Exploring the relationship between mobility and COVID- 19 infection rates for the second peak in the United States using phase-wise association.
Journal
BMC public health
ISSN: 1471-2458
Titre abrégé: BMC Public Health
Pays: England
ID NLM: 100968562
Informations de publication
Date de publication:
14 09 2021
14 09 2021
Historique:
received:
15
04
2021
accepted:
09
08
2021
entrez:
15
9
2021
pubmed:
16
9
2021
medline:
21
9
2021
Statut:
epublish
Résumé
Human mobility plays an important role in the dynamics of infectious disease spread. Evidence from the initial nationwide lockdowns for COVID- 19 indicates that restricting human mobility is an effective strategy to contain the spread. While a direct correlation was observed early on, it is not known how mobility impacted COVID- 19 infection growth rates once lockdowns are lifted, primarily due to modulation by other factors such as face masks, social distancing, and the non-linear patterns of both mobility and infection growth. This paper introduces a piece-wise approach to better explore the phase-wise association between state-level COVID- 19 incidence data and anonymized mobile phone data for various states in the United States. Prior literature analyzed the linear correlation between mobility and the number of cases during the early stages of the pandemic. However, it is important to capture the non-linear dynamics of case growth and mobility to be usable for both tracking and forecasting COVID- 19 infections, which is accomplished by the piece-wise approach. The associations between mobility and case growth rate varied widely for various phases of the epidemic curve when the stay-at-home orders were lifted. The mobility growth patterns had a strong positive association of 0.7 with the growth in the number of cases, with a lag of 5 to 7 weeks, for the fast-growth phase of the pandemic, for only 20 states that had a peak between July 1st and September 30, 2020. Overall though, mobility cannot be used to predict the rise in the number of cases after initial lockdowns have been lifted. Our analysis explores the gradual diminishing value of mobility associations in the later stage of the outbreak. Our analysis indicates that the relationship between mobility and the increase in the number of cases, once lockdowns have been lifted, is tenuous at best and there is no strong relationship between these signals. But we identify the remnants of the last associations in specific phases of the growth curve.
Identifiants
pubmed: 34521372
doi: 10.1186/s12889-021-11657-0
pii: 10.1186/s12889-021-11657-0
pmc: PMC8438287
doi:
Types de publication
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
1669Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2021. The Author(s).
Références
Hopkins J. “Coronavirus Resource Center,” Johns Hopkins Coronavirus Resource Center; 2021.
Buckee CO, Balsari S, Chan J, Crosas M, Dominici F, Gasser U, et al. Aggregated mobility data could help fight COVID-19. Science (80-. ). 2020;368(6487):145–6. https://doi.org/10.1126/science.abb8021 .
doi: 10.1126/science.abb8021
Hadfield J, Megill C, Bell SM, Huddleston J, Potter B, Callender C, et al. NextStrain: real-time tracking of pathogen evolution. Bioinformatics. 2018;34(23):4121–3. https://doi.org/10.1093/bioinformatics/bty407 .
doi: 10.1093/bioinformatics/bty407
pubmed: 29790939
pmcid: 6247931
Franch-Pardo I, Napoletano BM, Rosete-Verges F, Billa L. Spatial analysis and GIS in the study of COVID-19. A review. Sci. Total Environ. 2020;739:140033. https://doi.org/10.1016/j.scitotenv.2020.140033 .
doi: 10.1016/j.scitotenv.2020.140033
pubmed: 32534320
pmcid: 7832930
Gao S, Rao J, Kang Y, Liang Y, Kruse J. Mapping county-level mobility pattern changes in the United States in response to COVID-19. SIGSPATIAL Spec. 2020;12(1):16–26. https://doi.org/10.1145/3404820.3404824 .
doi: 10.1145/3404820.3404824
Oliver N, et al. Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle. Sci Adv. 2020;6(23). https://doi.org/10.1126/sciadv.abc0764 .
Warren MS, Skillman SW. Mobility changes in response to COVID-19; 2020.
Aktay, Ahmet et al., “Google COVID-19 Community Mobility Reports: Anonymization Process Description (version 1.1).” 2020.
Hartley DM, Perencevich EN. Public health interventions for COVID-19: emerging evidence and implications for an evolving public health crisis. JAMA - J Am Med Assoc. 2020;323(19):1908–9. https://doi.org/10.1001/jama.2020.5910 .
doi: 10.1001/jama.2020.5910
Kuchler T, Russel D, Stroebel J. The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook; 2021.
Badr HS, Hongru D, Marshall M, Dong E, Squire MM, Gardner LM. Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study. Lancet Infect Dis. 2020;20(11):1247–54. https://doi.org/10.1016/S1473-3099(20)30553-3 .
doi: 10.1016/S1473-3099(20)30553-3
pubmed: 32621869
pmcid: 7329287
Basellini U, et al. Linking excess mortality to mobility data during the first wave of COVID-19 in England and Wales. SSM - Population Health. 2021;14:100799. https://doi.org/10.1016/j.ssmph.2021.100799 .
doi: 10.1016/j.ssmph.2021.100799
pubmed: 33898726
pmcid: 8058100
Courtemanche C, Garuccio J, Le A, Pinkston J, Yelowitz A. Strong social distancing measures in the United States reduced the covid-19 growth rate. Health Aff. 2020;39(7):1237–46. https://doi.org/10.1377/hlthaff.2020.00608 .
doi: 10.1377/hlthaff.2020.00608
Engle S, Stromme J, Zhou A. Staying at Home: Mobility Effects of COVID-19. SSRN Electron J. 2020. https://doi.org/10.2139/ssrn.3565703 .
Gao S, et al. Association of Mobile Phone Location Data Indications of Travel and Stay-at-Home Mandates With COVID-19 Infection Rates in the US. JAMA Netw Open. 2020;3(9):e2020485. https://doi.org/10.1001/jamanetworkopen.2020.20485 .
doi: 10.1001/jamanetworkopen.2020.20485
pubmed: 32897373
pmcid: 7489834
Glaeser EL, Gorback C, Redding SJ. JUE insight: How much does COVID-19 increase with mobility? Evidence from New York and four other U.S. cities; 2020.
doi: 10.3386/w27519
Iacus SM, Santamaria C, Sermi F, Spyratos S, Tarchi D, Vespe M. Human mobility and COVID-19 initial dynamics. Nonlinear Dyn. 2020;101(3):1901–19. https://doi.org/10.1007/s11071-020-05854-6 .
doi: 10.1007/s11071-020-05854-6
Linka K, Peirlinck M, Costabal FS, Kuhl E. Outbreak dynamics of COVID-19 in Europe and the effect of travel restrictions. Comput Methods Biomech Biomed Engin. 2020;23(11):710–7. https://doi.org/10.1080/10255842.2020.1759560 .
doi: 10.1080/10255842.2020.1759560
pubmed: 32367739
pmcid: 7429245
Vinceti M, et al. Lockdown timing and efficacy in controlling COVID-19 using mobile phone tracking. EClinicalMedicine. 2020;25:100457. https://doi.org/10.1016/j.eclinm.2020.100457 .
doi: 10.1016/j.eclinm.2020.100457
pubmed: 32838234
pmcid: 7355328
Gao S, et al. Mobile phone location data reveal the effect and geographic variation of social distancing on the spread of the COVID-19 epidemic; 2020. https://doi.org/10.1001/jamanetworkopen.2020.20485 .
doi: 10.1001/jamanetworkopen.2020.20485
Gatalo O, Tseng K, Hamilton A, Lin G, Klein E. Associations between phone mobility data and COVID-19 cases. Lancet Infect. Dis. 2021;21(5):e111. https://doi.org/10.1016/S1473-3099(20)30725-8 .
doi: 10.1016/S1473-3099(20)30725-8
pubmed: 32946835
Maier BF, Brockmann D. Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China. Science (80- ). 2020;368(6492):742–6. https://doi.org/10.1126/science.abb4557 .
doi: 10.1126/science.abb4557
Singer HM. The COVID-19 pandemic: Growth patterns, power law scaling, and saturation. Phys. Biol. 2020;17(5). https://doi.org/10.1088/1478-3975/ab9bf5 .
Vasconcelos GL, Macêdo AMS, Duarte-Filho GC, Brum AA, Ospina R, Almeida FAG. Power law behaviour in the saturation regime of fatality curves of the COVID-19 pandemic. Sci Rep. 2021;11(1). https://doi.org/10.1038/s41598-021-84165-1 .
Wu K, Darcet D, Wang Q, Sornette D. Generalized logistic growth modeling of the COVID-19 outbreak: comparing the dynamics in the 29 provinces in China and in the rest of the world. Nonlinear Dyn. 2020;101(3):1561–81. https://doi.org/10.1007/s11071-020-05862-6 .
doi: 10.1007/s11071-020-05862-6
Batista M. Estimation of the final size of the second phase of the coronavirus COVID 19 epidemic by the logistic model. medRxiv. 2020. https://doi.org/10.1101/2020.03.11.20024901 .
New York Times, “Github. Coronavirus (Covid-19) Data in the United States.” 2020.
Dong E, Hongru D, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis. 2020;20(5):533–4. https://doi.org/10.1016/S1473-3099(20)30120-1 .
doi: 10.1016/S1473-3099(20)30120-1
pubmed: 32087114
pmcid: 7159018
Haberman R, Models M. Mechanical vibrations, population dynamics, and traffic flow. SIAM; 1998. https://doi.org/10.1137/1.9781611971156 .
Newville M, Ingargiola A, Stensitzki T, Allen DB. LMFIT: non-linear Least-Square minimization and curve-fitting for Python. Zenodo; 2014.
Im J, Jensen JR. A change detection model based on neighborhood correlation image analysis and decision tree classification. Remote Sens Environ. 2005;99(3):326–40. https://doi.org/10.1016/j.rse.2005.09.008 .
doi: 10.1016/j.rse.2005.09.008
Khan S, Batteau P. Should the government directly intervene in stock market during a crisis? Q Rev Econ Financ. 2011;51(4):350–9. https://doi.org/10.1016/j.qref.2011.07.003 .
doi: 10.1016/j.qref.2011.07.003
Jondeau E, Rockinger M. The copula-GARCH model of conditional dependencies: an international stock market application. J Int Money Financ. 2006;25(5):827–53. https://doi.org/10.1016/j.jimonfin.2006.04.007 .
doi: 10.1016/j.jimonfin.2006.04.007
Rice JJ, Yuhai T, Stolovitzky G. Reconstructing biological networks using conditional correlation analysis. Bioinformatics. 2005;21(6):765–73. https://doi.org/10.1093/bioinformatics/bti064 .
doi: 10.1093/bioinformatics/bti064
pubmed: 15486043