Effectiveness of Localized Lockdowns in the COVID-19 Pandemic
Open accessPreprint — published as 10.1093/aje/kwac008
Abstract
Non-pharmaceutical interventions, such as social distancing and lockdowns, have been essential to control the COVID-19 pandemic. In particular, localized lockdowns in small geographic areas have become an important policy intervention to prevent viral spread in cases of resurgence. These localized lockdowns can result in lower social and economic costs compared to larger-scale suppression strategies. Using an integrated dataset from Chile (March 3 through June 15, 2020) and a novel synthetic control approach, in this paper we estimate the effect of localized lockdowns, disentangling its direct and indirect causal effects on SARS-CoV-2 transmission. Our results show that the effects of localized lockdowns are strongly modulated by their duration and are influenced by indirect effects from neighboring geographic areas. Our estimates suggest that extending localized lockdowns can slow down the pandemic; however, localized lockdowns on their own are insufficient to control pandemic growth in the presence of indirect effects from contiguous neighboring areas that do not have lockdowns. These results provide critical empirical evidence about the effectiveness of localized lockdowns in interconnected geographic areas.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Recent studies in Chile have suggested a link between SES and the effectiveness of nonpharmaceutical interventions such as stay-at-home orders.
“In Chile, recent studies have suggested a link between SES and the effectiveness of nonpharmaceutical interventions such as stay-at-home orders (12, 13, 29).”
From Socioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile (2021) · cites this paper as [29]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 59 · Cited by 58
Research works printed but not linked: 55
- [1]1. Flaxman S, Mishra S, Gandy A, et al. Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature 2020;584:257-61.
- [2]2. Hsiang S, Allen D, Annan-Phan S, et al. The effect of large-scale anti-contagion policies on the COVID-19 pandemic. Nature 2020;584:262-7.
- [3]3. Walker PGT, Whittaker C, Watson OJ, et al. The impact of COVID-19 and strategies for mitigation and suppression in low- and middle-income countries. Science 2020;369(6502):413-22.
- [4]4. Lavezzo E, Franchin E, Ciavarella C, et al. Suppression of a SARS-CoV-2 outbreak in the Italian municipality of Vo'. Nature 2020;584:425-9.
- [5]5. Cowling BJ, Ali ST, Ng TWY, et al. Impact assessment of non-pharmaceutical interventions against coronavirus disease 2019 and influenza in Hong Kong: an observational study. Lancet Public Health 2020;5(5):e279-e88. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint medRxiv preprint doi: https://doi.org/10.1101/2020.08.25.20182071; this version posted November 17, 2021. The copyright holder for this [It is made available under a CC-BY 4.0 International license .](http://creativecommons.org/licenses/by/4.0/)
- [6]6. Hao X, Cheng S, Wu D, et al. Reconstruction of the full transmission dynamics of COVID-19 in Wuhan. Nature 2020;584:420-4.
- [8]8. Kissler SM, Tedijanto C, Goldstein E, et al. Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period. Science 2020;368(6493):860-8.
- [9]9. Chinazzi M, Davis JT, Ajelli M, et al. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science 2020;368(6489):395-400.
- [11]11. Shea K, Runge MC, Pannell D, et al. Harnessing multiple models for outbreak management. Science 2020;368(6491):577-9.
- [12]12. Imbens GW, Rubin DB. Causal inference in statistics, social, and biomedical sciences . Cambridge University Press; 2015.
- [14]14. Haber NA, Clarke-Deelder E, Salomon JA, et al. Policy evaluation in COVID-19: A graphical guide to common design issues. arXiv preprint arXiv:200901940 2020.
- [15]15. Hernán MA, Robins JM. Using big data to emulate a target trial when a randomized trial is not available. Am J Epidemiol 2016;183(8):758-64.
- [16]16. Danaei G, Rodríguez LAG, Cantero OF, et al. Electronic medical records can be used to emulate target trials of sustained treatment strategies. J Clin Epidemiol 2018;96:12-22.
- [17]17. Dickerman BA, García-Albéniz X, Logan RW, et al. Avoidable flaws in observational analyses: an application to statins and cancer. Nat Med 2019;25(10):1601-6. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint medRxiv preprint doi: https://doi.org/10.1101/2020.08.25.20182071; this version posted November 17, 2021. The copyright holder for this [It is made available under a CC-BY 4.0 International license .](http://creativecommons.org/licenses/by/4.0/)
- [18]18. Ben-Michael E, Feller A, Stuart EA. A trial emulation approach for policy evaluations with group-level longitudinal data. arXiv preprint arXiv:201105826 2020.
- [20]20. Chowdhury R, Luhar S, Khan N, et al. Long-term strategies to control COVID-19 in low and middle-income countries: an options overview of community-based, nonpharmacological interventions. Eur J Epidemiol 2020;35:743-8.
- [21]21. Mahase E. Covid-19: How does local lockdown work, and is it effective? BMJ 2020;370:m2679.
- [22]22. Pradelski B, Oliu-Barton M. Green bridges: Reconnecting Europe to avoid economic disaster. In: Bénassy-Quéré A, Mauro BWd, eds. Europe in the Time of Covid-19 . London: Centre for Economic Policy Research Press, 2020:83-7.
- [24]24. Firth JA, Hellewell J, Klepac P, et al. Using a real-world network to model localized COVID-19 control strategies. Nat Med 2020;26:1616-22.
- [25]25. Cuadrado C, Monsalves MJ, Gajardo J, et al. Impact of small-area lockdowns for the control of the COVID-19 pandemic. medRxiv 2020:2020.05.05.20092106.
- [26]26. Pan A, Liu L, Wang C, et al. Association of Public Health Interventions With the Epidemiology of the COVID-19 Outbreak in Wuhan, China. JAMA 2020;323(19):191523. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint medRxiv preprint doi: https://doi.org/10.1101/2020.08.25.20182071; this version posted November 17, 2021. The copyright holder for this [It is made available under a CC-BY 4.0 International license .](http://creativecommons.org/licenses/by/4.0/)
- [30]30. Abadie A, Diamond A, Hainmueller J. Synthetic control methods for comparative case studies: Estimating the effect of California's tobacco control program. J Am Stat Assoc 2010;105(490):493-505.
- [32]32. Sobel ME. What do randomized studies of housing mobility demonstrate? Causal inference in the face of interference. J Am Stat Assoc 2006;101(476):1398-407.
- [33]33. Hudgens MG, Halloran ME. Toward Causal Inference With Interference. J Am Stat Assoc 2008;103(482):832-42.
- [34]34. Ministerio de Salud. Paso a Paso Nos Cuidamos. Plan de Acción Coronavirus Covid-19. Santiago: Ministerio de Salud, Gobierno de Chile; 2020. (https://www.gob.cl/pasoapaso/). (Accessed Abril 13 2021). is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint medRxiv preprint doi: https://doi.org/10.1101/2020.08.25.20182071; this version posted November 17, 2021. The copyright holder for this [It is made available under a CC-BY 4.0 International license .](http://creativecommons.org/licenses/by/4.0/)
- [36]36. Rubin DB. Estimating causal effects of treatments in randomized and nonrandomized studies. J Educ Psychol 1974;66(5):688.
- [37]37. Neyman J. Sur les applications de la thar des probabilities aux experiences Agaricales: Essay des principle. Excerpts reprinted (1990) in English. Stat Sci 1923;5(4):463-72.
- [41]41. Killick R, Fearnhead P, Eckley IA. Optimal detection of changepoints with a linear computational cost. J Am Stat Assoc 2012;107(500):1590-8.
- [42]42. Zhao Q, Ju N, Bacallado S. Commentary: Using Symptom Onset Instead of Incidence Results in Time-Lagged Estimate of R(t). JAMA 2020;323(19):1924. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint medRxiv preprint doi: https://doi.org/10.1101/2020.08.25.20182071; this version posted November 17, 2021. The copyright holder for this [It is made available under a CC-BY 4.0 International license .](http://creativecommons.org/licenses/by/4.0/)
- [43]43. Cori A, Ferguson NM, Fraser C, et al. A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics. Am J Epidemiol 2013;178(9):1505-12.
- [44]44. Gostic KM, McGough L, Baskerville E, et al. Practical considerations for measuring the effective reproductive number, Rt. PLoS Comput Biol 2020;16(12):e1008409.
- [45]45. Li Q, Guan X, Wu P, et al. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N Engl J Med 2020;382(13):1199-207.
- [46]46. Ogburn EL, VanderWeele TJ. Causal diagrams for interference. Stat Sci 2014;29(4):55978.
- [49]49. Lee EC, Wada NI, Grabowski MK, et al. The engines of SARS-CoV-2 spread. Science 2020;370(6515):406-7.
- [50]50. Wang Z, Ma W, Zheng X, et al. Household transmission of SARS-CoV-2. J Infect 2020;81:179-82.
- [51]51. Park YJ, Choe YJ, Park O, et al. Contact Tracing during Coronavirus Disease Outbreak, South Korea, 2020. Emerg Infect Dis 2020;26(10):2465.
- [52]52. Madewell ZJ, Yang Y, Longini IM, Jr, et al. Household Transmission of SARS-CoV-2: A Systematic Review and Meta-analysis. JAMA Network Open 2020;3(12):e2031756-e.
- [53]53. Programa de Vialidad y Transporte Urbano. Actualización y recolección de información del sistema de transporte urbano, IX Etapa: Encuesta Origen Destino Santiago 2012. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint medRxiv preprint doi: https://doi.org/10.1101/2020.08.25.20182071; this version posted November 17, 2021. The copyright holder for this [It is made available under a CC-BY 4.0 International license .](http://creativecommons.org/licenses/by/4.0/) Encuesta origen destino de viajes 2012. Santiago, Chile: Ministerio de Transporte y Telecomunicaciones; 2014. (http://www.sectra.gob.cl/biblioteca/detalle1.asp?mfn=3253). (Accessed December 22 2020).
- [56]56. Hasell J, Mathieu E, Beltekian D, et al. A cross-country database of COVID-19 testing. Sci Data 2020;7(1):345.
- [57]57. Guan W-j, Ni Z-y, Hu Y, et al. Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med 2020;382:1708-20.
- [58]58. Cao J, Hu X, Cheng W, et al. Clinical features and short-term outcomes of 18 patients with corona virus disease 2019 in intensive care unit. Intensive Care Med 2020;46:851-3.
- [59]59. Wu JT, Leung K, Bushman M, et al. Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China. Nat Med 2020;26:506-10.
- [60]60. Jung S-m, Akhmetzhanov AR, Hayashi K, et al. Real-time estimation of the risk of death from novel coronavirus (COVID-19) infection: inference using exported cases. J Clin Med 2020;9(2):523.
- [61]61. Li R, Pei S, Chen B, et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (COVID-19). Science 2020;368(6490):489-93. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint medRxiv preprint doi: https://doi.org/10.1101/2020.08.25.20182071; this version posted November 17, 2021. The copyright holder for this [It is made available under a CC-BY 4.0 International license .](http://creativecommons.org/licenses/by/4.0/)
- [62]62. Wiersinga WJ, Rhodes A, Cheng AC, et al. Pathophysiology, Transmission, Diagnosis, and Treatment of Coronavirus Disease 2019 (COVID-19): A Review. JAMA 2020;324(8):782-93.
- [64]64. Aum S, Lee SYT, Shin Y. COVID-19 Doesn't Need Lockdowns to Destroy Jobs: The Effect of Local Outbreaks in Korea. Labour Econ 2021;70:101993.
- [66]66. Asahi K, Undurraga EA, Valdes R, et al. The effect of COVID-19 on the economy: evidence from an early adopter of localized lockdowns. J Glob Health 2021;11:05002.
- [68]68. Bennett M. All things equal? Heterogeneity in policy effectiveness against COVID-19 spread in chile. World Dev 2021;137:105208. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint medRxiv preprint doi: https://doi.org/10.1101/2020.08.25.20182071; this version posted November 17, 2021. The copyright holder for this [It is made available under a CC-BY 4.0 International license .](http://creativecommons.org/licenses/by/4.0/) Figure
- [1]1. Incidence of COVID-19 cases in Chile, March 1 to July 15, 2020. The majority (66%) of cases were reported in Greater Santiago (solid line). <!-- image --> is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint medRxiv preprint doi: https://doi.org/10.1101/2020.08.25.20182071; this version posted November 17, 2021. The copyright holder for this [It is made available under a CC-BY 4.0 International license .](http://creativecommons.org/licenses/by/4.0/) Figure
- [2]2. Lockdowns and instantaneous reproduction number. (A) Municipality-level lockdowns in Greater Santiago on March 31, April 15, April 30, and May 15, 2020. (B) Variation of the instantaneous reproduction number ( 𝑅𝑡 ) and the proportion of the population under lockdown in Greater Santiago over time. <!-- image --> is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint medRxiv preprint doi: https://doi.org/10.1101/2020.08.25.20182071; this version posted November 17, 2021. The copyright holder for this [It is made available under a CC-BY 4.0 International license .](http://creativecommons.org/licenses/by/4.0/) Figure
- [3]3. Histogram of daily Rt and alternative lockdown interventions. (A) Histogram of daily Rt for all municipalities in Chile from March 15 to June 15, 2020, divided into the periods before lockdown, during the first lockdown, during the first reopening, and during the second lockdown. Municipalities with fewer than 10 COVID19 cases were excluded. (B) Illustration of alternative lockdown interventions of varying durations (blue area) and proportion of neighbors under lockdown (grey line). <!-- image --> is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint medRxiv preprint doi: https://doi.org/10.1101/2020.08.25.20182071; this version posted November 17, 2021. The copyright holder for this [It is made available under a CC-BY 4.0 International license .](http://creativecommons.org/licenses/by/4.0/) Figure
- [4]4. Distribution of cumulative cases of COVID-19 and illustration of lockdown implementation. (A) <!-- image --> Cumulative COVID-19 cases in Chile and (B) Greater Santiago before May 15, 2020. (C) Municipalities of Lo Barnechea, Providencia, and Santiago (red diamonds), and their corresponding neighbors (blue for the immediate neighbors under lockdown and white otherwise) on the last date under lockdown, April 13. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint medRxiv preprint doi: https://doi.org/10.1101/2020.08.25.20182071; this version posted November 17, 2021. The copyright holder for this [It is made available under a CC-BY 4.0 International license .](http://creativecommons.org/licenses/by/4.0/) <!-- image --> A Figure
- [5]5. Estimated instantaneous reproduction number 𝑹𝒕 and incidence It under different lockdown interventions by municipality. In (A), the grey solid and blue dashed lines show the proportion of the population in the surrounding municipalities under lockdown, respectively for the observed and the intervened 𝑃𝑡 ( 𝑃𝑡 𝑜𝑏𝑠. and 𝑃𝑡 𝑖𝑛𝑡. ). In (A, B), the lighter shade of blue extends the duration of the lockdown. The solid black lines show the reproduction number 𝑅𝑡 (A) and daily incidence 𝐼 𝑡 (B), and the dashed lines show the predicted 𝑅𝑡 and case incidence 𝐼 𝑡 for the extended lockdown. The direct and total effects of the extended lockdown are the difference between the solid black line and dashed line with grey ( 𝑅𝑡 𝑑𝑖𝑟. , 𝐼 𝑡 dir. ) and blue ( 𝑅𝑡 𝑡𝑜𝑡. , 𝐼 𝑡 tot. ) bands, respectively. The bands around the curves indicate 95% confidence intervals (additional results in the Supplementary Material). is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint medRxiv preprint doi: https://doi.org/10.1101/2020.08.25.20182071; this version posted November 17, 2021. The copyright holder for this [It is made available under a CC-BY 4.0 International license .](http://creativecommons.org/licenses/by/4.0/) Figure
- [6]6. Duration and indirect effects strongly modulate the effectiveness of lockdowns. From top to bottom, the municipalities in the figures are Lo Barnechea, Providencia, and Santiago, March 15 to May 15, 2020. (A) Average daily COVID-19 incidence over three weeks with varying duration of extended lockdown ( Δ𝐷 = 0-14 days) and a varying proportion of the neighboring population under lockdown ( 𝑃𝑡 = 0-1). (B) Prediction of the instantaneous reproduction number 𝑅𝑡 as a function of time with 50 percent of the neighboring population under lockdown ( 𝑃𝑡 = 0.5) since intervention (lockdown extended for 0-14 days). <!-- image --> is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint medRxiv preprint doi: https://doi.org/10.1101/2020.08.25.20182071; this version posted November 17, 2021. The copyright holder for this [It is made available under a CC-BY 4.0 International license .](http://creativecommons.org/licenses/by/4.0/) Figure
- [7]7. Effectiveness of lockdowns for different target areas. (A) In blue, municipalities of Greater Santiago under lockdown at different points in time; outlined in red (Ñuñoa), orange (cluster of six municipalities), and green (Greater Santiago), lockdown target areas of increasing size. (B-D) Estimated instantaneous reproduction number <!-- image --> is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint medRxiv preprint doi: https://doi.org/10.1101/2020.08.25.20182071; this version posted November 17, 2021. The copyright holder for this [It is made available under a CC-BY 4.0 International license .](http://creativecommons.org/licenses/by/4.0/) 𝑅𝑡 , with changing proportions of the population under lockdown in each geographic area and its immediate neighboring areas. From top to bottom the figure shows the municipality of Ñuñoa, a cluster of six municipalities, and the city Greater Santiago. (B-C) Also show the predicted 𝑅𝑡 (dashed line) had the lockdowns in the geographic area and for its immediate neighbors been extended. We estimate that the epidemic would have continued to grow ( 𝑅𝑡 ≥ 1 ) even with the extended localized lockdowns. The epidemic kept growing until Greater Santiago was put under lockdown on May 15, 2020.
- Authors
- Li, Y., Undurraga, E. A., & Zubizarreta, J. R.
- Affiliations
- Harvard University (US)
- Corresponding author
- Zubizarreta, José R.
- Venue
- American Journal of Epidemiology
- Publisher
- openRxiv
- Published
- 2020-08-31
- Volume
- 191
- Issue
- 5
- Pages
- 812-824
- ISSN
- 0002-9262 (ISSN-L)
- PMC
- PMC8807239
- OpenAlex
- W4205667523
- Cited by
- 58
- References
- 59
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 12 · 2023: 15 · 2022: 11 · 2021: 7 · 2020: 2
- FWCI
- 6.69
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- American Journal of Epidemiology (publishedVersion)
- medRxiv (acceptedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Alfred P. Sloan Foundation (g-2018-10118)
- Patient-Centered Outcomes Research Institute (ME‐2019C1‐16172)
- University of Pennsylvania
- Harvard University
- University of Oxford
- Universidad de Chile
- Pontificia Universidad Católica de Chile
- Agencia Nacional de Investigación y Desarrollo (15110017, NCN17_081)
- Fondo de Financiamiento de Centros de Investigación en Áreas Prioritarias (15110017)
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Social Sciences › Sociology and Political Science › COVID-19 Prevention and Impact
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Keywords
- COVID-19 pandemic, SARS-CoV-2 transmission, non-pharmaceutical interventions, social distancing, synthetic control method, lockdown duration, pandemic response, Chile
- MeSH terms
- Physical Distancing, COVID-19, SARS-CoV-2, Communicable Disease Control, Humans, Pandemics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review