One Health Institute, School of Veterinary Medicine, University of California, Davis, USA.
University of California, Davis, USA.
Sci Rep. 2021 Mar 25;11(1):6946. doi: 10.1038/s41598-021-86124-2.
Innovative tools for modeling infectious agents are essential for better understanding disease spread given the inherent complexity of changing and interacting ecological, environmental, and demographic factors. We leveraged fine-scale satellite data on urban areas to build a road-connected geospatial network upon which to model disease spread. This model was tested by simulating the spread of the 2009 pandemic influenza in Rwanda and also used to determine the effects of vaccination regimens on outbreak spread and impact. Our results were comparable to data collected during the actual pandemic in Rwanda, determining the initial places affected after outbreak introduction in Kigali. They also highlighted the effectiveness of preventing outbreaks by targeting mitigation efforts at points of outbreak origin. This modeling approach can be valuable for planning and control purposes in real-time disease situations, providing helpful baseline scenarios during initial phases of outbreaks, and can be applied to other infectious diseases where high population mobility promotes rapid disease propagation.
创新的传染病建模工具对于更好地理解疾病传播至关重要，因为不断变化和相互作用的生态、环境和人口因素具有内在的复杂性。我们利用城市地区的精细卫星数据构建了一个道路连接的地理空间网络，以用于模拟疾病传播。该模型通过模拟 2009 年流感大流行在卢旺达的传播进行了测试，并用于确定疫苗接种方案对疫情传播和影响的作用。我们的结果与卢旺达实际大流行期间收集的数据相媲美，确定了基加利疫情爆发后最初受影响的地点。这些结果还突出了通过在疫情爆发源头采取缓解措施来预防疫情的有效性。这种建模方法对于实时疾病情况下的规划和控制目的非常有价值，在疫情爆发的初始阶段提供了有用的基线情景，并且可以应用于其他传染病，在这些传染病中，高人口流动性会促进疾病的快速传播。
