STATISTICAL MODELING OF MALARIA CASES IN NIGERIA
Abstract
One major challenge facing malaria control efforts is the inability to accurately predict future disease occurrences and identify areas at high risk. Malaria transmission is influenced by several factors, including climatic conditions, environmental changes, population movement, and healthcare access, making its pattern complex and difficult to understand.
Although large volumes of malaria surveillance data are collected annually, these data are often underutilized for forecasting and decision-making purposes. The lack of effective statistical models capable of capturing the trends and determinants of malaria cases limits the ability of policymakers and healthcare providers to implement proactive interventions.
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