COVID-19 early-alert signals using human behavior alternative data.

Alternative data sources COVID-19 Digital epidemiology Predictive analytics

Journal

Social network analysis and mining
ISSN: 1869-5450
Titre abrégé: Soc Netw Anal Min
Pays: Germany
ID NLM: 101616226

Informations de publication

Date de publication:
2021
Historique:
received: 13 07 2020
revised: 02 11 2020
accepted: 02 01 2021
entrez: 9 2 2021
pubmed: 10 2 2021
medline: 10 2 2021
Statut: ppublish

Résumé

Google searches create a window into population-wide thoughts and plans not just of individuals, but populations at large. Since the outbreak of COVID-19 and the non-pharmaceutical interventions introduced to contain it, searches for socially distanced activities have trended. We hypothesize that trends in the volume of search queries related to activities associated with COVID-19 transmission correlate with subsequent COVID-19 caseloads. We present a preliminary analytics framework that examines the relationship between Google search queries and the number of newly confirmed COVID-19 cases in the United States. We designed an experimental tool with search volume indices to track interest in queries related to two themes: isolation and mobility. Our goal was to capture the underlying social dynamics of an unprecedented pandemic using alternative data sources that are new to epidemiology. Our results indicate that the net movement index we defined correlates with COVID-19 weekly new case growth rate with a lag of between 10 and 14 days for the United States at-large, as well as at the state level for 42 out of 50 states with the exception of 8 states (DE, IA, KS, NE, ND, SD, WV, WY) from March to June 2020. In addition, an increasing caseload was seen over the summer in some southern US states. A sharp rise in mobility indices was followed by a sharp increase, respectively, in the case growth data, as seen in our case study of Arizona, California, Florida, and Texas. A sharp decline in mobility indices is often followed by a sharp decline, respectively, in the case growth data, as seen in our case study of Arizona, California, Florida, Texas, and New York. The digital epidemiology framework presented here aims to discover predictors of the pandemic's curve, which could supplement traditional predictive models and inform early warning systems and public health policies.

Identifiants

pubmed: 33558823
doi: 10.1007/s13278-021-00723-5
pii: 723
pmc: PMC7859099
doi:

Types de publication

Journal Article

Langues

eng

Pagination

18

Informations de copyright

© The Author(s), under exclusive licence to Springer-Verlag GmbH, AT part of Springer Nature 2021.

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Auteurs

Anasse Bari (A)

Computer Science Department, Courant Institute of Mathematical Sciences, New York University, New York, NY USA.

Aashish Khubchandani (A)

Computer Science Department, Courant Institute of Mathematical Sciences, New York University, New York, NY USA.

Junzhang Wang (J)

Computer Science Department, Courant Institute of Mathematical Sciences, New York University, New York, NY USA.

Matthias Heymann (M)

Computer Science Department, Courant Institute of Mathematical Sciences, New York University, New York, NY USA.

Megan Coffee (M)

Division of Infectious Diseases and Immunology, Grossman School of Medicine, New York University, New York, NY USA.

Classifications MeSH