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    4 Comments for this article
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    Weather and COVID-19
    Peter Byass, PhD | Umeå University
    Thanks for this interesting paper.

    Your findings are generally in line with my analysis of the spread of COVID-19 around China (excluding Wuhan) in relation to weather conditions there.

    https://www.tandfonline.com/doi/full/10.1080/16549716.2020.1760490

    It seemed in my analysis that solar radiation at the surface (also from the ERA-5 model) was also important. Of course latitude to some extent is a proxy for this, but misses cloudiness. This might be even more important in tropical areas moving from hot sunny dry seasons to cool cloudy wet seasons.

    Humidity will also be to some extent dependent on cloudiness and
    thus solar radiation. I looked at rainfall rather than humidity, simply because in public health terms it's more intuitive to talk about wet weather than humidity. Maybe should have done both!
    CONFLICT OF INTEREST: None Reported
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    When a virus does not cause chronic infection there are two possibilities
    Giovanni Ghirga, Pediatrician | San Paolo Hospital, Civitavecchia, Italy
    Substantial community transmission of COVID-19 should take into account that when a virus does not cause a chronic infection there are two possibilities. The first one is generally rare and it is the death of the infected individual followed by the death of the virus itself. The second possibility is characterized by a variable period of active replication within the host until the immune system will clear the virus. In both cases, the virus has a limited time to stay alive unless it is capable of infecting new individuals. Therefore, wearing a mask and distancing should be extremely effective in controlling the spread of the SARS-CoV-2.
    CONFLICT OF INTEREST: None Reported
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    Temperature, Humidity, and Latitude Analysis to Estimate Potential Spread and Seasonality of Coronavirus Disease 2019 (COVID-19)
    Daniela Werneck, MD PhD in Medicine | Fundação Hemominas
    The authors based on a model only for the countries of the northern hemisphere.
    I would like to know what the study design would look like for tropical countries with maximum positive winter temperatures and who have a broad public vaccination program with coverage above 80% for influenza (trivalent vaccine)?
    CONFLICT OF INTEREST: None Reported
    Daylight May Drive Seasonal Variation in SARS-CoV-2 Infectivity
    Andy Goren, MD | Clinical Hospital Center Sestre Milosrdnice Zagreb, Croatia
    SARS-CoV-2 infectivity is dependent on proteolysis of its spike protein by the TMPRSS2 enzyme expressed on the surface of type II pneumocytes (1). In humans, the only known promoter of the TMPRSS2 gene is an androgen response element; therefore, androgen receptor expression is likely to determine COVID-19 disease severity (2). In support of the androgen driven COVID-19 hypothesis, a recent study from Italy (3) demonstrated a significant protective effect of androgen depravation therapy in COVID-19 prostate cancer patients OR 4.05 (95% CI: 1.55-10.59). Androgen receptor expression is mediated by the period circadian protein homolog 1 (Per1). Per1 overexpression inhibits the transactivation of the androgen receptor(4). Per1 expression follows a circadian cycle determined by the length of the daylight. Rats exposed to a longer photoperiod (16 hours light and 8 hours darkness) exhibit higher expression of Per1 compared to rats exposed to a shorter photoperiod (8 hours light and 16 hours darkness) (5). In conclusion, during the fall and the winter months when daylight is short, TMPRSS2 expression is likely to be increased which may lead to increased SARS-CoV-2 infectivity.

    1. Hoffmann M, Kleine-Weber H, Schroeder S, et al. SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor. Cell. 2020;181(2):271‐280.e8. doi:10.1016/j.cell.2020.02.052
    2. Wambier CG, Goren A, Vaño-Galván S, et al. Androgen sensitivity gateway to COVID-19 disease severity [published online ahead of print, 2020 May 15]. Drug Dev Res. 2020;10.1002/ddr.21688. doi:10.1002/ddr.21688
    3. Montopoli M, Zumerle S, Vettor R, et al. Androgen-deprivation therapies for prostate cancer and risk of infection by SARS-CoV-2: a population-based study (n=4532) [published online ahead of print, 2020 May 4]. Ann Oncol. 2020;S0923-7534(20)39797-0. doi:10.1016/j.annonc.2020.04.479
    4. Cao Q, Gery S, Dashti A, et al. A Role for the Clock Gene Per1 in Prostate Cancer. Cancer Res. 2009;69(19):7619-7625. doi:10.1158/0008-5472.CAN-08-4199
    5. Sumová A, Sládek M, Jác M, Illnerová H. The circadian rhythm of Per1 gene product in the rat suprachiasmatic nucleus and its modulation by seasonal changes in daylength. Brain Res. 2002;947(2):260‐270. doi:10.1016/s0006-8993(02)02933-5
    CONFLICT OF INTEREST: None Reported
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    Original Investigation
    Infectious Diseases
    June 11, 2020

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

    Author Affiliations
    • 1Institute of Human Virology, University of Maryland School of Medicine, Baltimore
    • 2Global Virus Network, Baltimore, Maryland
    • 3Persian BayanGene Research and Training Center, Shiraz University of Medical Sciences, Shiraz, Iran
    • 4Earth System Science Interdisciplinary Center, University of Maryland, College Park
    • 5Infectious Diseases and Tropical Medicine Research, Shaheed Beheshti University of Medical Sciences, Tehran, Iran
    • 6Department of Atmospheric and Oceanic Science, University of Maryland, College Park
    • 7The Nature Conservancy, Arlington, Virginia
    JAMA Netw Open. 2020;3(6):e2011834. doi:10.1001/jamanetworkopen.2020.11834
    Key Points español 中文 (chinese)

    Question  Is severe acute respiratory syndrome coronavirus 2 associated with seasonality, and can its spread be estimated?

    Findings  In this cohort study of 50 cities with and without coronavirus disease 2019 (COVID-19), areas with substantial community transmission of COVID-19 had distribution roughly along the 30° N to 50° N latitude corridor with consistently similar weather patterns, consisting of mean temperatures of 5 to 11 °C combined with low specific and absolute humidity.

    Meaning  In this study, the distribution of substantial community outbreaks of COVID-19 along restricted latitude, temperature, and humidity measurements were consistent with the behavior of a seasonal respiratory virus; with modeling, it may be possible to estimate areas at high risk of substantial community transmission of COVID-19.

    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.

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