Trading contact tracing efficiency for finding patient zero.

As the COVID-19 pandemic has demonstrated, identifying the origin of a pandemic remains a challenging task. The search for patient zero may benefit from the widely-used and well-established toolkit of contact tracing methods, although this possibility has not been explored to date. We fill this gap by investigating the prospect of performing the source detection task as part of the contact tracing process, i.e., the possibility of tuning the parameters of the process in order to pinpoint the origin of the infection. To this end, we perform simulations on temporal networks using a recent diffusion model that recreates the dynamics of the COVID-19 pandemic. We find that increasing the budget for contact tracing beyond a certain threshold can significantly improve the identification of infected individuals but has diminishing returns in terms of source detection. Moreover, disease variants of higher infectivity make it easier to find the source but harder to identify infected individuals. Finally, we unravel a seemingly-intrinsic trade-off between the use of contact tracing to either identify infected nodes or detect the source of infection. This trade-off suggests that focusing on the identification of patient zero may come at the expense of identifying infected individuals.

The network limits of infectious disease control via occupation-based targeting.

Policymakers commonly employ non-pharmaceutical interventions to reduce the scale and severity of pandemics. Of non-pharmaceutical interventions, physical distancing policies-designed to reduce person-to-person pathogenic spread - have risen to recent prominence. In particular, stay-at-home policies of the sort widely implemented around the globe in response to the COVID-19 pandemic have proven to be markedly effective at slowing pandemic growth. However, such blunt policy instruments, while effective, produce numerous unintended consequences, including potentially dramatic reductions in economic productivity. In this study, we develop methods to investigate the potential to simultaneously contain pandemic spread while also minimizing economic disruptions. We do so by incorporating both occupational and contact network information contained within an urban environment, information that is commonly excluded from typical pandemic control policy design. The results of our methods suggest that large gains in both economic productivity and pandemic control might be had by the incorporation and consideration of simple-to-measure characteristics of the occupational contact network. We find evidence that more sophisticated, and more privacy invasive, measures of this network do not drastically increase performance.

Social mobilization and polarization can create volatility in COVID-19 pandemic control.

During the COVID-19 pandemic, political polarization has emerged as a significant threat that inhibits coordinated action of central and local institutions reducing the efficacy of non-pharmaceutical interventions (NPIs). Yet, it is not well-understood to what extent polarization can affect grass-roots, voluntary social mobilization targeted at mitigating the pandemic spread. Here, we propose a polarized mobilization model amidst the pandemic for demonstrating the differential responses to COVID-19 as mediated by the USA's political landscape. We use a novel dataset and models from time-critical social mobilization competitions, voting records, and a high-resolution county-wise friendship network. Our simulations show that a higher degree of polarization impedes the overall spread of mobilization and leads to a highly-heterogeneous impact among states. Our hypothetical compliance campaign to mitigate COVID-19 spread predicts grass-roots mitigation strategies' success before the dates of actual lockdowns in identically polarized states with more than three times of success rate than oppositely polarized states. Finally, we analyze the coupling of social mobilization leading to unrest and the growth of COVID-19 infections. These findings highlight social mobilization as both a collective precautionary measure and a potential threat to countermeasures, together with a warning message that the emerging polarization can be a significant hurdle of NPIs relying on coordinated action.