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

撒哈拉以南非洲仍然是受疟疾影响最严重的地区，尽管世界卫生组织（世卫组织）与疟疾流行国家合作，努力防治疟疾，但疟疾是可以治愈的。虽然气候、气候变化和疟疾之间的关系已经在较长时间内得到了解决，但本项目侧重于预测未来几周或几个月疟疾传播的可能性和局限性。我们进一步开发和应用亚季节到季节（S2S）预测在气候-疟疾模型链。特别是，我们研究了在联合气候-疟疾预测中考虑缩小尺度的高分辨率S2S预测是否以及在多大程度上对疟疾早期预警有益。我们的目标区域是努纳（布基纳法索）和基苏木（肯尼亚）的高流行疟疾区及其卫生和人口监测系统（HDSS）站点。事实上，关于哪些地区最容易传播疟疾以及预计的水文气象条件可能如何影响感染模式的预期知识，可能对疾病预防和控制产生重大影响。因此，在实现季节和分季节预报尺度时，数值模式的最新进展可能有助于减少/预防气候对疟疾的影响。在本研究中，我们将通过使用区域化S2S预测和网格细胞分布式VECTRI动态疟疾传播模型开发基于天气的疟疾预测，以针对疾病暴发进行针对性干预，从而为SSA的疟疾早期变暖举措做出贡献。为此，我们将：(1)进一步发展VECTRI的水文气象建模链，并对其积水描述进行调整；(2)利用现有观测产品（网格化降水数据集和再分析）进行多元偏倚校正，对S2S预报原始产品进行空间分解；(3)验证偏倚校正后的概率预报；5)评估S2S预测系统在疟疾行动计划决策中的经济收益；6)为Heat to Harvest研究（H2H）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.