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: 10682435
• 负责人: WILLIAM KUANG-YAO PAN
• 金额: $ 63.93万
• 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/10682435
• 关键词: Academia; Address; Affect; Area; Bayesian Modeling; Behavioral; Border Community; Border Crossings; Brazil; Case Study; Censuses; Climate; Collaborations; Colombia; Communicable Diseases; Communities; Community Networks; Community Relations; 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; Maps; Modeling; Morbidity - disease rate; Native-Born; Pan American Health Organization; Pathway Analysis; Pattern; Performance; Peru; Politics; Population; Probability; Prophylactic treatment; Reaction Time; Reporting; Research; Resources; Risk; Role; Route; Rural; Rural Population; Seasons; Social Distance; 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; comparison intervention; data infrastructure; experience; extreme weather; hydrology; improved; indexing; indigenous community; informant; innovation; land cover; malaria transmission; 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.

摘要 这项提议的目标是通过加强对何时发生疟疾的了解来改善亚马逊地区对疟疾的反应 在哪里，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，世界上没有一个地区的疟疾增长速度超过 亚马逊。几个事件促成了这一增长：极端天气(即厄尔尼诺现象)、扩大资源开采、 委内瑞拉的政治动荡，以及全球基金从南美撤出。史无前例的疟疾 在靠近边境的地区，移民和糟糕的医疗保健促进了人口复苏，这一现象尤为严重 变速箱。目前的监测系统对报告的病例延迟了4周，这是完全 不充分，导致被动干预策略与预防性干预策略的对比。作为回应，我们的团队开发了一种疟疾 在美国国家航空航天局的支持下，秘鲁洛雷托建立了早期预警系统(MEWS)，秘鲁90%以上的疟疾病例出现在那里 发生。MEWS预测疫情的敏感度为90%，特异度为75%。 区域(使用未观测分量模型的生态区域[UCM])和区域(通过空间贝叶斯模型)， 并适合基于社区的基于代理的模型(ABM)来评估与 变速箱。然而，差距依然存在：我们的MEWS在秘鲁以外的地方有未知的表现；它没有 纳入迁移；预测不会因热点检测而缩减；近期预测性能较差 边界地区；模型不是跨尺度整合的。我们有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展，并评估缩减的分区 预测；(2)评估基础设施、社会经济网络和移民之间的关系 国际边界与疟疾发病率；以及(3)评估潜在的疟疾干预方案 边界，以降低使用ABM的两国的疟疾风险。该项目将显著改善目前的状况 监测工作，通过使用最先进的气候提供疟疾的当前估计和预测， 水文和土地覆盖模型。通过从以下方面获得监测和人口数据，扩大了MEWS的规模 厄瓜多尔和巴西，并将这些数据与水文气象数据合并。忽视的新生态区 定义了行政边界，并应用了UCM。空间贝叶斯模型被用来估计两者 区级和降级的分区疟疾发病率。基础设施数据从公共来源获取 社会网络分析(和数据收集)将在边境地区(巴西-- 秘鲁、厄瓜多尔-秘鲁)。我们沿着确定的网络结构评估疟疾发病率，最远可达300公里 在边境社区测试模拟干预情景，以评估对疟疾的影响 变速箱。这项建议响应了世卫组织2016-2030年全球疟疾技术战略和 泛美卫生组织最近提出的以改善疟疾监测为核心的倡议 采取干预措施，改善对疟疾高负担的反应。