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Abstract

Dengue virus (DENV) causes over 390 million infections and around 40,000 deaths worldwide each year. DENV is primarily transmitted by the mosquito Aedes aegypti, and both the life cycle of these mosquitoes and dengue transmission are significantly impacted by temperature. In the temperate region of Central Argentina, where dengue outbreaks first began in 2009, outbreaks of dengue can only occur due to new introductions of DENV from other regions. Due to the relationships between temperature and dengue, the risk of outbreak changes throughout the year.

We develop a stochastic model including temperature-dependent mosquito life history traits and transmission-related parameters. We calculate the next-generation matrix to estimate the temperature dependent reproduction number (R0). We fit the model to climate and dengue case data collected in Córdoba, Argentina from the 2016 outbreak. We numerically solve the model and calculate epidemiologically relevant metrics.

We characterize how the timing of introduction of an infected person from endemic countries affects the probability of autochthonous transmission. We also characterize the percent of introductions that lead to large outbreaks. For outbreaks that occur, we also calculate length of outbreak, timing and magnitude of peak number of cases, and total number of persons infected throughout the outbreak. We also investigate how climate change may affect these outbreak statistics.

We discuss these results in the context of improving mosquito population and dengue epidemiological models and improving methods to include seasonal temperature in mechanistic-stochastic models.

Publication Date

2024

Keywords

dengue, modeling, stochastic, Aedes aegypti, dengue virus, Argentina, Cordoba, mosquito, simulation

Disciplines

Applied Mathematics | Numerical Analysis and Computation | Ordinary Differential Equations and Applied Dynamics | Other Applied Mathematics

Faculty Advisor/Mentor

Michael Robert and Cheng Ly

Is Part Of

VCU Graduate Research Posters

Characterizing Probabilities of Outbreaks of Dengue in Central Argentina using a Temperature-Dependent Stochastic Model

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