Quantifying Critical Parameter in Disease Transmission

Abstract

The values of each parameter introduced in a disease model play important role in providing the prediction of a disease transmission. Some parameters values are easily quantified through collected statistical data usually made available from clinical research. However, there may be some parameters that are not easily found. For such case, the parameters values are estimated through many trial-and-error numerical runs. In this paper, it is shown that a statistical modeling approach coupled with the Maximum Likelihood Estimate method can be used to quantify critical model parameters. A Hand-Foot-Mouth disease (HFMD) model was taken as a case study where infected population data provided by the Sarawak State of Health was fitted onto the SusceptibleInfected-Removal (SIR) model. The concerned parameter is the transmission coefficient of HFMD in the year 2012. Using the mentioned method, it was found that the value for the transmission coefficient of HFMD in 2012 is 1.2654 (CI: 1.15-1.43). It can be concluded that the critical parameter with 95% confidence interval in SIR model has been quantified effectively. Due to the possibility of obtaining other sets of infected population data, a web application called the Disease Modeling Parameter Calculator was developed to assist in estimating the transmission coefficient.