ENSO Impact on Global Chikungunya Virus Transmission, 2008–2024: A Multi-Country Distributed-Lag Time-Series Analysis
Viruses·
- DOI
- 10.3390/v18080918
- PMID
- —
- PMCID
- —
- OpenAlex
- —
- Study type
- Journal article
- Publisher
- MDPI AG
- Article type
- journal-article
- Integrity
- current
Why this research matters now
Embedding climate cycle predictions into routine monitoring programs may strengthen operational readiness in locations susceptible to weather-modulated viral circulation.
Structured evidence summary
Research question
The authors sought to evaluate how different phases of the El Niño–Southern Oscillation affect chikungunya case counts through temperature and rainfall patterns, while estimating future disease burden under various climate projections.
Study design
This investigation utilized a distributed-lag time-series framework applied to aggregated annual infection records spanning multiple nations and regions between 2008 and 2024.
Population and setting
The analysis encompassed worldwide national and subnational jurisdictions reporting chikungunya cases over a sixteen-year observational window.
Main findings
El Niño periods were associated with delayed elevations in case volumes, whereas La Niña intervals corresponded with substantial declines. Thermal climate associations primarily explained geographic differences in these outcomes. Scenario modeling suggested potential case increases under El Niño warming trajectories, while La Niña pathways indicated minor and highly variable reductions.
Public-health relevance
Embedding climate cycle predictions into routine monitoring programs may strengthen operational readiness in locations susceptible to weather-modulated viral circulation.
Important limitations
Because the assessment depends solely on the provided abstract and bibliographic details, a complete appraisal requires access to the full manuscript for decision-grade interpretation.
GIDS interpretation
This record offers thematic context on atmospheric oscillators and arbovirus spread, supporting literature retrieval and classification within environmental epidemiology and pathogen tracking databases.
Related GIDS surveillance
Literature context does not validate, explain, or change a surveillance signal. Exact and contextual relationships are shown separately.
Evidence relationships
This article has 8 auditable classifier relationships to diseases, places, topics, and study design.