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COVID-19: Early evening curfews and mobility.

ABSTRACT: During the COVID-19 pandemic, some countries introduced early evening curfews. Several studies try to measure the effectiveness of such measures across different countries, but disentangling competing effects can be elusive. We examined the impact of an early evening curfew on mobility by studying a shift in curfews from 9pm to 6pm in Greece using Google mobility data. We followed a difference-in-differences (DiD) econometric approach, where we compared trends in mobility in residential spaces as well as groceries and pharmacies, before and after the introduction of the 6pm curfew in Attica with trends in three other comparable Regions. We found little or no evidence of an effect of the early curfew on daily mobility relating to groceries and pharmacies, and that an 18.75% reduction in hours where people were allowed to leave home led to a relatively small increase in time spent in residential spaces. This less-than-proportionate reduction in mobility outside the household suggests a possibility that the curfew led to more people coinciding in indoor public spaces, such as grocery shops - which constitutes a contagion risk factor. Results should be treated with caution, especially with regards to the magnitude of any effect, as Google mobility data do not report the time of the day, so the time density of activities cannot be estimated. Lockdowns and other measures are necessary to tackle Covid-19, but it is important to avoid substitution by activities that contribute further to spreading the virus. Interventions should therefore be based on a thorough analysis of human behaviour.

SUBMITTER: Velias A

PROVIDER: S-EPMC8588835 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundContact mixing plays a key role in the spread of COVID-19. Thus, mobility restrictions of varying degrees up to and including nationwide lockdowns have been implemented in over 200 countries. To appropriately target the timing, location, and severity of measures intended to encourage social distancing at a country level, it is essential to predict when and where outbreaks will occur, and how widespread they will be.MethodsWe analyze aggregated, anonymized health data and cell phone mobility data from Israel. We develop predictive models for daily new cases and the test positivity rate over the next 7 days for different geographic regions in Israel. We evaluate model goodness of fit using root mean squared error (RMSE). We use these predictions in a five-tier categorization scheme to predict the severity of COVID-19 in each region over the next week. We measure magnitude accuracy (MA), the extent to which the correct severity tier is predicted.ResultsModels using mobility data outperformed models that did not use mobility data, reducing RMSE by 17.3% when predicting new cases and by 10.2% when predicting the test positivity rate. The best set of predictors for new cases consisted of 1-day lag of past 7-day average new cases, along with a measure of internal movement within a region. The best set of predictors for the test positivity rate consisted of 3-days lag of past 7-day average test positivity rate, along with the same measure of internal movement. Using these predictors, RMSE was 4.812 cases per 100,000 people when predicting new cases and 0.79% when predicting the test positivity rate. MA in predicting new cases was 0.775, and accuracy of prediction to within one tier was 1.0. MA in predicting the test positivity rate was 0.820, and accuracy to within one tier was 0.998.ConclusionsUsing anonymized, macro-level data human mobility data along with health data aids predictions of when and where COVID-19 outbreaks are likely to occur. Our method provides a useful tool for government decision makers, particularly in the post-vaccination era, when focused interventions are needed to contain COVID-19 outbreaks while mitigating the collateral damage from more global restrictions.

Project description:To prevent the outbreak of the Coronavirus disease (COVID-19), many countries around the world went into lockdown and imposed unprecedented containment measures. These restrictions progressively produced changes to social behavior and global mobility patterns, evidently disrupting social and economic activities. Here, using maritime traffic data collected via a global network of Automatic Identification System (AIS) receivers, we analyze the effects that the COVID-19 pandemic and containment measures had on the shipping industry, which accounts alone for more than 80% of the world trade. We rely on multiple data-driven maritime mobility indexes to quantitatively assess ship mobility in a given unit of time. The mobility analysis here presented has a worldwide extent and is based on the computation of: Cumulative Navigated Miles (CNM) of all ships reporting their position and navigational status via AIS, number of active and idle ships, and fleet average speed. To highlight significant changes in shipping routes and operational patterns, we also compute and compare global and local vessel density maps. We compare 2020 mobility levels to those of previous years assuming that an unchanged growth rate would have been achieved, if not for COVID-19. Following the outbreak, we find an unprecedented drop in maritime mobility, across all categories of commercial shipping. With few exceptions, a generally reduced activity is observable from March to June 2020, when the most severe restrictions were in force. We quantify a variation of mobility between -5.62 and -13.77% for container ships, between +2.28 and -3.32% for dry bulk, between -0.22 and -9.27% for wet bulk, and between -19.57 and -42.77% for passenger traffic. The presented study is unprecedented for the uniqueness and completeness of the employed AIS dataset, which comprises a trillion AIS messages broadcast worldwide by 50,000 ships, a figure that closely parallels the documented size of the world merchant fleet.

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COVID-19: Early evening curfews and mobility.

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