The DOI matches its Crossref record; 294 of the 294 references it cites were found in a registry or an index.
Non-pharmaceutical interventions during the COVID-19 pandemic: A review
Open accessAbstract
Infectious diseases and human behavior are intertwined. On one side, our movements and interactions are the engines of transmission. On the other, the unfolding of viruses might induce changes to our daily activities. While intuitive, our understanding of such feedback loop is still limited. Before COVID-19 the literature on the subject was mainly theoretical and largely missed validation. The main issue was the lack of empirical data capturing behavioral change induced by diseases. Things have dramatically changed in 2020. Non-pharmaceutical interventions (NPIs) have been the key weapon against the SARS-CoV-2 virus and affected virtually any societal process. Travel bans, events cancellation, social distancing, curfews, and lockdowns have become unfortunately very familiar. The scale of the emergency, the ease of survey as well as crowdsourcing deployment guaranteed by the latest technology, several Data for Good programs developed by tech giants, major mobile phone providers, and other companies have allowed unprecedented access to data describing behavioral changes induced by the pandemic. Here, I review some of the vast literature written on the subject of NPIs during the COVID-19 pandemic. In doing so, I analyze 348 articles written by more than 2518 authors in the first 12 months of the emergency. While the large majority of the sample was obtained by querying PubMed, it includes also a hand-curated list. Considering the focus, and methodology I have classified the sample into seven main categories: epidemic models, surveys, comments/perspectives, papers aiming to quantify the effects of NPIs, reviews, articles using data proxies to measure NPIs, and publicly available datasets describing NPIs. I summarize the methodology, data used, findings of the articles in each category and provide an outlook highlighting future challenges as well as opportunities.
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Citations
Cites
The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak
Chinazzi, M., Davis, J. T., Ajelli, M., Gioannini, C., Litvinova, M., Merler, S., Pastore y Piontti, A., Mu, K., Rossi, L., Sun, K., et al. (2020) · Science · doi:10.1126/science.aba9757 · Cited by 3,694
Estimates of the severity of coronavirus disease 2019: a model-based analysis
Verity, R., Okell, L. C., Dorigatti, I., Winskill, P., Whittaker, C., Imai, N., Cuomo-Dannenburg, G., Thompson, H., Walker, P. G. T., Fu, H., et al. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30243-7 · Cited by 3,660
The Health Belief Model and Preventive Health Behavior
Rosenstock, I. M. (1974) · Health Education Monographs · doi:10.1177/109019817400200405 · Cited by 3,643
Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe
Flaxman, S., Mishra, S., Gandy, A., Unwin, H. J. T., Mellan, T. A., Coupland, H., Whittaker, C., Zhu, H., Berah, T., Eaton, J. W., et al. (2020) · Nature · doi:10.1038/s41586-020-2405-7 · Cited by 3,350
The effect of human mobility and control measures on the COVID-19 epidemic in China
Kraemer, M. U. G., Yang, C.-H., Gutierrez, B., Wu, C.-H., Klein, B., Pigott, D. M., Open COVID-19 Data Working Group, du Plessis, L., Faria, N. R., Li, R., et al. (2020) · Science · doi:10.1126/science.abb4218 · Cited by 2,950
Prem, K., Liu, Y., Russell, T. W., Kucharski, A. J., Eggo, R. M., Davies, N., Flasche, S., Clifford, S., Pearson, C. A. B., Munday, J. D., et al. (2020) · The Lancet Public Health · doi:10.1016/s2468-2667(20)30073-6 · Cited by 2,114
Tian, H., Liu, Y., Li, Y., Wu, C.-H., Chen, B., Kraemer, M. U. G., Li, B., Cai, J., Xu, B., Yang, Q., et al. (2020) · Science · doi:10.1126/science.abb6105 · Cited by 2,010
Pan, A., Liu, L., Wang, C., Guo, H., Hao, X., Wang, Q., Huang, J., He, N., Yu, H., Lin, X., et al. (2020) · JAMA · doi:10.1001/jama.2020.6130 · Cited by 1,931
Maringe, C., Spicer, J., Morris, M., Purushotham, A., Nolte, E., Sullivan, R., Rachet, B., & Aggarwal, A. (2020) · The Lancet Oncology · doi:10.1016/s1470-2045(20)30388-0 · Cited by 1,807
Mobility network models of COVID-19 explain inequities and inform reopening
Chang, S., Pierson, E., Koh, P. W., Gerardin, J., Redbird, B., Grusky, D., & Leskovec, J. (2021) · Nature · doi:10.1038/s41586-020-2923-3 · Cited by 1,533
Cited by
Infodemics: a new challenge for public health
Briand, S. C., Cinelli, M., Nguyen, T., Lewis, R., Prybylski, D., Valensise, C. M., Colizza, V., Tozzi, A. E., Perra, N., Baronchelli, A., et al. (2021) · Cell · doi:10.1016/j.cell.2021.10.031 · Cited by 125
Metadata
- Authors
- Perra, N.
- Venue
- Physics Reports
- Publisher
- Elsevier BV
- Published
- 2021-05
- Volume
- 913
- Pages
- 1-52
- ISSN
- 0370-1573 (ISSN-L)
- OpenAlex
- W3116362583
- Cited by
- 531
- References
- 294
- FWCI
- 39.61
- Type
- journal article
- Language
- en
- Open access
- green
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.