Abstract
Malaria is a major public health concern in tropics and subtropics. Accurate malaria prediction is critical for reporting ongoing incidences of infection and its control. Hence, the purpose of this investigation was to evaluate the performances of different models of predicting malaria incidence in Marodijeh region, Somaliland. The study used monthly historical data from January 2011 to December 2020. Five deterministic and stochastic models, i.e. Seasonal Autoregressive Moving Average (SARIMA), Holt-Winters’ Exponential Smoothing, Harmonic Model, Seasonal and Trend Decomposition using Loess (STL) and Artificial Neural Networks (ANN), were fitted to the malaria incidence data. The study employed Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Scaled Error (MASE) to measure the accuracy of each model. The results indicated that the artificial neural network (ANN) model outperformed other models in terms of the lowest values of RMSE (39.4044), MAE (29.1615), MAPE (31.3611) and MASE (0.6618). The study also incorporated three meteorological variables (Humidity, Rainfall and Temperature) into the ANN model. The incorporation of these variables into the model enhanced the prediction of malaria incidence in terms of achieving better prediction accuracy measures (RMSE = 8.6565, MAE = 6.1029, MAPE = 7.4526 and MASE = 0.1385). The 2-year generated forecasts based on the ANN model implied a significant increasing trend. The study recommends the ANN model for forecasting malaria cases and for taking the steps to reduce malaria incidence during the times of year when high incidence is reported in the Marodijeh region.
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The data that suppport the findings of this study are not publicly available due to third party restrictions. With permission, the malaria incidence data from both public and private health facilities was obtained from the Somaliland Ministry of Health especially the department of Health Management Information System (HMIS). Metereological data was also collected from Somalia Water and Land Information Mangement (SWALIM) of the Food and Agriculture Organization (FAO).
Code availability
The code that supports the findings of this study is available from the corresponding author upon reasonable request.
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All authors contributed to the preparation of the manuscript. Jama Mohamed performed data analysis, first draft preparation and organization of the manuscript. Ahmed Ismail Mohamed wrote the introduction of the manuscript. Eid Ibrahim Daud contributed to the manuscript by collecting the data and writing the materials and methods section of the manuscript. All authors read and approved the final manuscript.
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Mohamed, J., Mohamed, A.I. & Daud, E.I. Evaluation of prediction models for the malaria incidence in Marodijeh Region, Somaliland. J Parasit Dis 46, 395–408 (2022). https://doi.org/10.1007/s12639-021-01458-y
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DOI: https://doi.org/10.1007/s12639-021-01458-y