School of Mathematics, University of Birmingham, Birmingham, UK.
Department of Plant Sciences, University of Cambridge, Cambridge, UK.
Spat Spatiotemporal Epidemiol. 2022 Jun;41:100391. doi: 10.1016/j.sste.2020.100391. Epub 2020 Nov 21.
Infectious diseases remain one of the major causes of human mortality and suffering. Mathematical models have been established as an important tool for capturing the features that drive the spread of the disease, predicting the progression of an epidemic and hence guiding the development of strategies to control it. Another important area of epidemiological interest is the development of geostatistical methods for the analysis of data from spatially referenced prevalence surveys. Maps of prevalence are useful, not only for enabling a more precise disease risk stratification, but also for guiding the planning of more reliable spatial control programmes by identifying affected areas. Despite the methodological advances that have been made in each area independently, efforts to link transmission models and geostatistical maps have been limited. Motivated by this fact, we developed a Bayesian approach that combines fine-scale geostatistical maps of disease prevalence with transmission models to provide quantitative, spatially-explicit projections of the current and future impact of control programs against a disease. These estimates can then be used at a local level to identify the effectiveness of suggested intervention schemes and allow investigation of alternative strategies. The methodology has been applied to lymphatic filariasis in East Africa to provide estimates of the impact of different intervention strategies against the disease.
传染病仍然是人类死亡和痛苦的主要原因之一。数学模型已成为捕捉驱动疾病传播的特征、预测疫情进展并指导制定控制策略的重要工具。流行病学另一个重要的研究领域是开发用于分析来自空间参考患病率调查数据的地统计学方法。患病率图不仅有助于更精确地进行疾病风险分层，还可以通过确定受影响的地区来指导更可靠的空间控制计划的规划。尽管在每个领域都取得了方法上的进展，但将传播模型和地统计学图联系起来的努力一直受到限制。受此事实的启发，我们开发了一种贝叶斯方法，将疾病患病率的精细尺度地统计学图与传播模型相结合，为当前和未来控制疾病的计划的影响提供定量的、空间明确的预测。然后可以在当地使用这些估计值来确定建议干预计划的有效性，并允许调查替代策略。该方法已应用于东非的淋巴丝虫病，以提供针对该疾病的不同干预策略的影响估计。
