Subseasonal to seasonal hydrometeorological- and dynamical Malaria transmission forecasting over Sub-Saharan Africa

撒哈拉以南非洲(SSA)仍然是受疟疾影响的人口最多的地区，尽管世界卫生组织(WHO)与流行国家合作开展了密集的抗击疟疾的努力，但该地区是可以治愈的。虽然气候、气候变化和疟疾之间的关系已经被讨论了更长的时间，但该项目侧重于预测未来几周和几个月疟疾传播的可能性和局限性。我们在气候-疟疾模型链中进一步开发和应用亚季节到季节性(S2S)预测。特别是，我们调查了气候-疟疾联合预测的缩减的高分辨率S2S预报的考虑是否以及在多大程度上为疟疾预警提供了好处。我们的目标区域是努纳(布基纳法索)和基苏木(肯尼亚)的疟疾高度流行地区及其卫生和人口监测系统地点。事实上，关于哪些地区最有利于疟疾传播的预期知识，以及预期的水文气象条件可能如何影响感染模式，可能会对疾病预防和控制产生重大影响。因此，在实现季节性和亚季节性预报尺度时，数值模式的最新进展可能有助于减少/防止气候对疟疾的影响。在这项研究中，我们将通过使用区域化的S2S预报和网格单元分布式VECTRI动态疟疾传播模型来开发基于天气的疟疾预测，以针对疾病暴发的干预措施，为SSA的疟疾早期变暖倡议做出贡献。为此，我们将1)进一步开发VECTRI的水文气象模型链并调整其池塘描述，2)应用多元偏差校正并使用现有观测产品(网格降水数据集和再分析)对S2S预测原始产品进行空间分解，3)验证偏差校正后的概率预测，4)估计模式链系统组件和最终疟疾预警系统(MEWS)的技能，5)评估S2S预测系统的经济收益，用于疟疾行动计划的决策，以及6)为“从高温到收获研究”(H2 H)做出贡献。我们的S2S预报链基于欧洲中期天气预报中心(ECMWF)的全球SEAS5系统。我们的研究将通过六个相互关联的目标、与七个研究单位项目的合作以及德国和非洲伙伴之间的密切合作来完成。

Sub-Saharan Africa (SSA) remains the most populous region affected by malaria, despite being curable and intense attempts from the World Health Organization (WHO) in collaboration with endemics countries to combat it. While the relationships between climate, climate change and malaria are addressed already for a longer time, this project focuses on possibilities and limitations to predict malaria transmission weeks and months ahead. We further develop and apply sub-seasonal to seasonal (S2S) prediction within a climate-malaria modeling chain. Particularly, we investigate if and how far the consideration of downscaled high resolution S2S forecasts of joint climate-malaria prediction offers benefit for malaria early warning. Our target region are the hyper-endemic malaria zones in Nouna (Burkina Faso) and Kisumu (Kenya) and their Health and Demographic Surveillance System (HDSS) sites. Indeed, anticipated knowledge on which areas are most conducive to malaria transmission and how expected hydrometeorological conditions likely influence infection patterns may have substantial consequences for disease prevention and control. Thus, recent advances in numerical models may help to reduce/prevent climate impacts on malaria when realizing seasonal and sub-seasonal forecasting scales. In this study, we will contribute to malaria early warming initiatives in SSA by developing a weather-based malaria predictions using regionalized S2S forecasts and the grid cell distributed VECTRI dynamical malaria transmission model to target interventions against disease outbreaks. To do so, we will 1) further develop the hydro-meteorological modeling chain for VECTRI and adapt its ponding descriptions, 2) apply multivariate bias-correction and spatially disaggregate the S2S forecasts raw product using available observational products (gridded precipitation datasets and re-analyses), 3) verify the probability forecast after bias correction, 4) estimate the skill of the components of the model chain system and the final malaria early warning system (MEWS), 5) assess the economic gain of the S2S forecasting system for decision-making in malaria action plans and 6) contribute to the Heat to Harvest study (H2H). Our S2S forecasting chain is based on the global SEAS5 system from the European Centre for Medium-Range Weather Forecasts (ECMWF). Our study will be accomplished through six interconnected aims, in collaboration with seven research unit projects, and in close collaboration between German and African partners.