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LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
1 79-87 Disease denotes COVID-19 MESH:C000657245
7 701-707 Species denotes people Tax:9606
8 159-167 Disease denotes COVID-19 MESH:C000657245
9 549-557 Disease denotes COVID-19 MESH:C000657245
10 692-700 Disease denotes infected MESH:D007239
11 1147-1155 Disease denotes COVID-19 MESH:C000657245
18 1264-1286 Species denotes 2019 novel coronavirus Tax:2697049
19 1298-1308 Species denotes SARS-CoV-2 Tax:2697049
20 1469-1476 Species denotes HCoV-19 Tax:2697049
21 1330-1338 Disease denotes COVID-19 MESH:C000657245
22 1883-1891 Disease denotes COVID-19 MESH:C000657245
23 2590-2598 Disease denotes COVID-19 MESH:C000657245
26 3342-3350 Disease denotes infected MESH:D007239
27 3739-3747 Disease denotes COVID-19 MESH:C000657245
35 3982-3992 Species denotes SARS-CoV-2 Tax:2697049
36 5053-5061 Species denotes children Tax:9606
37 3968-3976 Disease denotes infected MESH:D007239
38 4531-4536 Disease denotes fever MESH:D005334
39 4540-4545 Disease denotes cough MESH:D003371
40 4666-4674 Disease denotes COVID-19 MESH:C000657245
41 4734-4742 Disease denotes COVID-19 MESH:C000657245
45 5396-5404 Disease denotes infected MESH:D007239
46 5784-5792 Disease denotes infected MESH:D007239
47 6341-6349 Disease denotes infected MESH:D007239
51 6527-6535 Disease denotes COVID-19 MESH:C000657245
52 6657-6665 Disease denotes COVID-19 MESH:C000657245
53 6839-6847 Disease denotes COVID-19 MESH:C000657245
55 7938-7946 Disease denotes COVID-19 MESH:C000657245
61 9283-9287 Gene denotes ship Gene:3635
62 9205-9211 Species denotes people Tax:9606
63 9222-9228 Species denotes people Tax:9606
64 9313-9319 Species denotes people Tax:9606
65 9770-9779 Disease denotes infection MESH:D007239
67 9959-9967 Disease denotes COVID-19 MESH:C000657245
69 10493-10501 Disease denotes COVID-19 MESH:C000657245
73 10617-10625 Disease denotes infected MESH:D007239
74 10697-10702 Disease denotes fever MESH:D005334
75 10706-10711 Disease denotes cough MESH:D003371
84 11853-11863 Species denotes SARS-CoV-2 Tax:2697049
85 11500-11508 Disease denotes Infected MESH:D007239
86 11596-11601 Disease denotes fever MESH:D005334
87 11605-11610 Disease denotes cough MESH:D003371
88 11738-11743 Disease denotes fever MESH:D005334
89 11747-11752 Disease denotes cough MESH:D003371
90 11810-11815 Disease denotes fever MESH:D005334
91 11819-11824 Disease denotes cough MESH:D003371
95 12449-12457 Disease denotes infected MESH:D007239
96 12973-12981 Disease denotes infected MESH:D007239
97 13110-13118 Disease denotes infected MESH:D007239
105 13197-13205 Disease denotes infected MESH:D007239
106 13400-13405 Disease denotes fever MESH:D005334
107 13409-13414 Disease denotes cough MESH:D003371
108 13468-13473 Disease denotes fever MESH:D005334
109 13729-13737 Disease denotes infected MESH:D007239
110 13846-13854 Disease denotes infected MESH:D007239
111 14052-14060 Disease denotes infected MESH:D007239
114 14699-14706 Species denotes patient Tax:9606
115 14893-14901 Disease denotes COVID-19 MESH:C000657245
117 15458-15466 Disease denotes COVID-19 MESH:C000657245
131 18296-18303 Species denotes patient Tax:9606
132 18508-18515 Species denotes patient Tax:9606
133 16544-16549 Disease denotes fever MESH:D005334
134 16553-16558 Disease denotes cough MESH:D003371
135 16655-16660 Disease denotes fever MESH:D005334
136 16666-16671 Disease denotes cough MESH:D003371
137 16703-16708 Disease denotes fever MESH:D005334
138 16714-16719 Disease denotes cough MESH:D003371
139 16752-16757 Disease denotes fever MESH:D005334
140 16763-16768 Disease denotes cough MESH:D003371
141 16804-16809 Disease denotes fever MESH:D005334
142 16839-16844 Disease denotes fever MESH:D005334
143 17807-17812 Disease denotes fever MESH:D005334
145 15997-16002 Disease denotes error MESH:D012030
147 18849-18857 Disease denotes infected MESH:D007239
151 19268-19276 Disease denotes infected MESH:D007239
152 19932-19940 Disease denotes infected MESH:D007239
153 20197-20205 Disease denotes infected MESH:D007239
158 20961-20967 Species denotes people Tax:9606
159 20743-20751 Disease denotes infected MESH:D007239
160 20952-20960 Disease denotes infected MESH:D007239
161 21475-21485 Disease denotes infections MESH:D007239
163 21678-21687 Disease denotes infection MESH:D007239
165 22224-22229 Disease denotes fever MESH:D005334
168 23057-23065 Disease denotes infected MESH:D007239
169 23116-23124 Disease denotes COVID-19 MESH:C000657245
178 23217-23222 Disease denotes fever MESH:D005334
179 23226-23231 Disease denotes cough MESH:D003371
180 23605-23610 Disease denotes fever MESH:D005334
181 24002-24007 Disease denotes fever MESH:D005334
182 24020-24033 Disease denotes arrival fever MESH:D005334
183 24467-24472 Disease denotes fever MESH:D005334
184 24554-24562 Disease denotes infected MESH:D007239
185 24592-24597 Disease denotes fever MESH:D005334
187 24734-24742 Disease denotes infected MESH:D007239
190 25061-25069 Disease denotes infected MESH:D007239
191 25262-25272 Disease denotes infections MESH:D007239
199 26718-26724 Species denotes people Tax:9606
200 25572-25581 Disease denotes infection MESH:D007239
201 25701-25709 Disease denotes infected MESH:D007239
202 26159-26167 Disease denotes infected MESH:D007239
203 26338-26346 Disease denotes infected MESH:D007239
204 26544-26552 Disease denotes infected MESH:D007239
205 26822-26827 Disease denotes fever MESH:D005334
208 26957-26965 Disease denotes infected MESH:D007239
209 27521-27529 Disease denotes infected MESH:D007239
213 28832-28840 Disease denotes infected MESH:D007239
214 28947-28955 Disease denotes infected MESH:D007239
215 29155-29164 Disease denotes infection MESH:D007239
218 29398-29406 Disease denotes infected MESH:D007239
219 30493-30501 Disease denotes infected MESH:D007239
222 31189-31197 Disease denotes infected MESH:D007239
223 31843-31851 Disease denotes infected MESH:D007239
225 32930-32934 Gene denotes PRCC
227 32426-32434 Disease denotes infected MESH:D007239
229 33421-33429 Disease denotes infected MESH:D007239
237 34538-34546 Disease denotes COVID-19 MESH:C000657245
238 34734-34742 Disease denotes COVID-19 MESH:C000657245
239 34860-34868 Disease denotes COVID-19 MESH:C000657245
240 34960-34968 Disease denotes infected MESH:D007239
241 35243-35252 Disease denotes infection MESH:D007239
242 35425-35433 Disease denotes infected MESH:D007239
243 35671-35679 Disease denotes infected MESH:D007239
252 36641-36647 Species denotes people Tax:9606
253 35947-35955 Disease denotes COVID-19 MESH:C000657245
254 36265-36273 Disease denotes infected MESH:D007239
255 36452-36460 Disease denotes infected MESH:D007239
256 36632-36640 Disease denotes infected MESH:D007239
257 36765-36773 Disease denotes COVID-19 MESH:C000657245
258 36904-36912 Disease denotes infected MESH:D007239
259 36925-36930 Disease denotes fever MESH:D005334
264 37846-37850 Gene denotes ship Gene:3635
265 38010-38020 Species denotes SARS-CoV-2 Tax:2697049
266 37058-37066 Disease denotes infected MESH:D007239
267 38294-38312 Disease denotes infectious disease MESH:D003141
273 38834-38842 Species denotes Children Tax:9606
274 39137-39145 Species denotes children Tax:9606
275 39345-39351 Species denotes people Tax:9606
276 38449-38454 Disease denotes fever MESH:D005334
277 38458-38463 Disease denotes cough MESH:D003371
280 39573-39581 Disease denotes COVID-19 MESH:C000657245
281 39703-39711 Disease denotes infected MESH:D007239
284 40192-40198 Species denotes people Tax:9606
285 40227-40232 Disease denotes fever MESH:D005334
290 40739-40748 Disease denotes infection MESH:D007239
291 41041-41049 Disease denotes infected MESH:D007239
292 41359-41369 Disease denotes infections MESH:D007239
293 41453-41461 Disease denotes COVID-19 MESH:C000657245
295 42881-42889 Disease denotes infected MESH:D007239
298 43635-43640 Disease denotes fever MESH:D005334
299 43959-43979 Disease denotes respiratory symptoms MESH:D012818
301 44465-44475 Species denotes SARS-CoV-2 Tax:2697049
308 45187-45193 Species denotes people Tax:9606
309 45178-45186 Disease denotes infected MESH:D007239
310 45376-45384 Disease denotes COVID-19 MESH:C000657245
311 45763-45771 Disease denotes infected MESH:D007239
312 46116-46124 Disease denotes COVID-19 MESH:C000657245
313 46291-46301 Disease denotes infections MESH:D007239
321 46609-46617 Disease denotes infected MESH:D007239
322 46659-46664 Disease denotes fever MESH:D005334
323 46780-46788 Disease denotes infected MESH:D007239
324 46833-46838 Disease denotes fever MESH:D005334
325 47025-47030 Disease denotes fever MESH:D005334
326 47160-47165 Disease denotes fever MESH:D005334
327 47250-47258 Disease denotes Infected MESH:D007239
329 47671-47679 Disease denotes infected MESH:D007239
337 49213-49217 Gene denotes ship Gene:3635
338 49904-49908 Gene denotes ship Gene:3635
339 48836-48840 Gene denotes ship Gene:3635
340 49282-49291 Disease denotes infection MESH:D007239
341 49486-49491 Disease denotes fever MESH:D005334
342 49495-49500 Disease denotes cough MESH:D003371
343 49879-49889 Disease denotes infections MESH:D007239
345 50385-50393 Disease denotes COVID-19 MESH:C000657245
348 51090-51098 Disease denotes infected MESH:D007239
349 51598-51606 Disease denotes infected MESH:D007239
351 52175-52179 Gene denotes v2.1 Gene:28809
353 54353-54357 Gene denotes v2.1 Gene:28809

LitCovid-PD-FMA-UBERON

Id Subject Object Predicate Lexical cue fma_id
T1 8252-8256 Body_part denotes sole http://purl.org/sig/ont/fma/fma25000
T2 18056-18060 Body_part denotes body http://purl.org/sig/ont/fma/fma256135
T3 45833-45837 Body_part denotes face http://purl.org/sig/ont/fma/fma24728

LitCovid-PD-UBERON

Id Subject Object Predicate Lexical cue uberon_id
T1 8154-8159 Body_part denotes scale http://purl.obolibrary.org/obo/UBERON_0002542
T2 39813-39817 Body_part denotes tail http://purl.obolibrary.org/obo/UBERON_0002415
T3 44624-44629 Body_part denotes scale http://purl.obolibrary.org/obo/UBERON_0002542
T4 45833-45837 Body_part denotes face http://purl.obolibrary.org/obo/UBERON_0001456
T5 46502-46512 Body_part denotes extensions http://purl.obolibrary.org/obo/UBERON_2000106
T6 46549-46559 Body_part denotes Extensions http://purl.obolibrary.org/obo/UBERON_2000106
T7 50269-50274 Body_part denotes scale http://purl.obolibrary.org/obo/UBERON_0002542

LitCovid-PD-MONDO

Id Subject Object Predicate Lexical cue mondo_id
T1 79-87 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T2 159-167 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T3 549-557 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T4 1147-1155 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T5 1298-1306 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T6 1330-1338 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T7 1883-1891 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T8 2590-2598 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T9 3739-3747 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T10 3982-3990 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T11 4666-4674 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T12 4734-4742 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T13 4936-4945 Disease denotes Pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T14 5114-5123 Disease denotes Pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T15 6527-6535 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T16 6657-6665 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T17 6839-6847 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T18 7938-7946 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T19 9770-9779 Disease denotes infection http://purl.obolibrary.org/obo/MONDO_0005550
T20 9959-9967 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T21 10493-10501 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T22 11853-11861 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T23 14893-14901 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T24 15458-15466 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T25 21475-21485 Disease denotes infections http://purl.obolibrary.org/obo/MONDO_0005550
T26 21678-21687 Disease denotes infection http://purl.obolibrary.org/obo/MONDO_0005550
T27 23116-23124 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T28 25262-25272 Disease denotes infections http://purl.obolibrary.org/obo/MONDO_0005550
T29 25572-25581 Disease denotes infection http://purl.obolibrary.org/obo/MONDO_0005550
T30 29155-29164 Disease denotes infection http://purl.obolibrary.org/obo/MONDO_0005550
T31 34538-34546 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T32 34734-34742 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T33 34860-34868 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T34 35243-35252 Disease denotes infection http://purl.obolibrary.org/obo/MONDO_0005550
T35 35947-35955 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T36 36402-36411 Disease denotes influenza http://purl.obolibrary.org/obo/MONDO_0005812
T37 36765-36773 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T38 38010-38018 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T39 38294-38312 Disease denotes infectious disease http://purl.obolibrary.org/obo/MONDO_0005550
T40 38982-38991 Disease denotes Pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T41 39191-39200 Disease denotes Pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T42 39573-39581 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T43 40739-40748 Disease denotes infection http://purl.obolibrary.org/obo/MONDO_0005550
T44 41359-41369 Disease denotes infections http://purl.obolibrary.org/obo/MONDO_0005550
T45 41453-41461 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T46 44089-44098 Disease denotes influenza http://purl.obolibrary.org/obo/MONDO_0005812
T47 44465-44473 Disease denotes SARS-CoV http://purl.obolibrary.org/obo/MONDO_0005091
T48 45376-45384 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T49 46116-46124 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T50 46291-46301 Disease denotes infections http://purl.obolibrary.org/obo/MONDO_0005550
T51 49282-49291 Disease denotes infection http://purl.obolibrary.org/obo/MONDO_0005550
T52 49610-49619 Disease denotes Pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T53 49879-49889 Disease denotes infections http://purl.obolibrary.org/obo/MONDO_0005550
T54 50385-50393 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T55 52790-52793 Disease denotes DEB http://purl.obolibrary.org/obo/MONDO_0017608

LitCovid-PD-CLO

Id Subject Object Predicate Lexical cue
T1 222-225 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T2 233-234 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T3 300-301 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T4 1053-1054 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T5 1340-1343 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T6 1399-1402 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T7 1446-1458 http://purl.obolibrary.org/obo/OBI_0000245 denotes Organization
T8 1477-1480 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T9 1524-1529 http://purl.obolibrary.org/obo/NCBITaxon_10239 denotes virus
T10 1718-1730 http://purl.obolibrary.org/obo/OBI_0000245 denotes Organization
T11 2520-2523 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T12 2531-2532 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T13 3124-3130 http://purl.obolibrary.org/obo/CLO_0001658 denotes active
T14 3236-3237 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T15 3646-3647 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T16 4028-4029 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T17 4101-4102 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T18 5188-5190 http://purl.obolibrary.org/obo/CLO_0001022 denotes Li
T19 5188-5190 http://purl.obolibrary.org/obo/CLO_0007314 denotes Li
T20 5313-5314 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T21 5367-5368 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T22 5427-5428 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T23 5470-5471 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T24 5705-5706 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T25 6249-6250 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T26 6394-6395 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T27 6741-6744 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T28 7553-7565 http://purl.obolibrary.org/obo/OBI_0000245 denotes Organization
T29 8372-8375 http://purl.obolibrary.org/obo/CLO_0050236 denotes lag
T30 8601-8613 http://purl.obolibrary.org/obo/OBI_0000245 denotes Organization
T31 8809-8810 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T32 8887-8888 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T33 8985-8988 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T34 9644-9645 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T35 9669-9670 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T36 9678-9682 http://purl.obolibrary.org/obo/UBERON_0000473 denotes test
T37 9882-9885 http://purl.obolibrary.org/obo/PR_000001343 denotes aim
T38 10073-10078 http://purl.obolibrary.org/obo/NCBITaxon_10239 denotes virus
T39 10280-10281 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T40 10527-10530 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T41 10877-10889 http://purl.obolibrary.org/obo/OBI_0000245 denotes Organization
T42 10911-10923 http://purl.obolibrary.org/obo/OBI_0000245 denotes Organization
T43 11555-11556 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T44 11693-11694 http://purl.obolibrary.org/obo/CLO_0001021 denotes B
T45 13230-13231 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T46 13601-13602 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T47 13748-13751 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T48 14251-14252 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T49 14609-14610 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T50 15224-15225 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T51 15305-15306 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T52 16226-16229 http://purl.obolibrary.org/obo/CLO_0001313 denotes 3–6
T53 16292-16294 http://purl.obolibrary.org/obo/CLO_0001022 denotes Li
T54 16292-16294 http://purl.obolibrary.org/obo/CLO_0007314 denotes Li
T55 16774-16776 http://purl.obolibrary.org/obo/CLO_0053794 denotes 41
T56 16898-16904 http://purl.obolibrary.org/obo/CLO_0001658 denotes Active
T57 17247-17249 http://purl.obolibrary.org/obo/CLO_0001022 denotes Li
T58 17247-17249 http://purl.obolibrary.org/obo/CLO_0007314 denotes Li
T59 17611-17612 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T60 17942-17943 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T61 18043-18049 http://purl.obolibrary.org/obo/OBI_0000968 denotes device
T62 18604-18606 http://purl.obolibrary.org/obo/CLO_0053794 denotes 41
T63 18911-18912 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T64 19011-19016 http://purl.obolibrary.org/obo/CLO_0009985 denotes focus
T65 19252-19253 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T66 19299-19300 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T67 19338-19339 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T68 19427-19428 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T69 19913-19914 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T70 20074-20086 http://purl.obolibrary.org/obo/OBI_0000245 denotes Organization
T71 20131-20132 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T72 20797-20798 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T73 20897-20898 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T74 21258-21259 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T75 21298-21299 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T76 21342-21343 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T77 21421-21422 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T78 22714-22720 http://purl.obolibrary.org/obo/UBERON_0000473 denotes tested
T79 23126-23127 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T80 23354-23355 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T81 23857-23858 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T82 24247-24257 http://purl.obolibrary.org/obo/OBI_0000968 denotes instrument
T83 24718-24719 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T84 24761-24762 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T85 24976-24981 http://purl.obolibrary.org/obo/NCBITaxon_10239 denotes virus
T86 25097-25098 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T87 25500-25501 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T88 26131-26132 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T89 26155-26158 http://purl.obolibrary.org/obo/CLO_0050884 denotes ten
T90 26473-26474 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T91 26636-26637 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T92 26901-26902 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T93 26922-26923 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T94 27277-27278 http://purl.obolibrary.org/obo/CLO_0001021 denotes B
T95 27966-27967 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T96 28220-28221 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T97 28508-28509 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T98 28661-28662 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T99 28861-28862 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T100 29346-29347 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T101 29732-29738 http://purl.obolibrary.org/obo/UBERON_0000473 denotes tested
T102 30214-30220 http://purl.obolibrary.org/obo/UBERON_0000473 denotes tested
T103 30554-30555 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T104 31673-31674 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T105 31727-31728 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T106 31820-31821 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T107 32513-32514 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T108 32860-32861 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T109 33201-33204 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T110 33218-33219 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T111 33378-33379 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T112 33397-33398 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T113 33783-33784 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T114 34362-34363 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T115 34553-34556 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T116 34781-34782 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T117 34983-34984 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T118 35355-35356 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T119 35582-35592 http://purl.obolibrary.org/obo/CLO_0001658 denotes activities
T120 35697-35698 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T121 36087-36088 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T122 36156-36157 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T123 36438-36439 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T124 36975-36976 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T125 37081-37082 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T126 37489-37490 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T127 37708-37709 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T128 37837-37838 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T129 37990-37996 http://purl.obolibrary.org/obo/UBERON_0000473 denotes tested
T130 38266-38267 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T131 38490-38491 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T132 38575-38581 http://purl.obolibrary.org/obo/CLO_0001658 denotes active
T133 39056-39058 http://purl.obolibrary.org/obo/CLO_0001022 denotes Li
T134 39056-39058 http://purl.obolibrary.org/obo/CLO_0007314 denotes Li
T135 39813-39817 http://purl.obolibrary.org/obo/UBERON_0002415 denotes tail
T136 40005-40012 http://purl.obolibrary.org/obo/CLO_0009985 denotes focused
T137 40016-40017 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T138 40124-40130 http://purl.obolibrary.org/obo/CLO_0001658 denotes active
T139 40505-40506 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T140 40771-40772 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T141 41190-41191 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T142 41243-41244 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T143 41383-41384 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T144 41893-41905 http://purl.obolibrary.org/obo/OBI_0000245 denotes Organization
T145 42154-42157 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T146 42209-42210 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T147 42286-42287 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T148 42336-42339 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T149 42556-42557 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T150 42731-42732 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T151 43304-43305 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T152 43571-43572 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T153 43743-43744 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T154 44137-44142 http://purl.obolibrary.org/obo/UBERON_0000473 denotes tests
T155 44289-44294 http://purl.obolibrary.org/obo/UBERON_0000473 denotes tests
T156 44332-44336 http://purl.obolibrary.org/obo/UBERON_0000473 denotes test
T157 44456-44460 http://purl.obolibrary.org/obo/UBERON_0000473 denotes test
T158 44510-44513 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T159 45064-45071 http://purl.obolibrary.org/obo/NCBITaxon_10239 denotes viruses
T160 45152-45153 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T161 45445-45450 http://purl.obolibrary.org/obo/NCBITaxon_10239 denotes virus
T162 45533-45534 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T163 45833-45837 http://purl.obolibrary.org/obo/UBERON_0001456 denotes face
T164 47434-47435 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T165 47495-47499 http://purl.obolibrary.org/obo/UBERON_0000473 denotes test
T166 47789-47790 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T167 48093-48094 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T168 48387-48388 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T169 48458-48459 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T170 48562-48563 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T171 48778-48784 http://purl.obolibrary.org/obo/CLO_0001658 denotes active
T172 48827-48828 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T173 49112-49113 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T174 49416-49417 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T175 49749-49750 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T176 49821-49822 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T177 50102-50103 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T178 51337-51338 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T179 51375-51376 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T180 51445-51446 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T181 51489-51501 http://purl.obolibrary.org/obo/OBI_0000245 denotes Organization
T182 51584-51585 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T183 51748-51749 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T184 54189-54190 http://purl.obolibrary.org/obo/CLO_0001020 denotes a

LitCovid-PD-CHEBI

Id Subject Object Predicate Lexical cue chebi_id
T1 5188-5190 Chemical denotes Li http://purl.obolibrary.org/obo/CHEBI_30145
T2 16292-16294 Chemical denotes Li http://purl.obolibrary.org/obo/CHEBI_30145
T3 17247-17249 Chemical denotes Li http://purl.obolibrary.org/obo/CHEBI_30145
T4 28166-28171 Chemical denotes gamma http://purl.obolibrary.org/obo/CHEBI_30212
T5 28305-28310 Chemical denotes gamma http://purl.obolibrary.org/obo/CHEBI_30212
T6 34021-34032 Chemical denotes application http://purl.obolibrary.org/obo/CHEBI_33232
T7 39056-39058 Chemical denotes Li http://purl.obolibrary.org/obo/CHEBI_30145
T8 41530-41539 Chemical denotes explosive http://purl.obolibrary.org/obo/CHEBI_63490
T51853 50104-50109 Chemical denotes gamma http://purl.obolibrary.org/obo/CHEBI_30212
T41335 50253-50258 Chemical denotes gamma http://purl.obolibrary.org/obo/CHEBI_30212
T29870 50362-50367 Chemical denotes gamma http://purl.obolibrary.org/obo/CHEBI_30212
T48597 52790-52793 Chemical denotes DEB http://purl.obolibrary.org/obo/CHEBI_23704

LitCovid-PD-HP

Id Subject Object Predicate Lexical cue hp_id
T1 4531-4536 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T2 4540-4545 Phenotype denotes cough http://purl.obolibrary.org/obo/HP_0012735
T3 4936-4945 Phenotype denotes Pneumonia http://purl.obolibrary.org/obo/HP_0002090
T4 5114-5123 Phenotype denotes Pneumonia http://purl.obolibrary.org/obo/HP_0002090
T5 10697-10702 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T6 10706-10711 Phenotype denotes cough http://purl.obolibrary.org/obo/HP_0012735
T7 11596-11601 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T8 11605-11610 Phenotype denotes cough http://purl.obolibrary.org/obo/HP_0012735
T9 11738-11743 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T10 11747-11752 Phenotype denotes cough http://purl.obolibrary.org/obo/HP_0012735
T11 11810-11815 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T12 11819-11824 Phenotype denotes cough http://purl.obolibrary.org/obo/HP_0012735
T13 13400-13405 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T14 13409-13414 Phenotype denotes cough http://purl.obolibrary.org/obo/HP_0012735
T15 13468-13473 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T16 16544-16549 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T17 16553-16558 Phenotype denotes cough http://purl.obolibrary.org/obo/HP_0012735
T18 16655-16660 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T19 16666-16671 Phenotype denotes cough http://purl.obolibrary.org/obo/HP_0012735
T20 16703-16708 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T21 16714-16719 Phenotype denotes cough http://purl.obolibrary.org/obo/HP_0012735
T22 16752-16757 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T23 16763-16768 Phenotype denotes cough http://purl.obolibrary.org/obo/HP_0012735
T24 16804-16809 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T25 16839-16844 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T26 17807-17812 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T27 22224-22229 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T28 23217-23222 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T29 23226-23231 Phenotype denotes cough http://purl.obolibrary.org/obo/HP_0012735
T30 23605-23610 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T31 24002-24007 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T32 24028-24033 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T33 24467-24472 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T34 24592-24597 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T35 26822-26827 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T36 36925-36930 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T37 38449-38454 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T38 38458-38463 Phenotype denotes cough http://purl.obolibrary.org/obo/HP_0012735
T39 38982-38991 Phenotype denotes Pneumonia http://purl.obolibrary.org/obo/HP_0002090
T40 39191-39200 Phenotype denotes Pneumonia http://purl.obolibrary.org/obo/HP_0002090
T41 40227-40232 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T42 43635-43640 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T43 46659-46664 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T44 46833-46838 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T45 47025-47030 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T46 47160-47165 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T47 48500-48518 Phenotype denotes behavioral changes http://purl.obolibrary.org/obo/HP_0000708
T48 49486-49491 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945
T49 49495-49500 Phenotype denotes cough http://purl.obolibrary.org/obo/HP_0012735
T50 49610-49619 Phenotype denotes Pneumonia http://purl.obolibrary.org/obo/HP_0002090

LitCovid-PD-GO-BP

Id Subject Object Predicate Lexical cue
T1 22111-22117 http://purl.obolibrary.org/obo/GO_0060361 denotes flight
T2 35064-35070 http://purl.obolibrary.org/obo/GO_0040007 denotes growth
T3 38178-38192 http://purl.obolibrary.org/obo/GO_0019076 denotes viral shedding
T4 47728-47740 http://purl.obolibrary.org/obo/GO_0000003 denotes reproduction
T5 48128-48137 http://purl.obolibrary.org/obo/GO_0007610 denotes behaviors
T6 53181-53192 http://purl.obolibrary.org/obo/GO_0006412 denotes translation

2_test

Id Subject Object Predicate Lexical cue
32091395-19215720-27032124 2805-2809 19215720 denotes 2009
32091395-20353566-27032125 2827-2831 20353566 denotes 2010
32091395-32069388-27032126 4812-4816 32069388 denotes 2020
32091395-32064853-27032127 4984-4988 32064853 denotes 2020
32091395-32064853-27032128 5162-5166 32064853 denotes 2020
32091395-20353566-27032129 6142-6146 20353566 denotes 2010
32091395-32067043-27032130 7268-7272 32067043 denotes 2020
32091395-32069388-27032131 9357-9361 32069388 denotes 2020
32091395-32109013-27032132 16892-16896 32109013 denotes 2020
32091395-19215720-27032133 17892-17896 19215720 denotes 2009
32091395-21245928-27032134 17913-17917 21245928 denotes 2011
32091395-26296847-27032135 17931-17935 26296847 denotes 2015
32091395-18572196-27032136 20638-20642 18572196 denotes 2008
32091395-18572196-27032137 30685-30689 18572196 denotes 2008
32091395-32069388-27032138 38067-38071 32069388 denotes 2020
32091395-15071187-27032139 38360-38364 15071187 denotes 2004
32091395-32064853-27032140 39030-39034 32064853 denotes 2020
32091395-32064853-27032141 39239-39243 32064853 denotes 2020
32091395-20353566-27032142 40899-40903 20353566 denotes 2010
32091395-16292310-27032143 41322-41326 16292310 denotes 2005
32091395-16292310-27032144 41580-41584 16292310 denotes 2005
32091395-32064853-27032145 49658-49662 32064853 denotes 2020

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T1 0-87 Sentence denotes Estimated effectiveness of symptom and risk screening to prevent the spread of COVID-19
T2 89-97 Sentence denotes Abstract
T3 98-274 Sentence denotes Traveller screening is being used to limit further spread of COVID-19 following its recent emergence, and symptom screening has become a ubiquitous tool in the global response.
T4 275-456 Sentence denotes Previously, we developed a mathematical model to understand factors governing the effectiveness of traveller screening to prevent spread of emerging pathogens (Gostic et al., 2015).
T5 457-602 Sentence denotes Here, we estimate the impact of different screening programs given current knowledge of key COVID-19 life history and epidemiological parameters.
T6 603-708 Sentence denotes Even under best-case assumptions, we estimate that screening will miss more than half of infected people.
T7 709-935 Sentence denotes Breaking down the factors leading to screening successes and failures, we find that most cases missed by screening are fundamentally undetectable, because they have not yet developed symptoms and are unaware they were exposed.
T8 936-1073 Sentence denotes Our work underscores the need for measures to limit transmission by individuals who become ill after being missed by a screening program.
T9 1074-1220 Sentence denotes These findings can support evidence-based policy to combat the spread of COVID-19, and prospective planning to mitigate future emerging pathogens.
T10 1222-1234 Sentence denotes Introduction
T11 1235-1551 Sentence denotes As of February 20, 2020, the 2019 novel coronavirus (now named SARS-CoV-2, causing the disease COVID-19) has caused over 75,000 confirmed cases inside of China and has spread to 25 other countries (World Health Organization, 2020b). (HCoV-19 has been proposed as an alternate name for the virus; Jiang et al., 2020).
T12 1552-1791 Sentence denotes Until now, local transmission remained limited outside of China, but as of this week, new epidemic hotspots have become apparent on multiple continents (World Health Organization, 2020a; Jankowicz, 2020; Sang-Hun, 2020; Schnirring, 2020a).
T13 1792-1918 Sentence denotes Many jurisdictions have imposed traveller screening in an effort to prevent importation of COVID-19 cases to unaffected areas.
T14 1919-2148 Sentence denotes Some high-income countries have escalated control measures beyond screening-based containment policies, and now restrict or quarantine inbound travellers from countries known to be experiencing substantial community transmission.
T15 2149-2301 Sentence denotes Meanwhile, in many other countries, screening remains the primary barrier to case importation (Guardian reporting team, 2020; Schengen Visa Info, 2020).
T16 2302-2488 Sentence denotes Even in countries with the resources to enforce quarantine measures, expanded arrival screening may be the first logical response as the source epidemic expands to regions outside China.
T17 2489-2664 Sentence denotes Furthermore, symptom screening has become a ubiquitous tool in the effort to contain local spread of COVID-19, in settings from affected cities to cruise ships to quarantines.
T18 2665-2716 Sentence denotes Our analysis is pertinent to all of these contexts.
T19 2717-3166 Sentence denotes It is widely recognized that screening is an imperfect barrier to spread (Bitar et al., 2009; Cowling et al., 2010; Gostic et al., 2015; Mabey et al., 2014; Quilty et al., 2020), due to: the absence of detectable symptoms during the incubation period; variation in the severity and detectability of symptoms once the disease begins to progress; imperfect performance of screening equipment or personnel; or active evasion of screening by travellers.
T20 3167-3413 Sentence denotes Previously we estimated the effectiveness of traveller screening for a range of pathogens that have emerged in the past, and found that arrival screening would miss 50–75% of infected cases even under optimistic assumptions (Gostic et al., 2015).
T21 3414-3633 Sentence denotes Yet the quantitative performance of different policies matters for planning interventions and will influence how public health authorities prioritize different measures as the international and domestic context changes.
T22 3634-3908 Sentence denotes Here we use a mathematical model to analyse the expected performance of different screening measures for COVID-19, based on what is currently known about its natural history and epidemiology and on different possible combinations of departure and arrival screening policies.
T23 3909-4062 Sentence denotes First we assess the probability that any single individual infected with SARS-CoV-2 would be detected by screening, as a function of time since exposure.
T24 4063-4278 Sentence denotes This individual-level analysis is not a comprehensive measure of program success, but serves to illustrate the various ways in which screening can succeed or fail (and in turn the ways it can or cannot be improved).
T25 4279-4442 Sentence denotes Further, these analyses emphasize the importance of the incubation period, and the fraction of subclinical cases, as determinants of individual screening outcomes.
T26 4443-4632 Sentence denotes We define subclinical cases as those too mild to show symptoms detectable in screening (fever or cough) after passing through the incubation period (i.e. once any symptoms have manifested).
T27 4633-4858 Sentence denotes The true fraction of subclinical COVID-19 cases remains unknown, but anecdotally, many lab-confirmed COVID-19 cases have not shown detectable symptoms on diagnosis (Hoehl et al., 2020; Nishiura et al., 2020; Hu et al., 2020).
T28 4859-5267 Sentence denotes About 80% of clinically attended cases have been mild (The Novel Coronavirus Pneumonia Emergency Response Epidemiology Team, 2020), and clinically attended cases have been conspicuously rare in children and teens (Chen et al., 2020; The Novel Coronavirus Pneumonia Emergency Response Epidemiology Team, 2020; Huang et al., 2020; Li et al., 2020), raising the possibility that subclinical cases may be common.
T29 5268-5563 Sentence denotes Next, we assess the overall effectiveness of a screening program by modeling screening outcomes in a hypothetical population of infected travellers, each with a different time since exposure (and hence a different probability of having progressed through incubation to show detectable symptoms).
T30 5564-5755 Sentence denotes Crucially, the distribution of times since exposure will depend on the epidemiology of the source population, so this overall measure is not a simple average of the individual-level outcomes.
T31 5756-5876 Sentence denotes We estimate the fraction of infected travellers detected, breaking down the ways in which screening can succeed or fail.
T32 5877-6148 Sentence denotes An alternate measure of program success is the extent to which screening delays the first importation of cases into the community, possibly providing additional time to train medical staff, deploy public health responders or refine travel policies (Cowling et al., 2010).
T33 6149-6357 Sentence denotes To quantify the potential for screening to delay case importation, we estimate the probability that a given screening program would detect the first n or more imported cases before missing an infected person.
T34 6358-6508 Sentence denotes Screening will be less effective in a growing epidemic, due to an excess of recently-exposed and not-yet-symptomatic travellers (Gostic et al., 2015).
T35 6509-6692 Sentence denotes In the context of COVID-19, we consider both growing and stable epidemic scenarios, but place greater emphasis on the realistic assumption that the COVID-19 epidemic is still growing.
T36 6693-6996 Sentence denotes Since late January 2020, the Chinese government has imposed strict travel restrictions and surveillance on population centers heavily affected by COVID-19 (BBC News, 2020; Cellan-Jones, 2020), and numerous other countries have imposed travel and quarantine restrictions on travellers inbound from China.
T37 6997-7013 Sentence denotes Until about Feb.
T38 7014-7438 Sentence denotes 20, 2020, these measures had appeared to successfully limit community transmission outside of China, but all the while multiple factors pointed to on-going risk, including evidence that transmission is possible prior to the onset of symptoms (Yu et al., 2020; Hu et al., 2020), and reports of citizens seeking to elude travel restrictions or leaving before restrictions were in place (Ma and Pinghui, 2020; Mahbubani, 2020).
T39 7439-7470 Sentence denotes Now, in the week following Feb.
T40 7471-7674 Sentence denotes 20, 2020, new source epidemics have appeared on multiple continents (World Health Organization, 2020a), and the the risk of exportation of cases from beyond the initial travel-restricted area is growing.
T41 7675-7850 Sentence denotes As the epidemic continues to expand geographically, arrival screening will likely be continued or expanded to prevent importation of cases to areas without established spread.
T42 7851-8076 Sentence denotes At the same time, there is great concern about potential public health consequences if COVID-19 spreads to developing countries that lack health infrastructure and resources to combat it effectively (de Salazar et al., 2020).
T43 8077-8178 Sentence denotes Limited resources also could mean that some countries cannot implement large-scale arrival screening.
T44 8179-8318 Sentence denotes In this scenario, departure screening implemented elsewhere would be the sole barrier -- however leaky -- to new waves of case importation.
T45 8319-8622 Sentence denotes It is also important to recognize that, owing to the lag time in appearance of symptoms in imported cases, any weaknesses in screening would continue to have an effect on known case importations for up to two weeks, officially considered the maximum incubation period (World Health Organization, 2020c).
T46 8623-8753 Sentence denotes Accordingly, we consider scenarios with departure screening only, arrival screening only, or both departure and arrival screening.
T47 8754-8966 Sentence denotes The model can also consider the consequences when only a fraction of the traveller population is screened, due either to travel from a location not subject to screening, or due to deliberate evasion of screening.
T48 8967-9093 Sentence denotes Our analysis also has direct bearing on other contexts where symptom screening is being used, beyond international air travel.
T49 9094-9456 Sentence denotes This includes screening of travelers at rail stations and roadside spot checks, and screening of other at-risk people including people living in affected areas, health-care workers, cruise ship passengers, evacuees and people undergoing quarantine (Hoehl et al., 2020; Japan Ministry of Health, Labor and Welfare, 2020; Nishiura et al., 2020; Schnirring, 2020c).
T50 9457-9597 Sentence denotes Below, we chiefly frame our findings in terms of travel screening, but these other screening contexts are also in the scope of our analysis.
T51 9598-9869 Sentence denotes Any one-off screening effort is equivalent to a departure screen (i.e. a single test with no delay), and our findings on symptom screening effectiveness over the course of infection are directly applicable to longitudinal screening in quarantine or occupational settings.
T52 9870-10079 Sentence denotes The central aim of our analysis is to assess the expected effectiveness of screening for COVID-19, taking account of current knowledge and uncertainties about the natural history and epidemiology of the virus.
T53 10080-10228 Sentence denotes We therefore show results using the best estimates currently available, in the hope of informing policy decisions in this fast-changing environment.
T54 10229-10472 Sentence denotes We also make our model available for public use as a user-friendly online app, so that stakeholders can explore scenarios of particular interest, and results can be updated rapidly as our knowledge of this new viral threat continues to expand.
T55 10474-10481 Sentence denotes Results
T56 10483-10511 Sentence denotes Model for COVID-19 screening
T57 10512-10789 Sentence denotes The core model has been described previously (Gostic et al., 2015), but to summarize briefly, it assumes infected travellers can be detained due to the presence of detectable symptoms (fever or cough), or due to self-reporting of exposure risk via questionnaires or interviews.
T58 10790-10932 Sentence denotes These assumptions are consistent with WHO traveller screening guidelines (World Health Organization, 2020b; World Health Organization, 2020c).
T59 10933-11227 Sentence denotes Upon screening, travellers fall into one of four categories: (1) symptomatic but not aware of exposure risk, (2) aware of exposure risk but without detectable symptoms, (3) symptomatic and aware that exposure may have occurred, and (4) neither symptomatic nor aware of exposure risk (Figure 1).
T60 11228-11415 Sentence denotes Travellers in the final category are fundamentally undetectable, and travellers in the second category are only detectable if aware that they have been exposed and willing to self report.
T61 11416-11425 Sentence denotes Figure 1.
T62 11427-11499 Sentence denotes Model of traveller screening process, adapted from Gostic et al. (2015).
T63 11500-12033 Sentence denotes Infected travellers fall into one of five categories: (A) Cases aware of exposure risk and with fever or cough are detectable in both symptom screening and questionnaire-based risk screening. (B) Cases aware of exposure risk, but without fever or cough are only detectable using risk screening. (C) Cases with fever or cough, but unaware of exposure to SARS-CoV-2 are only detectable in symptom screening. (D–E) Subclinical cases who are unaware of exposure risk, and individuals that evade screening, are fundamentally undetectable.
T64 12034-12189 Sentence denotes In the model, screening for symptoms occurs prior to questionnaire-based screening for exposure risk, and detected cases do not progress to the next stage.
T65 12190-12311 Sentence denotes This allows us to track the fraction of cases detected using symptom screening or risk screening at arrival or departure.
T66 12312-12949 Sentence denotes Additionally, building on the four detectability classes explained above, the model keeps track of four ways in which screening can miss infected travellers: (1) due to imperfect sensitivity, symptom screening may fail to detect symptoms in travellers that display symptoms; (2) questionnaires may fail to detect exposure risk in travellers aware they have been exposed, owing to deliberate obfuscation or misunderstanding; (3) screening may fail to detect both symptoms and known exposure risk in travellers who have both and (4) travellers not exhibiting symptoms and with no knowledge of their exposure are fundamentally undetectable.
T67 12950-13017 Sentence denotes Here, we only consider infected travellers who submit to screening.
T68 13018-13172 Sentence denotes However, the supplementary app allows users to consider scenarios in which some fraction of infected travellers intentionally evade screening (Figure 1E).
T69 13173-13627 Sentence denotes The probability that an infected person is detectable in a screening program depends on: the incubation period (the time from exposure to onset of detectable symptoms); the proportion of subclinical cases (mild cases that lack fever or cough); the sensitivity of thermal scanners used to detect fever; the fraction of cases aware they have high exposure risk; and the fraction of those cases who would self-report truthfully on a screening questionnaire.
T70 13628-13788 Sentence denotes Further, the distribution of individual times since exposure affects the probability that any single infected traveller has progressed to the symptomatic stage.
T71 13789-13925 Sentence denotes If the source epidemic is still growing, the majority of infected cases will have been recently exposed, and will not yet show symptoms.
T72 13926-14146 Sentence denotes If the source epidemic is no longer growing (stable), times since exposure will be more evenly distributed, meaning that more infected travellers will have progressed through incubation and will show detectable symptoms.
T73 14147-14354 Sentence denotes We used methods described previously to estimate the distribution of individual times since exposure in a growing or stable epidemic, given various values of the reproductive number R0 (Gostic et al., 2015).
T74 14355-14551 Sentence denotes Briefly, early in the epidemic when the number of cases is still growing, the model draws on epidemiological theory to assume that the fraction of cases who are recently exposed increases with R0.
T75 14552-14912 Sentence denotes The distribution of times since exposure is truncated at a maximum value, which corresponds epidemiologically to the maximum time from exposure to patient isolation, after which point we assume cases will not attempt to travel. (Isolation may occur due to hospitalization, or due to confinement at home in response to escalating symptoms or COVID-19 diagnosis.
T76 14913-15180 Sentence denotes In the non-travel context, this would correspond to cases that have been hospitalized or otherwise diagnosed and isolated.) Here, we approximate the maximum time from exposure to isolation as the sum of the mean incubation time, and mean time from onset to isolation.
T77 15181-15382 Sentence denotes To consider the epidemiological context of a stable epidemic in the source population we assume times since exposure follow a uniform distribution across the time period between exposure and isolation.
T78 15384-15432 Sentence denotes Parameters, uncertainty and sensitivity analyses
T79 15433-15628 Sentence denotes As of February 20, 2020, COVID-19-specific estimates are available for most parameters, but many have been derived from limited or preliminary data and remain subject to considerable uncertainty.
T80 15629-15694 Sentence denotes Table 1 and the Methods summarize the current state of knowledge.
T81 15695-15788 Sentence denotes Here, we used two distinct approaches to incorporate parameter uncertainty into our analysis.
T82 15789-15797 Sentence denotes Table 1.
T83 15799-15912 Sentence denotes Parameter values estimated in currently available studies, along with accompanying uncertainties and assumptions.
T84 15913-16046 Sentence denotes Ranges in the final column reflect confidence interval, credible interval, standard error or range reported by each study referenced.
T85 16047-16148 Sentence denotes Parameter Best estimate (Used in Figure 2) Plausible range (Used in Figure 3) References and notes
T86 16149-16194 Sentence denotes Mean incubation period 5.5 days Sensitivity:
T87 16195-16409 Sentence denotes 4.5 or 6.5 days 4.5–6.5 days 3–6 days, n = 4 (Chan et al., 2020)* 5.2 (4.1–7.0) days, n < 425 (Li et al., 2020)† 5.2 (4.4–6.0) days, n = 101 (Lauer et al., 2020)† 6.5 (5.6–7.9) days, n = 88 (Backer et al., 2020)†
T88 16410-16510 Sentence denotes Incubation period distribution Gamma distribution with mean as above, and standard deviation = 2.25
T89 16511-16580 Sentence denotes Percent of cases subclinical (No fever or cough) Best case scenario:
T90 16581-16605 Sentence denotes 5% Middle case scenario:
T91 16606-16630 Sentence denotes 25% Worst case scenario:
T92 16631-16650 Sentence denotes 50% Clinical data:
T93 16651-17138 Sentence denotes 83% fever, 67% cough, n = 6 (Chan et al., 2020) 83% fever, 82% cough, n = 99 (Chen et al., 2020) 98% fever, 76% cough, n = 41 (Huang et al., 2020) 43.8% fever at hospital admission, 88.7% fever during hospitalization, n = 1099 (Guan et al., 2020) Active monitoring after repatriation flights or on cruise ships: % asymptomatic at diagnosis 31.2% (111/355) (Japan Ministry of Health, Labor and Welfare, 2020) 65.2% (5 of 8) (Nishiura et al., 2020) 70.0% (7 of 10) (Dorigatti et al., 2020)
T94 17139-17182 Sentence denotes R0 No effect in individual-level analysis.
T95 17184-17542 Sentence denotes 1.5–4.0 2.2 (1.4–3.8) (Riou and Althaus, 2020) 2.2 (1.4–3.9) (Li et al., 2020) 2.6 (1.5–3.5) (Imai et al., 2020) 2.7 (2.5–2.9) (Wu et al., 2020) 4.5 (4.4-4.6) (Liu et al., 2020) 3.8 (3.6-4.0) (Read et al., 2020) 4.08 (3.37–4.77) (Cao et al., 2020) 4.7 (2.8–7.6) (Sanche et al., 2020) 6.3 (3.3-11.3) (Sanche et al., 2020) 6.47 (5.71–7.23) (Tang et al., 2020)
T96 17543-17761 Sentence denotes Percent of travellers aware of exposure risk 20% 5–40% We assume a low percentage, as no specific risk factors have been identified, and known times or sources of exposure are rarely reported in existing line lists.
T97 17762-17937 Sentence denotes Sensitivity of infrared thermal scanners for fever 70% 60–90% Most studies estimated sensitivity between 60–88% (Bitar et al., 2009; Priest et al., 2011; Tay et al., 2015).
T98 17938-18002 Sentence denotes But a handful of studies estimated very low sensitivity (4–30%).
T99 18003-18099 Sentence denotes In general, sensitivity depended on the device used, body area targeted and ambient temperature.
T100 18100-18268 Sentence denotes Probability that travellers self-report exposure risk 25% 5–25% 25% is an upper-bound estimate based on outcomes of past screening initiatives. (Gostic et al., 2015)
T101 18269-18401 Sentence denotes Time from symptom onset to patient isolation (After which we assume travel is not possible) No effect in individual-level analysis.
T102 18403-18684 Sentence denotes 3–7 days Median 7 days from onset to hospitalization (n = 6) (Chan et al., 2020) Mean 2.9 days onset to patient isolation (n = 164) (Liu et al., 2020) Median 7 days from onset to hospitalization (n = 41) (Huang et al., 2020) As awareness increases, times to isolation may decline.
T103 18685-18707 Sentence denotes * From family cluster.
T104 18708-18805 Sentence denotes † Parametric distributions fit to cases with known dates of exposure or travel to and from Wuhan.
T105 18806-19007 Sentence denotes First, to estimate the probability that an infected individual would be detected or missed we considered a range of plausible values for the mean incubation time, and the fraction of subclinical cases.
T106 19008-19146 Sentence denotes We focus on the incubation period and subclinical fraction of cases because screening outcomes are particularly sensitive to their values.
T107 19147-19229 Sentence denotes All other parameters were fixed to the best available estimates listed in Table 1.
T108 19230-19405 Sentence denotes Second, we considered a population of infected travellers, each with a unique time of exposure, and in turn a unique probability of having progressed to the symptomatic stage.
T109 19406-19654 Sentence denotes Here, the model used a resampling-based approach to simultaneously consider uncertainty from both stochasticity in any single individual’s screening outcome, and uncertainty as to the true underlying natural history parameters driving the epidemic.
T110 19655-19839 Sentence denotes Details are provided in the methods, but briefly, we constructed 1000 candidate parameter sets, drawn using Latin hypercube sampling from plausible ranges for each parameter (Table 1).
T111 19840-19953 Sentence denotes Using each parameter set, we simulated one set of screening outcomes for a population of 30 infected individuals.
T112 19954-20175 Sentence denotes As of February 20, 2020, 30 approximates the maximum known number of cases imported to any single country (World Health Organization, 2020b), and thus our analysis incorporates a reasonable degree of binomial uncertainty.
T113 20176-20441 Sentence denotes The actual number of infected travellers passing through screening in any given location may be higher or lower than 30, and will depend on patterns of global connectivity, and on the duration of the source epidemic (Chinazzi et al., 2020; de Salazar et al., 2020).
T114 20442-20644 Sentence denotes Finally, we analysed the sensitivity of screening effectiveness (fraction of travellers detected) to each parameter, as measured by the partial rank correlation coefficient (PRCC) (Marino et al., 2008).
T115 20646-20683 Sentence denotes Individual probabilities of detection
T116 20684-20877 Sentence denotes First, the model estimated the probability that any single infected individual would be detected by screening as a function of the time between exposure and the initiation of travel (Figure 2).
T117 20878-21023 Sentence denotes Incubation time is a crucial driver of traveller screening effectiveness; infected people are most likely to travel before the onset of symptoms.
T118 21024-21134 Sentence denotes Here we considered three mean incubation times, which together span the range of most existing mean estimates:
T119 21135-21167 Sentence denotes 4.5, 5.5 and 6.5 days (Table 1).
T120 21168-21242 Sentence denotes Additionally, we considered three possible fractions of subclinical cases:
T121 21243-21400 Sentence denotes 50% represents a worst-case upper bound, 5% represents a best-case lower bound, and 25% represents a plausible middle case. (Table 1, Materials and methods).
T122 21401-21585 Sentence denotes Since resubmission, a new delay-adjusted estimate indicates that 34.6% of infections are asymptomatic (Mizumoto et al., 2020), intermediate between our middle and worst-case scenarios.
T123 21586-21595 Sentence denotes Figure 2.
T124 21597-21688 Sentence denotes Individual outcome probabilities for travellers who screened at given time since infection.
T125 21689-21841 Sentence denotes Columns show three possible mean incubation periods, and rows show best-case, middle-case and worst-case estimates of the fraction of subclinical cases.
T126 21842-22012 Sentence denotes Here, we assume screening occurs at both arrival and departure; see Figure 2—figure supplement 1 and Figure 2—figure supplement 2 for departure or arrival screening only.
T127 22013-22094 Sentence denotes The black dashed lines separate detected cases (below) from missed cases (above).
T128 22095-22345 Sentence denotes Here, we assume flight duration = 24 hr, the probability that an individual is aware of exposure risk is 0.2, the sensitivity of fever scanners is 0.7, and the probability that an individual will truthfully self-report on risk questionnaires is 0.25.
T129 22346-22383 Sentence denotes Table 1 lists all other input values.
T130 22384-22407 Sentence denotes Figure 2—source data 1.
T131 22409-22434 Sentence denotes Source data for Figure 2.
T132 22435-22525 Sentence denotes Raw, simulated data, and source data for Figure 2—figures supplement 1, 2 can be found as.
T133 22526-22572 Sentence denotes RData or. csv files in the supplementary code.
T134 22573-22602 Sentence denotes Figure 2—figure supplement 1.
T135 22604-22629 Sentence denotes Departure screening only.
T136 22630-22659 Sentence denotes Figure 2—figure supplement 2.
T137 22661-22684 Sentence denotes Arrival screening only.
T138 22685-22786 Sentence denotes Even within the narrow range tested, screening outcomes were sensitive to the incubation period mean.
T139 22787-23125 Sentence denotes For longer incubation periods, we found that larger proportions of departing travellers would not yet be exhibiting symptoms – either at departure or arrival – which in turn reduced the probability that screening would detect these cases, especially since we assume few infected travellers will realize they have been exposed to COVID-19.
T140 23126-23265 Sentence denotes A second crucial uncertainty is the proportion of subclinical cases, which lack detectable fever or cough even after the onset of symptoms.
T141 23266-23407 Sentence denotes We considered scenarios in which 5%, 25% and 50% of cases are subclinical, representing a best, middle and worst-case scenario, respectively.
T142 23408-23632 Sentence denotes The middle and worst-case scenarios have predictable and discouraging consequences for the effectiveness of traveller screening, since they render large fractions of the population undetectable by fever screening (Figure 2).
T143 23633-23738 Sentence denotes Furthermore, subclinical cases who are unaware of their exposure risk are never detectable, by any means.
T144 23739-23852 Sentence denotes This is manifested as the bright red ‘undetectable’ region which persists well beyond the mean incubation period.
T145 23853-24015 Sentence denotes For a screening program combining departure and arrival screening, as shown in Figure 2, the greatest contributor to case detection is the departure fever screen.
T146 24016-24349 Sentence denotes The arrival fever screen is the next greatest contributor, with its value arising from two factors: the potential to detect cases whose symptom onset occurred during travel, and the potential to catch cases missed due to imperfect instrument sensitivity in non-contact infrared thermal scanners used in traveller screening (Table 1).
T147 24350-24681 Sentence denotes Considering the effectiveness of departure or arrival screening only (Figure 2—figures supplement 1, 2), we see that fever screening is the dominant contributor in each case, but that the risk of missing infected travellers due to undetected fever is substantially higher when there is no redundancy from two successive screenings.
T148 24683-24789 Sentence denotes Overall screening effectiveness in a population of infected travellers during a growing or stable epidemic
T149 24790-24982 Sentence denotes Next we estimated the overall effectiveness of different screening programs, as well as the uncertainties arising from the current partial state of knowledge about this recently-emerged virus.
T150 24983-25174 Sentence denotes We modeled plausible population-level outcomes by tracking the fraction of 30 infected travellers detained, given a growing or stable epidemic and current uncertainty around parameter values.
T151 25175-25467 Sentence denotes We separately consider the best, middle and worst-case scenarios for the proportion of infections that are subclinical, and for each scenario we compare the impact of departure screening only (or equivalently, any on-the-spot screening), arrival screening only, or programs that include both.
T152 25468-25805 Sentence denotes The striking finding is that in a growing epidemic, even under the best-case assumptions, with just one infection in twenty being subclinical and all travellers passing through departure and arrival screening, the median fraction of infected travellers detected is only 0.30, with 95% interval extending from 0.10 up to 0.53 (Figure 3A).
T153 25806-26003 Sentence denotes The total fraction detected is lower for programs with only one layer of screening, with arrival screening preferable to departure screening owing to the possibility of symptom onset during travel.
T154 26004-26238 Sentence denotes Considering higher proportions of subclinical cases, the overall effectiveness of screening programs is further degraded, with a median of just one in ten infected travellers detected by departure screening in the worst-case scenario.
T155 26239-26424 Sentence denotes The key driver of these poor outcomes is that even in the best-case scenario, nearly two thirds of infected travellers will not be detectable (as shown by the red regions in Figure 3B).
T156 26425-26758 Sentence denotes There are three drivers of this outcome: (1) in a growing epidemic, the majority of travellers will have been recently infected and hence will not yet have progressed to exhibit any symptoms; (2) we assume that a fraction of cases never develop detectable symptoms; and (3) we assume that few people are aware of their exposure risk.
T157 26759-26838 Sentence denotes As above, the dominant contributor to successful detections is fever screening.
T158 26839-26848 Sentence denotes Figure 3.
T159 26850-26920 Sentence denotes Population-level outcomes of screening programs in a growing epidemic.
T160 26921-27121 Sentence denotes (A) Violin plots of the fraction of infected travellers detected, accounting for current uncertainties by running 1000 simulations using parameter sets randomly drawn from the ranges shown in Table 1.
T161 27122-27200 Sentence denotes Dots and vertical line segments show the median and central 95%, respectively.
T162 27201-27336 Sentence denotes Text above each violin shows the median and central 95% fraction detected. (B) Mean fraction of travellers with each screening outcome.
T163 27337-27550 Sentence denotes The black dashed lines separate detected cases (below) from missed cases (above). (C) Fraction of simulations in which screening successfully detects at least n cases before the first infected traveller is missed.
T164 27551-27574 Sentence denotes Figure 3—source data 1.
T165 27576-27602 Sentence denotes Source data for Figure 3A.
T166 27603-27693 Sentence denotes Raw, simulated data, and source data for Figure 3—figures supplement 1, 2 can be found as.
T167 27694-27740 Sentence denotes Rdata or. csv files in the supplementary code.
T168 27741-27764 Sentence denotes Figure 3—source data 2.
T169 27766-27792 Sentence denotes Source data for Figure 3B.
T170 27793-27816 Sentence denotes Figure 3—source data 3.
T171 27818-27844 Sentence denotes Source data for Figure 3C.
T172 27845-27874 Sentence denotes Figure 3—figure supplement 1.
T173 27876-27964 Sentence denotes Population-level screening outcomes given that the source epidemic is no longer growing.
T174 27965-27999 Sentence denotes (A-C) are as dscribed in Figure 3.
T175 28000-28029 Sentence denotes Figure 3—figure supplement 2.
T176 28031-28109 Sentence denotes Plausible incubation period distributions underlying the analyses in Figure 3.
T177 28110-28249 Sentence denotes Each line shows the probability density function of the gamma distribution with different plausible means and a standard deviation of 2.25.
T178 28250-28382 Sentence denotes The parameter values were picked based on the best-fit gamma distributions reported in Backer et al. (2020) and Lauer et al. (2020).
T179 28383-28566 Sentence denotes In an epidemic that is no longer growing (Figure 3—figures supplement 1), screening effectiveness is considerably higher, as a lower proportion of travellers will be recently exposed.
T180 28567-28657 Sentence denotes This is shown by the smaller, red ‘undetectable’ region in Figure 3—figures supplement 1B.
T181 28658-28967 Sentence denotes In a stable epidemic, under the middle-case assumption that 25% of cases are subclinical, we estimate that arrival screening alone would detect roughly one third (17–53%) of infected travelers, and that a combination of arrival and departure screening would detect nearly half (23–63%) of infected travellers.
T182 28968-29196 Sentence denotes In short, holding all other things equal, screening effectiveness will increase as the source epidemic transitions from growing to stable, owing simply to changes in the distribution of ‘infection ages,’ or times since exposure.
T183 29197-29430 Sentence denotes To assess the potential for screening to delay introduction of undiagnosed cases, we evaluated the fraction of simulations in which screening during a growing epidemic would detect the first n or more infected travellers (Figure 3C).
T184 29431-29724 Sentence denotes Depending on the screening strategy (arrival, departure or both) and assumed subclinical fraction (5%, 25%, or 50%), the probability of detecting at least the first two cases ranged from 0.02 to 0.11, and the probability of detecting three or more cases was never better than 0.04 (Figure 3C).
T185 29725-29883 Sentence denotes In all tested scenarios, more than half of simulations failed to detect the first imported case, consistent with probabilities of case detection in Figure 3A.
T186 29884-30230 Sentence denotes Probabilities of detecting the first n consecutive cases were marginally higher in the stable epidemic context (Figure 3—figures supplement 1), but still the probability of detecting at least the first three cases was never better than 0.13, and the probability of detecting the first four cases was never better than 0.06 in any tested scenario.
T187 30231-30425 Sentence denotes Taken together, these results indicate that screening in any context is very unlikely to delay case importation beyond the first 1–3 cases, and often will not delay the first importation at all.
T188 30426-30513 Sentence denotes What duration of delay this yields will depend on the frequency of infected travellers.
T189 30515-30535 Sentence denotes Sensitivity analysis
T190 30536-30877 Sentence denotes In the context of a growing epidemic, sensitivity analysis using the method of Latin hypercube sampling and partial rank correlation (Marino et al., 2008) showed that the fraction of travellers detected was moderately sensitive to all parameters considered -- most coefficient estimates fell between 0.1 and 0.3 in absolute value (Figure 4).
T191 30878-31006 Sentence denotes Sensitivity to R0 was somewhat higher than sensitivity to other parameters, but the difference was not statistically remarkable.
T192 31007-31104 Sentence denotes R0 and the mean incubation period were negatively associated with the fraction of cases detected.
T193 31105-31358 Sentence denotes An increase in either of these parameters implies an increase in the probability an infected traveller will be undetectable, either because they have been recently exposed (R0), or have not yet progressed to the symptomatic stage (mean incubation time).
T194 31359-31566 Sentence denotes The positive association between the fraction of cases detected and the sensitivity of thermal scanners, sensitivity of risk questionnaires, or the fraction of travellers aware of exposure risk is intuitive.
T195 31567-31720 Sentence denotes Finally, the duration from onset to isolation effectively describes the window of time in which we assume a symptomatic individual could initiate travel.
T196 31721-31883 Sentence denotes Here, a wider window is associated with increased screening effectiveness, because it will lead to a higher proportion of infected travellers who are symptomatic.
T197 31884-31976 Sentence denotes Figure 4 shows results from the middle case scenario, in which 25% of cases are subclinical.
T198 31977-32278 Sentence denotes Considering scenarios where more or fewer cases are subclinical, we see increased influence of the factors based on exposure risk (questionnaire sensitivity and the fraction of cases aware of their exposure) as the proportion of cases with detectable symptoms declines (Figure 4—figures supplement 1).
T199 32279-32288 Sentence denotes Figure 4.
T200 32290-32455 Sentence denotes Sensitivity analysis showing partial rank correlation coefficient (PRCC) between each parameter and the fraction (per-simulation) of 30 infected travellers detected.
T201 32456-32577 Sentence denotes Outcomes were obtained from 1000 simulations, each using a candidate parameter sets drawn using Latin hypercube sampling.
T202 32578-32711 Sentence denotes Text shows PRCC estimate, and * indicates statistical significance after Bonferroni correction (threshold = 9e-4 for 54 comparisons).
T203 32712-32735 Sentence denotes Figure 4—source data 1.
T204 32737-32797 Sentence denotes Source data for Figure 4, and Figure 4—figures supplement 1.
T205 32798-32898 Sentence denotes Source data for Figure 4—figures supplement 2 can be found as a. csv file in the supplementary code.
T206 32899-32928 Sentence denotes Figure 4—figure supplement 1.
T207 32930-33006 Sentence denotes PRCC analysis comparing cases where 5%, 25% or 50% of cases are subclinical.
T208 33007-33084 Sentence denotes (Middle panel is identical to Figure 4, but repeated for ease of comparison).
T209 33085-33114 Sentence denotes Figure 4—figure supplement 2.
T210 33116-33180 Sentence denotes PRCC analysis assuming the source epidemic is no longer growing.
T211 33181-33234 Sentence denotes By construction, R0 has no impact in a flat epidemic.
T212 33235-33359 Sentence denotes Small PRCC estimates for R0 arise from stochasticity in simulated outcomes, but are never significantly different from zero.
T213 33360-33673 Sentence denotes In the context of a stable epidemic, a greater proportion of infected travellers will have progressed to show detectable symptoms, and so screening effectiveness was more sensitive to parameters that impact symptom screening efficacy (thermal scanner sensitivity, and to the time from symptom onset to isolation).
T214 33674-33944 Sentence denotes Note that by construction, model outcomes are insensitive to parameter R0 in the stable epidemic context. As a result, R0 coefficient estimates are very small (non-zero due to stochasticity in simulation outcomes), and never significant. (Figure 4—figures supplement 2).
T215 33946-33983 Sentence denotes Interactive online app for public use
T216 33984-34190 Sentence denotes We have developed an interactive web application using the R package Shiny (Chang et al., 2019) in which users can replicate our analyses using parameter inputs that reflect the most up-do-date information.
T217 34191-34298 Sentence denotes The supplementary user interface can be accessed at https://faculty.eeb.ucla.edu/lloydsmith/screeningmodel.
T218 34299-34494 Sentence denotes Please note that while the results in Figures 3 and 4 consider a range of plausible values for each parameter, the outputs of the Shiny app are calculated using fixed, user-specified values only.
T219 34496-34506 Sentence denotes Discussion
T220 34507-34774 Sentence denotes The international expansion of COVID-19 cases has led to widespread adoption of symptom and risk screening measures, in travel-associated and other contexts, and programs may still be adopted or expanded as source epidemics of COVID-19 emerge in new geographic areas.
T221 34775-35106 Sentence denotes Using a mathematical model of screening effectiveness, with preliminary estimates of COVID-19 epidemiology and natural history, we estimate that screening will detect less than half of infected travellers in a growing epidemic, and that screening effectiveness will increase marginally as growth of the source epidemic decelerates.
T222 35107-35180 Sentence denotes We found that two main factors influenced the effectiveness of screening.
T223 35181-35354 Sentence denotes First, symptom screening depends on the natural history of an infection: individuals are increasingly likely to show detectable symptoms with increasing time since exposure.
T224 35355-35603 Sentence denotes A fundamental shortcoming of screening is the difficulty of detecting infected individuals during their incubation period, or early after the onset of symptoms, at which point they still feel healthy enough to undertake normal activities or travel.
T225 35604-35788 Sentence denotes This difficulty is amplified when the incubation period is longer; infected individuals have a longer window in which they may mix socially or travel with low probability of detection.
T226 35789-35942 Sentence denotes Second, screening depends on whether exposure risk factors exist that would facilitate specific and reasonably sensitive case detection by questionnaire.
T227 35943-36108 Sentence denotes For COVID-19, there is so far limited evidence for specific risk factors; we therefore assumed that at most 40% of travellers would be aware of a potential exposure.
T228 36109-36344 Sentence denotes It is plausible that many individuals aware of a potential exposure would voluntarily avoid travel and practice social distancing--if so, the population of infected travellers will be skewed toward those unaware they have been exposed.
T229 36345-36583 Sentence denotes Furthermore, based on screening outcomes during the 2009 influenza pandemic, we assumed that a minority of infected travellers would self-report their exposure honestly, which led to limited effectiveness in questionnaire-based screening.
T230 36584-36694 Sentence denotes The confluence of these two factors led to many infected people being fundamentally undetectable in our model.
T231 36695-36862 Sentence denotes Even under our most generous assumptions about the natural history of COVID-19, the presence of undetectable cases made the greatest contribution to screening failure.
T232 36863-36993 Sentence denotes Correctable failures, such as missing an infected person with fever or awareness of their exposure risk, played a more minor role.
T233 36994-37330 Sentence denotes Our conclusion that screening would detect no more than half of infected travellers in a growing epidemic is consistent with recent studies that have compared country-specific air travel volumes with detected case counts to estimate that roughly two thirds of imported cases remain undetected (Niehus et al., 2020; Bhatia et al., 2020).
T234 37331-37478 Sentence denotes Furthermore, the finding that the majority of cases missed by screening are fundamentally undetectable is consistent with observed outcomes so far.
T235 37479-37771 Sentence denotes Analyzing a line list of 290 cases imported into various countries (Dorigatti et al., 2020), we found that symptom onset occurred after the date of inbound travel for 72% (75/104) of cases for whom both dates were available, and a further 14% (15/104) had symptom onset on the date of travel.
T236 37772-38164 Sentence denotes Even among passengers of repatriation flights, or quarantined on a cruise ship off the coast of Japan (who are all demonstrably at high risk), numerous cases have been undetectable in symptom screening, but have still tested positive for SARS-CoV-2 by PCR (Dorigatti et al., 2020; Hoehl et al., 2020; Japan Ministry of Health, Labor and Welfare, 2020; Nishiura et al., 2020; Hu et al., 2020).
T237 38165-38366 Sentence denotes The onset of viral shedding prior to the onset of symptoms, or in cases that remain asymptomatic, is a classic factor that makes infectious disease outbreaks difficult to control (Fraser et al., 2004).
T238 38367-38554 Sentence denotes Our results emphasize that the true fraction of subclinical cases (those who lack fever or cough at symptom onset) remains a crucial unknown, and strongly impacts screening effectiveness.
T239 38555-38740 Sentence denotes Reviewing data from active surveillance of passengers on cruise ships or repatriation flights, we estimate that up to half of cases show no detectable symptoms at the time of diagnosis.
T240 38741-38833 Sentence denotes To complicate matters further, the fraction of subclinical cases may vary across age groups.
T241 38834-39073 Sentence denotes Children and young adults have been conspicuously underrepresented, even in very large clinical data sets (Chen et al., 2020; The Novel Coronavirus Pneumonia Emergency Response Epidemiology Team, 2020; Huang et al., 2020; Li et al., 2020).
T242 39074-39245 Sentence denotes Only 2.1% of the first 44,672 confirmed cases were observed in children under 20 years of age (The Novel Coronavirus Pneumonia Emergency Response Epidemiology Team, 2020).
T243 39246-39352 Sentence denotes The possibility cannot be ruled out that large numbers of subclinical cases are occurring in young people.
T244 39353-39507 Sentence denotes If an age-by-severity interaction does indeed exist, then the mean age of travellers should be taken into account when estimating screening effectiveness.
T245 39508-39551 Sentence denotes There are some limitations to our analysis.
T246 39552-39793 Sentence denotes Parameter values for COVID-19 may be affected by bias or censoring, particularly in the early stages of an outbreak when most cases have been recently infected, and when severe or hospitalized cases are overrepresented in the available data.
T247 39794-39929 Sentence denotes In particular, the tail of the incubation period distribution is difficult to characterize with precision using limited or biased data.
T248 39930-40080 Sentence denotes As country-specific screening policies can change rapidly in real-time, we focused on a general screening framework rather than specific case studies.
T249 40081-40152 Sentence denotes We also assumed traveller adherence and no active evasion of screening.
T250 40153-40324 Sentence denotes However, there are informal reports of people taking antipyretics to beat fever screening (Mahbubani, 2020), which would further reduce the effectiveness of these methods.
T251 40325-40523 Sentence denotes With travel restrictions in place, individuals may also take alternative routes (e.g. road rather than air), which would in effect circumvent departure and/or arrival screening as a control measure.
T252 40524-40626 Sentence denotes Our quantitative findings may overestimate screening effectiveness if many travellers evade screening.
T253 40627-40731 Sentence denotes Our results have several implications for the design and implementation of traveller screening policies.
T254 40732-40956 Sentence denotes If the infection is not yet present in a region, then arrival screening could delay the introduction of cases, but consistent with previous analyses, (Cowling et al., 2010), our results indicate such delays would be minimal.
T255 40957-41124 Sentence denotes Our findings indicate that for every case detected by travel screening, one or more infected travellers were not caught, and must be found and isolated by other means.
T256 41125-41328 Sentence denotes We note that even with high R0 and no control measures in place, a single case importation is not guaranteed to start a sustained chain of transmission (Kucharski et al., 2020; Lloyd-Smith et al., 2005).
T257 41329-41586 Sentence denotes This is particularly true for infections that exhibit a tendency toward superspreading events, as increasingly reported for COVID-19, but the flipside is that outbreaks triggered by superspreading are explosive when they do occur (Lloyd-Smith et al., 2005).
T258 41587-41681 Sentence denotes We did not analyze second-order benefits from screening, such as potential to raise awareness.
T259 41682-41914 Sentence denotes Official recommendations emphasize that screening is an opportunity for ‘risk communication’ in which travellers can be instructed how to proceed responsibly if symptoms develop at the destination (World Health Organization, 2020d).
T260 41915-42121 Sentence denotes Alongside increased general surveillance/alertness in healthcare settings, such measures could help reduce the risk of local transmission and superspreading, but their quantitative effectiveness is unknown.
T261 42122-42296 Sentence denotes Once limited local transmission has begun, arrival screening could still have merit as a means to restrict the number of parallel chains of transmission present in a country.
T262 42297-42509 Sentence denotes Once there is generalized spread which has outpaced contact tracing, departure screening to prevent export of cases to new areas will be more valuable than arrival screening to identify additional incoming cases.
T263 42510-42764 Sentence denotes Altogether, screening should not be viewed as a definitive barrier to case importation, but used alongside on-the-ground response strategies that help reduce the probability that any single imported case spreads to cause a self-sustaining local epidemic.
T264 42765-42959 Sentence denotes The cost-benefit tradeoff for any screening policy should be assessed in light of past experiences, where few or no infected travellers have been detected by such programs (Gostic et al., 2015).
T265 42960-43203 Sentence denotes While our findings indicate that the majority of screening failures arise from undetectable cases (i.e. those without symptoms or knowledge of their exposure), several factors could potentially strengthen the screening measures described here.
T266 43204-43438 Sentence denotes With improved efficiency of thermal scanners or other symptom detection technology, we would expect a smaller difference between the effectiveness of arrival-only screening and combined departure and arrival screening in our analysis.
T267 43439-43761 Sentence denotes Alternatively, the benefits of redundant screening (noted above for programs with departure and arrival screens) could be gained in a single-site screening program by simply having two successive fever-screening stations that travellers pass through (or taking multiple measurements of each traveller at a single station).
T268 43762-43864 Sentence denotes As risk factors become better known, questionnaires could be refined to identify more potential cases.
T269 43865-44106 Sentence denotes Alternatively, less stringent definition of high exposure risk (e.g. contact with anyone with respiratory symptoms) would be more sensitive, but at the expense of large numbers of false positives detained, especially during influenza season.
T270 44107-44280 Sentence denotes The availability of rapid PCR tests would also be beneficial for case identification at arrival, and would help address concerns with false-positive detections by screening.
T271 44281-44553 Sentence denotes If such tests were fast, there may be potential to test suspected cases in real time based on questionnaire responses, travel origin, or borderline symptoms; at least one PCR test for SARS-CoV-2 claimed to take less than an hour has already been announced (Biomeme, 2020).
T272 44554-44630 Sentence denotes However, such measures could prove highly expensive if implemented at scale.
T273 44631-44849 Sentence denotes There is also scope for new tools to improve the ongoing tracking of travellers who pass through screening, such as smartphone-based self-reporting of temperature or symptoms in incoming cases (Dorigatti et al., 2020).
T274 44850-45009 Sentence denotes Smartphone or diary-based surveillance would be cheaper and more scalable than intense, on-the-ground follow-up, but is likely to be limited by user adherence.
T275 45010-45321 Sentence denotes Our analysis underscores the reality that respiratory viruses are difficult to detect by symptom and risk screening programs, particularly if a substantial fraction of infected people show mild or indistinct symptoms, if incubation periods are long, and if transmission is possible before the onset of symptoms.
T276 45322-45512 Sentence denotes Quantitative estimates of screening effectiveness for COVID-19 will improve as more is learned about this recently-emerged virus, and will vary with the precise design of screening programs.
T277 45513-45778 Sentence denotes However, we present a robust qualitative finding: in any situation where there is widespread epidemic transmission in source populations from which travellers are drawn, travel screening programs can slow (marginally) but not stop the importation of infected cases.
T278 45779-45858 Sentence denotes Screening programs implemented in other settings will face the same challenges.
T279 45859-46044 Sentence denotes By decomposing the factors leading to success or failure of screening efforts, our work supports decision-making about program design, and highlights key questions for further research.
T280 46045-46302 Sentence denotes We hope that these insights may help to mitigate the global impacts of COVID-19 by guiding effective decision-making in both high- and low-resource countries, and may contribute to prospective improvements in screening policy for future emerging infections.
T281 46304-46325 Sentence denotes Materials and methods
T282 46327-46344 Sentence denotes Modeling strategy
T283 46345-46473 Sentence denotes The model’s structure is summarized above (Figure 1), and detailed methods have been described previously (Gostic et al., 2015).
T284 46474-46547 Sentence denotes Here, we summarize relevant extensions, assumptions and parameter inputs.
T285 46549-46559 Sentence denotes Extensions
T286 46560-46720 Sentence denotes Our previous model tracked all the ways in which infected travellers can be detected by screening (fever screen, or risk factor screen at arrival or departure).
T287 46721-46941 Sentence denotes Here, we additionally keep track of the many ways in which infected travellers can be missed (i.e. missed given fever present, missed given exposure risk present, missed given both present, or missed given undetectable).
T288 46942-47102 Sentence denotes Cases who have not yet passed the incubation period are considered undetectable by fever screening, even if they will eventually develop symptoms in the future.
T289 47103-47249 Sentence denotes In other words, no traveller is considered ‘missed given fever present’ until they have passed the incubation period and show detectable symptoms.
T290 47250-47404 Sentence denotes Infected travellers who progress to symptoms during their journey are considered undetectable by departure screening, but detectable by arrival screening.
T291 47405-47558 Sentence denotes Additionally, we now provide a supplementary user interface, which allows stakeholders to test input parameters of interest using up-to-date information.
T292 47559-47720 Sentence denotes Here, in addition to the analyses presented in this study, we implemented the possibility that some fraction of infected travellers deliberately evade screening.
T293 47722-47751 Sentence denotes Basic reproduction number, R0
T294 47752-47858 Sentence denotes Existing point estimates for R0 span a wide range (2.2–6.47), but most fall between 2.0 and 4.0 (Table 1).
T295 47859-47997 Sentence denotes The vast majority of these estimates are informed by data collected very early in the outbreak, before any control measures were in place.
T296 47998-48207 Sentence denotes However, several studies already demostrate decreases in the reproductive number over time, as a consequence of social distancing behaviors, and containment measures (Kucharski et al., 2020; Liu et al., 2020).
T297 48208-48363 Sentence denotes Realistically, R0 will vary considerably over time, and across locations, depending on the social context, resource availability, and containment policies.
T298 48364-48666 Sentence denotes Our analysis considers a plausible range of R0 values spanning 1.5–4.0, with 4.0 representing a plausible maximum in the absence of any behavioral changes or containment efforts, and 1.5 reflecting a plausible lower bound, given containment measures may already be in place at the time of introduction.
T299 48668-48697 Sentence denotes Fraction of subclinical cases
T300 48698-48903 Sentence denotes To estimate the upper-bound fraction of subclinical cases, we draw on data from active surveillance of passengers quarantined on a cruise ship off the coast of Japan, or passengers of repatriation flights.
T301 48904-49069 Sentence denotes These data show that 50–70% of cases are asymptomatic at the time of diagnosis (Dorigatti et al., 2020; Nishiura et al., 2020; Schnirring, 2020c; Schnirring, 2020b).
T302 49070-49335 Sentence denotes We estimate that 50% subclinical cases is a reasonable upper bound: due to intensive monitoring, cases in repatriated individuals or in cruise ship passengers will be detected earlier than usual in the course of infection--and possibly before the onset of symptoms.
T303 49336-49684 Sentence denotes From clinical data (where severe cases are likely overrepresented), we estimate a lower bound of 5%: even among clinically attended cases, 2–15% lack fever or cough, and would be undetectable in symptom screening (Chan et al., 2020; Chen et al., 2020; The Novel Coronavirus Pneumonia Emergency Response Epidemiology Team, 2020; Huang et al., 2020).
T304 49685-49820 Sentence denotes In addition to the upper and lower bound scenarios, we consider a plausible middle-case scenario in which 25% of cases are subclinical.
T305 49821-50062 Sentence denotes A very recent delay-adjusted estimate indicates 30-40% of infections on the cruise ship quarantined off the coast of Japan are asymptomatic, so the truth may fall somewhere between our middle and worst-case scenarios (Mizumoto et al., 2020).
T306 50064-50094 Sentence denotes Incubation period distribution
T307 50095-50160 Sentence denotes We use a gamma distribution to model individual incubation times.
T308 50161-50344 Sentence denotes We choose this form over the Weibull and lognormal distribution for ease of interpretation (gamma shape and scale parameters can be transformed easily to mean and standard deviation).
T309 50345-50529 Sentence denotes So far, best-fit gamma distributions to COVID-19 data have had mean 6.5 and standard deviation 2.6 (Backer et al., 2020), or mean 5.46 and standard deviation 1.94 (Lauer et al., 2020).
T310 50530-50767 Sentence denotes Here, to model uncertainty around the true mean incubation time, we fix the standard deviation to 2.25 (intermediate between the two existing estimates), and allow the mean to vary between 4.5 and 6.5 days (Figure 2—figure supplement 2).
T311 50768-51018 Sentence denotes The 95th percentile of the distributions we consider fall between 8.7 and 10.6 days, slightly below the officially accepted maximum incubation time of 14 days, and consistent with existing estimates (Table 1; Backer et al., 2020; Lauer et al., 2020).
T312 51020-51065 Sentence denotes Effectiveness of exposure risk questionnaires
T313 51066-51257 Sentence denotes The probability that an infected traveller is detectable using questionnaire-based screening for exposure risk will be highest if risk factors with high sensitivity and specificity are known.
T314 51258-51510 Sentence denotes Currently, official guidance recommends asking whether travellers have visited a country with epidemic transmission, a healthcare facility with confirmed cases, or had close contact with a confirmed or suspected case (World Health Organization, 2020c).
T315 51511-51727 Sentence denotes Given the relative anonymity of respiratory transmission, we assume that a minority of infected travellers would realize that they have been exposed before symptoms develop (20% in Figure 2, range 5–40% in Figure 3).
T316 51728-51904 Sentence denotes Further, relying on a previous upper-bound estimate (Gostic et al., 2015) we assume that only 25% of travellers would self-report truthfully if aware of elevated exposure risk.
T317 51905-52000 Sentence denotes Table 1 summarizes the state of knowledge about additional parameters, as of February 20, 2020.
T318 52002-52028 Sentence denotes Code and data availability
T319 52029-52280 Sentence denotes All code and source data used to perform analyses and generate figures is archived at https://github.com/kgostic/traveller_screening/releases/tag/v2.1. (Gostic, 2020; copy archived at https://github.com/elifesciences-publications/traveller_screening).
T320 52282-52301 Sentence denotes Funding Information
T321 52302-52351 Sentence denotes This paper was supported by the following grants:
T322 52352-52396 Sentence denotes http://dx.doi.org/10.13039/100000913James S.
T323 52397-52494 Sentence denotes McDonnell Foundation Postdoctoral fellowship in dynamic and multiscale systems to Katelyn Gostic.
T324 52495-52574 Sentence denotes http://dx.doi.org/10.13039/100004440Wellcome 206250/Z/17/Z to Adam J Kucharski.
T325 52575-52725 Sentence denotes http://dx.doi.org/10.13039/501100002322Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Science without borders fellowship to Ana CR Gomez.
T326 52726-52825 Sentence denotes http://dx.doi.org/10.13039/100000001National Science Foundation DEB-1557022 to James O Lloyd-Smith.
T327 52826-52946 Sentence denotes http://dx.doi.org/10.13039/100000185Defense Advanced Research Projects Agency PREEMPT D18AC00031 to James O Lloyd-Smith.
T328 52947-53102 Sentence denotes http://dx.doi.org/10.13039/100013316Strategic Environmental Research and Development Program RC-2635 to Ana C R Gomez, Riley O Mummah, James O Lloyd-Smith.
T329 53104-53120 Sentence denotes Acknowledgements
T330 53121-53204 Sentence denotes We are grateful to Miaka McClenahan and Pieter de Ganon for translation assistance.
T331 53205-53249 Sentence denotes We thank the Cobey lab for helpful comments.
T332 53251-53273 Sentence denotes Additional information
T333 53274-53293 Sentence denotes Competing interests
T334 53294-53326 Sentence denotes No competing interests declared.
T335 53327-53347 Sentence denotes Author contributions
T336 53348-53563 Sentence denotes Conceptualization, Resources, Data curation, Software, Formal analysis, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing.
T337 53564-53678 Sentence denotes Resources, Software, Validation, Visualization, Methodology, Project administration, Writing - review and editing.
T338 53679-53747 Sentence denotes Data curation, Project administration, Writing - review and editing.
T339 53748-53831 Sentence denotes Conceptualization, Visualization, Writing - original draft, Project administration.
T340 53832-53992 Sentence denotes Conceptualization, Supervision, Funding acquisition, Investigation, Methodology, Project administration, Writing - review and editing, Writing - original draft.
T341 53994-54010 Sentence denotes Additional files
T342 54011-54037 Sentence denotes Transparent reporting form
T343 54039-54056 Sentence denotes Data availability
T344 54057-54097 Sentence denotes There are no data inputs into our model.
T345 54098-54177 Sentence denotes All parameter input values are specified in Table 1, or in the manuscript text.
T346 54178-54443 Sentence denotes We provide a link to the github repository containing all code necessary to run the analyses and generate figures (https://github.com/kgostic/traveller_screening/releases/tag/v2.1, copy archived at https://github.com/elifesciences-publications/traveller_screening).

MyTest

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