Epidemics of chikungunya, Zika, and COVID-19 reveal bias in case-based mapping

Abstract

Explosive epidemics of chikungunya, Zika, and COVID-19 have recently occurred worldwide, all of which featured large proportions of subclinical infections. Spatial studies of infectious disease epidemics typically use symptomatic infections (cases) to estimate incidence rates (cases/total population), often misinterpreting them as infection risks (infections/total population) or disease risks (cases/infected population). We examined these three measures in a pediatric cohort (N≈3,000) over two chikungunya epidemics and one Zika epidemic and in a household cohort (N=1,793) over one COVID-19 epidemic in Nicaragua. Across different analyses and all epidemics, case incidence rates considerably underestimated both risk-based measures. Spatial infection risk differed from spatial disease risk, and typical case-only approaches precluded a full understanding of the spatial seroprevalence patterns. For epidemics of pathogens that cause many subclinical infections, relying on case-only datasets and misinterpreting incidence rates, as is common, results in substantial bias, a general finding applicable to many pathogens of high human concern.