Quantifying bias of COVID-19 prevalence and severity estimates in Wuhan, China that depend on reported cases in international travelers

Rene Niehus; Pablo M De Salazar; Aimee Taylor; Marc Lipsitch

During the outbreak of a new virus SARS-Cov2 and its associated disease COVID-19, infection in travelers has been used to estimate the risk of infection in Wuhan, Hubei Province, China, the epicenter of the outbreak 1 . This approach is similar to that used for the the 2009 influenza pandemic where infections in tourists returning from Mexico were used to estimate the time-specific risk of infection (incidence or cumulative incidence) with the novel pandemic H1N1 influenza strain in Mexico (or parts thereof). The idea was that surveillance for the novel virus was not intense during the early days of the pandemic in Mexico, the source country, and that detection would be far more sensitive in travelers leaving Mexico, who would be screened when returning home as a means of preventing introductions of cases into destination countries 2, 3 . Reports that health systems in Wuhan are overwhelmed and many cases are not being counted have led to the use of outgoing traveler data to estimate the time-specific risk of COVID-19 in Wuhan 4 . This estimate, in turn, has been used to estimate the cumulative incidence of infection by a certain date in Wuhan, and from there (often assuming exponential growth and no appreciable depletion of susceptibles) the cumulative number of cases. Two important assumption underlie this calculation: i) that the detection of cases in the destination country has been 100% sensitive and specific, whether they are detected at the airport (prevalent cases with symptoms) or later after arrival at their destination (cases that were incubating during travel); ii) that travelers have the same prevalence of infection as the average resident of Hubei , so the prevalence inferred in travelers may be directly applied in Hubei. Here we consider the extent to which these two assumptions are justified. We conclude that the first assumption is strongly inconsistent with observed data, resulting in potentially substantial underestimates of prevalence in Hubei and corresponding overestimates of case-severity measures that are normalised by case counts.