APPLICATION OF HIDDEN MARKOV MODEL (HMM) FOR THE PREDICTION OF DENGUE FEVER OUTBREAK IN NIGERIA

Dengue fever remains a pressing public health concern in Nigeria, marked by recurrent outbreaks and complex transmission patterns. This study employs a Hidden Markov Model (HMM) to predict dengue fever outbreak trends by modeling the latent outbreak risk levels based on reported case data from 2014 to 2023. The model leverages the Baum-Welch algorithm for parameter estimation, while the Viterbi and Forward algorithms are used for state sequence inference and sequence probability computation, respectively. By integrating discrete observed data with probabilistic state transitions, the HMM captures the underlying dynamics of disease progression. The model's performance was evaluated using statistical measures such as precision, recall, F1-score, and accuracy, revealing its effectiveness in learning outbreak patterns and identifying potential epidemic phases. A ten-year forecast (2024–2033) was also produced, offering valuable insights for early warning systems and strategic health planning. This research highlights the utility of HMM in epidemiological modeling and reinforces its potential for guiding data-driven decision-making in infectious disease control.