Using observational data to quantify bias of traveller-derived COVID-19 prevalence estimates in Wuhan, China

Abstract

Background: The incidence of coronavirus disease 2019 (COVID-19) in Wuhan, China, has been estimated using imported case counts of international travellers, generally under the assumptions that all cases of the disease in travellers have been ascertained and that infection prevalence in travellers and residents is the same. However, findings indicate variation among locations in the capacity for detection of imported cases. Singapore has had very strong epidemiological surveillance and contact tracing capacity during previous infectious disease outbreaks and has consistently shown high sensitivity of case-detection during the COVID-19 outbreak.

Methods: We used a Bayesian modelling approach to estimate the relative capacity for detection of imported cases of COVID-19 for 194 locations (excluding China) compared with that for Singapore. We also built a simple mathematical model of the point prevalence of infection in visitors to an epicentre relative to that in residents.

Findings: The weighted global ability to detect Wuhan-to-location imported cases of COVID-19 was estimated to be 38% (95% highest posterior density interval [HPDI] 22-64) of Singapore's capacity. This value is equivalent to 2·8 (95% HPDI 1·5-4·4) times the current number of imported and reported cases that could have been detected if all locations had had the same detection capacity as Singapore. Using the second component of the Global Health Security index to stratify likely case-detection capacities, the ability to detect imported cases relative to Singapore was 40% (95% HPDI 22-67) among locations with high surveillance capacity, 37% (18-68) among locations with medium surveillance capacity, and 11% (0-42) among locations with low surveillance capacity. Treating all travellers as if they were residents (rather than accounting for the brief stay of some of these travellers in Wuhan) contributed modestly to underestimation of prevalence.

Interpretation: Estimates of case counts in Wuhan based on assumptions of 100% detection in travellers could have been underestimated by several fold. Furthermore, severity estimates will be inflated several fold since they also rely on case count estimates. Finally, our model supports evidence that underdetected cases of COVID-19 have probably spread in most locations around the world, with greatest risk in locations of low detection capacity and high connectivity to the epicentre of the outbreak.

Funding: US National Institute of General Medical Sciences, and Fellowship Foundation Ramon Areces.

Figure 1

Posterior distributions of detection probabilities relative to Singapore Upper panel is a density plot of θ_global. Lower panel shows posterior distributions of θ_low, θ_medium, θ_high. Solid vertical lines show median estimates. Shaded areas show 80% HPDI. Curved lines show the 95% HPDI. HPDI=highest posterior density interval.

Figure 2

Ratio of infection prevalence in temporary visitors relative to that in residents Plot shows the ratio over a range of durations of visit (in days) and a range of durations of detectable infection (time to recovery in days). Upper panel shows an epidemic doubling time of 5 days; lower panel shows an epidemic doubling time of 7 days. Ratios are shown as decimals rounded to 2 decimal places, with lighter areas as the ratio approaches 1·00. In this base case, we assume that the hazard of infection is the same for residents and visitors.