Reliable testing data for new infectious diseases like COVID-19 is scarce in developing countries making it difficult to rapidly diagnose spatial disease transmission and identify at-risk areas. We propose a method that uses readily available data on bi-lateral migration channels combined with COVID-19 cases at respective migrant destinations to construct a spatially oriented risk index. We find significant and consistent association between our measure and various types of outcomes including actual COVID-19 cases and deaths, indices of government policy responses, and community mobility patterns. Results suggest that future pandemic models should incorporate migration-linkages to predict regional socio-economic and health risk exposure.