Using Simple Dynamic Analytic Framework To Characterize And Forecast Epidemics

Abstract

Mathematical modeling provides a powerful analytic framework to investigate the transmission and control of infectious diseases. However, the reliability of the results stemming from modeling studies heavily depend on the validity of assumptions underlying the models as well as the quality of data that is employed to calibrate them. When substantial uncertainty about the epidemiology of newly emerging diseases (e.g. the generation interval, asymptomatic transmission) hampers the application of mechanistic models that incorporate modes of transmission and parameters characterizing the natural history of the disease, phenomenological growth models provide a starting point to make inferences about key transmission parameters, such as the reproduction number, and forecast the trajectory of the epidemic in order to inform public health policies. We describe in detail the methodology and application of three phenomenological growth models, the generalized-growth model, generalized logistic growth model and the Richards model in context of the COVID-19 epidemic in Pakistan.