A systematic review of mathematical models with optimal control strategies for malaria management from 2010 to 2024

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Malaria remains a significant global public health concern, particularly in tropical and subtropical regions. Mathematical modeling has played a critical role in understanding malaria transmission dynamics and informing control strategies. This systematic review aims to synthesize existing research on mathematical models that incorporate optimal control strategies for malaria management between 2010 and 2024. A comprehensive literature search was conducted across databases, including Google Scholar, Science Direct, PubMed, Taylor & Francis, Web of Science and Wiley Online Library, following the PRISMA 2020 guidelines. From an initial pool of 4866 articles, 61 studies met the inclusion criteria. Key data such as model type, control strategies, optimization techniques and model outcomes were extracted and analyzed. The results reveal that deterministic models remain the most commonly used framework, although stochastic approaches are gaining attention for their ability to capture real-world variability. Optimal control theory, particularly Pontryagin's maximum principle was frequently employed to evaluate interventions such as insecticide-treated nets, indoor residual spraying, treatment and vaccination. Despite this, emerging tools like attractive targeted sugar baits (ATSBs), endectocides and biological controls remain underrepresented in these models. This review highlights both the strengths and limitations of current modeling approaches and identifies gaps in incorporating integrated and novel intervention strategies. This calls for the development of more robust, stochastic and multi-scale models to better support malaria control and elimination efforts under varying epidemiological contexts.