Effectiveness of traveller screening for emerging pathogens is shaped by epidemiology and natural history of infection

Abstract

During outbreaks of high-consequence pathogens, airport screening programs have been deployed to curtail geographic spread of infection. The effectiveness of screening depends on several factors, including pathogen natural history and epidemiology, human behavior, and characteristics of the source epidemic. We developed a mathematical model to understand how these factors combine to influence screening outcomes. We analyzed screening programs for six emerging pathogens in the early and late stages of an epidemic. We show that the effectiveness of different screening tools depends strongly on pathogen natural history and epidemiological features, as well as human factors in implementation and compliance. For pathogens with longer incubation periods, exposure risk detection dominates in growing epidemics, while fever becomes a better target in stable or declining epidemics. For pathogens with short incubation, fever screening drives detection in any epidemic stage. However, even in the most optimistic scenario arrival screening will miss the majority of cases.

Keywords: ecology; emerging diseases; entry screening; epidemiology; fever screening; global health; incubation period; pathogen natural history; viruses.

Figure 1.. 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 single screening test were detained. (B) Passengers who did not present with fever would always pass through symptom screening, but could still be identified during questionnaire screening. (C) Passengers who were not aware of exposure risk would always pass through questionnaire screening. (D) Passengers with neither fever nor knowledge of exposure would go undetected. DOI: http://dx.doi.org/10.7554/eLife.05564.013

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 are given in Table 2. θ(d) describes the infection age distribution (times since exposure) in individuals attempting travel. δ(d) is the incubation period cumulative distribution function, which describes the probability that travellers have progressed to symptom onset at the time of attempted travel. ε describes the efficacy of each respective screening module. S is the probability that travellers develop symptoms in flight, given that they did not yet have symptoms at departure. DOI: http://dx.doi.org/10.7554/eLife.05564.014

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 interval. Red, influenza A/H7N9; purple influenza A/H1N1p; blue, MERS; green, SARS; orange, Ebola; black, Marburg. (B) Incubation period and fever at onset. Point shows median estimate, circle shows joint 95% CI, generated using a binomial distribution for fever symptoms and fitted parametric distributions given by references in Table 3 for incubation period. DOI: http://dx.doi.org/10.7554/eLife.05564.004

Figure 3.. 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 journey. We assume a 70% probability that fever screening will identify febrile patients, and a 25% probability that a traveller with a known history of risky exposure will report it on a questionnaire. We assume 24 hr travel time. The white lines denote the point at which travellers board their flight; the black dashed line shows the median time from exposure to hospitalization for each pathogen. DOI: http://dx.doi.org/10.7554/eLife.05564.005

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.

DOI: http://dx.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.

DOI: http://dx.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.

DOI: http://dx.doi.org/10.7554/eLife.05564.008

Figure 4.. 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 risk questionnaire, or both. Lines show 95% bootstrapped CI. (B) Proportion of infected travellers missed by both departure and arrival screening in a stable epidemic. (C) Proportion of infected individuals who fly that are missed by arrival screening in a stable epidemic. (D) Proportion of infected arrivals missed by point of entry screening in a stable epidemic. We assume 25% probability traveller will report if they know exposure and 70% probability screening with identify visibly febrile patients. We assume R0 = 2 and a 24 hr travel time. DOI: http://dx.doi.org/10.7554/eLife.05564.009

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. DOI: http://dx.doi.org/10.7554/eLife.05564.010