A mathematical approach for classifying input parameters for infectious disease spread

The spread of infectious diseases in space and time has been broadly studied through mathematical and simulation models. Recent epidemics signal the need for continuing research in the area. Among the primary goals of this field is to trace the factors that contribute to the disease occurrence. Although it is a primary goal, it is also one of the major difficulties as it comprises a large and diverse range of factors, such as: population size, vector (transmitting agent) population size, rate of contacts between individuals, rate of contact between individual and vector, percentage of non-symptomatic cases, to name a few. So, identifying the factors that are the most important is expected to be the first step in any disease spread model research. A brief analysis of the literature would prove this wrong. A possible reason is that many of the studies have been conducted by epidemiologists, entomologists and healthcare workers with a lack of IISE background. Therefore, this work aims to provide a discussion and a preliminary mathematical method, by using ANOVA and Shannon entropy, to classify the factors according to specific response criteria. Examples include number of infected people, epidemic length, number of severe cases and hospitals utilization.

A mathematical approach for classifying input parameters for infectious disease spread Conference Paper