Tarun K. Martheswaran
Vol. 2 Issue 1. p. 1- 8 (2020)
ABSTRACT: Dengue Fever is a debilitating viral disease of the tropics, transmitted by female Aedes mosquitoes, causing sudden fever, acute pains in the joints and hemorrhaging. The World Health Organization (WHO) estimated more than 25,000 deaths annually. To date, no vaccine has been developed for Dengue Fever due to the existence of four virus serotypes, against which, developing an immunity is very difficult. Early detection of Dengue is proving to be the only viable option of mitigating the transmission of this disease and potentially containing it. The purpose of this project is to innovate a novel approach to detect the outbreak of Dengue disease. Using 2005 Dengue disease outbreak data collected from Singapore’s Health Database, as well as ten years’ worth of Singaporean climate data, the average temperature and vector-human population were calculated. Thousands of simulations were then performed with varying susceptible human populations and mosquito bite rates to achieve high correlation values, and formed a quartic function relationship between the climate and bite rate for use in future simulations. The resulting model was then tested on data from 2013, 2014, and 2015 Singapore Dengue Outbreaks. Statistical testing using Pearson’s values showed a significant correlation between the test data and actual Dengue outbreaks, with 2013 R-value of 0.74, 2014 R-value of 0.53, and 2015 R-value of 0.61. These positive results indicate that this novel model can be used to predict Dengue disease outbreaks accurately and as a tool for early detection to employ sufficient vector, or mosquito, control measures in place, which ultimately reduce the number of deaths associated with this disease.
This tool will be the only viable measure until a promising vaccine is developed to control and curb this fatal, century-long disease, that has killed more than 5 million humans worldwide to date.
KEYWORDS: Mathematics; Biology; Modeling; Early Detection; Dengue Fever