Using Geographically Weighted Regression to Assess the Relationship Between Weather and Sociodemographic Indicators and COVID-19 Infection in Iraq

The impact of COVID-19 is still being recognised due to increased research, demand for the communication of uncertainties and evidence based information to help decision makers to design appropriate mitigation policies overtime. Weather and sociodemographic indicators are important to comprehensively understand rapid spread of the disease at a given spatial scale. The spatial pattern in the occurrence of disease may hint at the mechanisms that produce the disease, hence spatial analysis is very useful in studying the causes of a disease. This study evaluates the influence of weather variables, sociodemographic characteristics and their corresponding records of COVID-19 in Iraq cities. The assessments of these relationships were based on R 0 estimated from the time series data of COVID-19 infections, and by using geographically weighted regression (GWR) and linear regression modelling. Global estimates of these relationships from the linear regressions are generally poor. On the contrary, the results derived from the GWR show spatially varying patterns. The lag7 of weather variables performed better compared to other lags. Among weather variables, increasing wind speed leads to risen COVID-19 infection. Population density is one of the sociodemographic characteristics that contributes to higher COVID-19 infection. COVID-19 infections, on the other hand, decreased in cities with a good health index and access to piped water. The findings of this study are therefore of great value to policy makers to design appropriate measures to reduce COVID-19 infection in Iraq and elsewhere.