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<p>Malaria remains a major public health concern in sub-Saharan Africa, with Nigeria, particularly Rivers State, experiencing significant incidence due to its ecological conditions and fluctuating transmission patterns. The absence of reliable, region-specific forecasting tools has hindered effective malaria control strategies. This study addresses this gap by applying a discrete-time Markov Chain (DTMC) model to malaria incidence data, with states representing varying levels of malaria burden. The transition probability matrix indicates a high likelihood of remaining in lower disease states, while transitions to severe states are rare, suggesting some natural stability in malaria transmission. Steady-state analysis reveals that malaria cases are likely to stabilize at moderate incidence levels over time. Predictions for 2021 show that state 1, representing moderate malaria cases, consistently had the highest probability (over 50%), while severe states (states 3 and 4) remained below 7%. Strong alignment between predicted and observed states validates the model’s robustness. Standard error analysis and 95% confidence intervals further confirm its precision, providing policymakers with reliable estimates for planning interventions. A sensitivity analysis, performed using a Monte Carlo simulation, examined how small changes in state 0 (very low incidence) influence transitions to state 4 (severe incidence). Results show a linear relationship: even slight increases in state 0 can significantly raise the risk of severe outbreaks. This highlights the importance of early intervention. The proposed model offers a valuable forecasting tool for malaria management, emphasizing the need for targeted interventions such as enhanced vector control and seasonal forecasting to curb malaria spread effectively.</p>

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Forecasting Malaria Cases: A Markov Chain-Based Predictive Model for Enhanced Public Health Planning

  • 期刊论文

  • 发表日期：

    2026年5月25日

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  • Chrysogonus Chinagorom Nwaigwe,
  • Desmond Chekwube Bartholomew,
  • Sylvia Ezinne Egbom,
  • Godwin Onyeka Nwafor

摘要

Malaria remains a major public health concern in sub-Saharan Africa, with Nigeria, particularly Rivers State, experiencing significant incidence due to its ecological conditions and fluctuating transmission patterns. The absence of reliable, region-specific forecasting tools has hindered effective malaria control strategies. This study addresses this gap by applying a discrete-time Markov Chain (DTMC) model to malaria incidence data, with states representing varying levels of malaria burden. The transition probability matrix indicates a high likelihood of remaining in lower disease states, while transitions to severe states are rare, suggesting some natural stability in malaria transmission. Steady-state analysis reveals that malaria cases are likely to stabilize at moderate incidence levels over time. Predictions for 2021 show that state 1, representing moderate malaria cases, consistently had the highest probability (over 50%), while severe states (states 3 and 4) remained below 7%. Strong alignment between predicted and observed states validates the model’s robustness. Standard error analysis and 95% confidence intervals further confirm its precision, providing policymakers with reliable estimates for planning interventions. A sensitivity analysis, performed using a Monte Carlo simulation, examined how small changes in state 0 (very low incidence) influence transitions to state 4 (severe incidence). Results show a linear relationship: even slight increases in state 0 can significantly raise the risk of severe outbreaks. This highlights the importance of early intervention. The proposed model offers a valuable forecasting tool for malaria management, emphasizing the need for targeted interventions such as enhanced vector control and seasonal forecasting to curb malaria spread effectively.