Improving Response to Malaria Outbreaks in Amazon-Basin Countries

摘要 该提案的目的是通过加强了解何时 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，世界上没有任何一个地区的疟疾发病率上升幅度超过 亚马逊几个事件导致了这种上升：极端天气（即，厄尔尼诺），扩大资源开采， 委内瑞拉的政治动荡，以及全球基金从南美洲撤出。史无前例的疟疾 在移民和保健条件差的边境地区， 传输目前的监测系统对报告的病例有4周的延迟，这完全是因为 不充分，导致反应性干预战略相对于预防性干预战略。作为回应，我们的团队开发了一种疟疾疫苗， 早期预警系统（MEWS）与美国航天局的支持，洛雷托，秘鲁，那里超过90%的疟疾病例在秘鲁 发生. MEWS预测疫情的敏感性>90%，特异性>75%，提前8-12周， 区域（使用未观测成分模型[UCM]的生态区域）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），以评估与 传输然而，差距仍然存在：我们的MEWS在秘鲁以外的地区性能未知; 合并迁移;预测不因热点检测而缩减;预测性能在接近 边界地区;模型没有跨尺度整合。我们通过三个目标来解决这些差距：（1） 评估MEWS扩展到厄瓜多尔和巴西亚马逊地区的情况，并评估缩小分区规模的情况 （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;以及（3）评价沿着可能的疟疾干预措施的设想方案 在两国边境使用ABM降低疟疾风险。该项目将大大改善目前 监测工作，利用最新的气候提供疟疾的当前估计和预测， 水文学和土地覆盖模型。MEWS通过从以下来源获得监测和人口数据而得到扩展： 厄瓜多尔和巴西，并将其与水文气象数据合并。新的生态区忽视了 确定行政边界并应用统一管理模式。空间贝叶斯模型用于估计 县一级和缩小的分区一级疟疾发病率。基础设施数据来自公共来源 将在沿着边境地区（巴西- 秘鲁、厄瓜多尔-秘鲁）。我们评估疟疾发病率沿着确定的网络结构，最远300公里， 在边境社区测试模拟干预方案，以评估对疟疾的影响 传输该提案响应了世卫组织2016-2030年全球疟疾技术战略和 泛美卫生组织最近提出的倡议，呼吁加强疟疾监测， 采取干预措施，改善对疟疾高负担的应对。

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.