Estimating the COVID-19 Spread Through Real-time Population Mobility Patterns: Surveillance in Low- and Middle-Income Countries

Abstract

Background On January 21, 2020, the World Health Organization reported the first case of severe acute respiratory syndrome coronavirus 2, which rapidly evolved to the COVID-19 pandemic. Since then, the virus has also rapidly spread among Latin American, Caribbean, and African countries. Objective The first aim of this study is to identify new emerging COVID-19 clusters over time and space (from January 21 to mid-May 2020) in Latin American, Caribbean, and African regions, using a prospective space–time scan measurement approach. The second aim is to assess the impact of real-time population mobility patterns between January 21 and May 18, 2020, under the implemented government interventions, measurements, and policy restrictions on COVID-19 spread among those regions and worldwide. Methods We created a global COVID-19 database, of 218 countries and territories, merging the World Health Organization daily case reports with other measures such as population density and country income levels for January 21 to May 18, 2020. A score of government policy interventions was created for low, intermediate, high, and very high interventions. The population’s mobility patterns at the country level were obtained from Google community mobility reports. The prospective space–time scan statistic method was applied in five time periods between January and May 2020, and a regression mixed model analysis was used. Results We found that COVID-19 emerging clusters within these five periods of time increased from 7 emerging clusters to 28 by mid-May 2020. We also detected various increasing and decreasing relative risk estimates of COVID-19 spread among Latin American, Caribbean, and African countries within the period of analysis. Globally, population mobility to parks and similar leisure areas during at least a minimum of implemented intermediate-level control policies (when compared to low-level control policies) was related to accelerated COVID-19 spread. Results were almost consistent when regional stratified analysis was applied. In addition, worldwide population mobility due to working during high implemented control policies and very high implemented control policies, when compared to low-level control policies, was related to positive COVID-19 spread. Conclusions The prospective space–time scan is an approach that low-income and middle-income countries could use to detect emerging clusters in a timely manner and implement specific control policies and interventions to slow down COVID-19 transmission. In addition, real-time population mobility obtained from crowdsourced digital data could be useful for current and future targeted public health and mitigation policies at a global and regional level.

Parks and the Pandemic: A Scoping Review of Research on Green Infrastructure Use and Health Outcomes during COVID-19

“…Two of the articles found varying results within the study, as Kartal, Depren, and Depren [ 32 ] noted park use as related to higher COVID-19 deaths but having no impact on cases. Tyrovolas et al [ 35 ], by contrast, found an overall pattern of park mobility indicating lower COVID-19 spread, except in areas of lockdown, where it was associated with higher spread. Of the four studies that found a positive association between park mobility and COVID-19 outcomes, two specifically noted that park results were weaker than other forms of mobility [ 30 , 33 ].…”

“…Those more detailed results are provided in Appendix B . This closer look reveals a wide variation in locations included in this research, including one global study that also specifically addressed the regions of Africa, Latin America, and the Caribbean [ 35 ], and country-specific studies of England [ 31 ], India [ 34 ], Portugal [ 29 ], Turkey [ 32 ], and the US [ 30 , 33 ]. As previously noted, all studies used Google community mobility data that provided aggregated mobility scores for six sets of destinations: retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, and residential destinations.…”

“…The studies so far cover a wide range of geographies, which is good, but most focus on variations over time or between regions in a single country. Two studies did find that GI use results vary based on other location characteristics, such as the nature and distribution of GI [ 31 ] and COVID-19 lockdown regulations [ 35 ]. Casa Nova et al [ 29 ] discussed, but did not test for, an association with population density.…”

“… Author (Year) Title Theme Casa Nova et al (2021) [ 29 ] Are mobility and COVID-19 related? A dynamic analysis for Portuguese districts Theme 1: COVID-specific Outcomes DePhillipo et al (2021) [ 30 ] Mobile phone GPS data and prevalence of COVID-19 infections: Quantifying parameters of social distancing in the U.S. Theme 1: COVID-specific Outcomes Johnson et al (2021) [ 31 ] Associations between COVID-19 transmission rates, park use, and landscape structure Theme 1: COVID-specific Outcomes Kartal, Depren, and Depren (2021) [ 32 ] The relationship between mobility and COVID-19 pandemic: Daily evidence from an emerging country by causality analysis Theme 1: COVID-specific Outcomes Noland (2021) [ 33 ] Mobility and the effective reproduction rate of COVID-19 Theme 1: COVID-specific Outcomes Praharaj and Han (2021) [ 34 ] Human mobility impacts on the surging incidence of COVID-19 in India Theme 1: COVID-specific Outcomes Tyrovolas et al (2021) [ 35 ] Estimating the COVID-19 spread through real-time population mobility patterns: Surveillance in low- and middle-income countries Theme 1: COVID-specific Outcomes Corley et al (2021) [ 36 ] Home garden use during COVID-19: Associations with physical and mental wellbeing in older adults Theme 2: Non-COVID Health Outcomes Gola et al (2021) [ 37 ...…”

“… General discussion of mobility as tied to transmission, but no park-specific mechanism was discussed. Noland (2021) [ 33 ] USA State Log-linear models COVID-19 effective reproduction rate Destination-based mobility metrics Positive correlation between all mobility categories except residential and reproductive rate Park-based mobility has the weakest relationship (smallest coefficient) of all mobility categories, which authors suggest indicates less viral spread at parks compared to other destinations Praharaj and Han (2021) [ 34 ] India State Poisson log-linear model COVID-19 cases Destination-based mobility metrics No significant relationship between park mobility and COVID-19 cases Tyrovolas et al (2021) [ 35 ] Global, with regional analyses for Latin America, Africa, the Caribbean Country Negative binomial mixed model Daily new COVID-19 cases Countries’ preparedness in epidemics (INFORM index), COVID-19 testing policy, COVID-19 type of transmission for each country through time, populations’ real-time mobility patterns, their interaction with each level of government control policy, and the country’s income level Park mobility interacted with government control measures—it exhibited a negative relationship to daily cases overall, but a positive relationship where control measures were in place. This held at the regional level for Latin America and the Caribbean, but not Africa Individual behavior, dynamic network, and seasonality may be factors, but no discussion offered specifically for parks …”