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<p>The drylands vesper mouse (<i>Calomys musculinus)</i> is the primary host for <i>Junin mammarenavirus</i> (JUNV), the etiological agent of Argentine hemorrhagic fever in humans. We assessed the potential distribution of <i>C. musculinus</i> and identified disease transmission hotspots under current climatic conditions and projected future scenarios, including severe (Representative Concentration Pathway 8.5) and intermediate (Representative Concentration Pathway 4.5) climate change scenarios in 2050 and 2070. Utilizing tree-based machine learning algorithms, we modeled <i>C. musculinus</i> distribution by incorporating bioclimatic and landscape predictors. The model showed strong performance, achieving <i>F</i>-scores between 80.22 and 83.09%. Key predictors indicated that <i>C. musculinus</i> prefers warm temperatures, moderate annual precipitation, low precipitation variability, and low pasture coverage. Under the severe climate change scenario, suitable areas for the rodent and hotspots for potential disease decreased. The intermediate scenario showed an expansion in <i>C. musculinus</i> distribution alongside increased potential hotspot zones. Despite the complexity of ecological systems and the limitations of the model, our findings offer a framework for preventive measures and ecological studies in regions prone to the expansion of <i>C. musculinus</i> and in hotspots for disease transmission driven by climate change.</p>

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Climate Change Impact on Human-Rodent Interfaces: Modeling Junin Virus Reservoir Shifts

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月27日

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  • Nuri Flores-Pérez,
  • Pranav Kulkarni,
  • Marcela Uhart,
  • Pranav S. Pandit

摘要

The drylands vesper mouse (Calomys musculinus) is the primary host for Junin mammarenavirus (JUNV), the etiological agent of Argentine hemorrhagic fever in humans. We assessed the potential distribution of C. musculinus and identified disease transmission hotspots under current climatic conditions and projected future scenarios, including severe (Representative Concentration Pathway 8.5) and intermediate (Representative Concentration Pathway 4.5) climate change scenarios in 2050 and 2070. Utilizing tree-based machine learning algorithms, we modeled C. musculinus distribution by incorporating bioclimatic and landscape predictors. The model showed strong performance, achieving F-scores between 80.22 and 83.09%. Key predictors indicated that C. musculinus prefers warm temperatures, moderate annual precipitation, low precipitation variability, and low pasture coverage. Under the severe climate change scenario, suitable areas for the rodent and hotspots for potential disease decreased. The intermediate scenario showed an expansion in C. musculinus distribution alongside increased potential hotspot zones. Despite the complexity of ecological systems and the limitations of the model, our findings offer a framework for preventive measures and ecological studies in regions prone to the expansion of C. musculinus and in hotspots for disease transmission driven by climate change.