Modelling the impact of social distancing and targeted vaccination on the spread of COVID-19 through a real city-scale contact network.

We use mobile device data to construct empirical interpersonal physical contact networks in the city of Portland, Oregon, both before and after social distancing measures were enacted during the COVID-19 pandemic. These networks reveal how social distancing measures and the public's reaction to the incipient pandemic affected the connectivity patterns within the city. We find that as the pandemic developed there was a substantial decrease in the number of individuals with many contacts. We further study the impact of these different network topologies on the spread of COVID-19 by simulating an SEIR epidemic model over these networks and find that the reduced connectivity greatly suppressed the epidemic. We then investigate how the epidemic responds when part of the population is vaccinated, and we compare two vaccination distribution strategies, both with and without social distancing. Our main result is that the heavy-tailed degree distribution of the contact networks causes a targeted vaccination strategy that prioritizes high-contact individuals to reduce the number of cases far more effectively than a strategy that vaccinates individuals at random. Combining both targeted vaccination and social distancing leads to the greatest reduction in cases, and we also find that the marginal benefit of a targeted strategy as compared to a random strategy exceeds the marginal benefit of social distancing for reducing the number of cases. These results have important implications for ongoing vaccine distribution efforts worldwide.

Spatial and temporal patterns in civil violence: Guatemala, 1977-1986.

Civil violence is a complex and often horrific phenomenon whose characteristics have varied by era, setting, and circumstance. Its objective analysis has rarely been feasible at spatial and temporal scales great enough and resolutions fine enough to reveal patterns useful in prevention, intervention, or adjudication. An extraordinary data set simultaneously meeting scale and resolution criteria was collected during conflict in Guatemala from 1977 through 1986. Reported here is its spatial-temporal analysis; reported as well is a putatively novel method for estimating power-law exponents from aggregate data. Analysis showed that the relationship between ethnic mix and killing was smooth yet highly nonlinear, that the temporal texture of killings was rough, and that the distribution of killing-event sizes was dichotomous, with nongenocidal and genocidal conflict periods displaying Zipf and non-Zipf distributions, respectively. These results add statistical support to claims that the Guatemalan military operated under at least two directives with respect to killing and that one of these effected a genocidal campaign against an indigenous people, the Mayans. Implications for group-behavioral modeling, conflict prevention, peace-keeping intervention, human-rights monitoring, and transitional justice are noted.