Testing, contact tracing, and isolation (TTI) is an epidemic management and control approach that is difficult to implement at scale because it relies on manual tracing of contacts. Exposure notification apps have been developed to digitally scale up TTI by harnessing contact data obtained from mobile devices; however, exposure notification apps provide users only with limited binary information when they have been directly exposed to a known infection source. Here we demonstrate a scalable improvement to TTI and exposure notification apps that uses data assimilation (DA) on a contact network. Network DA exploits diverse sources of health data together with the proximity data from mobile devices that exposure notification apps rely upon. It provides users with continuously assessed individual risks of exposure and infection, which can form the basis for targeting individual contact interventions. Simulations of the early COVID-19 epidemic in New York City prove the concepts. In the simulations, network DA identifies up to a factor 2 more infections than contact tracing when both harness the same contact data and diagnostic test data. This remains true even when only a relatively small fraction of the population uses network DA. When a sufficiently large fraction of the population ($\gtrsim 75\%$) uses network DA and complies with individual contact interventions, targeting contact interventions with network DA reduces deaths by up to a factor 4 relative to TTI. Network DA can be implemented by expanding the computational backend of existing exposure notification apps, thus greatly enhancing their capabilities. Implemented at scale, it has the potential to precisely and effectively control future epidemics while minimizing economic disruption.
翻译:接触通知应用程序已经开发,通过利用移动设备获得的接触数据,数字化地推广TTI;然而,接触通知应用程序只向直接接触已知感染源的用户提供有限的二进制信息。在这里,我们展示了对TTI和接触通知通知应用程序的可扩缩改进,在联系网络上使用数据同化(DA)的接触通知应用程序。网络DA利用不同的健康数据来源以及接触通知所依赖的移动装置的近距离数据,开发了接触通知应用程序,以数字方式扩大TTI;通过利用从移动设备获得的接触数据;开发了接触通知应用程序,以数字方式扩大TTI的接触风险和感染风险;然而,对纽约市早期COVID-19流行病的模拟证明了这些概念。在模拟中,网络DA发现比在使用相同的接触数据和诊断测试数据进行接触跟踪时,感染程度要高2倍于接触跟踪。即使只有相对较少一部分的人口使用网络DADA。 当相当大一部分的人口使用接触风险和感染风险通知的用户能够有效地评估个人接触和感染风险和感染风险程度时,Tgrsim 75* 将个人联系到DA 。