CovidFranceV1, Main, Exploration, bibRecord, 000502

Temperature, Humidity, and Latitude Analysis to Estimate Potential Spread and Seasonality of Coronavirus Disease 2019 (COVID-19).

Identifieur interne : 000502 ( Main/Exploration ); précédent : 000501; suivant : 000503

Temperature, Humidity, and Latitude Analysis to Estimate Potential Spread and Seasonality of Coronavirus Disease 2019 (COVID-19).

Auteurs : Mohammad M. Sajadi [États-Unis] ; Parham Habibzadeh [Iran] ; Augustin Vintzileos [États-Unis] ; Shervin Shokouhi [Iran] ; Fernando Miralles-Wilhelm [États-Unis] ; Anthony Amoroso [États-Unis]

Source :

JAMA network open [ 2574-3805 ] ; 2020.

RBID : pubmed:32525550

Descripteurs français

KwdFr :
- Betacoronavirus (MeSH), Contrôle des maladies transmissibles (MeSH), Géographie médicale (MeSH), Humains (MeSH), Humidité (MeSH), Infections à coronavirus (prévention et contrôle), Infections à coronavirus (transmission), Infections à coronavirus (virologie), Pandémies (prévention et contrôle), Pneumopathie virale (prévention et contrôle), Pneumopathie virale (transmission), Pneumopathie virale (virologie), Saisons (MeSH), Température (MeSH), Villes (épidémiologie), Études de cohortes (MeSH).
MESH :
- prévention et contrôle : Infections à coronavirus, Pandémies, Pneumopathie virale.
- virologie : Infections à coronavirus, Pneumopathie virale.
- épidémiologie : Infections à coronavirus, Pneumopathie virale, Villes.
- Betacoronavirus, Contrôle des maladies transmissibles, Géographie médicale, Humains, Humidité, Saisons, Température, Études de cohortes.

English descriptors

KwdEn :
- Betacoronavirus (MeSH), Cities (epidemiology), Cohort Studies (MeSH), Communicable Disease Control (MeSH), Coronavirus Infections (prevention & control), Coronavirus Infections (transmission), Coronavirus Infections (virology), Geography, Medical (MeSH), Humans (MeSH), Humidity (MeSH), Pandemics (prevention & control), Pneumonia, Viral (prevention & control), Pneumonia, Viral (transmission), Pneumonia, Viral (virology), Seasons (MeSH), Temperature (MeSH).
MESH :
- geographic , epidemiology : Cities.
- prevention & control : Coronavirus Infections, Pandemics, Pneumonia, Viral.
- transmission : Coronavirus Infections, Pneumonia, Viral.
- virology : Coronavirus Infections, Pneumonia, Viral.
- Betacoronavirus, Cohort Studies, Communicable Disease Control, Geography, Medical, Humans, Humidity, Seasons, Temperature.

Abstract

Importance

Coronavirus disease 2019 (COVID-19) infection has resulted in a global crisis. Investigating the potential association of climate and seasonality with the spread of this infection could aid in preventive and surveillance strategies.

Objective

To examine the association of climate with the spread of COVID-19 infection.

Design, Setting, and Participants

This cohort study examined climate data from 50 cities worldwide with and without substantial community spread of COVID-19. Eight cities with substantial spread of COVID-19 (Wuhan, China; Tokyo, Japan; Daegu, South Korea; Qom, Iran; Milan, Italy; Paris, France; Seattle, US; and Madrid, Spain) were compared with 42 cities that have not been affected or did not have substantial community spread. Data were collected from January to March 10, 2020.

Main Outcomes and Measures

Substantial community transmission was defined as at least 10 reported deaths in a country as of March 10, 2020. Climate data (latitude, mean 2-m temperature, mean specific humidity, and mean relative humidity) were obtained from ERA-5 reanalysis.

Results

The 8 cities with substantial community spread as of March 10, 2020, were located on a narrow band, roughly on the 30° N to 50° N corridor. They had consistently similar weather patterns, consisting of mean temperatures of between 5 and 11 °C, combined with low specific humidity (3-6 g/kg) and low absolute humidity (4-7 g/m3). There was a lack of substantial community establishment in expected locations based on proximity. For example, while Wuhan, China (30.8° N) had 3136 deaths and 80 757 cases, Moscow, Russia (56.0° N), had 0 deaths and 10 cases and Hanoi, Vietnam (21.2° N), had 0 deaths and 31 cases.

Conclusions and Relevance

In this study, the distribution of substantial community outbreaks of COVID-19 along restricted latitude, temperature, and humidity measurements was consistent with the behavior of a seasonal respiratory virus. Using weather modeling, it may be possible to estimate the regions most likely to be at a higher risk of substantial community spread of COVID-19 in the upcoming weeks, allowing for concentration of public health efforts on surveillance and containment.

DOI: 10.1001/jamanetworkopen.2020.11834
PubMed: 32525550
PubMed Central: PMC7290414

Affiliations:

Links toward previous steps (curation, corpus...)

to stream Main, to step Corpus: 000E42
to stream Main, to step Curation: 000E42

Le document en format XML

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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Betacoronavirus (MeSH)</term>
<term>Cities (epidemiology)</term>
<term>Cohort Studies (MeSH)</term>
<term>Communicable Disease Control (MeSH)</term>
<term>Coronavirus Infections (prevention & control)</term>
<term>Coronavirus Infections (transmission)</term>
<term>Coronavirus Infections (virology)</term>
<term>Geography, Medical (MeSH)</term>
<term>Humans (MeSH)</term>
<term>Humidity (MeSH)</term>
<term>Pandemics (prevention & control)</term>
<term>Pneumonia, Viral (prevention & control)</term>
<term>Pneumonia, Viral (transmission)</term>
<term>Pneumonia, Viral (virology)</term>
<term>Seasons (MeSH)</term>
<term>Temperature (MeSH)</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>Betacoronavirus (MeSH)</term>
<term>Contrôle des maladies transmissibles (MeSH)</term>
<term>Géographie médicale (MeSH)</term>
<term>Humains (MeSH)</term>
<term>Humidité (MeSH)</term>
<term>Infections à coronavirus (prévention et contrôle)</term>
<term>Infections à coronavirus (transmission)</term>
<term>Infections à coronavirus (virologie)</term>
<term>Pandémies (prévention et contrôle)</term>
<term>Pneumopathie virale (prévention et contrôle)</term>
<term>Pneumopathie virale (transmission)</term>
<term>Pneumopathie virale (virologie)</term>
<term>Saisons (MeSH)</term>
<term>Température (MeSH)</term>
<term>Villes (épidémiologie)</term>
<term>Études de cohortes (MeSH)</term>
</keywords>
<keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Cities</term>
</keywords>
<keywords scheme="MESH" qualifier="prevention & control" xml:lang="en"><term>Coronavirus Infections</term>
<term>Pandemics</term>
<term>Pneumonia, Viral</term>
</keywords>
<keywords scheme="MESH" qualifier="prévention et contrôle" xml:lang="fr"><term>Infections à coronavirus</term>
<term>Pandémies</term>
<term>Pneumopathie virale</term>
</keywords>
<keywords scheme="MESH" qualifier="transmission" xml:lang="en"><term>Coronavirus Infections</term>
<term>Pneumonia, Viral</term>
</keywords>
<keywords scheme="MESH" qualifier="virologie" xml:lang="fr"><term>Infections à coronavirus</term>
<term>Pneumopathie virale</term>
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<keywords scheme="MESH" qualifier="virology" xml:lang="en"><term>Coronavirus Infections</term>
<term>Pneumonia, Viral</term>
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<front><div type="abstract" xml:lang="en"><p><b>Importance</b>
</p>
<p>Coronavirus disease 2019 (COVID-19) infection has resulted in a global crisis. Investigating the potential association of climate and seasonality with the spread of this infection could aid in preventive and surveillance strategies.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>Objective</b>
</p>
<p>To examine the association of climate with the spread of COVID-19 infection.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>Design, Setting, and Participants</b>
</p>
<p>This cohort study examined climate data from 50 cities worldwide with and without substantial community spread of COVID-19. Eight cities with substantial spread of COVID-19 (Wuhan, China; Tokyo, Japan; Daegu, South Korea; Qom, Iran; Milan, Italy; Paris, France; Seattle, US; and Madrid, Spain) were compared with 42 cities that have not been affected or did not have substantial community spread. Data were collected from January to March 10, 2020.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>Main Outcomes and Measures</b>
</p>
<p>Substantial community transmission was defined as at least 10 reported deaths in a country as of March 10, 2020. Climate data (latitude, mean 2-m temperature, mean specific humidity, and mean relative humidity) were obtained from ERA-5 reanalysis.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>Results</b>
</p>
<p>The 8 cities with substantial community spread as of March 10, 2020, were located on a narrow band, roughly on the 30° N to 50° N corridor. They had consistently similar weather patterns, consisting of mean temperatures of between 5 and 11 °C, combined with low specific humidity (3-6 g/kg) and low absolute humidity (4-7 g/m3). There was a lack of substantial community establishment in expected locations based on proximity. For example, while Wuhan, China (30.8° N) had 3136 deaths and 80 757 cases, Moscow, Russia (56.0° N), had 0 deaths and 10 cases and Hanoi, Vietnam (21.2° N), had 0 deaths and 31 cases.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>Conclusions and Relevance</b>
</p>
<p>In this study, the distribution of substantial community outbreaks of COVID-19 along restricted latitude, temperature, and humidity measurements was consistent with the behavior of a seasonal respiratory virus. Using weather modeling, it may be possible to estimate the regions most likely to be at a higher risk of substantial community spread of COVID-19 in the upcoming weeks, allowing for concentration of public health efforts on surveillance and containment.</p>
</div>
</front>
</TEI>
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<ArticleTitle>Temperature, Humidity, and Latitude Analysis to Estimate Potential Spread and Seasonality of Coronavirus Disease 2019 (COVID-19).</ArticleTitle>
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<Abstract><AbstractText Label="Importance">Coronavirus disease 2019 (COVID-19) infection has resulted in a global crisis. Investigating the potential association of climate and seasonality with the spread of this infection could aid in preventive and surveillance strategies.</AbstractText>
<AbstractText Label="Objective">To examine the association of climate with the spread of COVID-19 infection.</AbstractText>
<AbstractText Label="Design, Setting, and Participants">This cohort study examined climate data from 50 cities worldwide with and without substantial community spread of COVID-19. Eight cities with substantial spread of COVID-19 (Wuhan, China; Tokyo, Japan; Daegu, South Korea; Qom, Iran; Milan, Italy; Paris, France; Seattle, US; and Madrid, Spain) were compared with 42 cities that have not been affected or did not have substantial community spread. Data were collected from January to March 10, 2020.</AbstractText>
<AbstractText Label="Main Outcomes and Measures">Substantial community transmission was defined as at least 10 reported deaths in a country as of March 10, 2020. Climate data (latitude, mean 2-m temperature, mean specific humidity, and mean relative humidity) were obtained from ERA-5 reanalysis.</AbstractText>
<AbstractText Label="Results">The 8 cities with substantial community spread as of March 10, 2020, were located on a narrow band, roughly on the 30° N to 50° N corridor. They had consistently similar weather patterns, consisting of mean temperatures of between 5 and 11 °C, combined with low specific humidity (3-6 g/kg) and low absolute humidity (4-7 g/m3). There was a lack of substantial community establishment in expected locations based on proximity. For example, while Wuhan, China (30.8° N) had 3136 deaths and 80 757 cases, Moscow, Russia (56.0° N), had 0 deaths and 10 cases and Hanoi, Vietnam (21.2° N), had 0 deaths and 31 cases.</AbstractText>
<AbstractText Label="Conclusions and Relevance">In this study, the distribution of substantial community outbreaks of COVID-19 along restricted latitude, temperature, and humidity measurements was consistent with the behavior of a seasonal respiratory virus. Using weather modeling, it may be possible to estimate the regions most likely to be at a higher risk of substantial community spread of COVID-19 in the upcoming weeks, allowing for concentration of public health efforts on surveillance and containment.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Sajadi</LastName>
<ForeName>Mohammad M</ForeName>
<Initials>MM</Initials>
<AffiliationInfo><Affiliation>Institute of Human Virology, University of Maryland School of Medicine, Baltimore.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Global Virus Network, Baltimore, Maryland.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Habibzadeh</LastName>
<ForeName>Parham</ForeName>
<Initials>P</Initials>
<AffiliationInfo><Affiliation>Persian BayanGene Research and Training Center, Shiraz University of Medical Sciences, Shiraz, Iran.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Vintzileos</LastName>
<ForeName>Augustin</ForeName>
<Initials>A</Initials>
<AffiliationInfo><Affiliation>Earth System Science Interdisciplinary Center, University of Maryland, College Park.</Affiliation>
</AffiliationInfo>
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<Author ValidYN="Y"><LastName>Shokouhi</LastName>
<ForeName>Shervin</ForeName>
<Initials>S</Initials>
<AffiliationInfo><Affiliation>Infectious Diseases and Tropical Medicine Research, Shaheed Beheshti University of Medical Sciences, Tehran, Iran.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Miralles-Wilhelm</LastName>
<ForeName>Fernando</ForeName>
<Initials>F</Initials>
<AffiliationInfo><Affiliation>Department of Atmospheric and Oceanic Science, University of Maryland, College Park.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>The Nature Conservancy, Arlington, Virginia.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Amoroso</LastName>
<ForeName>Anthony</ForeName>
<Initials>A</Initials>
<AffiliationInfo><Affiliation>Institute of Human Virology, University of Maryland School of Medicine, Baltimore.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Global Virus Network, Baltimore, Maryland.</Affiliation>
</AffiliationInfo>
</Author>
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<Language>eng</Language>
<GrantList CompleteYN="Y"><Grant><GrantID>R01 AI147870</GrantID>
<Acronym>AI</Acronym>
<Agency>NIAID NIH HHS</Agency>
<Country>United States</Country>
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<affiliations><list><country><li>Iran</li>
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	Serveur d'exploration sur la COVID en France
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Temperature, Humidity, and Latitude Analysis to Estimate Potential Spread and Seasonality of Coronavirus Disease 2019 (COVID-19).

Temperature, Humidity, and Latitude Analysis to Estimate Potential Spread and Seasonality of Coronavirus Disease 2019 (COVID-19).

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