An Enhanced Early Detection Model of Dengue Fever Outbreaks Using SEIR Infectious Disease Epidemiological Compartments, Generalized Linear Regression Relationships, and Statistical Computing

Overview

Dengue Fever is a debilitating viral disease, with more than 25,000 deaths annually. To date, no vaccine has been developed for Dengue Fever due to the existence of four virus serotypes. This project aims to innovate a novel approach to detect outbreaks of Dengue disease, and one that is adaptable to several different regions of the world. The SEIR model was applied to track the population dynamics of transmission between mosquitoes (vectors) and humans. Using outbreak data collected from Singapore’s Health Database, a regression relationship was found between climatic variables and the Density of Infectious Mosquitos. The collected variables were Temperature, Precipitation, and Humidity, which have been shown to relate to increased mosquito breeding rates in entomological studies. Simulations were then performed with varying the parameter of Density of Infectious Mosquitoes in the SEIR model, based on generalized linear regression relationships with the climate variables. The model was then tested using this relationship on actual 2013 to 2019 Singapore Dengue Outbreaks, 2019 Honduras Dengue Outbreak and 2019 Cambodia Dengue Outbreak with successful results. Statistical testing using cross- correlation showed a significant relationship between the test data and reported Dengue case count. With this early detection model, the authorities can effectively plan and put measures in place to fight the outbreak with enough lead time. This tool will be the only viable measure until a promising vaccine is developed to control and curb this century-long disease that, to date, has killed more than 5 million humans worldwide.

Awards (1)

• 2 nd Place Medicine & Health/Behavioral Sciences

Competition history

• JSHS 2020 Category not listed

Resources

• National JSHS abstract book ↗

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