Identifiability Analysis of the H1N1 Influenza and COVID-19 Viruses

Mathematics is useful in modeling biological phenomena, such as the spread of infectious diseases in a population. This research applies mathematical modeling to investigate the spread of H1N1 influenza in the U.S. and COVID-19 in Florida. The model parameters represent epidemiological characteristics of the disease and validating the model with data allows for the estimation of model parameter values. The identifiability of the model, or the reliability of parameter estimates, is determined with Monte Carlo simulations. This research demonstrated successful curve-fitting of H1N1 influenza and COVID-19 data to a mathematical model and generating identifiable parameter estimations. Furthermore, this research quantified the effectiveness of social distancing in preventing COVID-19 spread and demonstrated that social distancing prevented about 185,000 weekly COVID-19 incidences and about 8,500 weekly deaths. Using mathematical modeling, epidemiologists and public health officials can possibly direct the implementation of disease control measures such as vaccines, treatments, mask-wearing, and social distancing.