Modelling the impact of climate variability on malaria morbidity in the Tamale Metropolitan Area: a time series analysis

Malaria 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.

Monthly 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.

The 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.

Climatic 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.

Not applicable.