Effect of Temperature on the Transmission of COVID-19: A Machine Learning Case Study in Spain

Abdollahi, Amir; Rahbaralam, Maryam

doi:10.1101/2020.05.01.20087759

Cited by 20 publications

(18 citation statements)

References 31 publications

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“…Equation (9a), however stresses that temperature and humidity are of secondary relevance to the virus spread rate, since they are inter-regime influences, rather than intra-regime ones. The very existence of this relevance (together with its being of limited or secondary nature only) seems to be in line with the reported disparity among the cited references [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52]. Qualitative reasoning [30, [35][36][37] show that, according to (9a), temperature and humidity have opposing influences on virus spread rate.…”

Section: Derivation Of the Virus Spread Rate Via The Dimensional Basissupporting

confidence: 67%

“…A notable exception to the general trend in the papers surveyed is the work of Jamil et al [50], who tested the hypothesis that COVID-19 spread is temperature-dependent using data derived from nations across the world and provinces in China, and found no evidence of a pattern between spread rates and ambient temperature, suggesting that the SARS-CoV-2 is unlikely to behave as a seasonal respiratory virus. Abdollahi and Rahbaralam [51] found that there is a moderate inverse correlation between temperature and the daily number of infections. Briz-Redón and Serrano-Aroca [52] suggest that 'the disparate findings reported seem to indicate that the estimated impact of hot weather on the transmission risk is not large enough to control the pandemic.'…”

Section: Derivation Of the Virus Spread Rate Via The Dimensional Basismentioning

confidence: 99%

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Modeling Coronavirus Spread Rate Utilizing Dimensional Analysis via an Irredundant Set of Fundamental Quantities

Rushdi

2020

IJPR

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The phenomenon of spread of a (pathogenic) virus involves many physical variables, and is not amenable to satisfactory analysis via conventional methods. Dimensional Analysis (DA) is singled out as a simple and accessible way that can determine (at least qualitatively) how virus spread is related to seven physical quantities that are thought to influence it. However, classical DA deduces four dimensionless products only, none of which incorporates temperature and humidity, despite the obvious relevance of these two meteorological factors. This paper proposes an alternative version of dimensional analysis using a novel irredundant set of three fundamental quantities only. This new DA version produces five dimensionless products, four of which are essentially a replication of the old ones, while the fifth is a novel product that relates both humidity and temperature to other influencing factors. Our novel DA solution is a significant contribution, since it provides a more realistic model for virus spread rate, and it does not ignore any of the essential influencing factors. Such a model might lead to a better understanding of the determinants of spread for the novel coronavirus SARS-CoV-2 that causes the ongoing COVID-19 fatal pandemic.

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Section: Derivation Of the Virus Spread Rate Via The Dimensional Basissupporting

confidence: 67%

Section: Derivation Of the Virus Spread Rate Via The Dimensional Basismentioning

confidence: 99%

Modeling Coronavirus Spread Rate Utilizing Dimensional Analysis via an Irredundant Set of Fundamental Quantities

Rushdi

2020

IJPR

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“…Most studies (33 out of 61) suggest a negative correlation between COVID-19 and temperature. A negative correlation was found in worldwide studies (Arumugam et al, 2020;Caspi et al, 2020;Chiyomaru and Takemoto, 2020;Notari, 2020;Pirouz et al, 2020;Sajadi et al, 2020;X Wu et al, 2020;Yu, 2020), in California (Gupta and Gupta, 2020), Japan (Ujiie et al, 2020), Ghana (Abdul et al, 2020), Spain (Abdollahi and Rahbaralam, 2020;Tobías and Molina, 2020), Italy (Livadiotis, 2020) and in China (Oliveiros et al, 2020;Qi et al, 2020;Shi et al, 2020;Sil and Kumar, 2020). However, other studies came to the opposite conclusion (6 out of 61): a positive correlation between COVID-19 and temperature in Jakarta (Tosepu et al, 2020) and New York (Bashir et al, 2020), or no association (9 out of 61) in countries such as Spain (Briz-Redón and Serrano-Aroca, 2020), Iran (Ahmadi et al, 2020;Jahangiri et al, 2020), Nigeria (Taiwo and Fashola, 2020) and in a worldwide study (Jamil et al, 2020).…”

Section: Findings 31 Meteorological Variablesmentioning

confidence: 89%

The effect of climate on the spread of the COVID-19 pandemic: A review of findings, and statistical and modelling techniques

Briz‐Redón¹,

Serrano‐Aroca

2020

Progress in Physical Geography: Earth and Environment

138

147

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The new SARS-CoV-2 coronavirus has spread rapidly around the world since it was first reported in humans in Wuhan, China, in December 2019 after being contracted from a zoonotic source. This new virus produces the so-called coronavirus 2019 or COVID-19. Although several studies have supported the epidemiological hypothesis that weather patterns may affect the survival and spread of droplet-mediated viral diseases, the most recent have concluded that summer weather may offer partial or no relief of the COVID-19 pandemic to some regions of the world. Some of these studies have considered only meteorological variables, while others have included non-meteorological factors. The statistical and modelling techniques considered in this research line have included correlation analyses, generalized linear models, generalized additive models, differential equations, or spatio-temporal models, among others. In this paper we provide a systematic review of the recent literature on the effects of climate on COVID-19’s global expansion. The review focuses on both the findings and the statistical and modelling techniques used. The disparate findings reported seem to indicate that the estimated impact of hot weather on the transmission risk is not large enough to control the pandemic, although the wide range of statistical and modelling approaches considered may have partly contributed to the inconsistency of the findings. In this regard, we highlight the importance of being aware of the limitations of the different mathematical approaches, the influence of choosing geographical units and the need to analyse COVID-19 data with great caution. The review seems to indicate that governments should remain vigilant and maintain the restrictions in force against the pandemic rather than assume that warm weather and ultraviolet exposure will naturally reduce COVID-19 transmission.

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“…Contrastingly, Bashir et al (2020) and Oliveiros et al (2020) have not observed any correlation of COVID-19 pandemic spread with wind speed in China. Abdollahi and Rahbaralam (2020) have also witnessed a weak correlation of wind speed with COVID-19 in Spain. Again, rainfall has not been found associated with COVID-19 pandemic spread in NCT of Delhi (Table 1), which has been found consistent with the results reported by Tosepu et al (2020).…”

Section: Correlation Between Climatic Variables and Covid-19 Pandemicmentioning

confidence: 99%

Association between climatic variables and COVID-19 pandemic in National Capital Territory of Delhi, India

Singh

Bhardwaj²,

Kumar³

2020

Environ Dev Sustain

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Globally, since the end of December 2019, coronavirus disease (COVID-19) has been recognized as a severe infectious disease. Therefore, this study has been attempted to examine the linkage between climatic variables and COVID-19 particularly in National Capital Territory of Delhi (NCT of Delhi), India. For this, daily data of COVID-19 has been used for the period March 14 to June 11, 2020, (90 days). Eight climatic variables such as maximum, minimum and mean temperature (°C), relative humidity (%), bright sunshine hours, wind speed (km/h), evaporation (mm), and rainfall (mm) have been analyzed in relation to COVID-19. To study the relationship among different climatic variables and COVID-19 spread, Karl Pearson's correlation analysis has been performed. The Mann-Kendall method and Sen's slope estimator have been used to detect the direction and magnitude of COVID-19 trends, respectively. The results have shown that out of eight selected climatic variables, six variables, viz. maximum temperature, minimum temperature, mean temperature, relative humidity, evaporation, and wind speed are positively associated with coronavirus disease cases (statistically significant at 95 and 99% confidence levels). No association of coronavirus disease has been found with bright sunshine hours and rainfall. Besides, COVID-19 cases and deaths have shown increasing trends, significant at 99% confidence level. The results of this study suggest that climatic conditions in NCT of Delhi are favorable for COVID-19 and the disease may spread further with the increasing temperature, relative humidity, evaporation and wind speed. This is the only study which has presented the analysis of COVID-19 spread in relation to several climatic variables for the most densely populated and rapidly growing city of India. Thus, considering the results obtained, effective policies and actions are necessary especially by identifying the areas where the spread rate is increasing rapidly in this megacity. The prevention and protection measures should be adopted aiming at to reduce the further transmission of disease in the city.

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Effect of Temperature on the Transmission of COVID-19: A Machine Learning Case Study in Spain

Cited by 20 publications

References 31 publications

Modeling Coronavirus Spread Rate Utilizing Dimensional Analysis via an Irredundant Set of Fundamental Quantities

Modeling Coronavirus Spread Rate Utilizing Dimensional Analysis via an Irredundant Set of Fundamental Quantities

The effect of climate on the spread of the COVID-19 pandemic: A review of findings, and statistical and modelling techniques

Association between climatic variables and COVID-19 pandemic in National Capital Territory of Delhi, India

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