Technological University Dublin, Grangegorman, Dublin, Ireland.
BMC Public Health. 2021 Mar 12;21(1):499. doi: 10.1186/s12889-021-10513-5.
In order to be prepared for an infectious disease outbreak it is important to know what interventions will or will not have an impact on reducing the outbreak. While some interventions might have a greater effect in mitigating an outbreak, others might only have a minor effect but all interventions will have a cost in implementation. Estimating the effectiveness of an intervention can be done using computational modelling. In particular, comparing the results of model runs with an intervention in place to control runs where no interventions were used can help to determine what interventions will have the greatest effect on an outbreak.
To test the effects of a school closure policy on the spread of an infectious disease (in this case measles) we run simulations closing schools based on either the proximity of the town to the initial outbreak or the centrality of the town within the network of towns in the simulation. To do this we use a hybrid model that combines an agent-based model with an equation-based model. In our analysis, we use three measures to compare the effects of different intervention strategies: the total number of model runs leading to an outbreak, the total number of infected agents, and the geographic spread of outbreaks.
Our results show that closing down the schools in the town where an outbreak begins and the town with the highest in degree centrality provides the largest reduction in percent of runs leading to an outbreak as well as a reduction in the geographic spread of the outbreak compared to only closing down the town where the outbreak begins. Although closing down schools in the town with the closest proximity to the town where the outbreak begins also provides a reduction in the chance of an outbreak, we do not find the reduction to be as large as when the schools in the high in degree centrality town are closed.
Thus we believe that focusing on high in degree centrality towns during an outbreak is important in reducing the overall size of an outbreak.
为了应对传染病的爆发，了解哪些干预措施将对减少疫情有影响或没有影响非常重要。虽然某些干预措施可能在减轻疫情方面具有更大的效果，但其他干预措施可能只有较小的效果，但所有干预措施在实施时都会产生成本。干预措施的有效性可以通过计算模型来估计。特别是，将实施干预措施后的模型运行结果与未使用干预措施的对照运行结果进行比较，可以帮助确定哪些干预措施对疫情的影响最大。
为了测试学校关闭政策对传染病（在本例中为麻疹）传播的影响，我们根据城镇与初始疫情的接近程度或城镇在模拟城镇网络中的中心程度，运行模拟关闭学校。为此，我们使用一种混合模型，将基于代理的模型与基于方程的模型相结合。在我们的分析中，我们使用三个措施来比较不同干预策略的效果：导致疫情爆发的模型运行总数、受感染代理的总数和疫情爆发的地理传播。
我们的结果表明，关闭疫情爆发的城镇和度中心性最高的城镇的学校，与仅关闭疫情爆发的城镇相比，能最大程度地减少导致疫情爆发的运行比例，同时也减少了疫情爆发的地理传播。尽管关闭距离疫情爆发地最近的城镇的学校也能降低疫情爆发的可能性，但我们发现，与关闭度中心性高的城镇的学校相比，这种降低幅度较小。
因此，我们认为在疫情期间关注度中心性高的城镇对于减少疫情的总体规模非常重要。
