Climate is a major driver of vector proliferation and arbovirus transmission, with temperature being a primary focus of research. Unlike other mosquito-borne diseases, Zika virus transmission involves both sexual tran...Climate is a major driver of vector proliferation and arbovirus transmission, with temperature being a primary focus of research. Unlike other mosquito-borne diseases, Zika virus transmission involves both sexual transmission between humans and environmental transmission pathways, a characteristic largely overlooked in existing studies. This paper develops a temperature-dependent transmission model based on the unique transmission characteristics of the Zika virus. We estimated the historical transmission of Zika virus in Brazil using a temperature-dependent basic reproduction number to assess the impact of climate change on Zika virus spread in the region. Results indicate that the temperature range for Zika virus outbreaks is between 23.34˚C and 33.99˚C, peaking at 3.2 at 29.4˚C. This range and peak temperature are approximately 1˚C lower than those found in models that do not consider environmental transmission pathways. By incorporating seasonal variations into the model and categorizing ten Brazilian cities into five climatic types based on temperature changes, we simulated historical and future daily average temperatures using the GFDL-ESM4 temperature model. We analyzed the control periods and virus risks across different regions and projected Zika virus transmission risk in Brazil under four Shared Socioeconomic Pathways (SSP126, SSP245, SSP370, and SSP585). The results suggest that under the SSP126 scenario, the control periods will extend by 2 - 3 months with rising temperatures. This study concludes by discussing the impact of temperature changes on control measures, emphasizing the importance of reducing adult mosquito populations through the Sterile Insect Technique (SIT) to mitigate future risks.展开更多
Risk management is relevant for every project that which seeks to avoid and suppress unanticipated costs, basically calling for pre-emptive action. The current work proposes a new approach for handling risks based on ...Risk management is relevant for every project that which seeks to avoid and suppress unanticipated costs, basically calling for pre-emptive action. The current work proposes a new approach for handling risks based on predictive analytics and machine learning (ML) that can work in real-time to help avoid risks and increase project adaptability. The main research aim of the study is to ascertain risk presence in projects by using historical data from previous projects, focusing on important aspects such as time, task time, resources and project results. t-SNE technique applies feature engineering in the reduction of the dimensionality while preserving important structural properties. This process is analysed using measures including recall, F1-score, accuracy and precision measurements. The results demonstrate that the Gradient Boosting Machine (GBM) achieves an impressive 85% accuracy, 82% precision, 85% recall, and 80% F1-score, surpassing previous models. Additionally, predictive analytics achieves a resource utilisation efficiency of 85%, compared to 70% for traditional allocation methods, and a project cost reduction of 10%, double the 5% achieved by traditional approaches. Furthermore, the study indicates that while GBM excels in overall accuracy, Logistic Regression (LR) offers more favourable precision-recall trade-offs, highlighting the importance of model selection in project risk management.展开更多
文摘Climate is a major driver of vector proliferation and arbovirus transmission, with temperature being a primary focus of research. Unlike other mosquito-borne diseases, Zika virus transmission involves both sexual transmission between humans and environmental transmission pathways, a characteristic largely overlooked in existing studies. This paper develops a temperature-dependent transmission model based on the unique transmission characteristics of the Zika virus. We estimated the historical transmission of Zika virus in Brazil using a temperature-dependent basic reproduction number to assess the impact of climate change on Zika virus spread in the region. Results indicate that the temperature range for Zika virus outbreaks is between 23.34˚C and 33.99˚C, peaking at 3.2 at 29.4˚C. This range and peak temperature are approximately 1˚C lower than those found in models that do not consider environmental transmission pathways. By incorporating seasonal variations into the model and categorizing ten Brazilian cities into five climatic types based on temperature changes, we simulated historical and future daily average temperatures using the GFDL-ESM4 temperature model. We analyzed the control periods and virus risks across different regions and projected Zika virus transmission risk in Brazil under four Shared Socioeconomic Pathways (SSP126, SSP245, SSP370, and SSP585). The results suggest that under the SSP126 scenario, the control periods will extend by 2 - 3 months with rising temperatures. This study concludes by discussing the impact of temperature changes on control measures, emphasizing the importance of reducing adult mosquito populations through the Sterile Insect Technique (SIT) to mitigate future risks.
文摘Risk management is relevant for every project that which seeks to avoid and suppress unanticipated costs, basically calling for pre-emptive action. The current work proposes a new approach for handling risks based on predictive analytics and machine learning (ML) that can work in real-time to help avoid risks and increase project adaptability. The main research aim of the study is to ascertain risk presence in projects by using historical data from previous projects, focusing on important aspects such as time, task time, resources and project results. t-SNE technique applies feature engineering in the reduction of the dimensionality while preserving important structural properties. This process is analysed using measures including recall, F1-score, accuracy and precision measurements. The results demonstrate that the Gradient Boosting Machine (GBM) achieves an impressive 85% accuracy, 82% precision, 85% recall, and 80% F1-score, surpassing previous models. Additionally, predictive analytics achieves a resource utilisation efficiency of 85%, compared to 70% for traditional allocation methods, and a project cost reduction of 10%, double the 5% achieved by traditional approaches. Furthermore, the study indicates that while GBM excels in overall accuracy, Logistic Regression (LR) offers more favourable precision-recall trade-offs, highlighting the importance of model selection in project risk management.
