Suprunenko Yevhen F, Gilligan Christopher A
Department of Plant Sciences, University of Cambridge, Cambridge, UK.
R Soc Open Sci. 2025 May 14;12(5):250012. doi: 10.1098/rsos.250012. eCollection 2025 May.
Epidemiological modelling plays an important role in global food security by informing strategies for the control and management of invasion and spread of crop diseases. However, the underlying data on spatial locations of host crops that are susceptible to a pathogen are often incomplete and inaccurate, thus reducing the accuracy of model predictions. Obtaining and refining datasets that fully represent a host landscape across territories can be a major challenge when predicting disease outbreaks. Therefore, it would be an advantage to prioritize areas in which data refinement efforts should be directed to improve the accuracy of epidemic prediction. In this paper, we present an analytical method to identify areas where potential errors in mapped host data would have the largest impact on modelled pathogen invasion and short-term spread. The method is based on an analytical approximation for the rate at which susceptible host crops become infected at the start of an epidemic. We show how implementing spatial prioritization for data refinement in a cassava-growing region in sub-Saharan Africa could be an effective means for improving accuracy when modelling the dispersal and spread of the crop pathogen cassava brown streak virus.
流行病学建模通过为控制和管理作物病害的入侵与传播提供策略，在全球粮食安全中发挥着重要作用。然而，易受病原体感染的寄主作物空间位置的基础数据往往不完整且不准确，从而降低了模型预测的准确性。在预测疾病爆发时，获取并完善能全面代表各地区寄主景观的数据集可能是一项重大挑战。因此，优先确定应进行数据完善工作的区域，以提高疫情预测的准确性，将是一个优势。在本文中，我们提出一种分析方法，以识别地图上寄主数据中的潜在误差对模拟病原体入侵和短期传播影响最大的区域。该方法基于对疫情开始时易感寄主作物感染率的解析近似。我们展示了在撒哈拉以南非洲的一个木薯种植区实施数据完善的空间优先级排序，如何能成为提高作物病原体木薯褐色条纹病毒传播和扩散建模准确性的有效手段。
