Effectiveness of traveller screening for emerging pathogens is shaped by epidemiology and natural history of infection
- University of California, Los Angeles, United States
- London School of Tropical Hygiene and Medicine, United Kingdom
- Fogarty International Center, National Institutes of Health, United States
Figures
Figure 1 with 1 supplement
Model of traveller screening process.
(A) Upon airport arrival, passengers passed through screening for fever, followed by screening for risk factors. We assumed a one-strike policy: passengers identified as potentially infected by any …
Figure 1—figure supplement 1
Detailed model formulation with parameters.
Each case represents a different detectability class. Travellers are assigned to detectability classes with probabilities f (presence of fever) and g (awareness of exposure risk). Values for f and g …
Figure 2
Parameters characterizing natural history of infection and epidemiological knowledge.
(A) Proportion of infected individuals who report known exposure risk and show fever at onset. Point shows median estimate, using data in Tables 2, 3; circle shows joint 95% binomial confidence …
Figure 3 with 3 supplements
Impact of infection age on effectiveness of screening measures.
Expected fraction of passengers detected by fever and risk factor screening, at arrival and departure, as a function of the time between an individual’s exposure and the departure leg of their …
Figure 3—figure supplement 1
Expected proportions detected by screening when efficacy of fever screening is 50% and proportion of cases with known exposure history who report correctly is 0.25.
https://doi.org/10.7554/eLife.05564.006
Figure 3—figure supplement 2
Expected proportions detected by screening when efficacy of fever screening is 70% and proportion of cases with known exposure history who report correctly is 0.1.
https://doi.org/10.7554/eLife.05564.007
Figure 3—figure supplement 3
Expected proportions detected by screening when efficacy of fever screening is 50% and proportion of cases with known exposure history who report correctly is 0.1.
https://doi.org/10.7554/eLife.05564.008
Figure 4 with 1 supplement
Proportion of infected travellers that would be missed by each of four screening scenarios.
(A) Proportion of 50 infected travellers that would be missed by both departure and arrival screening in a growing epidemic. Figure shows three possible screening methods: fever screen, exposure …
Figure 4—figure supplement 1
Different time from exposure to departure functions used in model.
Red, influenza A/H7N9; purple influenza A/H1N1p; blue, MERS; green, SARS; orange, Ebola; black, Marburg. (A) Growing epidemic with R0 = 1.5. (B) Stable situation.
Tables
Table 1
Airport screening measures during past disease outbreaks
| Pathogen | Date | Location | Direction | Screened | Detained | Positive | Source |
|---|---|---|---|---|---|---|---|
| Influenza A/H1N1p | 27 April–22 June 2009 | Auckland, New Zealand | Inbound | 456,518 | 406 | 4 | (Hale et al., 2012) |
| 28 April–18 June 2009 | Sydney, Australia | Inbound | 625,147 | 5845 | 3 | (Gunaratnam et al., 2014) | |
| 28 April–18 June 2009 | Tokyo, Japan | Inbound | 471,733 | 805 | 15 | (Nishiura and Kamiya, 2011) | |
| SARS Co-V | 5 April–16 June 2003 | Australia | Inbound | 1,840,000 | 794 | 0 | (Samaan et al., 2004) |
| 31 March–31 May 2003 | Singapore | Inbound | 442,973 | 176 | 0 | (Wilder-Smith et al., 2003) | |
| 14 May–5 July 2003 | Toronto, Canada | Inbound | 349,754 | 1264 | 0 | (St John et al., 2005) | |
| 14 May–5 July 2003 | Toronto, Canada | Outbound | 495,492 | 411 | 0 | (St John et al., 2005) | |
| MERS Co-V | 24 September 2012–15 October 2013 | England | Inbound | NR | 77 | 2 | (Thomas et al., 2014) |
| Ebola virus | August–September 2014 | Guinea, Liberia, Sierra Leone | Outbound | 36,000 | 77 | 0 | (Centers for Disease Control and Prevention, 2014a) |
| 11 October–22 October 2014 | United States | Inbound | 762 | 3 | 0 | (Apuzzo and Fernandez, 2014; CBS, 2014) |
Table 2
Natural history parameters: f is the proportion of cases with fever, g is the proportion of cases aware of exposure risk
| Pathogen | Parameter | Mean | Sample size | Reference |
|---|---|---|---|---|
| A/H7N9 | f | 0.79 | 85 | (Cowling et al., 2013) |
| f | 1.00 | 46 | (Gong et al., 2014; Sun et al., 2014) | |
| f | 1.00 | 111 | (Gao et al., 2013) | |
| g | 0.75 | 123 | (Cowling et al., 2013) | |
| g | 0.56 | 111 | (Gao et al., 2013) | |
| g | 0.78 | 46 | (Gong et al., 2014; Sun et al., 2014) | |
| A/H1N1 | f | 0.67 | 426 | (Cao et al., 2009) |
| f | 0.89 | 1088 | (Louie et al., 2009) | |
| g | 0.29 | 426 | (Cao et al., 2009) | |
| SARS | f | 0.94 | 1452 | (Donnelly et al., 2004) |
| g | 0.29 | 1192 | (Lau et al., 2004) | |
| MERS | f | 0.87 | 23 | (Assiri et al., 2013) |
| g | 0 | 10,000 | (Cauchemez et al., 2014) | |
| Ebola | f | 0.87 | 1151 | (WHO Ebola Response Team, 2014) |
| g | 0.86 | 142 | (Pattyn, 1978) | |
| Marburg | f | 0.93 | 129 | (Bausch et al., 2006) |
| f | 0.47 | 15 | (Bausch et al., 2003) | |
| g | 0.67 | 39 | (Roddy et al., 2010) |
Table 3
Time from exposure to onset (i.e., incubation period) and onset to hospitalization for different pathogens
| Pathogen | Time from | Mean (days) | Reference |
|---|---|---|---|
| Influenza A/H7N9 | Exposure-to-onset | 4.3 | (Cowling et al., 2013) |
| Onset-to-hospitalization | 5 | (Gao et al., 2013; Sun et al., 2014) | |
| Influenza A/H1N1 | Exposure-to-onset | 4.3 | (Tuite et al., 2010) |
| Exposure-to-onset | 2.05 | (Ghani et al., 2009) | |
| Onset-to-recovery | 7 | (Tuite et al., 2010) | |
| SARS-CoV | Exposure-to-onset | 6.4 | (Donnelly et al., 2003) |
| Onset-to-hospitalization | 4.85 | (Donnelly et al., 2003) | |
| MERS-CoV | Exposure-to-onset | 5.2 | (Assiri et al., 2013) |
| Exposure-to-onset | 5.5 | (Cauchemez et al., 2014) | |
| Onset-to-hospitalization | 5 | (Assiri et al., 2013) | |
| Ebola | Exposure-to-onset | 9.1 | (WHO Ebola Response Team, 2014) |
| Onset-to-hospitalization | 5 | (WHO Ebola Response Team, 2014) | |
| Marburg | Exposure-to-onset | 6.8 | (Martini, 1973) |
| Onset-to-hospitalization | 5* |
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*
As there was limited data for onset-to-hospitalization for Marburg, we assumed the same value as for Ebola.
Additional files
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Source code 1
(A) Internal Functions Filename—Code_distribution_functions.R. This script contains user-defined functions and distributions that are called by the master script. (B) Screening Model Filename—Code_Screening_model.R. This script defines the core probabilistic model described in this manuscript. This function is called by the master script. (C) Master Script Filename—Plot_results.R. This script integrates all the provided code to perform analyses and generate figures presented in this manuscript.
- https://doi.org/10.7554/eLife.05564.015
