首页|University of Sannio Reports Findings in Machine Learning (A reproducible ensemb le machine learning approach to forecast dengue outbreaks)
University of Sannio Reports Findings in Machine Learning (A reproducible ensemb le machine learning approach to forecast dengue outbreaks)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news originating from Benevento, Italy, by N ewsRx correspondents, research stated, "Dengue fever, a prevalent and rapidly sp reading arboviral disease, poses substantial public health and economic challeng es in tropical and sub-tropical regions worldwide. Predicting infectious disease outbreaks on a countrywide scale is complex due to spatiotemporal variations in dengue incidence across administrative areas." Our news journalists obtained a quote from the research from the University of S annio, "To address this, we propose a machine learning ensemble model for foreca sting the dengue incidence rate (DIR) in Brazil, with a focus on the population under 19 years old. The model integrates spatial and temporal information, provi ding one-month-ahead DIR estimates at the state level. Comparative analyses with a dummy model and ablation studies demonstrate the ensemble model's qualitative and quantitative efficacy across the 27 Brazilian Federal Units. Furthermore, w e showcase the transferability of this approach to Peru, another Latin American country with differing epidemiological characteristics. This timely forecast sys tem can aid local governments in implementing targeted control measures. The stu dy advances climate services for health by identifying factors triggering dengue outbreaks in Brazil and Peru, emphasizing collaborative efforts with intergover nmental organizations and public health institutions. The innovation lies not on ly in the algorithms themselves but in their application to a domain marked by d ata scarcity and operational scalability challenges. We bridge the gap by integr ating well-curated ground data with advanced analytical methods, addressing a si gnificant deficiency in current practices. The successful transfer of the model to Peru and its consistent performance during the 2019 outbreak in Brazil showca se its scalability and practical application. While acknowledging limitations in handling extreme values, especially in regions with low DIR, our approach excel s where accurate predictions are critical. The study not only contributes to adv ancing DIR forecasting but also represents a paradigm shift in integrating advan ced analytics into public health operational frameworks. This work, driven by a collaborative spirit involving intergovernmental organizations and public health institutions, sets a precedent for interdisciplinary collaboration in addressin g global health challenges."
BeneventoItalyEuropeCyborgsEmerg ing TechnologiesEpidemiologyHealth and MedicineMachine LearningPublic He alth
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