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This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (3541) Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (1049) Public (e)Health, Digital Epidemiology and Public Health Informatics (1142) mHealth for Data Collection and Research (1285) Epidemiology (217) Statistical Methods for Surveillance and Population Health (196) Infectious Diseases (non-STD/STI) (1767) Data Science (406) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1547)

Published on in Vol 8 (2024)

This is a member publication of National University of Singapore

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/55013, first published .
Nonrepresentativeness of Human Mobility Data and its Impact on Modeling Dynamics of the COVID-19 Pandemic: Systematic Evaluation

Nonrepresentativeness of Human Mobility Data and its Impact on Modeling Dynamics of the COVID-19 Pandemic: Systematic Evaluation

Nonrepresentativeness of Human Mobility Data and its Impact on Modeling Dynamics of the COVID-19 Pandemic: Systematic Evaluation

Authors of this article:

Chuchu Liu1, 2 Author Orcid Image ;   Petter Holme3, 4 Author Orcid Image ;   Sune Lehmann5 Author Orcid Image ;   Wenchuan Yang2 Author Orcid Image ;   Xin Lu2 Author Orcid Image
Article Authors Cited by (2) Tweetations (1) Metrics

Journals

  1. Lu X, Feng J, Lai S, Holme P, Liu S, Du Z, Yuan X, Wang S, Li Y, Zhang X, Bai Y, Duan X, Mei W, Yu H, Tan S, Liljeros F. Human mobility in epidemic modeling. Physics Reports 2026;1157:1 View
  2. Mazzoli M, Varela-Lasheras I, Namorado S, Pereira Caetano C, Leite A, Hermans L, Hens N, Türkmen P, Kalimeri K, Ferres L, Cattuto C, Paolotti D, Verhulst S. Nontraditional Data in Pandemic Preparedness and Response: Identifying and Addressing First- and Last-Mile Challenges. Journal of Medical Internet Research 2026;28:e85540 View