Understanding incidence, spread, prevalence, and control of an infectious disease requires a multidisciplinary approach that encompasses many fields of inquiry in natural and social sciences. Several biological, environmental and economic/social/demographic factors govern the disease spread in a population. The overall pattern of a disease incidence is an outcome of the interaction of all these processes acting at different scales-from genetic epidemiology to public health-making it a complex multi-scale and interdisciplinary study.
Mathematical modelling of the disease process has been one of the oldest areas of study in Mathematical Biology. It has contributed significantly to the understanding of basic infection process, predicting future incidence to aid in taking immediate control measures, drug discovery, and health policy development. It uses application of concepts from different areas in mathematics, statistics and computational algorithms for data analysis and visualization. Each theoretical approach incorporates information from the biological, environmental, and social sciences, and offers understanding at different scales.
In this talk Prof. Somdatta Sinha will outline studies at three different scales to highlight the type of data required, variety of methods of analysis, and kinds of inferences/information that the analysis offers. She will show that comparative whole genome analysis of HIV-1, the pathogen responsible for AIDS, offers some insights into the differential evolution of HIV-1 genes; Understanding HIV-1 Reverse Transcriptase (RT) wild-type and mutant protein structures using graph theory allows us to uncover the drug resistance mechanisms in RT-drug mutants. Finally, at the population level modelling of disease spread, Prof. Sinha will discuss studies of malaria using mathematical, statistical, and graphical approaches suitable for a diversity of fine and coarse-grained data from India.