Niels Bohr Institute, University of Copenhagen, 2100, Copenhagen, Denmark.
Sci Rep. 2021 May 27;11(1):11191. doi: 10.1038/s41598-021-90666-w.
Epidemics are regularly associated with reports of superspreading: single individuals infecting many others. How do we determine if such events are due to people inherently being biological superspreaders or simply due to random chance? We present an analytically solvable model for airborne diseases which reveal the spreading statistics of epidemics in socio-spatial heterogeneous spaces and provide a baseline to which data may be compared. In contrast to classical SIR models, we explicitly model social events where airborne pathogen transmission allows a single individual to infect many simultaneously, a key feature that generates distinctive output statistics. We find that diseases that have a short duration of high infectiousness can give extreme statistics such as 20% infecting more than 80%, depending on the socio-spatial heterogeneity. Quantifying this by a distribution over sizes of social gatherings, tracking data of social proximity for university students suggest that this can be a approximated by a power law. Finally, we study mitigation efforts applied to our model. We find that the effect of banning large gatherings works equally well for diseases with any duration of infectiousness, but depends strongly on socio-spatial heterogeneity.
单个个体感染了许多其他人。我们如何确定这些事件是由于人们天生就是生物超级传播者，还是仅仅由于随机机会？我们提出了一个可分析求解的空气传播疾病模型，该模型揭示了社会空间异质空间中传染病的传播统计数据，并为可以进行比较的数据提供了基准。与经典的 SIR 模型不同，我们明确地模拟了社会事件，在这些事件中，空气传播的病原体传播允许一个个体同时感染许多个体，这是产生独特输出统计数据的关键特征。我们发现，具有短暂高传染性的疾病可能会产生极端的统计数据，例如 20%的人感染超过 80%的人，具体取决于社会空间的异质性。通过对社交聚会规模的分布进行量化，并追踪大学生社交接近度的数据，我们发现这可以用幂律来近似。最后，我们研究了应用于我们模型的缓解措施。我们发现，禁止大型聚会的效果对任何传染性疾病都同样有效，但强烈依赖于社会空间的异质性。
