Forecasting malaria dynamics based on causal relations between control interventions, climatic factors, and disease incidence in western Kenya.

ABSTRACT:

Background

Malaria remains one of the deadliest diseases worldwide, especially among young children in sub-Saharan Africa. Predictive models are necessary for effective planning and resource allocation; however, statistical models suffer from association pitfalls. In this study, we used empirical dynamic modelling (EDM) to investigate causal links between climatic factors and intervention coverage with malaria for short-term forecasting.

Methods

Based on data spanning the period from 2008 to 2022, we used convergent cross-mapping (CCM) to identify suitable lags for climatic drivers and investigate their effects, interaction strength, and suitability ranges on malaria incidence. Monthly malaria cases were collected at St. Elizabeth Lwak Mission Hospital. Intervention coverage

SUBMITTER: Nyawanda BO 

PROVIDER: S-EPMC11466501 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature