Effects of temperature variation and humidity on the mortality of COVID-19 in Wuhan

Ma, Yueling; Zhao, Yingfeng; Liu, Jiangtao; He, Xiaokai; Wang, Bo; Fu, Shihua; Yan, Jun; Niu, Jingping; Luo, Bin

doi:10.1101/2020.03.15.20036426

Cited by 64 publications

(70 citation statements)

References 40 publications

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“…As for other viruses, temperature and humidity seem to be factors with some influence on the spread of SARS-CoV-2, but recent studies led to contrasting results. In general, a negative relationship is found between air temperature and the spread of SARS-CoV-2 virus 13,14 , but a positive one 15 and a lack of effect 16 have also been reported. In the case of humidity, limited 17,18 , positive 19 and negative 13 effects on the infection rate have been reported.…”

Section: Introductionmentioning

confidence: 97%

Climatic factors influence COVID-19 outbreak as revealed by worldwide mortality

Quilodrán

Currat

Montoya‐Burgos

2020

Preprint

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The COVID-19 outbreak is triggering a global crisis that is challenging governments, health systems and the scientific community worldwide 1 . A central question in the pandemic is whether climatic factors modulate its progression. This information is key to epidemiologists and healthcare decision-makers for improving their management plans 2 .Previous attempts to assess the impact of climatic parameters have yielded ambiguous results, either because they were using geographically or temporally restricted data, or because they were comparing infection rates across countries, which were measured differently depending on local screening strategies. In March 2020, the spread of COVID-19 dramatically increased the number of countries recording deaths 3 , providing an opportunity to use mortality rate, which is measured more homogeneously across countries than infection rates, as a descriptor of the COVID-19 outbreak over a large latitudinal range. Here, using data recorded in 208 territories from 88 countries, we show that mortality rate is negatively influenced by warmer air temperature and positively affected by higher relative humidity. Each additional Celsius degree decreases mortality rate by ~4%, while a 1% increase in relative humidity raises . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) : medRxiv preprint mortality rate by ~2%. Temperature is positively correlated with UV-index, for which one unit of increase results in a ~15% decrease in the mortality rate. We also show that other factors contribute to the dynamics of the COVID-19 outbreak, such as the proportion of vulnerable age classes in the population, access to a non-overwhelmed health system, as well as governmental travel restrictions for controlling the spread of the disease. All of them are critical factors impacting the mortality rate of COVID-19. The influence of climatic factors is a warning to all southern hemisphere countries where winter is coming soon. Northern hemisphere countries should also be warned that climatic factors alone will likely not be sufficient to contain the disease.

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Section: Introductionmentioning

confidence: 97%

Climatic factors influence COVID-19 outbreak as revealed by worldwide mortality

Quilodrán

Currat

Montoya‐Burgos

2020

Preprint

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“…Some studies have shown that COVID-19 mortality has a signi cant negative correlation to temperature, showing a signi cant negative correlation [20]. When the temperature increased, the mortality of ordinary and severe patients decreased signi cantly [22].…”

Section: Discussionmentioning

confidence: 99%

“…Current studies have reported potential factors affecting the prevalence rate (PR) of COVID-19, such as temperature and humidity. The results show that both temperature and humidity will change the spread and mortality of COVID-19 [17][18][19][20][21]; however, a few studies have examined the factors affecting the proportion of local infection (PLI). In the longest incubation period before the onset of con rmed cases, if there is a history of staying in Wuhan or other provinces and cities, with a history of exposure to new coronavirus pneumonia cases, we de ned these cases as imported cases.…”

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confidence: 99%

The relationship between key natural and social factors and the transmission of novel coronavirus disease 2019 in China

Lin

Rui

et al. 2020

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Background Novel coronavirus disease 2019 (COVID-19) has become a global pandemic. This study aims to explore the relationship between key natural and social factors and the transmission of COVID-19 in China. Methods This study collected the number of confirmed cases of COVID-19 in 21 provinces and cities in China as of February 28, 2020. Three provinces were included in the sample: Hainan, Guizhou, and Qinghai. The 18 cities included Shanghai, Tianjin and so on. Key natural factors comprised monthly average temperatures in the January and February 2020 and spatial location as determined by longitude and latitude. Social factors were population density, Gross Domestic Product (GDP), number of medical institutions and health practitioners; as well as the per capita values for GDP, medical institutions, and health practitioners. Excel was used to collate the data and draw the temporal and spatial distribution map of the prevalence rate (PR) and the proportion of local infection (PLI). The influencing factors were analyzed by SPSS 21.0 statistical software, and the relationship between the dependent and independent variables was simulated by 11 models. Finally, we choose the exponential model according to the value of R2 and the applicability of the model. Results The temporal and spatial distribution of the PR varies across the 21 provinces and cities identified. The PR generally decreases with distance from Hubei, except in the case of Shenzhen City, where the converse is observed. The results of the exponential model simulation show that the monthly minimum, median, and maximum average temperatures in January and February, and the latitude and population density are significant and thus will affect the PLI. The corresponding values of R2 are 0.297, 0.322, 0.349, 0.290, 0.314, 0.339, 0.344, and 0.301. The effects of other factors were not statistically significant. Conclusions Among the selected key natural and social factors, higher temperatures may decrease the transmission of COVID-19. From this analysis, it is evident that if the temperature decreases by 1℃, the average PLI increases by 0.01. Further, it was established that locations at more northern latitudes had a higher PLI, and population density showed an inverse relationship with PLI.

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“…The impact of mobility on the spread rate of COVID19 is presented by Alexander F et al [65]. Biqing Chen et al [66], Yueling Maaet al [67] and Peng Shi et al [68] presented the impact of environmental factors on death count and spread rate of COVID-19. This analysis is based on the parameters listed as earlier and the details of this analysis are summarized in table 3.…”

Section: Stochastic Theory/ Mathematical Modelsmentioning

confidence: 99%

Forecasting Models for Coronavirus (COVID-19): A Survey of the State-of-the-Art

Mahalle¹,

Kalamkar²,

Dey³

et al. 2020

Preprint

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When new virus and its respective disease cause more infections, it is very important to decide the strategies to control the spread and determine its impact. Considering the recent example of Coronavirus initially identified in Wuhan China has now targeted Italy badly, It is very important to study different forecasting models to control this pandemic. In the view of this, this study present the comparative analysis of various forecasting models, their classification and the techniques used. The detail analysis of forecasting models with respect to the parameters like data source, techniques, algorithms, mathematical parameters is also presented in this study. In the sequel, this study also presents useful recommendations to help government and healthcare community in designing better strategies and in taking productive decisions to control this outbreak.<div><br></div>

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Effects of temperature variation and humidity on the mortality of COVID-19 in Wuhan

Cited by 64 publications

References 40 publications

Climatic factors influence COVID-19 outbreak as revealed by worldwide mortality

Climatic factors influence COVID-19 outbreak as revealed by worldwide mortality

The relationship between key natural and social factors and the transmission of novel coronavirus disease 2019 in China

Forecasting Models for Coronavirus (COVID-19): A Survey of the State-of-the-Art

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