ArticleEcoHealth2025
Climate Change Impact on Human-Rodent Interfaces: Modeling Junin Virus Reservoir Shifts.
Article in EcoHealth, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Junín virus and Argentine haemorrhagic fever: Virology, pathogenesis, diagnosis, and prevention strategies.World journal of virology · 2026Review
- Climate-driven changes in zoonotic risk of arenaviral hemorrhagic fevers in South America.Npj viruses · 2026Article
- Essential contributions of wildlife health surveillance to the United Nations Sustainable Development Goals.One health outlook · 2025Review
- Rodent-Borne Parasites and Human Disease: A Growing Public Health Concern.Animals : an open access journal from MDPI · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.