Demystifying a Hidden Trend: Do Temperature Variations Affect COVID-19 Virus Spread?

Marvi, Murk; Arfeen, Asad; Rauf, Zahid

doi:10.2139/ssrn.3567084

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…Marvi and Arfeen [9] have examine the relationship between average temperature and rate of spread of COVID-19 across various regions around the globe. Their finding manifests a bounded relationship between factors under consideration, that is, the spread rate of the virus has found to be slower in regions with extreme temperatures.…”

Section: Related Workmentioning

confidence: 99%

Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches

Malki

Atlam

Hassanien

et al. 2020

Chaos, Solitons & Fractals

190

129

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Nowadays, a significant number of infectious diseases such as human coronavirus disease (COVID-19) are threatening the world by spreading at an alarming rate. Some of the literatures pointed out that the pandemic is exhibiting seasonal patterns in its spread, incidence and nature of the distribution. In connection to the spread and distribution of the infection, scientific analysis that answers the questions whether the next summer can save people from COVID-19 is required. Many researchers have been exclusively asked whether high temperature during summer can slow down the spread of the COVID-19 as it has with other seasonal flues. Since there are a lot of questions that are unanswered right now, and many mysteries aspects about the COVID-19 that is still unknown to us, in-depth study and analysis of associated weather features are required. Moreover, understanding the nature of COVID-19 and forecasting the spread of COVID-19 request more investigation of the real effect of weather variables on the transmission of the COVID-19 among people. In this work, various regressor machine learning models are proposed to extract the relationship between different factors and the spreading rate of COVID-19. The machine learning algorithms employed in this work estimate the impact of weather variables such as temperature and humidity on the transmission of COVID-19 by extracting the relationship between the number of confirmed cases and the weather variables on certain regions. To validate the proposed method, we have collected the required datasets related to weather and census features and necessary prepossessing is carried out. From the experimental results, it is shown that the weather variables are more relevant in predicting the mortality rate when compared to the other census variables such as population, age, and urbanization. Thus, from this result, we can conclude that temperature and humidity are important features for predicting COVID-19 mortality rate. Moreover, it is indicated that the higher the value of temperature the lower number of infection cases.

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Section: Related Workmentioning

confidence: 99%

Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches

Malki

Atlam

Hassanien

et al. 2020

Chaos, Solitons & Fractals

190

129

View full text Add to dashboard Cite

show abstract

“…According to Marvi and coauthors [71] work, the relationship between average temperature and the rate of spread of COVID-19 in several regions of the world was investigated, and the authors inferred that the rate of spread of the virus is slower in regions with extreme temperatures. A laboratory study published by Chin and coauthors [72] reported that SARS-CoV-2 was highly stable at 4 °C but sensitive to heat; the virus survival time was reduced to 5 min as the incubation temperature increased to 70 °C.…”

Section: Weather Conditions and Covid-19mentioning

confidence: 99%

How Have Particulate Matter, Weather Conditions and Smoking Contributed to the Transmission and Aggravation of the COVID-19 Pandemic?

Santa-Helena,

Falco,

Gioda

2023

Braz. arch. biol. technol.

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One of the most important issues, according to the World Health Organization, to be addressed during the COVID-19 pandemic is understanding the risk factors for the severity of the disease. Some studies show a possible association of SARS-CoV-2 with risk factors such as particulate matter and some weather conditions, in addition to behavioral factors such as smoking. The present review aimed to evaluate the correlation between COVID-19 and these potential risk factors. The Web of Science database searched for eligible studies covering experimental and epidemiological studies from March 2020 to March 2022. The recognized risk groups for an unfavorable evolution are the elderly, those with chronic diseases (such as diabetes, hypertension, and pulmonary diseases), and those who use immunobiological or chemotherapy drugs. But some environmental factors may come to transmit the severity of COVID-19 cases. Lifestyle may be responsible for the variability in the severity of COVID-19, with smoking being one of the main factors, yet environmental factors such as particulate matter levels, humidity, and temperature have been shown to affect the transmissibility of SARS-CoV-2.

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“…Demongeot et al [8] demonstrated the impact of temperature on spreading of coronavirus diseases. Marvi and Arfeen [9] identified the relationship between rate of COVID-19 spread across different regions worldwide and the average temperature. ey considered two factors, namely the spread rate of the virus and temperatures.…”

Section: Related Workmentioning

confidence: 99%

Impact of Weather Predictions on COVID‐19 Infection Rate by Using Deep Learning Models

et al. 2021

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Nowadays, the whole world is facing a pandemic situation in the form of coronavirus diseases (COVID-19). In connection with the spread of COVID-19 confirmed cases and deaths, various researchers have analysed the impact of temperature and humidity on the spread of coronavirus. In this paper, a deep transfer learning-based exhaustive analysis is performed by evaluating the influence of different weather factors, including temperature, sunlight hours, and humidity. To perform all the experiments, two data sets are used: one is taken from Kaggle consists of official COVID-19 case reports and another data set is related to weather. Moreover, COVID-19 data are also tested and validated using deep transfer learning models. From the experimental results, it is shown that the temperature, the wind speed, and the sunlight hours make a significant impact on COVID-19 cases and deaths. However, it is shown that the humidity does not affect coronavirus cases significantly. It is concluded that the convolutional neural network performs better than the competitive model.

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Demystifying a Hidden Trend: Do Temperature Variations Affect COVID-19 Virus Spread?

Cited by 5 publications

References 8 publications

Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches

Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches

How Have Particulate Matter, Weather Conditions and Smoking Contributed to the Transmission and Aggravation of the COVID-19 Pandemic?

Impact of Weather Predictions on COVID‐19 Infection Rate by Using Deep Learning Models

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