Associations of Government-Mandated Closures and Restrictions With Aggregate Mobility Trends and SARS-CoV-2 Infections in Nigeria

Abstract

IMPORTANCE To prepare for future coronavirus disease 2019 (COVID-19) waves, Nigerian policy makers need insights into community spread of COVID-19 and changes in rates of infection associated with government-mandated closures and restrictions. OBJECTIVES To measure the association of closures and restrictions with aggregate mobility and the association of mobility with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and to characterize community spread of COVID-19. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study used aggregated anonymized mobility data from smartphone users in Nigeria who opted to provide location history (from a pool of up to 40 million individuals) collected between February 27 and July 21, 2020. The analyzed data included daily counts of confirmed SARS-CoV-2 infections and daily changes in aggregate mobility across 6 categories: retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, and residential. Closures and restrictions were initiated on March 30, 2020, and partially eased on May 4, 2020. MAIN OUTCOMES AND MEASURES Interrupted time series were used to measure associations of closures and restrictions with aggregate mobility. Negative binomial regression was used to evaluate associations between confirmed SARS-CoV-2 infections and mobility categories. Averted infections were estimated by subtracting cumulative confirmed infections from estimated infections assuming no closures and restrictions. RESULTSClosures and restrictions had negative associations with mean change in daily aggregate mobility in retail and recreation ] percentage points; P < .001), grocery and pharmacy ] percentage points; P < .001), parks ] percentage points; P < .001), transit stations percentage points; P < .001), and workplaces (-53.07 [95% CI, -67.75 to -38.39] percentage points; P < .001) and a positive association with mobility in residential areas (24.10 [95% CI, 19.14 to 29.05] percentage points; P < .001). Most of these changes reversed after closures and restrictions were partially eased (retail and recreation: 14.63 [95% CI, 10.95 to 18.30] percentage points;

Effectiveness of public health measures in reducing the incidence of covid-19, SARS-CoV-2 transmission, and covid-19 mortality: systematic review and meta-analysis

“…Isolation or stay at home was an effective measure in reducing the transmission of SARS-CoV-2, but the included studies used results for mobility to assess stay at home or isolation and therefore could have been limited by potential flaws in publicly available phone data, 41 58 102 and variations in the enforcement of public health measures in different states or regions were not assessed. 55 58 102 Quarantine was found to be as effective in reducing the incidence of covid-19 and transmission of SARS-CoV-2, yet variation in testing and case detection in low income environments was substantial. 59 96 98 Another study reported that quarantine was effective in reducing the transmission of SARS-CoV-2 in a cohort with a low prevalence of the virus, yet it is unknown if the same effect would be observed with higher prevalence.…”

On the role of financial support programs in mitigating the SARS-CoV-2 spread in Brazil

“…November 2020, all the states in the Northeast region faced their first waves of infections, as Figs. S.8-S. 16 show. The social isolation index presented small values, between 38.5% to 40.5% during 2020.…”

“…As expected, the correlation between the SII and the reproduction number R (t) , as well as the correlation between their daily variations were mainly negative. The estimated median time between a change in SII to imply a change in R (t) in the Southeast region ranged from 15 days (50% CI: [11][12][13][14][15][16][17] in ES to 20 days (50% CI: 20-22) in MG. In both cases, correlation approached zero or became positive basically during periods when SII reached values below 40% or when SII and R (t) stabilized around fixed values.…”

“…Using an agent-based spatial model and mobility data from Facebook in different countries, Kishore et al [ 15 ] provided that mobility restrictions effectively reduced the contact between individuals and controlled outbreaks. In Erim et al [ 16 ], interrupted time series were applied to analyze mobility data from mobile phones in Nigeria and their association with reports of COVID-19 infections, finding that mobility restrictions were associated with the reduction in the number of cases.…”

On the Role of Financial Support Programs in Mitigating the Sars-CoV-2 Spread in Brazil

“…Figure S.6 illustrates.The correlation between the daily variations in SII and in the reproduction number R(t) was mainly negative, indicating that, an increase (or decrease) in SII is linked to a decrease (or increase) in R(t). The median time between a change in SII to imply a change in R(t) in the North region ranged from 14 days (50% CI: 12-17) in AC and 22 days (50% CI:[12][13][14][15][16][17][18][19][20][21][22] in AP. Correlation approached zero or became positive basically during periods when SII reached values below 40% or when SII and R(t) stabilized around fixed values.…”