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

Abstract

In response to the ongoing COVID-19 pandemic, several countries adopted measures of social distancing to a different degree. For many countries, after successfully curbing the initial wave, lockdown measures were gradually lifted. In Belgium, such relief started on May 4th with phase 1, followed by several subsequent phases over the next few weeks. We analysed the expected impact of relaxing stringent lockdown measures taken according to the phased Belgian exit strategy. We developed a stochastic, data-informed, meta-population model that accounts for mixing and mobility of the age-structured population of Belgium. The model is calibrated to daily hospitalization data and serological data and is able to reproduce the outbreak at the national level. We consider different scenarios for relieving the lockdown, quantified in terms of relative reductions in pre-pandemic social mixing and mobility. We validate our assumptions by making comparisons with social contact data collected during and after the lockdown. Our model is able to successfully describe the initial wave of COVID-19 in Belgium and identifies interactions during leisure/other activities as pivotal in the exit strategy. Indeed, we find a smaller impact of school re-openings as compared to restarting leisure activities and re-openings of work places. We also assess the impact of case isolation of new (suspected) infections, and find that it allows re-establishing relatively more social interactions while still ensuring epidemic control. Scenarios predicting a second wave of hospitalizations were not observed, suggesting that the per-contact probability of infection has changed with respect to the pre-lockdown period. Community contacts are found to be most influential, followed by professional contacts and school contacts, respectively, for an impending second wave of COVID-19. Regular re-assessment is crucial to adjust to evolving behavioral changes that can affect epidemic diffusion. In addition to social distancing, sufficient capacity for extensive testing and contact tracing is essential for successful mitigation.

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

“…From this ensemble, we selected a single best parameter set based on the average log-likelihood function value to match the observed hospital admissions over time, since this is the model outcome of main interest. The per-case average number of secondary cases in a susceptible population, which corresponds to the basic reproduction number R 0 , was estimated to be 3.42, which is in line with estimates from a meta-analysis [19] and other modelling studies for Belgium [20, 21]. Within our final model parameter ensemble, the reproduction number ranged between (3.41–3.49).…”

Modeling the early phase of the Belgian COVID-19 epidemic using a stochastic compartmental model and studying its implied future trajectories

“…Using this model, we evaluated the expected impact of the lockdown and exit strategies for the control of COVID-19 transmission in the population. The basic reproduction number prior to lockdown was estimated to be 2.900 (2.885, 2.918) which is in line with estimates for the epidemic growth in Europe prior to the implementation of nationwide intervention measures and epidemiological modeling in different countries [22, 23, 24, 25, 26, 27], based on recent meta-analytic results [28, 29] and on other modeling exercises specifically tailored to the Belgian setting [30, 31]. Moreover, the intervention measures taken clearly flattened the epidemic curve followed by a progressive reduction of the number of (confirmed) cases over time and the number of new hospitalizations.…”

“…Several mathematical approaches have been considered in the context of the SARS-CoV-2/COVID-19 epidemic in Belgium, all having different merits and limitations [31, 30]. For example, the individual-based model by Willem et al [31] enabled the direct study of contact tracing and case isolation as a control measure.…”

On mobility trends analysis of COVID-19 dissemination in Mexico City

“…The metapopulation concept is to subdivide the entire population into distinct sub-populations, each of which has independent dynamics, together with limited interaction between the sub-populations. This approach has been used to great effect within the ecological literature [33] and recently to model the spread of COVID-19 see e.g., [10,20,35,38,57]. In this work, we calibrate the metapopulation model proposed by Li et.…”