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Effectiveness of social distancing strategies for protecting a community from a pandemic with a data-driven contact network based on census and real-world mobility data.
1. School closures do not have a major impact on controlling the epidemic; despite closing them, infections keep occurring within the households and the community layers.
2. Passive social distance strategies are not enough to contain the epidemic, indicating that active strategies need to be established. For instance, large-scale testing, remote symptoms monitoring, isolation and contact tracing.
3. School closures and self-distancing at 90% of adoption are a feasible strategy for minimizing the effects of the epidemic, but only if they are applied for a long time.
4. A full confinement is not feasible and will not solve the problem, without active measures in place after the confinement, since there would be a new outbreak.
5. If high resolution mobility data is available, our data-driven approach with the real-world data can be easily replicated for new cities or countries to measure the impact of social distance strategies and the epidemic.
The current situation of emergency is global. As of today, March 22nd 2020, there are more than 23 countries with more than 1.000 infected cases by COVID-19, in the exponential growth phase of the disease.
Furthermore, there are different mitigation and suppression strategies in place worldwide, but many of them are based on enforcing, to a more or less extent, the so-called social distancing.
The impact and outcomes of the adopted measures are yet to be contrasted and quantified.
Therefore, realistic modeling approaches could provide important clues about what to expect and the best course of action. Such modeling efforts could potentially save thousands, if not millions of lives.
Our report contains preliminary results that aim at answering the following questions in relation to the spread and control of the COVID-19 pandemic:
– What is the expected impact of current social distancing strategies?
– How long should such measures need to be in place?
– How many people will be infected and at which social level?
– How do R(t) and the epidemic dynamic change based on the adopted strategies?
– What is the probability of having a second outbreak, i.e., a reemergence?
– If there is a reemergence, how much time do we have to get ready?
– What is the best strategy to minimize the current epidemic and get ready for a second wave?
In this report, we provide details of the data analyzed, the methodology (and its limitations) employed as well as a quantitative and qualitative assessment of strategies based on social distancing and corresponding what-if-scenarios for control and mitigation.
We use real-world mobility and census data of the Boston area to build a co-location network at three different layers (community, households and schools), and a data-driven SEIR model that allows testing six different social distancing strategies, namely, (i) school closures, (ii) self-distancing and teleworking, (iii) self-distancing and teleworking plus School closure (iv) Restaurants, nightlife
and cultural closures, (v) non-essential workplace closures, and (vi) total confinement.
We test the impact of establishing these strategies at different stages of the epidemic evolution and different time periods.