LitCovid-sentences  

PMC:7060038 / 1222-52280 JSONTXT 13 Projects

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Id Subject Object Predicate Lexical cue
T10 0-12 Sentence denotes Introduction
T11 13-329 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 330-569 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 570-696 Sentence denotes Many jurisdictions have imposed traveller screening in an effort to prevent importation of COVID-19 cases to unaffected areas.
T14 697-926 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 927-1079 Sentence denotes Meanwhile, in many other countries, screening remains the primary barrier to case importation (Guardian reporting team, 2020; Schengen Visa Info, 2020).
T16 1080-1266 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 1267-1442 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 1443-1494 Sentence denotes Our analysis is pertinent to all of these contexts.
T19 1495-1944 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 1945-2191 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 2192-2411 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 2412-2686 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 2687-2840 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 2841-3056 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 3057-3220 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 3221-3410 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 3411-3636 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 3637-4045 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 4046-4341 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 4342-4533 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 4534-4654 Sentence denotes We estimate the fraction of infected travellers detected, breaking down the ways in which screening can succeed or fail.
T32 4655-4926 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 4927-5135 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 5136-5286 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 5287-5470 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 5471-5774 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 5775-5791 Sentence denotes Until about Feb.
T38 5792-6216 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 6217-6248 Sentence denotes Now, in the week following Feb.
T40 6249-6452 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 6453-6628 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 6629-6854 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 6855-6956 Sentence denotes Limited resources also could mean that some countries cannot implement large-scale arrival screening.
T44 6957-7096 Sentence denotes In this scenario, departure screening implemented elsewhere would be the sole barrier -- however leaky -- to new waves of case importation.
T45 7097-7400 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 7401-7531 Sentence denotes Accordingly, we consider scenarios with departure screening only, arrival screening only, or both departure and arrival screening.
T47 7532-7744 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 7745-7871 Sentence denotes Our analysis also has direct bearing on other contexts where symptom screening is being used, beyond international air travel.
T49 7872-8234 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 8235-8375 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 8376-8647 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 8648-8857 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 8858-9006 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 9007-9250 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 9252-9259 Sentence denotes Results
T56 9261-9289 Sentence denotes Model for COVID-19 screening
T57 9290-9567 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 9568-9710 Sentence denotes These assumptions are consistent with WHO traveller screening guidelines (World Health Organization, 2020b; World Health Organization, 2020c).
T59 9711-10005 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 10006-10193 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 10194-10203 Sentence denotes Figure 1.
T62 10205-10277 Sentence denotes Model of traveller screening process, adapted from Gostic et al. (2015).
T63 10278-10811 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 10812-10967 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 10968-11089 Sentence denotes This allows us to track the fraction of cases detected using symptom screening or risk screening at arrival or departure.
T66 11090-11727 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 11728-11795 Sentence denotes Here, we only consider infected travellers who submit to screening.
T68 11796-11950 Sentence denotes However, the supplementary app allows users to consider scenarios in which some fraction of infected travellers intentionally evade screening (Figure 1E).
T69 11951-12405 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 12406-12566 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 12567-12703 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 12704-12924 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 12925-13132 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 13133-13329 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 13330-13690 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 13691-13958 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 13959-14160 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 14162-14210 Sentence denotes Parameters, uncertainty and sensitivity analyses
T79 14211-14406 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 14407-14472 Sentence denotes Table 1 and the Methods summarize the current state of knowledge.
T81 14473-14566 Sentence denotes Here, we used two distinct approaches to incorporate parameter uncertainty into our analysis.
T82 14567-14575 Sentence denotes Table 1.
T83 14577-14690 Sentence denotes Parameter values estimated in currently available studies, along with accompanying uncertainties and assumptions.
T84 14691-14824 Sentence denotes Ranges in the final column reflect confidence interval, credible interval, standard error or range reported by each study referenced.
T85 14825-14926 Sentence denotes Parameter Best estimate (Used in Figure 2) Plausible range (Used in Figure 3) References and notes
T86 14927-14972 Sentence denotes Mean incubation period 5.5 days Sensitivity:
T87 14973-15187 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 15188-15288 Sentence denotes Incubation period distribution Gamma distribution with mean as above, and standard deviation = 2.25
T89 15289-15358 Sentence denotes Percent of cases subclinical (No fever or cough) Best case scenario:
T90 15359-15383 Sentence denotes 5% Middle case scenario:
T91 15384-15408 Sentence denotes 25% Worst case scenario:
T92 15409-15428 Sentence denotes 50% Clinical data:
T93 15429-15916 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 15917-15960 Sentence denotes R0 No effect in individual-level analysis.
T95 15962-16320 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 16321-16539 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 16540-16715 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 16716-16780 Sentence denotes But a handful of studies estimated very low sensitivity (4–30%).
T99 16781-16877 Sentence denotes In general, sensitivity depended on the device used, body area targeted and ambient temperature.
T100 16878-17046 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 17047-17179 Sentence denotes Time from symptom onset to patient isolation (After which we assume travel is not possible) No effect in individual-level analysis.
T102 17181-17462 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 17463-17485 Sentence denotes * From family cluster.
T104 17486-17583 Sentence denotes † Parametric distributions fit to cases with known dates of exposure or travel to and from Wuhan.
T105 17584-17785 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 17786-17924 Sentence denotes We focus on the incubation period and subclinical fraction of cases because screening outcomes are particularly sensitive to their values.
T107 17925-18007 Sentence denotes All other parameters were fixed to the best available estimates listed in Table 1.
T108 18008-18183 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 18184-18432 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 18433-18617 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 18618-18731 Sentence denotes Using each parameter set, we simulated one set of screening outcomes for a population of 30 infected individuals.
T112 18732-18953 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 18954-19219 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 19220-19422 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 19424-19461 Sentence denotes Individual probabilities of detection
T116 19462-19655 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 19656-19801 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 19802-19912 Sentence denotes Here we considered three mean incubation times, which together span the range of most existing mean estimates:
T119 19913-19945 Sentence denotes 4.5, 5.5 and 6.5 days (Table 1).
T120 19946-20020 Sentence denotes Additionally, we considered three possible fractions of subclinical cases:
T121 20021-20178 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 20179-20363 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 20364-20373 Sentence denotes Figure 2.
T124 20375-20466 Sentence denotes Individual outcome probabilities for travellers who screened at given time since infection.
T125 20467-20619 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 20620-20790 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 20791-20872 Sentence denotes The black dashed lines separate detected cases (below) from missed cases (above).
T128 20873-21123 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 21124-21161 Sentence denotes Table 1 lists all other input values.
T130 21162-21185 Sentence denotes Figure 2—source data 1.
T131 21187-21212 Sentence denotes Source data for Figure 2.
T132 21213-21303 Sentence denotes Raw, simulated data, and source data for Figure 2—figures supplement 1, 2 can be found as.
T133 21304-21350 Sentence denotes RData or. csv files in the supplementary code.
T134 21351-21380 Sentence denotes Figure 2—figure supplement 1.
T135 21382-21407 Sentence denotes Departure screening only.
T136 21408-21437 Sentence denotes Figure 2—figure supplement 2.
T137 21439-21462 Sentence denotes Arrival screening only.
T138 21463-21564 Sentence denotes Even within the narrow range tested, screening outcomes were sensitive to the incubation period mean.
T139 21565-21903 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 21904-22043 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 22044-22185 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 22186-22410 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 22411-22516 Sentence denotes Furthermore, subclinical cases who are unaware of their exposure risk are never detectable, by any means.
T144 22517-22630 Sentence denotes This is manifested as the bright red ‘undetectable’ region which persists well beyond the mean incubation period.
T145 22631-22793 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 22794-23127 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 23128-23459 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 23461-23567 Sentence denotes Overall screening effectiveness in a population of infected travellers during a growing or stable epidemic
T149 23568-23760 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 23761-23952 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 23953-24245 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 24246-24583 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 24584-24781 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 24782-25016 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 25017-25202 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 25203-25536 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 25537-25616 Sentence denotes As above, the dominant contributor to successful detections is fever screening.
T158 25617-25626 Sentence denotes Figure 3.
T159 25628-25698 Sentence denotes Population-level outcomes of screening programs in a growing epidemic.
T160 25699-25899 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 25900-25978 Sentence denotes Dots and vertical line segments show the median and central 95%, respectively.
T162 25979-26114 Sentence denotes Text above each violin shows the median and central 95% fraction detected. (B) Mean fraction of travellers with each screening outcome.
T163 26115-26328 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 26329-26352 Sentence denotes Figure 3—source data 1.
T165 26354-26380 Sentence denotes Source data for Figure 3A.
T166 26381-26471 Sentence denotes Raw, simulated data, and source data for Figure 3—figures supplement 1, 2 can be found as.
T167 26472-26518 Sentence denotes Rdata or. csv files in the supplementary code.
T168 26519-26542 Sentence denotes Figure 3—source data 2.
T169 26544-26570 Sentence denotes Source data for Figure 3B.
T170 26571-26594 Sentence denotes Figure 3—source data 3.
T171 26596-26622 Sentence denotes Source data for Figure 3C.
T172 26623-26652 Sentence denotes Figure 3—figure supplement 1.
T173 26654-26742 Sentence denotes Population-level screening outcomes given that the source epidemic is no longer growing.
T174 26743-26777 Sentence denotes (A-C) are as dscribed in Figure 3.
T175 26778-26807 Sentence denotes Figure 3—figure supplement 2.
T176 26809-26887 Sentence denotes Plausible incubation period distributions underlying the analyses in Figure 3.
T177 26888-27027 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 27028-27160 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 27161-27344 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 27345-27435 Sentence denotes This is shown by the smaller, red ‘undetectable’ region in Figure 3—figures supplement 1B.
T181 27436-27745 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 27746-27974 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 27975-28208 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 28209-28502 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 28503-28661 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 28662-29008 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 29009-29203 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 29204-29291 Sentence denotes What duration of delay this yields will depend on the frequency of infected travellers.
T189 29293-29313 Sentence denotes Sensitivity analysis
T190 29314-29655 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 29656-29784 Sentence denotes Sensitivity to R0 was somewhat higher than sensitivity to other parameters, but the difference was not statistically remarkable.
T192 29785-29882 Sentence denotes R0 and the mean incubation period were negatively associated with the fraction of cases detected.
T193 29883-30136 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 30137-30344 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 30345-30498 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 30499-30661 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 30662-30754 Sentence denotes Figure 4 shows results from the middle case scenario, in which 25% of cases are subclinical.
T198 30755-31056 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 31057-31066 Sentence denotes Figure 4.
T200 31068-31233 Sentence denotes Sensitivity analysis showing partial rank correlation coefficient (PRCC) between each parameter and the fraction (per-simulation) of 30 infected travellers detected.
T201 31234-31355 Sentence denotes Outcomes were obtained from 1000 simulations, each using a candidate parameter sets drawn using Latin hypercube sampling.
T202 31356-31489 Sentence denotes Text shows PRCC estimate, and * indicates statistical significance after Bonferroni correction (threshold = 9e-4 for 54 comparisons).
T203 31490-31513 Sentence denotes Figure 4—source data 1.
T204 31515-31575 Sentence denotes Source data for Figure 4, and Figure 4—figures supplement 1.
T205 31576-31676 Sentence denotes Source data for Figure 4—figures supplement 2 can be found as a. csv file in the supplementary code.
T206 31677-31706 Sentence denotes Figure 4—figure supplement 1.
T207 31708-31784 Sentence denotes PRCC analysis comparing cases where 5%, 25% or 50% of cases are subclinical.
T208 31785-31862 Sentence denotes (Middle panel is identical to Figure 4, but repeated for ease of comparison).
T209 31863-31892 Sentence denotes Figure 4—figure supplement 2.
T210 31894-31958 Sentence denotes PRCC analysis assuming the source epidemic is no longer growing.
T211 31959-32012 Sentence denotes By construction, R0 has no impact in a flat epidemic.
T212 32013-32137 Sentence denotes Small PRCC estimates for R0 arise from stochasticity in simulated outcomes, but are never significantly different from zero.
T213 32138-32451 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 32452-32722 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 32724-32761 Sentence denotes Interactive online app for public use
T216 32762-32968 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 32969-33076 Sentence denotes The supplementary user interface can be accessed at https://faculty.eeb.ucla.edu/lloydsmith/screeningmodel.
T218 33077-33272 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 33274-33284 Sentence denotes Discussion
T220 33285-33552 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 33553-33884 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 33885-33958 Sentence denotes We found that two main factors influenced the effectiveness of screening.
T223 33959-34132 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 34133-34381 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 34382-34566 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 34567-34720 Sentence denotes Second, screening depends on whether exposure risk factors exist that would facilitate specific and reasonably sensitive case detection by questionnaire.
T227 34721-34886 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 34887-35122 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 35123-35361 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 35362-35472 Sentence denotes The confluence of these two factors led to many infected people being fundamentally undetectable in our model.
T231 35473-35640 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 35641-35771 Sentence denotes Correctable failures, such as missing an infected person with fever or awareness of their exposure risk, played a more minor role.
T233 35772-36108 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 36109-36256 Sentence denotes Furthermore, the finding that the majority of cases missed by screening are fundamentally undetectable is consistent with observed outcomes so far.
T235 36257-36549 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 36550-36942 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 36943-37144 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 37145-37332 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 37333-37518 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 37519-37611 Sentence denotes To complicate matters further, the fraction of subclinical cases may vary across age groups.
T241 37612-37851 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 37852-38023 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 38024-38130 Sentence denotes The possibility cannot be ruled out that large numbers of subclinical cases are occurring in young people.
T244 38131-38285 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 38286-38329 Sentence denotes There are some limitations to our analysis.
T246 38330-38571 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 38572-38707 Sentence denotes In particular, the tail of the incubation period distribution is difficult to characterize with precision using limited or biased data.
T248 38708-38858 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 38859-38930 Sentence denotes We also assumed traveller adherence and no active evasion of screening.
T250 38931-39102 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 39103-39301 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 39302-39404 Sentence denotes Our quantitative findings may overestimate screening effectiveness if many travellers evade screening.
T253 39405-39509 Sentence denotes Our results have several implications for the design and implementation of traveller screening policies.
T254 39510-39734 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 39735-39902 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 39903-40106 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 40107-40364 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 40365-40459 Sentence denotes We did not analyze second-order benefits from screening, such as potential to raise awareness.
T259 40460-40692 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 40693-40899 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 40900-41074 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 41075-41287 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 41288-41542 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 41543-41737 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 41738-41981 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 41982-42216 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 42217-42539 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 42540-42642 Sentence denotes As risk factors become better known, questionnaires could be refined to identify more potential cases.
T269 42643-42884 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 42885-43058 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 43059-43331 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 43332-43408 Sentence denotes However, such measures could prove highly expensive if implemented at scale.
T273 43409-43627 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 43628-43787 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 43788-44099 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 44100-44290 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 44291-44556 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 44557-44636 Sentence denotes Screening programs implemented in other settings will face the same challenges.
T279 44637-44822 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 44823-45080 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 45082-45103 Sentence denotes Materials and methods
T282 45105-45122 Sentence denotes Modeling strategy
T283 45123-45251 Sentence denotes The model’s structure is summarized above (Figure 1), and detailed methods have been described previously (Gostic et al., 2015).
T284 45252-45325 Sentence denotes Here, we summarize relevant extensions, assumptions and parameter inputs.
T285 45327-45337 Sentence denotes Extensions
T286 45338-45498 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 45499-45719 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 45720-45880 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 45881-46027 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 46028-46182 Sentence denotes Infected travellers who progress to symptoms during their journey are considered undetectable by departure screening, but detectable by arrival screening.
T291 46183-46336 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 46337-46498 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 46500-46529 Sentence denotes Basic reproduction number, R0
T294 46530-46636 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 46637-46775 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 46776-46985 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 46986-47141 Sentence denotes Realistically, R0 will vary considerably over time, and across locations, depending on the social context, resource availability, and containment policies.
T298 47142-47444 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 47446-47475 Sentence denotes Fraction of subclinical cases
T300 47476-47681 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 47682-47847 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 47848-48113 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 48114-48462 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 48463-48598 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 48599-48840 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 48842-48872 Sentence denotes Incubation period distribution
T307 48873-48938 Sentence denotes We use a gamma distribution to model individual incubation times.
T308 48939-49122 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 49123-49307 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 49308-49545 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 49546-49796 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 49798-49843 Sentence denotes Effectiveness of exposure risk questionnaires
T313 49844-50035 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 50036-50288 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 50289-50505 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 50506-50682 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 50683-50778 Sentence denotes Table 1 summarizes the state of knowledge about additional parameters, as of February 20, 2020.
T318 50780-50806 Sentence denotes Code and data availability
T319 50807-51058 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).