Machine learning models for predicting residual malaria infections using environmental factors: A case study of the Jazan region, Kingdom of Saudi Arabia
Background: Malaria is a global public health problem affecting more than 100 countries. Meteorological factors on the other hand represent a major driving force behind malaria transmission and other vector-borne diseases. This study aims to predict and forecast malaria incidence while exploring its...
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KeAi Communications Co., Ltd.
2024-01-01
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| Series: | Decoding Infection and Transmission |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949924024000065 |
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| author | Idris Zubairu Sadiq Yakubu Saddeeq Abubakar Abdulkadir Rabiu Salisu Babangida Sanusi Katsayal Umar Saidu Sani I. Abba Abdullahi Garba Usman |
| author_facet | Idris Zubairu Sadiq Yakubu Saddeeq Abubakar Abdulkadir Rabiu Salisu Babangida Sanusi Katsayal Umar Saidu Sani I. Abba Abdullahi Garba Usman |
| author_sort | Idris Zubairu Sadiq |
| collection | DOAJ |
| description | Background: Malaria is a global public health problem affecting more than 100 countries. Meteorological factors on the other hand represent a major driving force behind malaria transmission and other vector-borne diseases. This study aims to predict and forecast malaria incidence while exploring its correlation with environmental factors. Method: Three Machine learning (ML) models, namely Artificial Neural Network (ANN), Random Forest Regression (RFR), and Regularized Linear Regression (RLR), were employed, along with a simple seasonal model, to predict and forecast malaria cases. Results: The ANN model outperformed the RFR and RLR models, with the lowest Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) of 0.313 and 0.146 respectively. A total of 10,778 malaria cases were reported from 2015 to 2020, with a monthly mean of 150 malaria infections. The study unveils no significant increase in malaria cases from 2020 to 2030. Furthermore, a strong negative correlation was found between monthly average malaria incidence and average temperature, minimum and maximum temperatures at p < 0.001. On the other hand, a strong positive correlation was observed between monthly average malaria incidence and relative humidity, which was statistically significant at p < 0.01. Conclusion: The Artificial Neural Network model is effective in predicting malaria cases compared to other models. The study revealed a negative correlation between malaria incidence and temperature, alongside a positive correlation with relative humidity. Although no significant increase in malaria cases is projected from 2020 to 2030, continuous monitoring of environmental factors and malaria prevalence remains crucial for effective disease control. |
| format | Article |
| id | doaj-art-f221fe8612e94dea89118f449f291b83 |
| institution | OA Journals |
| issn | 2949-9240 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Decoding Infection and Transmission |
| spelling | doaj-art-f221fe8612e94dea89118f449f291b832025-08-20T02:33:42ZengKeAi Communications Co., Ltd.Decoding Infection and Transmission2949-92402024-01-01210002210.1016/j.dcit.2024.100022Machine learning models for predicting residual malaria infections using environmental factors: A case study of the Jazan region, Kingdom of Saudi ArabiaIdris Zubairu Sadiq0Yakubu Saddeeq Abubakar1Abdulkadir Rabiu Salisu2Babangida Sanusi Katsayal3Umar Saidu4Sani I. Abba5Abdullahi Garba Usman6Department of Biochemistry, Faculty of Life Sciences, Ahmadu Bello University, Zaria, Nigeria; Corresponding author.Department of Biochemistry, Faculty of Life Sciences, Ahmadu Bello University, Zaria, NigeriaDepartment of Environmental Health Science, Faculty of Allied Health Sciences, Bayero University, Kano, NigeriaDepartment of Biochemistry, Faculty of Life Sciences, Ahmadu Bello University, Zaria, NigeriaDepartment of Biochemistry, Faculty of Life Sciences, Ahmadu Bello University, Zaria, NigeriaDepartment of Chemical Engineering, Prince Mohammad Bin Fahd University, Al Khobar, 31952, Saudi Arabia; Water Research Centre, Prince Mohammad Bin Fahd University, Al Khobar, 31952, Saudi ArabiaOperational Research Centre in Healthcare, Near East University, Nicosia, Cyprus; Department of Analytical Chemistry, Faculty of Pharmacy, Near East University, Nicosia, CyprusBackground: Malaria is a global public health problem affecting more than 100 countries. Meteorological factors on the other hand represent a major driving force behind malaria transmission and other vector-borne diseases. This study aims to predict and forecast malaria incidence while exploring its correlation with environmental factors. Method: Three Machine learning (ML) models, namely Artificial Neural Network (ANN), Random Forest Regression (RFR), and Regularized Linear Regression (RLR), were employed, along with a simple seasonal model, to predict and forecast malaria cases. Results: The ANN model outperformed the RFR and RLR models, with the lowest Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) of 0.313 and 0.146 respectively. A total of 10,778 malaria cases were reported from 2015 to 2020, with a monthly mean of 150 malaria infections. The study unveils no significant increase in malaria cases from 2020 to 2030. Furthermore, a strong negative correlation was found between monthly average malaria incidence and average temperature, minimum and maximum temperatures at p < 0.001. On the other hand, a strong positive correlation was observed between monthly average malaria incidence and relative humidity, which was statistically significant at p < 0.01. Conclusion: The Artificial Neural Network model is effective in predicting malaria cases compared to other models. The study revealed a negative correlation between malaria incidence and temperature, alongside a positive correlation with relative humidity. Although no significant increase in malaria cases is projected from 2020 to 2030, continuous monitoring of environmental factors and malaria prevalence remains crucial for effective disease control.http://www.sciencedirect.com/science/article/pii/S2949924024000065Machine learningMalaria incidenceJazanClimatic factorsMeteorological factors |
| spellingShingle | Idris Zubairu Sadiq Yakubu Saddeeq Abubakar Abdulkadir Rabiu Salisu Babangida Sanusi Katsayal Umar Saidu Sani I. Abba Abdullahi Garba Usman Machine learning models for predicting residual malaria infections using environmental factors: A case study of the Jazan region, Kingdom of Saudi Arabia Decoding Infection and Transmission Machine learning Malaria incidence Jazan Climatic factors Meteorological factors |
| title | Machine learning models for predicting residual malaria infections using environmental factors: A case study of the Jazan region, Kingdom of Saudi Arabia |
| title_full | Machine learning models for predicting residual malaria infections using environmental factors: A case study of the Jazan region, Kingdom of Saudi Arabia |
| title_fullStr | Machine learning models for predicting residual malaria infections using environmental factors: A case study of the Jazan region, Kingdom of Saudi Arabia |
| title_full_unstemmed | Machine learning models for predicting residual malaria infections using environmental factors: A case study of the Jazan region, Kingdom of Saudi Arabia |
| title_short | Machine learning models for predicting residual malaria infections using environmental factors: A case study of the Jazan region, Kingdom of Saudi Arabia |
| title_sort | machine learning models for predicting residual malaria infections using environmental factors a case study of the jazan region kingdom of saudi arabia |
| topic | Machine learning Malaria incidence Jazan Climatic factors Meteorological factors |
| url | http://www.sciencedirect.com/science/article/pii/S2949924024000065 |
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