The Effectiveness of Community-based Social Distancing for Mitigating the Spread of the COVID-19 Pandemic in Turkey
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Objectives: The objective of this study was to demonstrate the effects of community-based social distancing interventions after the first coronavirus disease 2019 (COVID-19) case in Turkey on the course of the pandemic and to determine the number of prevented cases. Methods: In this ecological study, the interventions implemented in response to the first COVID-19 cases in Turkey were evaluated and the effect of the interventions was demonstrated by calculating the effective reproduction number (Rt) of severe acute respiratory syndrome coro navirus 2 (SARS-CoV-2) when people complied with community-based social distancing rules. Results: Google mobility scores decreased by an average of 36.33±22.41 points (range, 2.60 to 84.80) and a median of 43.80 points (interquartile range [IQR], 24.90 to 50.25). The interventions caused the calculated Rt to decrease to 1.88 (95% confidence interval, 1.87 to 1.89). The median growth rate was 19.90% (IQR, 10.90 to 53.90). A positive correlation was found between Google mobility data and Rt (r=0.783; p<0.001). The expected number of cases if the growth rate had not changed was predicted according to Google mobility categories, and it was estimated to be 1 381 922 in total. Thus, community-based interventions were estimated to have prevented 1 299 593 people from being infected. Conclusions: Community-based social distancing interventions significantly decreased the Rt of COVID-19 by reducing human mobility, and thereby prevented many people from becoming infected. Another important result of this study is that it shows health policy-makers that data on human mobility in the community obtained via mobile phones can be a guide for measures to be taken.
Abstract
Objectives: The objective of this study was to demonstrate the effects of community-based social distancing interventions after the first coronavirus disease 2019 (COVID-19) case in Turkey on the course of the pandemic and to determine the number of prevented cases. Methods: In this ecological study, the interventions implemented in response to the first COVID-19 cases in Turkey were evaluated and the effect of the interventions was demonstrated by calculating the effective reproduction number (Rt) of severe acute respiratory syndrome coro navirus 2 (SARS-CoV-2) when people complied with community-based social distancing rules. Results: Google mobility scores decreased by an average of 36.33±22.41 points (range, 2.60 to 84.80) and a median of 43.80 points (interquartile range [IQR], 24.90 to 50.25). The interventions caused the calculated Rt to decrease to 1.88 (95% confidence interval, 1.87 to 1.89). The median growth rate was 19.90% (IQR, 10.90 to 53.90). A positive correlation was found between Google mobility data and Rt (r=0.783; p<0.001). The expected number of cases if the growth rate had not changed was predicted according to Google mobility categories, and it was estimated to be 1 381 922 in total. Thus, community-based interventions were estimated to have prevented 1 299 593 people from being infected. Conclusions: Community-based social distancing interventions significantly decreased the Rt of COVID-19 by reducing human mobility, and thereby prevented many people from becoming infected. Another important result of this study is that it shows health policy-makers that data on human mobility in the community obtained via mobile phones can be a guide for measures to be taken.
참고문헌 (39)
- Liu SL, "Emerging viruses without borders : the Wuhan coronavirus", Viruses, 12(2), pp.130~ (2020)
- Hui DS, "The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health - the latest 2019 novel coronavirus outbreak in Wuhan, China", Int J Infect Dis, 91, pp.264~266 (2020)
- Lu R, "Genomic characterisation and epidemiology of 2019 novel coronavirus : implications for virus origins and receptor binding", Lancet, 395(10224), pp.565~574 (2020)
- Gralinski LE, "Return of the coronavirus : 2019-nCoV", Viruses, 12(2), pp.135~ (2020)
- Wang C, "A novel coronavirus outbreak of global health concern", Lancet, 395(10223), pp.470~473 (2020)
- Guo YR, "The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak - an update on the status", Mil Med Res, 7(1), pp.11~ (2020)
- Zhou P, "A pneumonia outbreak associated with a new coronavirus of probable bat origin", Nature, 579(7798), pp.270~273 (2020)
- "WHO Director-General’s opening remarks at the media briefing on COVID-19 - 11 March 2020"[URL]
- Parry J, "China coronavirus : cases surge as official admits human to human transmission", BMJ, 368, pp.m236~ (2020)
- Hartley DM, "Public health interventions for COVID-19 : emerging evidence and implications for an evolving public health crisis", JAMA, 323(19), pp.1908~1909 (2020)
- Pan A, "Association of public health interventions with the epidemiology of the COVID-19 outbreak in Wuhan, China", JAMA, 323(19), pp.1915~1923 (2020)
- Ucar A, "Nowcasting and forecasting the spread of COVID-19 and healthcare demand in Turkey, a modelling study", medRxiv 2020. doi: https:, doi.org, pp.10.1101, 2020.04.13.20063305[DOI]
- COVID-19 pandemic assessment report, "Şeker M", Turkish Academy of Sciences, 43~48 (2020)
- Demirbilek Y, "COVID-19 outbreak control, example of ministry of health of Turkey", Turk J Med Sci, 50(SI-1), pp.489~494 (2020)
- van den Driessche P, "Mathematical epidemiology", pp.159~178 (2008), Springer
- Wallinga J, "How generation intervals shape the relationship between growth rates and reproductive numbers", Proc Biol Sci, 274(1609), pp.599~604 (2007)
- Vrugt MT, "Effects of social distancing and isolation on epidemic spreading : a dynamical density functional theory model", arXiv preprint 2020;arXiv:2003.13967, ~ (2020)
- "Community mobility reports: see how your community is moving around differently due to COVID-19"[URL]
- "Digital 2020"[URL]
- "COVID-19 information page"[URL]
- "Coronavirus 10-day forecast"[URL]
- Obadia T, "The R0 package : a toolbox to estimate reproduction numbers for epidemic outbreaks", BMC Med Inform Decis Mak, 12, pp.147~ (2012)
- Cori A, "A new framework and software to estimate time-varying reproduction numbers during epidemics", Am J Epidemiol, 178(9), pp.1505~1512 (2013)
- Li Q, "Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia", N Engl J Med, 382(13), pp.1199~1207 (2020)
- Nelson KE, "Infectious disease epidemiology: theory and practice", pp.77~80 (2014), Jones & Bartlett Publishers
- Kouadio IK, "Infectious diseases following natural disasters : prevention and control measures", Expert Rev Anti Infect Ther, 10(1), pp.95~104 (2012)
- Lai CC, "Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)and coronavirus disease-2019(COVID-19) : the epidemic and the challenges", Int J Antimicrob Agents, 55(3), pp.105924~ (2020)
- Liu Y, "The reproductive number of COVID-19 is higher compared to SARS coronavirus", J Travel Med, 27(2), pp.taaa021~ (2020)
- Wilder-Smith A, "Isolation, quarantine, social distancing and community containment: pivotal role for old-style public health measures in the novel coronavirus (2019-nCoV) outbreak", J Travel Med, 27(2), pp.taaa020~ (2020)
- Rothe C, "Transmission of 2019-nCoV infection from an asymptomatic contact in Germany", N Engl J Med, 382(10), pp.970~971 (2020)
- Novel Coronavirus Pneumonia Emergency Response Epidemiology Team, "The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19)—China, 2020", China CDC Wkly, 2(8), pp.113~122 (2020)
- Ryu S, "Effect of nonpharmaceutical interventions on transmission of severe acute respiratory syndrome coronavirus 2, South Korea, 2020", Emerg Infect Dis, 26(10), pp.2406~2410 (2020)
- Ryu S, "Epidemiology and control of two epidemic waves of SARS-CoV-2 in South Korea", SSRN (2020)[DOI]
- Chinazzi M, "The effect of travel restrictions on the spread of the 2019 novel coronavirus(COVID-19)outbreak", Science, 368(6489), pp.395~400 (2020)
- Anderson RM, "How will country-based mitigation measures influence the course of the COVID-19 epidemic?", Lancet, 395(10228), pp.931~934 (2020)
- Cowling BJ, "Impact assessment of non-pharmaceutical interventions against coronavirus disease 2019 and influenza in Hong Kong : an observational study", Lancet Public Health, 5(5), pp.e279~e288 (2020)
- Bayham J, "Impact of school closures for COVID-19 on the US health-care workforce and net mortality : a modelling study", Lancet Public Health, 5(5), pp.e271~e278 (2020)
- Cheng VC, "The role of community-wide wearing of face mask for control of coronavirus disease 2019(COVID-19)epidemic due to SARS-CoV-2", J Infect, 81(1), pp.107~114 (2020)
- Eikenberry SE, "To mask or not to mask : modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic", Infect Dis Model, 5, pp.293~308 (2020)