COVID-19 Under-reporting: Spillovers and Stringent Containment Strategies of Global Cases*

Abstract Owing to the rapid spread of COVID-19 pandemic, accurately determining the true global infection count has become an exceedingly challenging endeavor. In this context, our study delves into the spatial spillover analysis of COVID-19 cases and assesses the impact of containment policy stringency on these spillovers. Furthermore, we explore the extent of under-reporting of COVID-19 cases at a country-specific level.To account for the diverse spatial dependencies, we employ a semiparametric spatial autoregressive model in which the coefficients are smooth unknown functions of countries' stringency indices. Country-specific under-reporting, represented as a one-sided deterministic function of exogenous variables, is estimated using the sieves method.Our analysis relies on COVID-19 infection data spanning from 2020 to 2021 for 57 countries. We have discovered that spillovers exhibit significant variations at different levels of containment stringency. Moreover, the true number of infections is estimated to be 1.38 to 17.12 times higher than the reported cases. These results align with prior research and bear significant policy implications for improving the precision of COVID-19 reporting and managing spillover effects effectively. JEL Classification No.: C23, C14, I18.