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LETTER TO THE EDITOR
Year : 2021  |  Volume : 14  |  Issue : 1  |  Page : 108-109  

Weather and COVID-19: Delhi


Deputy Assistant Director General, Dte. GHS, Ministry of Health and Family Welfare, New Delhi, India

Date of Submission01-Aug-2020
Date of Decision01-Nov-2020
Date of Acceptance10-Dec-2020
Date of Web Publication22-Jan-2021

Correspondence Address:
Manas Pratim Roy
Deputy Assistant Director General, Dte. GHS Ministry of Health and Family Welfare, New Delhi-110 011
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/mjdrdypu.mjdrdypu_433_20

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How to cite this article:
Roy MP. Weather and COVID-19: Delhi. Med J DY Patil Vidyapeeth 2021;14:108-9

How to cite this URL:
Roy MP. Weather and COVID-19: Delhi. Med J DY Patil Vidyapeeth [serial online] 2021 [cited 2021 Mar 3];14:108-9. Available from: https://www.mjdrdypv.org/text.asp?2021/14/1/108/307685



Dear Sir,

Recently, coronavirus disease 2019 (COVID-19) pandemic has emerged as one of the greatest public health crises. Semi-global lockdown, imposed as knee jerk reaction for mitigation of the challenge, has brought the planet to a halt. In India, after undergoing nation-wide lockdown since March 25, 2020, it was expected that the surge of the pandemic could be delayed. However, despite several restrictions, the number of cases is increasing continuously.

Among several opinions about the trend of the pandemic, few are concentrated on weather parameters. From the different parts of the world, the role of weather in affecting the spread of COVID-19 has drawn attention of the researchers.[1],[2] Therefore, such roles were examined for Delhi, the Capital of India.

Till May 29, 2020, Delhi has contributed more than 17,000 cases of COVID-19. For a better understanding of the impact of weather on the number of daily new cases, data for 7 weeks were considered, starting from April 11, 2020. Weekly data on the new diagnosed cases in Delhi were noted down.[3] Average temperature (maximum and minimum) and relative humidity (RH) were examined weekly with weekly new case load. Daily temperature range (DTR) was also considered and calculated as the difference between daily maximum and minimum temperature. Weekly average of DTR was taken into account. RH was recorded at 7:21 a.m. and 2:21 p.m. every day and termed as RH1 and RH2, respectively.[4] Correlation was tested between weekly new cases and different weather parameters.

While maximum and minimum temperatures have strong effect (r = 0.860 and r = 0.859, respectively) on the number of new cases, RH1 has moderately negative impact on the same (r = −0.504). [Figure 1] displays the change in temperature, with changes in weekly new cases. RH2 influences the weekly new case load (r = −0.229) to some extent while DTR affect the epidemic trend moderately (r = 0.471).
Figure 1: Relationship between temperature and weekly new cases of COVID-19: Delhi

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When maximum and minimum temperature, along with RH1 and RH2 were considered together through regression, none of the factors appeared significant. Other possible confounding factors such as rainfall and sunlight were beyond the scope of this paper. However, RH, one of factors considered in the article, depends on rainfall.

The findings suggest a positive correlation between temperature and new weekly case load, in contrary to the previous studies.[1],[2],[5] Being one of the first attempts from India, such findings break the myth of apparent immunity from infection in regions with the high temperature. One study claimed no impact of temperature on pandemic, after adjusting for RH and ultraviolet radiation.[6] It might be pertinent to mention that Middle East Respiratory Syndrome continued even at 45°C.[7]

It might happen that gradually increasing rate of testing was responsible for continuous rise in the number of positive cases. Effectiveness of lockdown, train movements between places might have certain influences on the curve of the pandemic. With lifting of lockdown, relaxation of restrictions and gradual opening of public places, further scopes would open to examine the possible role of other confounders.

For portraying a better prediction model, a multi-centric approach would be suitable, when data are captured over a period of time, after controlling for other variables. It has been rightly stated that risk prediction should not be based on weather alone.[8] However, with researches mostly dealing with clinical presentations, future focus is required on weather and the changes in the character of the pandemic. In addition, it is understood that with further spread of pandemic, absolute number of new cases may not be good indicator. Growth rate could be a better replacement instead. For understanding the uneven distribution of the pandemic, further research is the need of the hour.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Wu Y, Jing W, Liu J, Ma Q, Yuan J, Wang Y, et al. Effects of temperature and humidity on the daily new cases and new deaths of COVID-19 in 166 countries. Sci Total Environ 2020;729:139051. doi: 10.1016/j.scitotenv.2020.138201.  Back to cited text no. 1
    
2.
Şahin M. Impact of Weather on COVID-19 Pandemic in Turkey. Sci Total Environ 2020;728:138810.  Back to cited text no. 2
    
3.
Government of Delhi. Health Bulletin Updates. Available from: http://health.delhigovt.nic.in/wps/wcm/connect/doit_health/Health/Home/Covid19/Bulletin+Apr+2020. [Last accessed on 2020 May 30].  Back to cited text no. 3
    
4.
Indian Agricultural Research Institute. Daily Weather Data. Available from: https://www.iari.res.in/index.php?option=com_content&view=article&id=402&Itemid=322. [Last accessed on 2020 May 30].  Back to cited text no. 4
    
5.
Xie J, Zhu Y. Association between ambient temperature and COVID-19 infection in 122 cities from china. Sci Total Environ 2020;724:138201. doi: 10.1016/j.scitotenv.2020.138201..  Back to cited text no. 5
    
6.
Yao Y, Pan J, Liu Z, Meng X, Wang W, Kan H, et al. No association of COVID-19 transmission with temperature or UV radiation in Chinese cities. Eur Respir J 2020;55: 2000517.  Back to cited text no. 6
    
7.
Alshukairi AN, Zheng J, Zhao J, Nehdi A, Baharoon SA, Layqah L, et al. High prevalence of MERS-coV infection in camel workers in Saudi Arabia. mBio 2018;9:e01985–18.  Back to cited text no. 7
    
8.
O'Reilly KM, Auzenbergs M, Jafari Y, Liu Y, Flasche S, Lowe R, et al. Effective transmission across the globe: The role of climate in COVID-19 mitigation strategies. Lancet Planet Health 2020;4:e172.  Back to cited text no. 8
    


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