Simulating SARS-CoV-2 epidemics by region-specific variables and modeling contact tracing app containment

Abstract

Targeted contact-tracing through mobile phone apps has been proposed as an instrument to help contain the spread of COVID-19 and manage the lifting of nation-wide lock-downs currently in place in USA and Europe. However, there is an ongoing debate on its potential efficacy, especially in light of region-specific demographics. We built an expanded SIR model of COVID-19 epidemics that accounts for region-specific population densities, and we used it to test the impact of a contact-tracing app in a number of scenarios. Using demographic and mobility data from Italy and Spain, we used the model to simulate scenarios that vary in baseline contact rates, population densities, and fraction of app users in the population. Our results show that, in support of efficient isolation of symptomatic cases, app-mediated contact-tracing can successfully mitigate the epidemic even with a relatively small fraction of users, and even suppress altogether with a larger fraction of users. However, when regional differences in population density are taken into consideration, the epidemic can be significantly harder to contain in higher density areas, highlighting potential limitations of this intervention in specific contexts. This work corroborates previous results in favor of app-mediated contact-tracing as mitigation measure for COVID-19, and draws attention on the importance of region-specific demographic and mobility factors to achieve maximum efficacy in containment policies.

Fig. 1. Density and mobility influence contact tracing app effectiveness.

Compared to low density regions (a), epidemics may be significantly harder to contain through contact tracing apps in areas with very high population density (b).

Fig. 2. Model outline.

Graphical representation of the interactions among the different compartments of the extended SIR model. S susceptible, I infected, R recovered, A asymptomatic, P pre-symptomatic, QS quarantined susceptible, QI quarantined infected, QA quarantined asymptomatic, QP quarantined pre-symptomatic. Rates of transfer between compartments are a function of the parameters annotated on the arrows. A detailed description of such parameters is provided in “Methods” and Table 1.

Fig. 3. Symptomatic population and mortality.

Total symptomatic population (red) and simulated mortality (black) in the 48 scenarios. Density-dependent contact rate simulations on the left (plots a–f); fixed contact rate simulations on the right (plots g–l). Each curve results from the sum over 110 districts averaged over 50 replicates per district. Solid lines represent no app users; dashed, dotted and dashed–dotted lines show increasing fractions of the population using the app (25, 50, 75%). All simulations can be interactively explored at https://flowmaps.life.bsc.es/shiny/ct_app/.

Fig. 4. Successful/unsuccessful suppression in the first 50 days.

Detail of Fig. 3. Density-dependent contact rate simulations on the left (a, c, e); fixed contact rate simulations on the right (b, d, f). Suppression is achieved in the most optimistic scenario (contact rate 7.5, app users 75%) with fixed contact rate (b), whereas its success varies from district to district in scenarios with density-dependent contact rate.

Fig. 5. Epidemic curve by district.

Epidemic curve for individual nodes (districts) in the scenario with average velocity $\overline{v}=1.5$ and fraction of app users j = 25%. The epidemic outbreak is entirely sustained by the three highest-density districts, whereas in the others the effective reproductive number is below 1.