Overall screening effectiveness in a population of infected travellers during a growing or stable epidemic
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. 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. 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.
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). 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. 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. 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). 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. As above, the dominant contributor to successful detections is fever screening.
Figure 3.  Population-level outcomes of screening programs in a growing epidemic.
(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. Dots and vertical line segments show the median and central 95%, respectively. Text above each violin shows the median and central 95% fraction detected. (B) Mean fraction of travellers with each screening outcome. 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.
Figure 3—source data 1.  Source data for Figure 3A.
Raw, simulated data, and source data for Figure 3—figures supplement 1, 2 can be found as. Rdata or. csv files in the supplementary code.
Figure 3—source data 2.  Source data for Figure 3B.
Figure 3—source data 3.  Source data for Figure 3C.
Figure 3—figure supplement 1.  Population-level screening outcomes given that the source epidemic is no longer growing.
(A-C) are as dscribed in Figure 3.
Figure 3—figure supplement 2.  Plausible incubation period distributions underlying the analyses in Figure 3.
Each line shows the probability density function of the gamma distribution with different plausible means and a standard deviation of 2.25. The parameter values were picked based on the best-fit gamma distributions reported in Backer et al. (2020) and Lauer et al. (2020). 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. This is shown by the smaller, red ‘undetectable’ region in Figure 3—figures supplement 1B. 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. 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.
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). 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). 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. 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. 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. What duration of delay this yields will depend on the frequency of infected travellers.