Spatial interaction between weather and socio-demographic factor as influencing variables for COVID-19 spread in Iraq

Weather and sociodemographic indicators are important to comprehensively understand rapid spread of COVID-19 at a given spatial scale and spatial analysis is very useful in studying the causes of disease. This study evaluates the influence of weather variables, sociodemographic characteristics and their corresponding records of COVID-19 in Iraq governorates. 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. The results showed that global estimates of these relationships from the linear regressions are generally poor. On the contrary, GWR results show spatially varying patterns. Among weather variables, increasing wind speed leads to rising COVID-19 infection. Population density is one of the sociodemographic characteristics that contribute 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 policymakers to design appropriate measures to reduce COVID-19 infection. This demonstrates the importance of spatial information methods in quantifying the impact of different variables on disease spread.