Institute for Disease Modeling, Bill & Melinda Gates Foundation, Seattle, USA.
Malaria Atlas Project, Telethon Kids Institute, Perth, Australia.
Malar J. 2023 Apr 26;22(1):138. doi: 10.1186/s12936-023-04535-0.
As both mechanistic and geospatial malaria modeling methods become more integrated into malaria policy decisions, there is increasing demand for strategies that combine these two methods. This paper introduces a novel archetypes-based methodology for generating high-resolution intervention impact maps based on mechanistic model simulations. An example configuration of the framework is described and explored.
First, dimensionality reduction and clustering techniques were applied to rasterized geospatial environmental and mosquito covariates to find archetypal malaria transmission patterns. Next, mechanistic models were run on a representative site from each archetype to assess intervention impact. Finally, these mechanistic results were reprojected onto each pixel to generate full maps of intervention impact. The example configuration used ERA5 and Malaria Atlas Project covariates, singular value decomposition, k-means clustering, and the Institute for Disease Modeling's EMOD model to explore a range of three-year malaria interventions primarily focused on vector control and case management.
Rainfall, temperature, and mosquito abundance layers were clustered into ten transmission archetypes with distinct properties. Example intervention impact curves and maps highlighted archetype-specific variation in efficacy of vector control interventions. A sensitivity analysis showed that the procedure for selecting representative sites to simulate worked well in all but one archetype.
This paper introduces a novel methodology which combines the richness of spatiotemporal mapping with the rigor of mechanistic modeling to create a multi-purpose infrastructure for answering a broad range of important questions in the malaria policy space. It is flexible and adaptable to a range of input covariates, mechanistic models, and mapping strategies and can be adapted to the modelers' setting of choice.
随着机械和地理空间疟疾建模方法越来越多地融入疟疾政策决策中，对于将这两种方法结合起来的策略的需求也在不断增加。本文介绍了一种基于原型的新方法，用于根据机械模型模拟生成高分辨率干预影响图。描述并探讨了该框架的示例配置。
首先，应用降维和聚类技术对栅格化的地理空间环境和蚊子协变量进行处理，以找到典型的疟疾传播模式。接下来，在每个原型的一个代表性地点运行机械模型，以评估干预措施的影响。最后，将这些机械模型的结果重新投影到每个像素上，生成干预影响的全图。示例配置使用 ERA5 和疟疾地图集协变量、奇异值分解、k-均值聚类和疾病建模研究所的 EMOD 模型，探索了一系列主要集中在病媒控制和病例管理的三年期疟疾干预措施。
降雨、温度和蚊子丰度层聚类为具有不同特征的十个传播原型。示例干预影响曲线和地图突出了媒介控制干预措施在不同类型中的效果差异。敏感性分析表明，选择代表性地点进行模拟的程序在除一个原型之外的所有原型中都能很好地工作。
本文介绍了一种新的方法，它将时空映射的丰富性与机械建模的严谨性相结合，为回答疟疾政策领域的一系列重要问题创建了一种多用途的基础设施。它具有灵活性和适应性，可以适应各种输入协变量、机械模型和映射策略，并可以适应建模人员的选择。
