Relationship between Average Daily Temperature and Average Cumulative Daily Rate of Confirmed Cases of COVID-19

Golmohammadi, Amirsina; SaeidpourMasouleh, Hasti; Violini, G.; Pirouz, Behrouz

doi:10.1101/2020.04.10.20059337

Cited by 26 publications

(23 citation statements)

References 28 publications

(33 reference statements)

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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: 79%

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

136

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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Section: Findings 31 Meteorological Variablesmentioning

confidence: 79%

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

136

147

View full text Add to dashboard Cite

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“…In some studies, the temperature is expressed as the average daily temperature (13,14,20,22,25,29), or the average temperature over a period of time (7,9,23). Others use the 14-day exponential moving averages (EMAs) of daily average temperature (28).…”

Section: Methodsmentioning

confidence: 99%

Does Temperature Affect COVID-19 Transmission?

Kassem¹

2020

Front. Public Health

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This study utilizes the non-linear least squares method to estimate the impact of temperature on COVID-19 cases per million in forty-three countries, divided into three groups as follows: the first group is composed of thirteen countries that announced the first COVID-19 cases in January 2020, while the second and third groups contain thirteen and seventeen countries, respectively, that witnessed the pandemic for the first time in February and March of the same year. This relationship was measured after four time periods from the date of reporting the first case until April 1, April 15, May 15, and July 8, 2020. The results show an inverse relationship between COVID-19 cases per million and the temperature in the studies of the four-time periods for the three-country groups. These results were only significant statistically (p < 0.1) after 110.8, 164.8 days on average from the beginning of the pandemic in the case of “January” countries.

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“…Sociological and economic studies have considered changes in lifestyle and economic and sustainable development effects [2][3][4][5] in correlation with nonpharmacological actions aimed to control the pandemic spread, such as lockdown [6,7], border closure, and social distancing. Other studies focused on the mechanisms and times of incubation [8], resistance, dynamics of transmission [9][10][11], environmental factors, and conditions like the role of UV and the climate [12][13][14][15]. A large number of studies have been dedicated to statistical assessment methods and modeling of the outbreak,…”

Section: Introductionmentioning

confidence: 99%

The Role of Artificial Intelligence, MLR and Statistical Analysis in Investigations about the Correlation of Swab Tests and Stress on Health Care Systems by COVID-19

2020

Self Cite

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The outbreak of the new Coronavirus (COVID-19) pandemic has prompted investigations on various aspects. This research aims to study the possible correlation between the numbers of swab tests and the trend of confirmed cases of infection, while paying particular attention to the sickness level. The study is carried out in relation to the Italian case, but the result is of more general importance, particularly for countries with limited ICU (intensive care units) availability. The statistical analysis showed that, by increasing the number of tests, the trend of home isolation cases was positive. However, the trend of mild cases admitted to hospitals, intensive case cases, and daily deaths were all negative. The result of the statistical analysis provided the basis for an AI study by ANN. In addition, the results were validated using a multivariate linear regression (MLR) approach. Our main result was to identify a significant statistical effect of a reduction of pressure on the health care system due to an increase in tests. The relevance of this result is not confined to the COVID-19 outbreak, because the high demand of hospitalizations and ICU treatments due to this pandemic has an indirect effect on the possibility of guaranteeing an adequate treatment for other high-fatality diseases, such as, e.g., cardiological and oncological ones. Our results show that swab testing may play a significant role in decreasing stress on the health system. Therefore, this case study is relevant, in particular, for plans to control the pandemic in countries with a limited capacity for admissions to ICU units.

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Relationship between Average Daily Temperature and Average Cumulative Daily Rate of Confirmed Cases of COVID-19

Cited by 26 publications

References 28 publications

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

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

Does Temperature Affect COVID-19 Transmission?

The Role of Artificial Intelligence, MLR and Statistical Analysis in Investigations about the Correlation of Swab Tests and Stress on Health Care Systems by COVID-19

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