An important question in the context of compound disasters is the degree to which geophysical disasters amplify the transmission of infectious diseases during pandemics and how this relation-ship is influenced by the social vulnerability of affected populations. This article proposes a spatiotemporal modeling approach to understand spatially varying social, demographic and health drivers of vulnerability during pandemics co-occurring with geophysical hazards. A multilevel mixed-effects model is developed to investigate the dynamic association between census tract -level Covid-19 case count trajectories co-occurring with a hurricane and demographic, socioeconomic and health factors. A state-level analysis is conducted to identify the distinct geographical regions in which significant changes are seen in the infection count trends due to the hurricane. A subsequent region-level analysis is performed to describe, at a higher spatial resolution, the im-pact of social vulnerability on the infection count trajectories at a community level. The method provides an approach to systematically study the effects of compound hazards and distinct pat-terns of infectious disease spread during hurricanes by quantifying (1) dynamic associations between infection counts and social factors and (2) spatial heterogeneities of these associations between communities. A case study for modeling the spatiotemporal variation of social vulnerability with data from Covid-19 pandemic and Hurricane Sally in Florida is presented to illustrate the application of the approach.