Modelling the impact of climate variability on malaria morbidity in the Tamale Metropolitan Area: a time series analysis
BMC Infectious Diseases·
- DOI
- 10.1186/s12879-026-13796-4
- PMID
- 42288776
- PMCID
- —
- OpenAlex
- W7164685849
- Study type
- Journal article
- Publisher
- Springer Science and Business Media LLC
- Article type
- journal-article
- Integrity
- current
Why this research matters now
Integrating localized meteorological trends into forecasting tools may assist regional authorities in anticipating disease surges and optimizing preventive resource distribution.
Structured evidence summary
Research question
Does fluctuating weather influence malaria case rates in the Tamale Metropolitan Area, and how do these associations differ across immediate versus extended periods?
Study design
An observational monthly time-series analysis spanning seven years employed an Autoregressive Distributed Lag framework to quantify simultaneous short- and long-term statistical links between environmental metrics and disease incidence.
Population and setting
The analysis focuses on residents within the Tamale Metropolitan Area of northern Ghana, utilizing laboratory-verified infection counts standardized to per-thousand population denominators.
Main findings
Statistical modeling established a persistent long-term equilibrium connecting each examined weather parameter to malaria incidence. Shorter observation windows revealed that elevated heat, precipitation, and atmospheric moisture corresponded with increased case frequencies. The authors emphasize that these environmental drivers exert divergent influences depending on the temporal scale examined.
Public-health relevance
Integrating localized meteorological trends into forecasting tools may assist regional authorities in anticipating disease surges and optimizing preventive resource distribution.
Important limitations
The analytical framework excludes variables related to intervention deployment, healthcare infrastructure capacity, and potential delayed biological responses. The researchers explicitly recommend incorporating broader health system metrics and refined statistical approaches in future investigations.
GIDS interpretation
This manuscript documents historical environmental correlations with malaria burden in a defined Ghanaian locality. It serves as contextual background for understanding regional transmission drivers without implying current operational surveillance validation or real-time alert generation.
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.