Background: Malaria is a vector-borne disease which, despite recent scaled-up efforts to achieve control in Africa, continues to pose a major threat to child survival. The disease is caused by the protozoan parasite Plasmodium and requires mosquitoes and humans for transmission. Rainfall is a major factor in seasonal and secular patterns of malaria transmission along the East African coast.
Objective: The goal of the study was to develop a model to reliably forecast incidences of paediatric malaria admissions to Kilifi District Hospital (KDH).
Design: In this article, we apply several statistical models to look at the temporal association between monthly paediatric malaria hospital admissions, rainfall, and Indian Ocean sea surface temperatures. Trend and seasonally adjusted, marginal and multivariate, time-series models for hospital admissions were applied to a unique data set to examine the role of climate, seasonality, and long-term anomalies in predicting malaria hospital admission rates and whether these might become more or less predictable with increasing vector control.
Results: The proportion of paediatric admissions to KDH that have malaria as a cause of admission can be forecast by a model which depends on the proportion of malaria admissions in the previous 2 months. This model is improved by incorporating either the previous month's Indian Ocean Dipole information or the previous 2 months' rainfall.
Conclusions: Surveillance data can help build time-series prediction models which can be used to anticipate seasonal variations in clinical burdens of malaria in stable transmission areas and aid the timing of malaria vector control.
Keywords: Indian Ocean Dipole; auto-regressive models; forecasting; malaria; rainfall; seasonality; time-series.