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      Impact of temperature and relative humidity on the transmission of COVID-19: a modelling study in China and the United States

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          Abstract

          Objectives

          We aim to assess the impact of temperature and relative humidity on the transmission of COVID-19 across communities after accounting for community-level factors such as demographics, socioeconomic status and human mobility status.

          Design

          A retrospective cross-sectional regression analysis via the Fama-MacBeth procedure is adopted.

          Setting

          We use the data for COVID-19 daily symptom-onset cases for 100 Chinese cities and COVID-19 daily confirmed cases for 1005 US counties.

          Participants

          A total of 69 498 cases in China and 740 843 cases in the USA are used for calculating the effective reproductive numbers.

          Primary outcome measures

          Regression analysis of the impact of temperature and relative humidity on the effective reproductive number ( R value).

          Results

          Statistically significant negative correlations are found between temperature/relative humidity and the effective reproductive number ( R value) in both China and the USA.

          Conclusions

          Higher temperature and higher relative humidity potentially suppress the transmission of COVID-19. Specifically, an increase in temperature by 1°C is associated with a reduction in the R value of COVID-19 by 0.026 (95% CI (−0.0395 to −0.0125)) in China and by 0.020 (95% CI (−0.0311 to −0.0096)) in the USA; an increase in relative humidity by 1% is associated with a reduction in the R value by 0.0076 (95% CI (−0.0108 to −0.0045)) in China and by 0.0080 (95% CI (−0.0150 to −0.0010)) in the USA. Therefore, the potential impact of temperature/relative humidity on the effective reproductive number alone is not strong enough to stop the pandemic.

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          Most cited references40

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          A Novel Coronavirus from Patients with Pneumonia in China, 2019

          Na Zhu, Dingyu Zhang, Wenling Wang … (2020)
          Summary In December 2019, a cluster of patients with pneumonia of unknown cause was linked to a seafood wholesale market in Wuhan, China. A previously unknown betacoronavirus was discovered through the use of unbiased sequencing in samples from patients with pneumonia. Human airway epithelial cells were used to isolate a novel coronavirus, named 2019-nCoV, which formed a clade within the subgenus sarbecovirus, Orthocoronavirinae subfamily. Different from both MERS-CoV and SARS-CoV, 2019-nCoV is the seventh member of the family of coronaviruses that infect humans. Enhanced surveillance and further investigation are ongoing. (Funded by the National Key Research and Development Program of China and the National Major Project for Control and Prevention of Infectious Disease in China.)
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            Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia

            Qun Li, Xuhua Guan, Peng Wu … (2020)
            Abstract Background The initial cases of novel coronavirus (2019-nCoV)–infected pneumonia (NCIP) occurred in Wuhan, Hubei Province, China, in December 2019 and January 2020. We analyzed data on the first 425 confirmed cases in Wuhan to determine the epidemiologic characteristics of NCIP. Methods We collected information on demographic characteristics, exposure history, and illness timelines of laboratory-confirmed cases of NCIP that had been reported by January 22, 2020. We described characteristics of the cases and estimated the key epidemiologic time-delay distributions. In the early period of exponential growth, we estimated the epidemic doubling time and the basic reproductive number. Results Among the first 425 patients with confirmed NCIP, the median age was 59 years and 56% were male. The majority of cases (55%) with onset before January 1, 2020, were linked to the Huanan Seafood Wholesale Market, as compared with 8.6% of the subsequent cases. The mean incubation period was 5.2 days (95% confidence interval [CI], 4.1 to 7.0), with the 95th percentile of the distribution at 12.5 days. In its early stages, the epidemic doubled in size every 7.4 days. With a mean serial interval of 7.5 days (95% CI, 5.3 to 19), the basic reproductive number was estimated to be 2.2 (95% CI, 1.4 to 3.9). Conclusions On the basis of this information, there is evidence that human-to-human transmission has occurred among close contacts since the middle of December 2019. Considerable efforts to reduce transmission will be required to control outbreaks if similar dynamics apply elsewhere. Measures to prevent or reduce transmission should be implemented in populations at risk. (Funded by the Ministry of Science and Technology of China and others.)
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              The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak

              Matteo Chinazzi, Jessica Davis, Marco Ajelli … (2020)
              Motivated by the rapid spread of COVID-19 in Mainland China, we use a global metapopulation disease transmission model to project the impact of travel limitations on the national and international spread of the epidemic. The model is calibrated based on internationally reported cases, and shows that at the start of the travel ban from Wuhan on 23 January 2020, most Chinese cities had already received many infected travelers. The travel quarantine of Wuhan delayed the overall epidemic progression by only 3 to 5 days in Mainland China, but has a more marked effect at the international scale, where case importations were reduced by nearly 80% until mid February. Modeling results also indicate that sustained 90% travel restrictions to and from Mainland China only modestly affect the epidemic trajectory unless combined with a 50% or higher reduction of transmission in the community.
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                Author and article information

                Journal
                Journal ID (nlm-ta): BMJ Open
                Journal ID (iso-abbrev): BMJ Open
                Journal ID (hwp): bmjopen
                Journal ID (publisher-id): bmjopen
                Title: BMJ Open
                Publisher: BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                ISSN (Electronic): 2044-6055
                Publication date Collection: 2021
                Publication date (Electronic): 17 February 2021
                Volume: 11
                Issue: 2
                Electronic Location Identifier: e043863
                Affiliations
                [1 ]School of Computer Science and Engineering, Beihang University , Beijing, China
                [2 ]State Key Laboratory of Software Development Environment, Beihang University , Beijing, China
                [3 ]Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University , Beijing, China
                [4 ]departmentInstitute of Economics , School of Social Sciences, Tsinghua University , Beijing, China
                [5 ]Center for Population Health, University of Connecticut Health Center , Farmington, Connecticut, USA
                [6 ]departmentDepartment of Statistics , University of Connecticut , Storrs, Connecticut, USA
                [7 ]departmentDepartment of Population Health Sciences, Weill Cornell Medical College , Cornell University , New York, New York, USA
                Author notes
                [Correspondence to ] Professor Ke Tang; ketang@ 123456tsinghua.edu.cn
                Author information
                http://orcid.org/0000-0003-4049-030X
                Article
                Publisher ID: bmjopen-2020-043863
                DOI: 10.1136/bmjopen-2020-043863
                PMC ID: 7893211
                PubMed ID: 33597143
                SO-VID: 5854563b-b57e-473b-bfce-1038cca57d40
                Copyright © © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
                License:

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/.

                History
                Date received : 18 August 2020
                Date revision received : 05 January 2021
                Date accepted : 22 January 2021
                Funding
                Funded by: National Key Research & Development Program of China;
                Award ID: 2019YFB2102100
                Funded by: BRICS STI Framework Programme: Response to COVID-19 global pandemic;
                Award ID: MFQuantiC
                Funded by: National Natural Science Foundation of China;
                Award ID: 92046010, 71531001, 61572059
                Funded by: National Natural Science Foundation of China;
                Award ID: 61421003
                Categories
                Subject: Public Health
                Subject: 1506
                Subject: 2474
                Subject: 1724
                Series title: Original research
                Custom metadata
                special-feature unlocked

                ScienceOpen disciplines: Medicine
                Keywords: covid-19,epidemiology,public health
                Data availability:
                ScienceOpen disciplines: Medicine
                Keywords: covid-19, epidemiology, public health

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