Improving Response to Malaria Outbreaks in Amazon-Basin Countries

改善亚马逊流域国家对疟疾疫情的应对能力

• 批准号: 10477933
• 负责人: WILLIAM KUANG-YAO PAN
• 金额: $ 62.92万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10477933
• 关键词: Academia; Address; Affect; Area; Bayesian Modeling; Behavioral; Border Community; Brazil; Case Study; Censuses; Climate; Collaborations; Colombia; Communicable Diseases; Communities; Community Networks; Complement; Country; Data; Data Collection; Decentralization; Detection; Development; Disease; Disease Outbreaks; Economics; Ecuador; Ecuadorian; Ecuadorian Amazon; Effectiveness of Interventions; El Nino southern oscillation; Event; Funding; Geography; Goals; Government; Health; Health system; Healthcare; Incidence; Infrastructure; Internal Migrations; International; International Migrations; Intervention; Interview; Knowledge; Malaria; Meteorology; Modeling; Morbidity - disease rate; Native-Born; Pan American Health Organization; Pathway Analysis; Pattern; Performance; Peru; Politics; Population; Probability; Prophylactic treatment; Reporting; Research; Resources; Risk; Role; Route; Rural; Rural Population; Social Network; Source; South America; Specificity; Statistical Models; Structure; Surveys; System; Technical Expertise; Testing; Time; Transportation; United States National Aeronautics and Space Administration; Vector Ecology; Venezuela; Vulnerable Populations; Withdrawal; base; comparison intervention; data infrastructure; experience; extreme weather; hydrology; improved; indexing; indigenous community; informant; innovation; land cover; malaria transmission; meteorological data; migration; outbreak prediction; preventive intervention; response; risk sharing; social; socioeconomics; spatiotemporal; success; surveillance data; transmission process; vector management strategies

Abstract The objective of this proposal is to improve malaria response in the Amazon by enhancing knowledge on when where, and which targeted interventions will have the greatest impact. There is a critical need for improved malaria control—since 2011, no region in the world has experienced a larger increase in malaria than the Amazon. Several events contributed to this rise: extreme weather (i.e., El Nino), expanded resource extraction, political unrest in Venezuela, and withdrawal of the Global Fund from South America. The unprecedented malaria resurgence has been particularly high near border regions where migration and poor health care facilitate transmission. The current surveillance system has a 4-week delay in cases reported, which is completely inadequate, resulting in reactive vs. preventive intervention strategies. To respond, our team developed a Malaria Early Warning System (MEWS) with NASA support for Loreto, Peru, where over 90% of malaria cases in Peru occur. The MEWS forecasts outbreaks with >90% sensitivity and >75% specificity 8-12 weeks in advance in sub- regions (EcoRegions using unobserved component models [UCM]) and districts (via spatial Bayesian models), and fits community-based agent based models (ABMs) to evaluate behavioral factors associated with transmission. However, gaps remain: our MEWS has unknown performance outside of Peru; it does not incorporate migration; forecasts are not downscaled for hotspot detection; forecasting performance is poor near border regions; and the models are not integrated across scales. We address these gaps with three aims: (1) Evaluate MEWS expansion to the Ecuadorian and Brazilian Amazon and evaluate sub-district downscaled forecasts; (2) Evaluate the relationship between infrastructure, socioeconomic networks, and migration across international borders with malaria incidence; and (3) Evaluate scenarios of potential malaria interventions along borders to reduce malaria risk in both countries using ABMs. This project will significantly improve current surveillance efforts by providing both current estimates and forecasts of malaria using state-of-the-art climate, hydrology and land cover models. The MEWS is expanded by obtaining surveillance and population data from Ecuador and Brazil, and merging these with hydro-meteorological data. New EcoRegions that ignore administrative borders are defined and UCMs are applied. Spatial Bayesian models are used to estimate both district- and downscaled sub-district level malaria incidence. Infrastructure data are obtained from public sources and a social network analysis (and data collection) will be conducted in communities along border regions (Brazil- Peru, Ecuador-Peru). We evaluate malaria incidence along identified network structures up to 300km away from borders and test simulated intervention scenarios in border communities to evaluate effects on malaria transmission. This proposal responds to the WHO 2016-2030 Global Technical Strategy for Malaria and the recent initiatives by the Pan American Health Organization calling for improved malaria surveillance as a core intervention to improve response to high malaria burden.

抽象的 该建议的目的是通过增强何时的知识来改善亚马逊的疟疾反应 在哪里以及哪些目标干预措施将产生最大的影响。提高的迫切需要 疟疾控制 - 2011年，世界上没有哪个地区的疟疾增加比 亚马逊。几个事件导致了这一崛起：极端天气（即埃尔尼诺），扩大资源提取， 委内瑞拉的政治动荡，并从南美撤出全球基金。空前的疟疾 在边境地区，迁移和医疗保健设施差的边境地区附近的复兴特别高 传播。当前的监视系统报告的案件延迟了4周，这是完全的 不足，导致反应性与预防性干预策略。为了做出回应，我们的团队发展了疟疾 预警系统（MEWS）在NASA支持秘鲁Loreto的支持下，秘鲁超过90％的疟疾病例 发生。 MEWS预测敏感性> 90％的爆发和> 75％的特异性提前8-12周 区域（使用未观察到的组件模型[UCM]的生态区）和地区（通过空间贝叶斯模型）， 并适合基于社区的代理模型（ABM）来评估与 传播。但是，差距仍然存在：我们的MEWS在秘鲁以外的表现未知；它没有 纳入迁移；森林没有被降低以进行热点检测；预测性能很差 边境地区；并且这些模型不是在范围内集成的。我们以三个目的解决这些差距：（1） 评估MEWS扩展到厄瓜多尔人和巴西亚马逊，并评估分区的缩小 预测； （2）评估基础设施，社会经济网络和跨越跨越的关系 国际疟疾事件的边界； （3）评估潜在疟疾干预措施的方案 在两国使用ABM的边界降低疟疾风险。该项目将大大改善当前 通过使用最新气候提供当前疟疾的估计和森林来提供监视的努力， 水文和土地覆盖模型。通过从中获得监视和人口数据来扩展MEWS 厄瓜多尔和巴西，并将其与水电学数据合并。忽略的新生态区 定义了行政边界并应用UCM。空间贝叶斯模型用于估计 地区和缩小的分区水平疟疾事件。基础架构数据是从公共来源获得的 并将在边境地区的社区（巴西 - 秘鲁，厄瓜多尔 - 佩鲁）。我们在距离距离300公里的确定网络结构上评估疟疾事件 边界和测试模拟干预方案在边境社区中评估对疟疾的影响 传播。该提案对WHO 2016 - 2030年全球疟疾技术战略做出了回应 PAN American Health组织的最新举措呼吁改善疟疾监测为核心 干预以改善对高疟疾负担的反应。