University of Waterloo, Department of Mathematics, Waterloo, N2L 3G1, Canada.
University of Guelph, School of Environmental Sciences, Guelph, N1G 2W1, Canada.
Sci Rep. 2020 May 6;10(1):7611. doi: 10.1038/s41598-020-63849-0.
The resurgence of infectious diseases due to vaccine refusal has highlighted the role of interactions between disease dynamics and the spread of vaccine opinion on social networks. Shifts between disease elimination and outbreak regimes often occur through tipping points. It is known that tipping points can be predicted by early warning signals (EWS) based on characteristic dynamics near the critical transition, but the study of EWS in coupled behaviour-disease networks has received little attention. Here, we test several EWS indicators measuring spatial coherence and autocorrelation for their ability to predict a critical transition corresponding to disease outbreaks and vaccine refusal in a multiplex network model. The model couples paediatric infectious disease spread through a contact network to binary opinion dynamics of vaccine opinion on a social network. Through change point detection, we find that mutual information and join count indicators provided the best EWS. We also show the paediatric infectious disease natural history generates a discrepancy between population-level vaccine opinions and vaccine immunity status, such that transitions in the social network may occur before epidemiological transitions. These results suggest that monitoring social media for EWS of paediatric infectious disease outbreaks using these spatial indicators could be successful.
由于疫苗抵制而导致传染病死灰复燃，这凸显了疾病动态与疫苗观点在社交网络上传播之间相互作用的作用。疾病消除和爆发之间的转变通常通过临界点发生。已知可以通过基于临界转变附近特征动态的预警信号 (EWS) 来预测临界点，但在耦合行为-疾病网络中对 EWS 的研究却很少受到关注。在这里，我们测试了几种测量空间一致性和自相关性的 EWS 指标，以评估它们在多网络模型中预测与疾病爆发和疫苗抵制相对应的临界点的能力。该模型通过接触网络传播儿科传染病，并通过社交网络上的疫苗观点二进制动态对其进行耦合。通过变点检测，我们发现互信息和加入计数指标提供了最佳的 EWS。我们还表明，儿科传染病的自然史导致了人群级疫苗观点和疫苗免疫状态之间的差异，因此社交网络中的转变可能先于流行病学转变。这些结果表明，使用这些空间指标监测社交媒体上儿科传染病爆发的 EWS 可能会取得成功。
