<![CDATA[Acute Respiratory Infections (ISPA) are a significant health issue. According to the World Health Organization (WHO), ISPA is the leading cause of death among children under five worldwide. ISPA can be caused by environments with high levels of air pollution, particularly in urban areas. Predicting the spread of ISPA is a crucial step in controlling the disease. Since pollution sources are diverse, modeling and prediction can be difficult, which makes advanced methods such as the Kalman Filter (KF) desirable. This study compares two estimation methods, the Extended Kalman Filter (EKF) and the Ensemble Kalman Filter (EnKF), in predicting the spread of ISPA triggered by environmental pollution. Simulation results show that both methods can produce accurate estimations, but EnKF demonstrates superior performance in terms of RMSE compared to EKF. It predicts more accurately for susceptible (X) and infected (Y) populations with EnKF than with EKF. Based on the results of the EnKF for the X and Y populations, the RMSEs are 0.0660 and 0.1114, respectively. EnKF's advantage in handling uncertainty and non-linearity in the model makes it suitable for predicting the spread of ISPA.]]>
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