Modelling and simulation of dengue fever spread

Abstract

Being a country of a tropical climate, Singapore provides an optimal environment for Aedes mosquitoes to thrive, making Dengue endemic and persistent locally. This project presents the study and application of simulating and modelling dengue disease in Singapore by examining the Susceptible-Infected-Recovered (SIR) model in the DengueME framework. The determining parameter, that is the infection rate β from the SIR model was estimated and then used for prediction to analyse the dengue situation of the year. The Least Square Logarithmic Regression model was performed with the estimated β values to predict the possible number of weekly dengue incidence cases for the subsequent month. The results showed that modelling dengue disease is promising in observing the effectiveness of prevention and control measures, even with a simple model. This serves to prove that mathematical models have huge potential in aiding health policymakers to come up with suitable mitigation measures for dengue spread and extend the use of models to other diseases in Singapore.