Predicting malaria prevalence with machine learning models using satellite-based climate information: technical report
The current report presents a machine learning model developed to predict malaria prevalence based on rainfall patterns, specifically tailored to different regions within Senegal. The developed model takes into account the varying climate conditions across regions to provide a more localized and acc...
| Main Authors: | , , , , |
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| Format: | Informe técnico |
| Language: | Inglés |
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International Water Management Institute (IWMI)
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/139414 |
| _version_ | 1855513368842993664 |
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| author | Ileperuma, Kaveesha Jampani, Mahesh Sellahewa, Uvindu Panjwani, Shweta Amarnath, Giriraj |
| author_browse | Amarnath, Giriraj Ileperuma, Kaveesha Jampani, Mahesh Panjwani, Shweta Sellahewa, Uvindu |
| author_facet | Ileperuma, Kaveesha Jampani, Mahesh Sellahewa, Uvindu Panjwani, Shweta Amarnath, Giriraj |
| author_sort | Ileperuma, Kaveesha |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The current report presents a machine learning model developed to predict malaria prevalence based on rainfall patterns, specifically tailored to different regions within Senegal. The developed model takes into account the varying climate conditions across regions to provide a more localized and accurate prediction. The primary input parameters used for prediction include rainfall, month, and year, allowing the model to capture each region's seasonal variations and trends. This research aims to enhance the precision of malaria predictions, contributing to more effective and targeted public health measures. The model is designed to provide future forecasts, offering valuable insights into early warning signals to help anticipate and mitigate the impact of malaria outbreaks. This proactive approach enables authorities and healthcare professionals to prepare and implement preventive measures in advance, potentially reducing the severity of malaria-related issues and aiding in the allocation of resources where they are most needed. By tailoring the prediction model to the unique characteristics of each region in Senegal, the current research addresses the localized nature of malaria outbreaks, recognizing that factors such as climate, geography, and environmental conditions can significantly influence the prevalence of malaria. The integration of predictive analytics and models in public health initiatives allows for a more strategic and responsive approach to malaria management, ultimately contributing to the overall well-being of the affected communities. This report includes an explanation of the methodology used for the development of the prediction model, along with the results obtained and their implications for public health in Senegal. |
| format | Informe técnico |
| id | CGSpace139414 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | International Water Management Institute (IWMI) |
| publisherStr | International Water Management Institute (IWMI) |
| record_format | dspace |
| spelling | CGSpace1394142025-11-07T07:58:42Z Predicting malaria prevalence with machine learning models using satellite-based climate information: technical report Ileperuma, Kaveesha Jampani, Mahesh Sellahewa, Uvindu Panjwani, Shweta Amarnath, Giriraj malaria prediction machine learning models climatic data satellite observation rainfall patterns The current report presents a machine learning model developed to predict malaria prevalence based on rainfall patterns, specifically tailored to different regions within Senegal. The developed model takes into account the varying climate conditions across regions to provide a more localized and accurate prediction. The primary input parameters used for prediction include rainfall, month, and year, allowing the model to capture each region's seasonal variations and trends. This research aims to enhance the precision of malaria predictions, contributing to more effective and targeted public health measures. The model is designed to provide future forecasts, offering valuable insights into early warning signals to help anticipate and mitigate the impact of malaria outbreaks. This proactive approach enables authorities and healthcare professionals to prepare and implement preventive measures in advance, potentially reducing the severity of malaria-related issues and aiding in the allocation of resources where they are most needed. By tailoring the prediction model to the unique characteristics of each region in Senegal, the current research addresses the localized nature of malaria outbreaks, recognizing that factors such as climate, geography, and environmental conditions can significantly influence the prevalence of malaria. The integration of predictive analytics and models in public health initiatives allows for a more strategic and responsive approach to malaria management, ultimately contributing to the overall well-being of the affected communities. This report includes an explanation of the methodology used for the development of the prediction model, along with the results obtained and their implications for public health in Senegal. 2023-12-01 2024-02-15T06:43:36Z 2024-02-15T06:43:36Z Report https://hdl.handle.net/10568/139414 en Open Access application/pdf International Water Management Institute (IWMI) CGIAR Initiative on Climate Resilience Ileperuma, Kaveesha; Jampani, Mahesh; Sellahewa, Uvindu; Panjwani, Shweta; Amarnath, Giriraj. 2023. Predicting malaria prevalence with machine learning models using satellite-based climate information: technical report. Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Initiative on Climate Resilience. 32p. |
| spellingShingle | malaria prediction machine learning models climatic data satellite observation rainfall patterns Ileperuma, Kaveesha Jampani, Mahesh Sellahewa, Uvindu Panjwani, Shweta Amarnath, Giriraj Predicting malaria prevalence with machine learning models using satellite-based climate information: technical report |
| title | Predicting malaria prevalence with machine learning models using satellite-based climate information: technical report |
| title_full | Predicting malaria prevalence with machine learning models using satellite-based climate information: technical report |
| title_fullStr | Predicting malaria prevalence with machine learning models using satellite-based climate information: technical report |
| title_full_unstemmed | Predicting malaria prevalence with machine learning models using satellite-based climate information: technical report |
| title_short | Predicting malaria prevalence with machine learning models using satellite-based climate information: technical report |
| title_sort | predicting malaria prevalence with machine learning models using satellite based climate information technical report |
| topic | malaria prediction machine learning models climatic data satellite observation rainfall patterns |
| url | https://hdl.handle.net/10568/139414 |
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