School of Computer Science, Technological University Dublin, Dublin D24 FKT9, Ireland.
School of Computer Science, University College Dublin, Dublin D04 V1W8, Ireland.
Int J Environ Res Public Health. 2020 Apr 30;17(9):3119. doi: 10.3390/ijerph17093119.
In understanding the dynamics of the spread of an infectious disease, it is important to understand how a town's place in a network of towns within a region will impact how the disease spreads to that town and from that town. In this article, we take a model for the spread of an infectious disease in a single town and scale it up to simulate a region containing multiple towns. The model is validated by looking at how adding additional towns and commuters influences the outbreak in a single town. We then look at how the centrality of a town within a network influences the outbreak. Our main finding is that the commuters coming into a town have a greater effect on whether an outbreak will spread to a town than the commuters going out. The findings on centrality of a town and how it influences an outbreak could potentially be used to help influence future policy and intervention strategies such as school closure policies.
在理解传染病传播的动态时，了解一个城镇在该地区内的城镇网络中的位置如何影响疾病传播到该城镇以及从该城镇传播的方式非常重要。在本文中，我们采用了一种在单个城镇中传播传染病的模型，并将其扩展到模拟包含多个城镇的区域。通过观察添加更多城镇和通勤者如何影响单个城镇的疫情爆发，对模型进行了验证。然后，我们研究了城镇在网络中的中心地位如何影响疫情爆发。我们的主要发现是，进入城镇的通勤者对疫情是否会传播到城镇的影响大于离开城镇的通勤者。关于城镇的中心性及其如何影响疫情爆发的发现，可能有助于影响未来的政策和干预策略，例如学校关闭政策。
