The impact of contact tracing and household bubbles on deconfinement strategies for COVID-19: an individual-based modelling study

Abstract

<jats:p>Background. The rising COVID-19 pandemic caused many governments to impose policies restricting social interactions. These policies have slowed down the spread of the SARS-CoV-2 virus to the extent that restric- tions can be gradually lifted. Models can be useful to assess the consequences of deconfinement strategies with respect to business, school and leisure activities. Methods. We adapted the individual-based model "STRIDE" to simulate interactions between the 11 million inhabitants of Belgium at the levels of households, workplaces, schools and communities. We calibrated our model to observed hospital incidence and seroprevalence data. STRIDE can explore contact tracing options and account for repetitive leisure contacts in extended household settings (so called "household bubbles") with varying levels of connectivity. Findings. Household bubbles have the potential to reduce the number of COVID-19 hospital admissions by up to 90%. The effectiveness of contact tracing depends on its timing, as it becomes futile more than 4 days after the index case developed symptoms. Assuming that children have a lower level of susceptibility and lower probability to experience symptomatic SARS-CoV-2 infection, (partial) school closure options have relatively little impact on COVID-19 burden. Interpretation. Not only the absolute number and intensity of physical contacts drive the transmission dynamics and COVID-19 burden, also their repetitiveness is influential. Contact tracing seems essential for a controlled and persistent release of lockdown measures, but requires timely compliance to testing, reporting and self-isolation. Rapid tracing and testing, and communication ensuring continued involvement of the population are therefore essential.</jats:p>

Assessing the feasibility and effectiveness of household-pooled universal testing to control COVID-19 epidemics

“…In the main experiments, we assume that when isolation is imposed, individuals are able to isolate from household members as well. When individuals are aware of their infection status, as is the case when individual isolation is applied, this assumption is reasonable and in line with earlier work [19]. However, we argue that this is less straightforward to accomplish in the case of pool isolation.…”

“…We conduct a series of experiments to investigate the proposed universal testing procedure under different assumptions. All of the experiments consider the Belgian COVID-19 epidemic that was fitted to data observed during the epidemic [19]. We assume that individuals are less infectious when asymptomatic (50% reduction in infectiousness) [31].…”

A data-driven metapopulation model for the Belgian COVID-19 epidemic: assessing the impact of lockdown and exit strategies

“…As a fast and reliable contact tracing is of foremost importance, several digital solutions have been proposed to match the need for personal information with privacy concerns [51,52]. Other models have been applied to the emergence of COVID-19 in Belgium, either specifically [53,54,29] or in multi-country applications [55]. Using different model paradigms allows to focus on distinct aspects of the outbreak, like delay distributions of the clinical history of patients [29], a more detailed and age-specific handling of serological data with MCMC [54] or exploring individualspecific contact tracing options [53].…”

A proposed two-stage quarantine containment scheme against spreading of novel coronavirus (SARS-CoV-2)

“…Mathematical modelling concepts was used in this study to illustrate the effects on containment based on models reported in the literature. 4,13,29,[32][33][34][35][36][37][38] There are many stochastic models which can capture the inherent randomness in disease transmission observed in outbreaks. The probability of an infection in the next period of time (in a discrete time model) was postulated by Hamer 38 in 1906 to be proportional to the number of infectious individuals multiplied by the number of susceptible individuals.…”

“…The blocks and units can be assigned under different conditions of different countries. For example, a block 28,29 can be a tall residential building in dense urban areas. A unit can be an apartment inside.…”