Estimating the COVID-19 spread through real-time population mobility patterns: surveillance in Low- and Middle- income countries (Preprint)

BACKGROUND On January 30, 2020, the World Health Organization (WHO) declared the current novel coronavirus disease 2019 (COVID-19) as a public health emergency of international concern and later characterized it as a pandemic. Since then, the virus has also rapidly spread among Latin American, Caribbean and African countries. The first aim of this study was to identify new emerging COVID-19 clusters over time and in space in Latin American, Caribbean, and African regions [mostly low- and middle-income countries (LMICs)], using a prospective space-time scan measurement approach. The second aim was to assess the impact of real-time population mobility patterns between January 21st to May 18th, under the implemented government interventions, measurements and policy restrictions, on COVID-19 spread, among those regions and globally. An optimal assessment of monitoring and detection tools as well as population mobility patterns could help countries to better plan and prepare their strategies against the pandemic. OBJECTIVE We created a global COVID-19 database merging WHO daily case reports with other measures such as population density, country income levels for January 21st to May 15th, 2020. A score of government policy interventions was created ranging from "light", "intermediate", and "high", to "very high" interventions. Prospective space-time scan statistic methods were applied in five time periods between January to May 2020 and a regression mixed model analysis was used. METHODS We created a global COVID-19 database merging WHO daily case reports with other measures such as population density, country income levels for January 21st to May 15th, 2020. A score of government policy interventions was created ranging from "light", "intermediate", and "high", to "very high" interventions. Prospective space-time scan statistic methods were applied in five time periods between January to May 2020 and a regression mixed model analysis was used. RESULTS We found that COVID-19 emerging clusters within these five periods of time grew from 7 emerging clusters to 28 by mid-May. 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 with accelerated COVID-19 spread. Results that were almost consistent when the regional stratified analysis was applied. Also globally, population mobility due to working reasons during high and very high implemented control policies, when compared to low- level control policies, was related with positive COVID-19 spread. CONCLUSIONS Prospective space-time scan is an approach that LMICs 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. CLINICALTRIAL