Is temperature reducing the transmission of COVID-19 ?

Tobías, Aurelio; Molina, Tomás

doi:10.1016/j.envres.2020.109553

Cited by 141 publications

(146 citation statements)

References 14 publications

Supporting

Mentioning

125

Contrasting

Unclassified

Order By: Relevance

“…Similar studies reported in China, Singapore, New York, Norway, Italy, United States, Spain, Turkey and Indonesia have analyzed the association between climate indicators (temperature, rainfall, humidity, air quality) and COVID-19 and found a significant association between them (Bashir et al, 2020;Gupta et al, 2020;Méndez-Arriaga, 2020;Menebo, 2020;Pani et al, 2020;Sahin, 2020;Tobias et al, 2020;Tosepu et al, 2020;Xu et al, 2020;Yao et al, 2020). Temperature which is considered as an important parameter in development, prevention and control of an epidemic showed different correlation in different countries (Tobías and Molina, 2020). A significant positive correlation of COVID-19 cases with temperature is reported over Singapore, New York, Norway, Turkey and Indonesia highlighting the importance of temperature in COVID-19 epidemic whereas a negative correlation is reported over Mexico while no correlation between COVID-19 cases and temperature is observed in China.…”

Section: Accepted Manuscriptmentioning

confidence: 60%

COVID-19 and Air Pollution in Indian Cities: World’s Most Polluted Cities

Kumari¹,

Lakhani²,

Kumari³

2020

Aerosol Air Qual. Res.

View full text Add to dashboard Cite

In the present study, pollutants levels from 24 th March-31 st May in 2020 were compared with the same time period in 2019 to estimate the impact of lockdown on air pollutants levels in 39 different cities of India (including 10 Indian cities considered among the world's 20 most polluted cities). Data for air pollutants was obtained from the Central Pollution Control Board (CPCB) which was statistically analyzed. Tropospheric NO2 column retrieved from Ozone Monitoring Instrument (OMI) were compared between 2019 and 2020 to compare the NO2 levels. Impact of lockdown measures on Ghaziabad which is the world's most polluted city and Patiala which showed maximum reduction during the lockdown period in the present study was studied in detail. After the implementation of lockdown measures, air pollution decreased but with substantial variation among pollutants. The most significant reduction was observed for nitrogen dioxide (NO2) (3-79%) and carbon monoxide (CO) (2-61%), pollutants which are mainly related to traffic emissions. Ozone (O3) showed a mixed trend with increasing levels at some cities which may be attributed to lower titration of O3 by NO. Maximum reduction observed in PM10 and PM2.5 was 58 and 57%, respectively during the lockdown period in 2020 as compared to the previous year. Air quality of the cities also improved in 2020. During the lockdown period in 2020, AQI of only 15% of cities was in the 'Unhealthy' category (151-200) while in 2019, 56% of cities were in the 'Unhealthy' category. In Ghaziabad and Patiala all the pollutants showed significant reduction after lockdown implementation except O3. Diurnal patterns of PM10, PM2.5, CO and NO2 showed lower values during lockdown period in 2020 with less distinct bimodal patterns as compared to 2019. The present study provides evidence that widespread implementation of air pollution measures can result in immediate air quality benefits.

show abstract

Section: Accepted Manuscriptmentioning

confidence: 60%

COVID-19 and Air Pollution in Indian Cities: World’s Most Polluted Cities

Kumari¹,

Lakhani²,

Kumari³

2020

Aerosol Air Qual. Res.

View full text Add to dashboard Cite

show abstract

“…Our findings accord with those published cited reported worldwide, exhibiting how the number of confirmed cases increases above 25 °C and linearly increased afterward. Several studies have stated that the virus (SARS-CoV-2) is sensitive to temperature and humidity (Luo et al 2013 ; Ma et al 2020 ; Sajadi et al 2020 ; Liu et al 2020 ; Benvenuto et al 2020 ; Tobías and Molina 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Are meteorological factors enhancing COVID-19 transmission in Bangladesh? Novel findings from a compound Poisson generalized linear modeling approach

Islam

Hasanuzzaman

Shammi

et al. 2020

Environ Sci Pollut Res

View full text Add to dashboard Cite

Novel coronavirus (SARS-CoV-2) causing COVID-19 disease has arisen to be a pandemic. Since there is a close association between other viral infection cases by epidemics and environmental factors, this study intends to unveil meteorological effects on the outbreak of COVID-19 across eight divisions of Bangladesh from March to April 2020. A compound Poisson generalized linear modeling (CPGLM), along with a Monte-Carlo method and random forest (RF) model, was employed to explore how meteorological factors affecting the COVID-19 transmission in Bangladesh. Results showed that subtropical climate (mean temperature about 26.6 °C, mean relative humidity (MRH) 64%, and rainfall approximately 3 mm) enhanced COVD-19 onset. The CPGLM model revealed that every 1 mm increase in rainfall elevated by 30.99% (95% CI 77.18%, − 15.20%) COVID-19 cases, while an increase of 1 °C of diurnal temperature (TDN) declined the confirmed cases by − 14.2% (95% CI 9.73%, − 38.13%) on the lag 1 and lag 2, respectively. In addition, NRH and MRH had the highest increase (17.98% (95% CI 22.5%, 13.42%) and 19.92% (95% CI: 25.71%, 14.13%)) of COVID-19 cased in lag 4. The results of the RF model indicated that TDN and AH (absolute humidity) influence the COVID-19 cases most. In the Dhaka division, MRH is the most vital meteorological factor that affects COVID-19 deaths. This study indicates the humidity and rainfall are crucial factors affecting the COVID-19 case, which is contrary to many previous studies in other countries. These outcomes can have policy formulation for the suppression of the COVID-19 outbreak in Bangladesh.

show abstract

“…Clustering E is based on temperature pro le only and it clearly suggested that higher temperature produced higher R 0 . This is a unique nding in itself as the role of temperature in the spread of COVID-19 has been studied [15][16][17][18][19][20][21][22] and the predominant prediction was mostly the opposite; evidence from published studies documented negative associations between increasing temperature and COVID-19 transmission 15,17,22 .…”

Section: Discussionmentioning

confidence: 98%

“…Control effort proved one of the keys to prevent and contain the spatial spread 14 . The role of temperature in the spread of COVID-19 has been studied [15][16][17][18][19][20][21][22] and debated among policymakers.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 in China: Risk Factors and R0 Revisited

Khan

Zhang

Zafar

et al. 2020

Preprint

View full text Add to dashboard Cite

Abstract Background: The COVID-19 epidemic had spread rapidly through China and subsequently has proliferated globally leading to a pandemic situation around the globe. Human-to-human transmissions, as well as asymptomatic transmissions of the infection, have been confirmed. As of April 3rd, public health crisis in China due to COVID-19 is potentially under control. Methods: We compiled a daily dataset of case counts, mortality, recovery, temperature, population density, and demographic information for each prefecture during the period of January 11 to April 07, 2020 (excluding Wuhan from our analysis due to missing data). Understanding the characteristics of spatiotemporal clustering of the COVID-19 epidemic and R0 is critical in effectively preventing and controlling the ongoing global pandemic. The prefectures were grouped based on several relevant features using unsupervised machine learning techniques. We performed a computational analysis utilizing the reported cases in China to estimate the revised R0 among different regions for prevention planning in an ongoing global pandemic. Results: Finally, our results indicate that the impact of temperature and demographic (different age group percentage compared to the total population) factors on virus transmission may be characterized using a stochastic transmission model. Conclusions: Such predictions will help prioritize segments of a given community/ region for action and provide a visual aid in designing prevention strategies for a specific geographic region. Furthermore, revised estimation and our methodology will aid in improving the human health consequences of COVID-19 elsewhere.

show abstract

Is temperature reducing the transmission of COVID-19 ?

Cited by 141 publications

References 14 publications

COVID-19 and Air Pollution in Indian Cities: World’s Most Polluted Cities

COVID-19 and Air Pollution in Indian Cities: World’s Most Polluted Cities

Are meteorological factors enhancing COVID-19 transmission in Bangladesh? Novel findings from a compound Poisson generalized linear modeling approach

COVID-19 in China: Risk Factors and R0 Revisited

Contact Info

Product

Resources

About