Heterogeneous interventions reduce the spread of COVID-19 in simulations on real mobility data
Open accessAbstract
Major interventions have been introduced worldwide to slow down the spread of the SARS-CoV-2 virus. Large scale lockdown of human movements are effective in reducing the spread, but they come at a cost of significantly limited societal functions. We show that natural human movements are statistically diverse, and the spread of the disease is significantly influenced by a small group of active individuals and gathering venues. We find that interventions focused on these most mobile individuals and popular venues reduce both the peak infection rate and the total infected population while retaining high social activity levels. These trends are seen consistently in simulations with real human mobility data of different scales, resolutions, and modalities from multiple cities across the world. The observation implies that compared to broad sweeping interventions, more heterogeneous strategies that are targeted based on the network effects in human mobility provide a better balance between pandemic control and regular social activities.
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Strategies to reopen an economy from lockdowns have been studied using check-in data, Wi-Fi association logs and GPS traces.
“Strategies to reopen an economy from lockdowns has been studied [106,107] using check-in data, Wi-Fi association logs and GPS traces.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [107]
To reduce disease spread and retain social activities, reopening strategies need to consider the mobility of the people, for example, protect the most mobile people or restricting large gatherings.
“The primary conclusions are that to reduce the disease spread and retain social activities, reopening strategies need to consider the mobility of the people, for example, protect the most mobile people or restricting large gatherings.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [107]
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References 34 · Cited by 28
- Authors
- Wang, H., Ghosh, A., Ding, J., Sarkar, R., & Gao, J.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- University of Cambridge (GB)
- Shanghai Jiao Tong University (CN)
- University of Edinburgh (GB)
- Corresponding author
- Sarkar, Rik
- Venue
- Scientific Reports
- Published
- 2021-04-08
- Volume
- 11
- Issue
- 1
- Pages
- 7809-7809
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC8034422
- OpenAlex
- W3142527171
- Cited by
- 28
- References
- 34
- Citations by year
- 2025: 3 · 2024: 5 · 2023: 7 · 2022: 7 · 2021: 6
- FWCI
- 2.41
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion, cc-by)
- Funders
- National Science Foundation (1421300, 1618391, 1939459, 1737812)
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Mathematical and Theoretical Epidemiology and Ecology Models
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- COVID-19 pandemic, SARS-CoV-2 transmission, human mobility, targeted interventions, lockdown, heterogeneous mobility, social networks, social activity, mobility data, epidemic simulation
- MeSH terms
- COVID-19, SARS-CoV-2, Communicable Disease Control, Computer Simulation, Humans, Life Style, Social Networking
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