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Threshold dynamics of an almost periodic vector-borne disease model

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Abstract

Many infectious diseases cannot be transmitted from human to human directly, and the transmission needs to be done via a vector. It is well known that vectors’ life cycles are highly dependent on their living environment. In order to investigate dynamics of vector-borne diseases under environment influence, we propose a vector-borne disease model with almost periodic coefficients. We derive the basic reproductive number \({{\textbf {R}}}_0\) for this model and establish a threshold type result on its global dynamics in terms of \({{\textbf {R}}}_0\). As an illustrative example, we consider an almost periodic model of malaria transmission. Our numerical simulation results show that the basic reproductive number may be underestimated if almost periodic coefficients are replaced by their average values . Finally, we use our model to study the dengue fever transmission in Guangdong, China. The parameters are chosen to fit the reported data available for Guangdong. Numerical simulations indicate that the annual dengue fever case in Guangdong will increase steadily in the near future unless more effective control measures are implemented. Sensitivity analysis implies that the parameters with strong impact on the outcome are recovery rate, mosquito recruitment rate, mosquito mortality rate, baseline transmission rates between mosquito and human. This suggests that the effective control strategies may include intensive treatment, mosquito control, decreasing human contact number with mosquitoes (e.g., using bed nets and preventing mosquito bites), and environmental modification.

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Acknowledgements

Zhang’s research was supported by the Natural Science Basic Research Plan in Shaanxi Province of China (2022JM-023) and China Scholarship Council (201906565049), and Zhao’s research is supported in part by the NSERC of Canada (RGPIN-2019-05648).

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Correspondence to Tailei Zhang.

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Zhang, T., Zhao, XQ. Threshold dynamics of an almost periodic vector-borne disease model. J. Math. Biol. 87, 72 (2023). https://doi.org/10.1007/s00285-023-02002-7

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  • DOI: https://doi.org/10.1007/s00285-023-02002-7

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