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Estimating the COVID-19 spread through real-time population mobility patterns: Surveillance in low-and middle-income countries

Estimating the COVID-19 spread through real-time population mobility patterns: Surveillance in low-and middle-income countries
Estimating the COVID-19 spread through real-time population mobility patterns: Surveillance in low-and middle-income countries
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.
COVID-19, Database, Digital public health, Emerging countries, Estimate, Low and middle-income countries, Mobile data, Pattern, Policy, Real-time, Social distancing, Surveillance, Transmission
1438-8871
Tyrovolas, Stefanos
cae9e4bc-13cb-4240-8252-66c17cc10db6
Giné-Vázquez, Iago
f8f432ab-6308-4f01-a9e8-b37b53434669
Fernández, Daniel
8fd02322-30c5-4b52-b24e-e77da4a284da
Morena, Marianthi
9b2ca375-3d49-43a4-8549-0609d7c62764
Koyanagi, Ai
217cfa42-a476-47d9-b158-4cfc6aed2719
Janko, Mark
41c89bed-0bcf-45a6-ba13-58b341500f4e
Haro, Josep Maria
c75ada55-0ff0-4cfa-ab02-6f9bc2e01204
Lin, Yang
d4e84e6a-39d9-4608-af5b-6d5d0fedb38f
Lee, Paul
02620eab-ae7f-4a1c-bad1-8a50e7e48951
Pan, William
d7605f51-574c-47c5-8d77-6d58f7cde426
Panagiotakos, Demosthenes
0fa0d52c-a7b5-4e4c-8082-b3a96f0383ef
Molassiotis, Alex
f4f18817-07cb-48ca-a51e-9504aa886a79
et al.
Tyrovolas, Stefanos
cae9e4bc-13cb-4240-8252-66c17cc10db6
Giné-Vázquez, Iago
f8f432ab-6308-4f01-a9e8-b37b53434669
Fernández, Daniel
8fd02322-30c5-4b52-b24e-e77da4a284da
Morena, Marianthi
9b2ca375-3d49-43a4-8549-0609d7c62764
Koyanagi, Ai
217cfa42-a476-47d9-b158-4cfc6aed2719
Janko, Mark
41c89bed-0bcf-45a6-ba13-58b341500f4e
Haro, Josep Maria
c75ada55-0ff0-4cfa-ab02-6f9bc2e01204
Lin, Yang
d4e84e6a-39d9-4608-af5b-6d5d0fedb38f
Lee, Paul
02620eab-ae7f-4a1c-bad1-8a50e7e48951
Pan, William
d7605f51-574c-47c5-8d77-6d58f7cde426
Panagiotakos, Demosthenes
0fa0d52c-a7b5-4e4c-8082-b3a96f0383ef
Molassiotis, Alex
f4f18817-07cb-48ca-a51e-9504aa886a79

Tyrovolas, Stefanos, Giné-Vázquez, Iago, Fernández, Daniel, Lin, Yang and Lee, Paul , et al. (2021) Estimating the COVID-19 spread through real-time population mobility patterns: Surveillance in low-and middle-income countries. Journal of Medical Internet Research, 23 (6), [e22999]. (doi:10.2196/22999).

Record type: Article

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.

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More information

Accepted/In Press date: 21 February 2021
Published date: 1 June 2021
Additional Information: Funding Information: ST was supported by the Foundation for Education and European Culture, the Miguel Servet programme (reference CP18/00006), and the Fondos Europeos de Desarrollo Regional. DF is a Serra Húnter Fellow and was supported by Marsden grant E2987-3648 administrated by the Royal Society of New Zealand, and by grant 2017 SGR 622 (GRBIO) administrated by the Departament d'Economia i Coneixement de la Generalitat de Catalunya (Spain). WP was supported by NASA-ROSES Grant NNX15AP74G. Publisher Copyright: ©Stefanos Tyrovolas, Iago Giné-Vázquez, Daniel Fernández, Marianthi Morena, Ai Koyanagi, Mark Janko, Josep Maria Haro, Yang Lin, Paul Lee, William Pan, Demosthenes Panagiotakos, Alex Molassiotis.
Keywords: COVID-19, Database, Digital public health, Emerging countries, Estimate, Low and middle-income countries, Mobile data, Pattern, Policy, Real-time, Social distancing, Surveillance, Transmission

Identifiers

Local EPrints ID: 475541
URI: http://eprints.soton.ac.uk/id/eprint/475541
ISSN: 1438-8871
PURE UUID: 3d618863-15b0-448c-bb93-038e079f115c
ORCID for Paul Lee: ORCID iD orcid.org/0000-0002-5729-6450

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Date deposited: 21 Mar 2023 17:41
Last modified: 18 Mar 2024 04:09

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Contributors

Author: Stefanos Tyrovolas
Author: Iago Giné-Vázquez
Author: Daniel Fernández
Author: Marianthi Morena
Author: Ai Koyanagi
Author: Mark Janko
Author: Josep Maria Haro
Author: Yang Lin
Author: Paul Lee ORCID iD
Author: William Pan
Author: Demosthenes Panagiotakos
Author: Alex Molassiotis
Corporate Author: et al.

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