Modelling the influence of temperature and rainfall on malaria incidence in four endemic provinces of Zambia using semiparametric Poisson regression

- In Zambia, the role of climatic factors on malaria, has not been determined in combination of space and time in modelling.
- The reversal in malaria reduction after the year 2010 and its variation by transmission zones make it critical.
- Semiparametric Poisson regression modelled a strong positive association between malaria incidence and precipitation.
- Malaria risk was 95% and 68% lower in Lusaka and Western (APR = 0.05 and 0.31, 95% CI = 0.04 - 0.06 and 0.25 - 0.41 respectively) compared to Luapula.
- The unique behaviour and effects of minimum and maximum temperatures are an indication of geographical region effects.

Although malaria morbidity and mortality are greatly reduced globally owing to great control efforts, the disease remains the main contributor. In Zambia, all provinces are malaria endemic. However, the transmission intensities vary mainly depending on environmental factors as they interact with the vectors. Generally in Africa, possibly due to the varying perspectives and methods used, there is variation on the relative importance of malaria risk determinants. In Zambia, the role climatic factors play on malaria case rates has not been determined in combination of space and time using robust methods in modelling. This is critical considering the reversal in malaria reduction after the year 2010 and the variation by transmission zones. Using a geoadditive or structured additive semiparametric Poisson regression model, we determined the influence of climatic factors on malaria incidence in four endemic provinces of Zambia. We demonstrate a strong positive association between malaria incidence and precipitation as well as minimum temperature. The risk of malaria was 95% lower in Lusaka (ARR = 0.05, 95% CI = 0.04-0.06) and 68% lower in the Western Province (ARR = 0.31, 95% CI = 0.25-0.41) compared to Luapula Province. North-western Province did not vary from Luapula Province. The effects of geographical region are clearly demonstrated by the unique behaviour and effects of minimum and maximum temperatures in the four provinces. Environmental factors such as landscape in urbanised places may also be playing a role.