Epidemic Forest: A Spatiotemporal Model for Communicable Diseases

Abstract

Traditional epidemic models of communicable diseases focus on the temporal dimension. We propose a framework for the epidemic forest approach to spatializing epidemic modeling. An epidemic forest is formed by epidemic trees, each using the tree structure to represent an epidemic starting from a primary case. Each node on the tree represents an individual case, and each link represents a parent–child transmission linkage and can be modeled with the spatiotemporal and other information about the cases. Structural, spatiotemporal, and epidemiological information can be extracted from constructed trees to characterize the epidemic they represent and to correspond to environmental data. When multiple primary cases can be determined, the forest is ready to build. We applied this method to the 2013 dengue fever epidemic in Guangzhou City, China. With the constructed forest, we particularly calculated the case reproduction ratio (Rt), an index widely used for characterizing epidemics, at the global, tree-wise, and pixel-wise scales. We further calculated correlation coefficients between these Rts and climate variables. Rts at different scales, as well as their associations with climate variables, offer information at different levels that is all important in epidemiological studies and disease control practices. Through this study, we explored and demonstrated how to spatialize epidemic modeling, what information can be extracted from this spatialization, and then how to use the extracted information. We also point out that spatialization of Rt is the essential process of mapping a communicable disease, corresponding to the spatialization of incidence or prevalence in mapping a chronic disease.