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 该提案的目的是通过增强对何时发生疟疾的了解来改善亚马逊地区的疟疾应对措施 在哪里以及哪些有针对性的干预措施将产生最大的影响。迫切需要改进 疟疾控制——自 2011 年以来，世界上没有哪个地区的疟疾增幅比 亚马逊。有几个事件导致了这一增长：极端天气（即厄尔尼诺现象）、扩大资源开采、 委内瑞拉政局动荡，全球基金从南美洲撤出。史无前例的疟疾 在移民和医疗条件差的边境地区附近，疫情死灰复燃的几率特别高 传播。目前的监测系统报告病例有4周的延迟，这完全是 不充分，导致采取反应性干预策略与预防性干预策略。为了应对这种情况，我们的团队开发了一种疟疾 NASA 支持秘鲁洛雷托的早期预警系统 (MEWS)，秘鲁 90% 以上的疟疾病例都发生在该地区 发生。 MEWS 提前 8-12 周以 >90% 的敏感性和 >75% 的特异性预测疫情暴发 区域（使用未观测组件模型 [UCM] 的 EcoRegions）和地区（通过空间贝叶斯模型）， 并拟合基于社区的代理模型（ABM）来评估与 传播。然而，差距仍然存在：我们的 MEWS 在秘鲁以外的地区表现尚不清楚；它没有 纳入移民；热点检测不会缩小预测范围；附近的预测性能很差 边境地区；并且模型没有跨尺度集成。我们通过三个目标来解决这些差距：(1) 评估 MEWS 向厄瓜多尔和巴西亚马逊的扩张并评估缩小规模的分区 预测； (2) 评估基础设施、社会经济网络和跨地区移民之间的关系 疟疾发病率的国际边界； (3) 评估潜在疟疾干预措施的情景 两国使用反导措施来减少疟疾风险。该项目将显着改善目前 通过使用最先进的气候提供当前的疟疾估计和预测来开展监测工作， 水文和土地覆盖模型。通过从以下机构获取监测和人口数据，MEWS 得以扩展： 厄瓜多尔和巴西，并将这些数据与水文气象数据合并。忽略的新生态区域 界定行政边界并应用 UCM。空间贝叶斯模型用于估计 区级和分区级疟疾发病率下降。基础设施数据来自公共来源 并将在边境地区（巴西- 秘鲁、厄瓜多尔-秘鲁）。我们沿着已确定的网络结构评估疟疾发病率，最远可达 300 公里 边境并测试边境社区的模拟干预场景，以评估对疟疾的影响 传播。该提案响应了世界卫生组织 2016-2030 年全球疟疾技术战略和 泛美卫生组织最近发起的倡议呼吁以改善疟疾监测为核心 采取干预措施，改善对疟疾高负担的应对措施。