TY - Jour A2 - Turinici，Gabriel Au - Zheer，Muhammad Usman Au - Salman，Mo D. Au - Steneroden，Kay K. Au - Magzamen，Sheryl L. Au - Weber，Stephen E. Au-Case，Shaun Au - Rao，Sangeeta Py - 2020 da - 2020/11/21 - 在流行环境中适用空间明确随机仿真模型的应用：系统评价SP - 7841941 VL - 2020 AB - 仿真建模估计高度传染性动物疾病的传播变得共同。已经开发了几种模型来模仿特定地区或国家的口蹄疫（FMD）的传播，进行风险评估，使用历史数据或假设情景分析爆发，协助流行病的政策决策，制定准备计划，以及评估经济影响。大多数可用的FMD仿真模型是为无疾病国家设计的，而在FMD流行国家的使用情况下有限使用。本文的目标是报告对在空间明确的随机仿真（Sess）模型的对现有发表的原始研究文献进行审查的研究结果，专注于评估这些模型的流行环境潜在使用。目标是确定特定FMD的特定组成部分，以适应这些Sess模型在FMD流行设置中的潜在应用程序。该系统审查遵循PRISMA指南，并搜查了三个数据库，这导致了1176个引文。八十个人终于达到了纳入标准，并被列入定性综合，识别九个独特的Sess模型。 These SESS models were assessed for their potential application in endemic settings. The assessed SESS models can be adapted for use in FMD endemic countries by modifying the underlying code to include multiple cocirculating serotypes, routine prophylactic vaccination (RPV), and livestock population dynamics to more realistically mimic the endemic characteristics of FMD. The application of SESS models in endemic settings will help evaluate strategies for FMD control, which will improve livestock health, provide economic gains for producers, help alleviate poverty and hunger, and will complement efforts to achieve the Sustainable Development Goals. SN - 1748-670X UR - https://doi.org/10.1155/2020/7841941 DO - 10.1155/2020/7841941 JF - Computational and Mathematical Methods in Medicine PB - Hindawi KW - ER -