Mosquitoes populations modelling for early warning system and rapid response public by health authorities correlating climate, weather and spatial-tem

卫生当局将气候、天气和空间温度相关联，为早期预警系统和公众快速反应建立蚊子种群模型

• 批准号: NE/T013664/1
• 负责人: Patty Kostkova
• 金额: $ 64.69万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FT013664%2F1
• 关键词: Mosquitoes; populations; modelling; early; warning

As a result of the recent climate changes, mosquito-borne diseases (like Zika, dengue) are becoming endemic not only in sub-tropical regions of Africa and Latin America but in other parts of the world. This project will combine public health, mobile technology and climate modelling to evaluate the impacts of environmental changes on water providing breeding habitats for mosquitoes in Northeast Brazil. We aim to develop a series of spatial-temporal models to predict the burden of mosquito populations by deploying cutting-edge mobile and internet of things (IoT) technology leveraging multiple data sources from newly acquired climate, weather, mosquito surveillance, water and sanitation and socioeconomic data. This technology will include the use of mobile surveillance apps using gamification and citizen science technology co-developed with local stakeholders for reporting locations of water breeding points in Brazil. We will develop a data-driven early warning system to predict changes in occurrence and abundance of mosquito breeding points. This real-time system will alert public health and environmental authorities to mobilise community engagement for the prevention and rapid response to vector outbreaks. We will also develop educational content for public and community stakeholders to increase awareness of mosquito breeding habitats and water management. With public health stakeholders (WHO and Recife City Hall), we will co-develop community engagement strategies and evidence-based policies to improve standing water management and treatment. Most importantly, building on existing partnerships in the provinces in Northeast Brazil, where mosquito-borne diseases are endemic, we will work with academics and local stakeholder partners from Recife, Olinda and Campina Grande, and have a unique access to mosquito surveillance data to calibrate our predictive models in real-time via mobile app and IoT devices. Access to real-time datasets will not only provide a unique method for calibrating the predictive modelling results ? but also will put us in a position to evaluate the entire early-warning decision-support dashboard system with the authorities during their standard daily operations to ensure outstanding real-world impact on vector surveillance and public health policy. It is absolutely unique for a research project to have the opportunity to validate the research in the timeframe of the project while directly translating the results to public health authorities, policy makers, WHO, and stakeholders in Brazil, Turkey and other countries where vector-borne disease are soon to become endemic.

由于最近的气候变化，蚊媒疾病(如寨卡病毒、登革热)不仅在非洲和拉丁美洲的亚热带地区流行，而且在世界其他地区也成为地方病。该项目将结合公共卫生、移动技术和气候模拟，评估环境变化对为巴西东北部蚊子提供滋生栖息地的水的影响。我们的目标是通过部署尖端移动和物联网(IoT)技术，利用来自新获得的气候、天气、蚊子监测、水和卫生设施以及社会经济数据的多种数据源，开发一系列时空模型来预测蚊子种群的负担。这项技术将包括使用移动监测应用程序，使用游戏化和公民科学技术，与当地利益攸关方共同开发，以报告巴西水养殖点的位置。我们将开发一个数据驱动的早期预警系统，以预测蚊子滋生点的发生和丰度的变化。这一实时系统将提醒公共卫生和环境当局动员社区参与预防和快速应对媒介疾病暴发。我们还将为公众和社区利益攸关方开发教育内容，以提高对蚊子滋生栖息地和水管理的认识。我们将与公共卫生利益攸关方(世卫组织和累西腓市政厅)共同制定社区参与战略和循证政策，以改善常态水管理和处理。最重要的是，在蚊媒疾病流行的巴西东北部省份现有伙伴关系的基础上，我们将与来自累西腓、奥林达和坎皮纳格兰德的学者和当地利益攸关方合作，并拥有独特的蚊子监测数据访问权限，以通过移动应用程序和物联网设备实时校准我们的预测模型。访问实时数据集不仅将提供校准预测建模结果的独特方法？而且还将使我们能够在当局的日常运作中与当局一起评估整个预警决策支持仪表盘系统，以确保对病媒监测和公共卫生政策产生突出的现实影响。对于一个研究项目来说，有机会在项目的时间框架内验证研究结果，同时将结果直接传递给公共卫生当局、政策制定者、世卫组织和巴西、土耳其和其他病媒传播疾病即将成为地方病的国家的利益攸关方，这绝对是独一无二的。

项目成果

Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review.

• DOI: 10.3389/fpubh.2022.900077
• 发表时间: 2022
• 期刊: FRONTIERS IN PUBLIC HEALTH
• 影响因子: 5.2
• 作者: de Lima, Clarisse Lins;da Silva, Ana Clara Gomes;Moreno, Giselle Machado Magalhaes;da Silva, Cecilia Cordeiro;Musah, Anwar;Aldosery, Aisha;Dutra, Livia;Ambrizzi, Tercio;Borges, Iuri V. G.;Tunali, Merve;Basibuyuk, Selma;Yenigun, Orhan;Massoni, Tiago Lima;Browning, Ella;Jones, Kate;Campos, Luiza;Kostkova, Patty;da Silva Filho, Abel Guilhermino;dos Santos, Wellington Pinheiro
• 通讯作者: dos Santos, Wellington Pinheiro

The Response of Governments and Public Health Agencies to COVID-19 Pandemics on Social Media: A Multi-Country Analysis of Twitter Discourse.

• DOI: 10.3389/fpubh.2021.716333
• 发表时间: 2021
• 期刊: Frontiers in public health
• 影响因子: 5.2
• 作者: Li L;Aldosery A;Vitiugin F;Nathan N;Novillo-Ortiz D;Castillo C;Kostkova P
• 通讯作者: Kostkova P

MEWAR: Development of a Cross-Platform Mobile Application and Web Dashboard System for Real-Time Mosquito Surveillance in Northeast Brazil.

• DOI: 10.3389/fpubh.2021.754072
• 发表时间: 2021
• 期刊: Frontiers in public health
• 影响因子: 5.2
• 作者: Aldosery A;Musah A;Birjovanu G;Moreno G;Boscor A;Dutra L;Santos G;Nunes V;Oliveira R;Ambrizzi T;Massoni T;Dos Santos WP;Kostkova P
• 通讯作者: Kostkova P

Covid-19 Dynamic Monitoring and Real-Time Spatio-Temporal Forecasting.

Covid-19动态监测和实时时空预测。

• DOI: 10.3389/fpubh.2021.641253
• 发表时间: 2021
• 期刊: Frontiers in public health
• 影响因子: 5.2
• 作者: da Silva CC;de Lima CL;da Silva ACG;Silva EL;Marques GS;de Araújo LJB;Albuquerque Júnior LA;de Souza SBJ;de Santana MA;Gomes JC;Barbosa VAF;Musah A;Kostkova P;Dos Santos WP;da Silva Filho AG
• 通讯作者: da Silva Filho AG

Spatiotemporal forecasting for dengue, chikungunya fever and Zika using machine learning and artificial expert committees based on meta-heuristics

• DOI: 10.1007/s42600-022-00202-6
• 发表时间: 2022-02
• 期刊: Research on Biomedical Engineering
• 作者: Cecilia Cordeiro da Silva;Clarisse Lins de Lima;Ana Clara Gomes da Silva;G. Moreno;A. Musah;A. Aldosery;L. Dutra;T. Ambrizzi;I. V. G. Borges;M. Tunali;S. Basibuyuk;O. Yenigün;T. Massoni;Kate Jones;Luiza Campos;P. Kostkova;A. G. da Silva Filho;W. P. dos Santos