Disease Prevention Maps

Location-based data provided by Facebook’s “Disease Prevention Maps” is provided at 8-hourly intervals in map squares as small as 600x600 meters. The data also enables us to measure changes in... Location-based data provided by Facebook’s “Disease Prevention Maps” is provided at 8-hourly intervals in map squares as small as 600x600 meters. The data also enables us to measure changes in the number of tiles visited per day. The idea is as follows: if the number of tiles visited per day decreases, we then take this as a proxy for decreased mobility. In Figure 1 we see two “dips” with the August Danang lockdown causing a greater and sharper decline than the national lockdown in April. For the first time, the pattern in Danang has diverged from that in Ha Noi and Ho Chi Minh City (HCMC); this was not the case in preceding months, where changes in all three cities occurred uniformly.

Since early March 2020, the COVID-19 epidemic across the United Kingdom has led to a range of social distancing policies, which have resulted in reduced mobility across different regions. Crowd level... Since early March 2020, the COVID-19 epidemic across the United Kingdom has led to a range of social distancing policies, which have resulted in reduced mobility across different regions. Crowd level data on mobile phone usage can be used as a proxy for actual population mobility patterns and provide a way of quantifying the impact of social distancing measures on changes in mobility. We show that there was a substantial overall reduction in mobility, with the most rapid decline on the 24th March 2020, the day after the Prime Minister’s announcement of an enforced lockdown.

This paper presents a large-scale analysis of the impact of lockdown measures introduced in response to the spread of novel coronavirus disease 2019 (COVID-19) on socioeconomic conditions of Italian... This paper presents a large-scale analysis of the impact of lockdown measures introduced in response to the spread of novel coronavirus disease 2019 (COVID-19) on socioeconomic conditions of Italian citizens. We leverage a massive near–real-time dataset of human mobility and we model mobility restrictions as an exogenous shock to the economy, similar to a natural disaster.

Researchers analyzed key mobility trends before and during COVID-19’s lockdown in Italy, one of the first and more affected countries. This research looks at how many people stayed home during... Researchers analyzed key mobility trends before and during COVID-19’s lockdown in Italy, one of the first and more affected countries. This research looks at how many people stayed home during lockdown; what happened between the announcement and the actual implementation of restriction measures; and how population changed during lockdown, checking if people left cities and moved to marginal areas or urban belts.