ABSTRACT
The outbreak and communicability of infectious diseases across the world are driven by an array of complex interrelated factors and processes, such as rapid urbanization, human mobility, changes in public health policy, and global climate change (Cooley et al., 2008; Eastin et al., 2014; Hu et al., 2012; Kanobana et al., 2013; Wu et al., 2009). Infectious diseases such as malaria or dengue fever, pose a critical threat to vulnerable human populations such that timely responses are necessary to reduce the burden caused by the diseases. Dengue fever for instance is known to vary through time and space, due to a number of factors including human host, virus, mosquito acting as disease-vector and environment (Mammen et al., 2008). To implement appropriate control measures, public health organizations and policy makers must rely on accurate and timely predictions of disease for monitoring and analyzing them under critical space-time conditions. A better understanding on the space-time signature of infectious diseases such as their rate of transmission and tendency to cluster should help epidemiologists and public health officials better allocate prevention measures.
