MATHEMATICAL MODEL OF DENGUE INFECTION WITH AGE STRUCTURE

• Main Author: Wijayanti Puspita, Juni
• Format: Dissertations
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/74981
• Institution: Institut Teknologi Bandung

Dengue virus infection is still among the leading health problems in many endemic countries, including Indonesia, characterized by its high morbidity and widespread. It is known that the risk factor that influences the transmission intensity varies among the different age groups, which can have implications for dengue control strategies. This dissertation presents a mathematical model of dengue infection with a time-dependent infection rate and age structure in the human population. The population of humans and mosquitoes will be represented by the Susceptible- Infected-Recovered (SIR) model and the Susceptible-Infected (SI) model, respectively. A vaccination scenario as a strategy to control dengue infection is also applied in the model. Dengue case data from populated cities in Indonesia, such as Semarang City and Bandung City, was used to estimate the constant and timedependent infection rates. The main indicators can be obtained here to identify the changes in dengue transmission intensity in each age group, such as the force of infection (FoI) and reproduction number, both effective and basic reproduction numbers. Numerical simulation results indicate that the youngster age group is at high risk for dengue infection. Vaccination in this age group effectively reduced the number of dengue cases in the studied cities. Therefore, future vaccination strategies can be prioritized for the youngster to control the spread of dengue infection. The presence of early warning for dengue outbreaks can also assist in planning more effective control strategies. In this study, a cumulative generating operator is constructed using weekly dengue case data from Palu City to generate dynamic solutions of a host-vector mathematical model. Based on the calculation of the correlation over time, the effective reproduction number can be used as an early warning indicator to predict future trends of dengue incidence.