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1.
Cori  A, Ferguson  NM, Fraser  C, Cauchemez  S.  A new framework and software to estimate time-varying reproduction numbers during epidemics.   Am J Epidemiol. 2013;178(9):1505-1512. doi:10.1093/aje/kwt133PubMedGoogle ScholarCrossref
2.
Fraser  C.  Estimating individual and household reproduction numbers in an emerging epidemic.   PLoS One. 2007;2(8):e758-e758. doi:10.1371/journal.pone.0000758PubMedGoogle ScholarCrossref
3.
Wallinga  J, Teunis  P.  Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures.   Am J Epidemiol. 2004;160(6):509-516. doi:10.1093/aje/kwh255PubMedGoogle ScholarCrossref
4.
Children’s Hospital of Philadelphia. Exempt research. Accessed May 25, 2020. https://irb.research.chop.edu/exempt-research
5.
von Elm  E, Altman  DG, Egger  M, Pocock  SJ, Gøtzsche  PC, Vandenbroucke  JP; STROBE Initiative.  The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.   J Clin Epidemiol. 2008;61(4):344-349. doi:10.1016/j.jclinepi.2007.11.008PubMedGoogle ScholarCrossref
6.
New York Times. COVID-19 data. Updated June 26, 2020. Accessed June 29, 2020. https://github.com/nytimes/covid-19-data
7.
Unacast. Social distancing scoreboard. Accessed June 29, 2020. https://www.unacast.com/covid19/social-distancing-scoreboard#methodology
8.
Sheikh  A, Sheikh  Z, Sheikh  A.  Novel approaches to estimate compliance with lockdown measures in the COVID-19 pandemic.   J Glob Health. 2020;10(1):010348. doi:10.7189/jogh.10.010348PubMedGoogle Scholar
9.
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):1199-1207. doi:10.1056/NEJMoa2001316PubMedGoogle ScholarCrossref
10.
Lowen  AC, Mubareka  S, Steel  J, Palese  P.  Influenza virus transmission is dependent on relative humidity and temperature.   PLoS Pathog. 2007;3(10):1470-1476. doi:10.1371/journal.ppat.0030151PubMedGoogle ScholarCrossref
11.
Shaman  J, Kohn  M.  Absolute humidity modulates influenza survival, transmission, and seasonality.   Proc Natl Acad Sci U S A. 2009;106(9):3243-3248. doi:10.1073/pnas.0806852106PubMedGoogle ScholarCrossref
12.
Cheng  YT, Lung  SC, Hwang  JS.  New approach to identifying proper thresholds for a heat warning system using health risk increments.   Environ Res. 2019;170:282-292. doi:10.1016/j.envres.2018.12.059PubMedGoogle ScholarCrossref
13.
Ross  ME, Vicedo-Cabrera  AM, Kopp  RE,  et al.  Assessment of the combination of temperature and relative humidity on kidney stone presentations.   Environ Res. 2018;162:97-105. doi:10.1016/j.envres.2017.12.020PubMedGoogle ScholarCrossref
14.
Richardson  S, Hirsch  JS, Narasimhan  M,  et al; and the Northwell COVID-19 Research Consortium.  Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area.   JAMA. 2020;323(20):2052-2059. doi:10.1001/jama.2020.6775PubMedGoogle ScholarCrossref
15.
NAICS Association. Six-digit NAICS codes and titles. Accessed March 29, 2020. https://www.naics.com/six-digit-naics/
16.
Social Explorer. Data dictionary: ACS 2018 (5-year estimates). Accessed March 29, 2020. https://www.socialexplorer.com/data/ACS2018_5yr/metadata/?ds=SE&table
17.
US Census Bureau. Commuting flows. Accessed June 29, 2020. https://www.census.gov/topics/employment/commuting/guidance/flows.html
18.
US Centers for Disease Control and Prevention. 500 cities: local data for better health. Accessed March 25, 2020. https://www.cdc.gov/500cities/
19.
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):1915-1923. doi:10.1001/jama.2020.6130PubMedGoogle ScholarCrossref
20.
Nadaraya  EA.  On Estimating Regression.   Theory Probability Applications. 1964;9(1):141-142. doi:10.1137/1109020Google ScholarCrossref
21.
Bierens  HJ. The Nadaraya–Watson kernel regression function estimator. In:  Topics in Advanced Econometrics. New York: Cambridge University Press; 1994:212-247. doi:10.1017/CBO9780511599279.011
22.
Gasparrini  A, Armstrong  B, Kenward  MG.  Distributed lag non-linear models.   Stat Med. 2010;29(21):2224-2234. doi:10.1002/sim.3940PubMedGoogle ScholarCrossref
23.
Armstrong  B.  Models for the relationship between ambient temperature and daily mortality.   Epidemiology. 2006;17(6):624-631. doi:10.1097/01.ede.0000239732.50999.8fPubMedGoogle ScholarCrossref
24.
Tasian  GE, Pulido  JE, Gasparrini  A,  et al; Urologic Diseases in America Project.  Daily mean temperature and clinical kidney stone presentation in five U.S. metropolitan areas: a time-series analysis.   Environ Health Perspect. 2014;122(10):1081-1087. doi:10.1289/ehp.1307703PubMedGoogle ScholarCrossref
25.
Gasparrini  A, Guo  Y, Hashizume  M,  et al.  Mortality risk attributable to high and low ambient temperature: a multicountry observational study.   Lancet. 2015;386(9991):369-375. doi:10.1016/S0140-6736(14)62114-0PubMedGoogle ScholarCrossref
26.
Vicedo-Cabrera  AM, Goldfarb  DS, Kopp  RE, Song  L, Tasian  GE.  Sex differences in the temperature dependence of kidney stone presentations: a population-based aggregated case-crossover study.   Urolithiasis. 2020;48(1):37-46. doi:10.1007/s00240-019-01129-xPubMedGoogle ScholarCrossref
27.
Gasparrini  A.  Distributed lag linear and non-linear models in R: the package dlnm.   J Stat Softw. 2011;43(8):1-20. doi:10.18637/jss.v043.i08PubMedGoogle ScholarCrossref
28.
Sanche  S, Lin  YT, Xu  C, Romero-Severson  E, Hengartner  N, Ke  R.  High contagiousness and rapid spread of severe acute respiratory syndrome coronavirus 2.   Emerg Infect Dis. 2020;26(7):1470-1477. doi:10.3201/eid2607.200282PubMedGoogle ScholarCrossref
29.
van Doremalen  N, Bushmaker  T, Morris  DH,  et al.  Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1.   N Engl J Med. 2020;382(16):1564-1567. doi:10.1056/NEJMc2004973PubMedGoogle ScholarCrossref
30.
Bahl  P, Doolan  C, de Silva  C, Chughtai  AA, Bourouiba  L, MacIntyre  CR.  Airborne or droplet precautions for health workers treating coronavirus disease 2019?   J Infect Dis. Published April 16, 2020. doi:10.1093/infdis/jiaa189Google Scholar
31.
Poulsen  A, Qureshi  K, Lisse  I,  et al.  A household study of chickenpox in Guinea-Bissau: intensity of exposure is a determinant of severity.   J Infect. 2002;45(4):237-242. doi:10.1053/jinf.2002.1049PubMedGoogle ScholarCrossref
32.
Paulo  AC, Correia-Neves  M, Domingos  T, Murta  AG, Pedrosa  J.  Influenza infectious dose may explain the high mortality of the second and third wave of 1918-1919 influenza pandemic.   PLoS One. 2010;5(7):e11655-e11655. doi:10.1371/journal.pone.0011655PubMedGoogle ScholarCrossref
33.
Chu  C-M, Poon  LLM, Cheng  VCC,  et al.  Initial viral load and the outcomes of SARS.   CMAJ. 2004;171(11):1349-1352.PubMedGoogle ScholarCrossref
34.
Virlogeux  V, Fang  VJ, Wu  JT,  et al.  Brief report: incubation period duration and severity of clinical disease following severe acute respiratory syndrome coronavirus infection.   Epidemiology. 2015;26(5):666-669. doi:10.1097/EDE.0000000000000339PubMedGoogle ScholarCrossref
35.
Watanabe  T, Bartrand  TA, Weir  MH, Omura  T, Haas  CN.  Development of a dose-response model for SARS coronavirus.   Risk Anal. 2010;30(7):1129-1138. doi:10.1111/j.1539-6924.2010.01427.xPubMedGoogle ScholarCrossref
36.
Ijaz  MK, Brunner  AH, Sattar  SA, Nair  RC, Johnson-Lussenburg  CM.  Survival characteristics of airborne human coronavirus 229E.   J Gen Virol. 1985;66(Pt 12):2743-2748. doi:10.1099/0022-1317-66-12-2743PubMedGoogle ScholarCrossref
37.
Children’s Hospital of Philadelphia. COVID-lab mapping COVID-19 in your community. Accessed May 15, 2020. https://policylab.chop.edu/covid-lab-mapping-covid-19-your-community
38.
Wells  CR, Sah  P, Moghadas  SM,  et al.  Impact of international travel and border control measures on the global spread of the novel 2019 coronavirus outbreak.   Proc Natl Acad Sci U S A. 2020;117(13):7504-7509. doi:10.1073/pnas.2002616117PubMedGoogle ScholarCrossref
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    Original Investigation
    Infectious Diseases
    July 23, 2020

    Association of Social Distancing, Population Density, and Temperature With the Instantaneous Reproduction Number of SARS-CoV-2 in Counties Across the United States

    Author Affiliations
    • 1Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
    • 2Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
    • 3PolicyLab, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
    • 4Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
    • 5Division of Infectious Disease, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
    • 6Center for Pediatric Clinical Effectiveness, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
    • 7Department of Public Health Environments and Society, London School of Hygiene and Tropical Medicine, London, United Kingdom
    • 8Centre for Statistical Methodology, London School of Hygiene and Tropical Medicine, London, United Kingdom
    • 9Centre on Climate Change and Planetary Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
    • 10Data Science and Biostatistics Unit, Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
    • 11Perelman School of Medicine at the University of Pennsylvania, Philadelphia
    • 12Division of Urology, Department of Surgery, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
    • 13Department of Electrical Engineering, The Technion, Haifa, Israel
    JAMA Netw Open. 2020;3(7):e2016099. doi:10.1001/jamanetworkopen.2020.16099
    Key Points español 中文 (chinese)

    Question  How is the instantaneous reproduction number of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) associated with social distancing, wet-bulb temperature, and population density in counties across the United States?

    Findings  In this cohort study of 211 counties in 46 states, social distancing, temperate weather, and lower population density were associated with a decrease in the instantaneous reproduction number of SARS-CoV-2. Of these county-specific factors, social distancing appeared to have the most substantial association with a reduction in SARS-CoV-2 transmission.

    Meaning  In this study, the instantaneous reproduction number of SARS-CoV-2 varied substantially among counties; the associations between the reproduction number and county-specific factors could inform policies to reduce SARS-CoV-2 transmission in selective and heterogeneous communities.

    Abstract

    Importance  Local variation in the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across the United States has not been well studied.

    Objective  To examine the association of county-level factors with variation in the SARS-CoV-2 reproduction number over time.

    Design, Setting, and Participants  This cohort study included 211 counties, representing state capitals and cities with at least 100 000 residents and including 178 892 208 US residents, in 46 states and the District of Columbia between February 25, 2020, and April 23, 2020.

    Exposures  Social distancing, measured by percentage change in visits to nonessential businesses; population density; and daily wet-bulb temperatures.

    Main Outcomes and Measures  Instantaneous reproduction number (Rt), or cases generated by each incident case at a given time, estimated from daily case incidence data.

    Results  The 211 counties contained 178 892 208 of 326 289 971 US residents (54.8%). Median (interquartile range) population density was 1022.7 (471.2-1846.0) people per square mile. The mean (SD) peak reduction in visits to nonessential business between April 6 and April 19, as the country was sheltering in place, was 68.7% (7.9%). Median (interquartile range) daily wet-bulb temperatures were 7.5 (3.8-12.8) °C. Median (interquartile range) case incidence and fatality rates per 100 000 people were approximately 10 times higher for the top decile of densely populated counties (1185.2 [313.2-1891.2] cases; 43.7 [10.4-106.7] deaths) than for counties in the lowest density quartile (121.4 [87.8-175.4] cases; 4.2 [1.9-8.0] deaths). Mean (SD) Rt in the first 2 weeks was 5.7 (2.5) in the top decile compared with 3.1 (1.2) in the lowest quartile. In multivariable analysis, a 50% decrease in visits to nonessential businesses was associated with a 45% decrease in Rt (95% CI, 43%-49%). From a relative Rt at 0 °C of 2.13 (95% CI, 1.89-2.40), relative Rt decreased to a minimum as temperatures warmed to 11 °C, increased between 11 and 20 °C (1.61; 95% CI, 1.42-1.84) and then declined again at temperatures greater than 20 °C. With a 70% reduction in visits to nonessential business, 202 counties (95.7%) were estimated to fall below a threshold Rt of 1.0, including 17 of 21 counties (81.0%) in the top density decile and 52 of 53 counties (98.1%) in the lowest density quartile.2

    Conclusions and Relevance  In this cohort study, social distancing, lower population density, and temperate weather were associated with a decreased Rt for SARS-CoV-2 in counties across the United States. These associations could inform selective public policy planning in communities during the coronavirus disease 2019 pandemic.

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