Estimating Infectious Disease Underreporting at the Country Level: A Model and Application to the COVID-19 Pandemic

Abstract

Background: Underreporting of infectious diseases is a pervasive challenge in public health that has emerged as a central issue in characterizing the dynamics of the COVID-19 pandemic. Estimating reporting accuracy via analytical methods is not always feasible due to time and resource constraints. We developed a model that predicts reporting completeness at the country/pathogen level from input variables accessible from the literature. Methods: We performed a literature search that collected estimates of reporting completeness for 32 pathogens, representing reporting estimates from 52 countries. We combined epidemiological and social science theory to identify factors that may influence empirical reporting rates, developed a model that can be used to estimate reporting completeness. We then applied this model to estimate reporting rates for SARS-CoV-2. Findings: Pathogen- and country-specific factors were predictive of reporting rates. Country epidemic preparedness was positively associated with reporting completeness, while countries with high levels of media bias in favor of incumbent governments were less likely to report infectious disease cases. Deadlier pathogens and sexually transmitted pathogens were more likely to be reported. Our model predicts that COVID-19 cases are widely underreported, with the true number of infections ranging from approximately 2·5 to 43 times the reported cases.Interpretation: Underreporting is a complex phenomenon that is driven by pathogen-level, health system, and political factors. This model can characterize and correct for the uncertainty in reported infectious disease statistics, particularly when outbreak-specific empirical estimates of underreporting are unavailable. More precise estimates can inform control policies and improve the accuracy of infectious disease models.Funding: None reported.Declaration of Interests: No competing interests to declare.