COVID-19 under-reporting: spillovers and stringent containment strategies of global cases.

Due to the rapid spread of the COVID-19 pandemic, accurately determining the true global infection count has become an extremely challenging task. In this context, our study explores the spatial spillover analysis of COVID-19 cases and assesses the impact of containment policy stringency on these spillovers. Furthermore, we examine the extent of under-reporting of COVID-19 cases at the country level. To account for 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, modeled as a one-sided deterministic function of exogenous variables, is estimated using the sieves method. Our analysis relies on COVID-19 infection data from 57 countries, which span from 2020 to 2021. We find that spillovers vary significantly across different levels of containment stringency. In addition, the true number of infections is estimated to be 1.72 to 5.73 times higher than the reported cases. These results align with previous research and have important policy implications for improving the precision of COVID-19 reporting and managing spillover effects more effectively. [ABSTRACT FROM AUTHOR]