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PMC:7060038 / 24683-30513 JSONTXT

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LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
187 51-59 Disease denotes infected MESH:D007239
190 378-386 Disease denotes infected MESH:D007239
191 579-589 Disease denotes infections MESH:D007239
199 2035-2041 Species denotes people Tax:9606
200 889-898 Disease denotes infection MESH:D007239
201 1018-1026 Disease denotes infected MESH:D007239
202 1476-1484 Disease denotes infected MESH:D007239
203 1655-1663 Disease denotes infected MESH:D007239
204 1861-1869 Disease denotes infected MESH:D007239
205 2139-2144 Disease denotes fever MESH:D005334
208 2274-2282 Disease denotes infected MESH:D007239
209 2838-2846 Disease denotes infected MESH:D007239
213 4149-4157 Disease denotes infected MESH:D007239
214 4264-4272 Disease denotes infected MESH:D007239
215 4472-4481 Disease denotes infection MESH:D007239
218 4715-4723 Disease denotes infected MESH:D007239
219 5810-5818 Disease denotes infected MESH:D007239

LitCovid-PD-MONDO

Id Subject Object Predicate Lexical cue mondo_id
T28 579-589 Disease denotes infections http://purl.obolibrary.org/obo/MONDO_0005550
T29 889-898 Disease denotes infection http://purl.obolibrary.org/obo/MONDO_0005550
T30 4472-4481 Disease denotes infection http://purl.obolibrary.org/obo/MONDO_0005550

LitCovid-PD-CLO

Id Subject Object Predicate Lexical cue
T83 35-36 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T84 78-79 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T85 293-298 http://purl.obolibrary.org/obo/NCBITaxon_10239 denotes virus
T86 414-415 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T87 817-818 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T88 1448-1449 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T89 1472-1475 http://purl.obolibrary.org/obo/CLO_0050884 denotes ten
T90 1790-1791 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T91 1953-1954 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T92 2218-2219 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T93 2239-2240 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T94 2594-2595 http://purl.obolibrary.org/obo/CLO_0001021 denotes B
T95 3283-3284 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T96 3537-3538 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T97 3825-3826 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T98 3978-3979 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T99 4178-4179 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T100 4663-4664 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T101 5049-5055 http://purl.obolibrary.org/obo/UBERON_0000473 denotes tested
T102 5531-5537 http://purl.obolibrary.org/obo/UBERON_0000473 denotes tested

LitCovid-PD-CHEBI

Id Subject Object Predicate Lexical cue chebi_id
T4 3483-3488 Chemical denotes gamma http://purl.obolibrary.org/obo/CHEBI_30212
T5 3622-3627 Chemical denotes gamma http://purl.obolibrary.org/obo/CHEBI_30212

LitCovid-PD-HP

Id Subject Object Predicate Lexical cue hp_id
T35 2139-2144 Phenotype denotes fever http://purl.obolibrary.org/obo/HP_0001945

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T148 0-106 Sentence denotes Overall screening effectiveness in a population of infected travellers during a growing or stable epidemic
T149 107-299 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 300-491 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 492-784 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 785-1122 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 1123-1320 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 1321-1555 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 1556-1741 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 1742-2075 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 2076-2155 Sentence denotes As above, the dominant contributor to successful detections is fever screening.
T158 2156-2165 Sentence denotes Figure 3.
T159 2167-2237 Sentence denotes Population-level outcomes of screening programs in a growing epidemic.
T160 2238-2438 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 2439-2517 Sentence denotes Dots and vertical line segments show the median and central 95%, respectively.
T162 2518-2653 Sentence denotes Text above each violin shows the median and central 95% fraction detected. (B) Mean fraction of travellers with each screening outcome.
T163 2654-2867 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 2868-2891 Sentence denotes Figure 3—source data 1.
T165 2893-2919 Sentence denotes Source data for Figure 3A.
T166 2920-3010 Sentence denotes Raw, simulated data, and source data for Figure 3—figures supplement 1, 2 can be found as.
T167 3011-3057 Sentence denotes Rdata or. csv files in the supplementary code.
T168 3058-3081 Sentence denotes Figure 3—source data 2.
T169 3083-3109 Sentence denotes Source data for Figure 3B.
T170 3110-3133 Sentence denotes Figure 3—source data 3.
T171 3135-3161 Sentence denotes Source data for Figure 3C.
T172 3162-3191 Sentence denotes Figure 3—figure supplement 1.
T173 3193-3281 Sentence denotes Population-level screening outcomes given that the source epidemic is no longer growing.
T174 3282-3316 Sentence denotes (A-C) are as dscribed in Figure 3.
T175 3317-3346 Sentence denotes Figure 3—figure supplement 2.
T176 3348-3426 Sentence denotes Plausible incubation period distributions underlying the analyses in Figure 3.
T177 3427-3566 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 3567-3699 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 3700-3883 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 3884-3974 Sentence denotes This is shown by the smaller, red ‘undetectable’ region in Figure 3—figures supplement 1B.
T181 3975-4284 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 4285-4513 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 4514-4747 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 4748-5041 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 5042-5200 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 5201-5547 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 5548-5742 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 5743-5830 Sentence denotes What duration of delay this yields will depend on the frequency of infected travellers.