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

Carrillo, Fausto Andres Bustos; Mercado, Brenda Lopez; Monterrey, Jairo Carey; Collado, Damaris; Saborio, Saira; Miranda, Tatiana; Barilla, Carlos; Ojeda, Sergio; Sanchez, Nery; Plazaola, Miguel; Laguna, Harold Suazo; Elizondo, Douglas; Arguello, Sonia; Gajewski, Anna M.; Maier, Hannah E.; Latta, Krista; Carlson, Bradley; Coloma, Josefina; Katzelnick, Leah; Sturrock, Hugh; Balmaseda, Angel; Kuan, Guillermina; Gordon, Aubree; Harris, Eva

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.