Journal of Applied Mathematics and Physics

Volume 12, Issue 12 (December 2024)

ISSN Print: 2327-4352   ISSN Online: 2327-4379

Google-based Impact Factor: 1.00  Citations  

Temperature-Driven Zika Virus Risk Prediction Model and Control Strategies: A Case Study of Brazil

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DOI: 10.4236/jamp.2024.1212260    57 Downloads   299 Views  

ABSTRACT

Climate is a major driver of vector proliferation and arbovirus transmission, with temperature being a primary focus of research. Unlike other mosquito-borne diseases, Zika virus transmission involves both sexual transmission between humans and environmental transmission pathways, a characteristic largely overlooked in existing studies. This paper develops a temperature-dependent transmission model based on the unique transmission characteristics of the Zika virus. We estimated the historical transmission of Zika virus in Brazil using a temperature-dependent basic reproduction number to assess the impact of climate change on Zika virus spread in the region. Results indicate that the temperature range for Zika virus outbreaks is between 23.34˚C and 33.99˚C, peaking at 3.2 at 29.4˚C. This range and peak temperature are approximately 1˚C lower than those found in models that do not consider environmental transmission pathways. By incorporating seasonal variations into the model and categorizing ten Brazilian cities into five climatic types based on temperature changes, we simulated historical and future daily average temperatures using the GFDL-ESM4 temperature model. We analyzed the control periods and virus risks across different regions and projected Zika virus transmission risk in Brazil under four Shared Socioeconomic Pathways (SSP126, SSP245, SSP370, and SSP585). The results suggest that under the SSP126 scenario, the control periods will extend by 2 - 3 months with rising temperatures. This study concludes by discussing the impact of temperature changes on control measures, emphasizing the importance of reducing adult mosquito populations through the Sterile Insect Technique (SIT) to mitigate future risks.

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Yue, Z. , Ji, X. and Zhang, Y. (2024) Temperature-Driven Zika Virus Risk Prediction Model and Control Strategies: A Case Study of Brazil. Journal of Applied Mathematics and Physics, 12, 4213-4241. doi: 10.4236/jamp.2024.1212260.

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