ArticleBMC infectious diseases2026
Modelling the impact of climate variability on malaria morbidity in the Tamale Metropolitan Area: a time series analysis.
Article in BMC infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMalaria remains endemic in northern Ghana, with seasonal outbreaks placing significant strain on health facilities in the Tamale Metropolitan Area. Although climatic variability is known to influence malaria transmission, the specific short- and long-run relationships between temperature, rainfall, relative humidity, and malaria outcomes in this setting remain poorly understood. This study models the impact of climate variability on population-adjusted malaria morbidity in the Tamale Metropolitan Area using a time series approach.
methodsMonthly time series data on laboratory-confirmed malaria morbidity (converted to rates per 1,000 population), as well as temperature, rainfall, and relative humidity, were obtained from the District Health Information Management System and the Ghana Meteorological Agency for the period January 2014 to December 2020 (84 observations). The Autoregressive Distributed Lag (ARDL) bounds testing approach was selected for its ability to handle variables with mixed orders of integration (I(0) and I(1)) and to simultaneously estimate short- and long-run dynamics. No logarithmic transformation was applied, preserving direct interpretation of coefficients as changes in malaria rates per 1,000 population.
resultsThe ARDL cointegration test confirmed a long-run equilibrium relationship between each climatic variable and malaria morbidity. In the short run, increases in relative humidity, temperature, and rainfall were significantly associated with higher malaria morbidity (p < 0.05 to p < 0.001). In the long run, however, temperature showed a significant inverse relationship: a one-unit (1 °C) increase in temperature was associated with a 0.37 case per 1,000 population decrease in malaria morbidity (p = 0.038). Model diagnostic tests (Ljung-Box and ARCH-LM) indicated that residuals were white noise, supporting model validity.
conclusionClimatic variables, particularly temperature, play a significant but complex role in malaria transmission in the Tamale Metropolitan Area, with opposing short- and long-run effects. These findings support the development of climate-informed early warning systems tailored to northern Ghana. To strengthen local malaria control and climate adaptation strategies, future efforts should integrate intervention coverage and health system data, improve surveillance, and apply advanced time-series methods to better capture non-linear and delayed climate effects. CLINICAL TRIAL: Not applicable.
Indexed as
Identifiers
What OpenQuestion holds
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.