{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Vector Signals","title":"Predicting Dengue Risk with Machine Learning and Microclimate Data (October 2025)","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/02010972\"></iframe>","width":"100%","height":180,"duration":756,"description":"Briefing: Fine-Scale Predictive Modeling for Dengue Risk in MalaysiaSource: Dom, N.C., Abdullah, N.A.M.H., Dapari, R. et al. Fine-scale predictive modeling of Aedes mosquito abundance and dengue risk indicators using machine learning algorithms with microclimatic variables. Sci Rep 15, 37017 (2025). https://doi.org/10.1038/s41598-025-17191-yDate: Received - 01 February 2025 | Accepted - 21 August 2025 | Published - 23 October 2025Executive SummaryThis briefing document synthesizes the findings of a study on the use of machine learning (ML) for fine-scale prediction of Aedes mosquito abundance and dengue risk in Kuala Selangor, Malaysia. Faced with a doubling of dengue cases in 2023, the study addresses the limitations of coarse, regional forecasting models by incorporating daily microclimatic data (temperature, relative humidity, rainfall) to improve predictive accuracy at the neighborhood level.Key Takeaways:Variable Model Performance: No single machine learning algorithm—Artificial Neural Network (ANN), Random Forest (RF), or Support Vector Machine (SVM)—was universally superior. Performance was highly dependent on the specific mosquito species (Ae. aegypti vs. Ae. albopictus), the risk indicator being predicted (Aedes Index vs. Dengue Positive Trap Index), and the combination of microclimatic inputs. For instance, ANN excelled at predicting the Ae. aegypti Aedes Index, while SVM was most effective for predicting the Ae. albopictus Dengue Positive Trap Index.Impact of Predictor Complexity: Models incorporating multiple microclimatic variables (dual or triple combinations) generally yielded lower error metrics than single-variable models. However, increasing model complexity did not always improve accuracy and, in some cases, led to overfitting and higher prediction errors, particularly for ANN models. This highlights a critical trade-off between model complexity and predictive power.Moderate and Time-Lagged Climatic Influence: While statistically significant,...","thumbnail_url":"https://img.transistorcdn.com/qJYlR2Phxe3IMx6KHnsmKp1D71DIqj8LuYMSrDKo9Jc/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS82MDgx/MDIyNmJkNWU5YmIz/NzJhZDVmZjYyOGZi/NTgxMi53ZWJw.webp","thumbnail_width":300,"thumbnail_height":300}