Contact tracing for epidemic control: designing efficient quarantine protocols for the covid-19 pandemic

Abstract

The emergence and re-emergence of infectious diseases are recurrent threats to public health. The emergence of covid-19, caused by the SARS-CoV-2 virus, has not only caused high morbidity and mortality rates, but has also posed the problem of implementing effective quarantine protocols without causing major disruptions to economies around the world. To characterize and control the spread of the disease, epidemic models fed by large-scale data about human behavior have been applied in different scenarios. Here, we use the mathematical framework of network Science together with the availability of anonymized GPS data on the mobility of individuals to monitor the evolution of the covid-19 pandemic in the state of Ceará, Brazil. Using the residents’ mobility patterns, we built the network of contacts between these individuals and their temporal evolution, which determines the transmission chain of the disease in the state. Complementing the contact networks with epidemiological surveillance data, we observed that the persistence of superspreading structures of the disease, characterized by the k-cores of the networks, can prolong the pandemic even with the implementation of severe control measures (lockdowns). By identifying these structures, it was possible to design efficient strategies to break the transmission chain by isolating a minimum number of individuals, those with the highest betweenness centrality values, connecting the large k-cores. Thus, with the availability of anonymous information related to human behavior and the implementation of digital resources for real-time contact tracing, our results become important for the design of efficient quarantine protocols with minimal disruptions to world economies.