Effects of temperature variation and humidity on the death of COVID-19 in Wuhan, China

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

doi:10.1016/j.scitotenv.2020.138226

Cited by 798 publications

(869 citation statements)

References 38 publications

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“…Based on our study, environmental factors did not demonstrate to be substantially influential when compared to COVID-19 incidence. However, in China, Ma et al (2020) found a significant association with diurnal temperature range and COVID-19, particularly pertaining to daily mortality. Further studies may consider temperature anomalies to analyze the severity of COVID-19 across the continental United…”

Section: Spatial Error Model (Sem)mentioning

confidence: 87%

GIS-based spatial modeling of COVID-19 incidence rate in the continental United States

Mollalo

Vahedi

Rivera

2020

Science of The Total Environment

556

547

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Journal Pre-proof J o u r n a l P r e -p r o o f 2 GIS-based Spatial Modeling of COVID-19 Incidence Rate in the Continental United States AbstractDuring the first 90 days of the COVID-19 outbreak in the United States, over 675,000 confirmed cases of the disease have been announced, posing unprecedented socioeconomic burden to the country. Due to inadequate research on geographic modeling of COVID-19, we investigated county-level variations of disease incidence across the continental United States. We compiled a geodatabase of 35 environmental, socioeconomic, topographic, and demographic variables that could explain the spatial variability of disease incidence. Further, we employed spatial lag and spatial error models to investigate spatial dependence and geographically weighted regression (GWR) and multiscale GWR (MGWR) models to locally examine spatial non-stationarity. The results suggested that even though incorporating spatial autocorrelation could significantly improve the performance of the global ordinary least square model; these models still represent a significantly poor performance compared to the local models. Moreover, MGWR could explain the highest variations (adj. R 2 : 68.1%) with the lowest AICc compared to the others. Mapping the effects of significant explanatory variables (i.e., income inequality, median household income, the proportion of black females, and the proportion of nurse practitioners) on spatial variability of COVID-19 incidence rates using MGWR could provide useful insights to policymakers for targeted interventions.

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Section: Spatial Error Model (Sem)mentioning

confidence: 87%

GIS-based spatial modeling of COVID-19 incidence rate in the continental United States

Mollalo

Vahedi

Rivera

2020

Science of The Total Environment

556

547

View full text Add to dashboard Cite

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“…for dynamics of technological and economic change in society the studies by Coccia (2005aCoccia ( , 2005bCoccia ( , 2008Coccia ( , 2008Coccia ( , 2009Coccia ( , 2015aCoccia ( , 2017e, 2017f, 2017g, 2018aCoccia ( , 2019aCoccia ( , 2019bCoccia ( , 2019cCoccia ( , 2019dCoccia and Finardi, 2012;Coccia and Rolfo, 2008). 6 Some studies show that in addition to human-to-human contact, ambient temperature is an important factor in the transmission and survival of coronaviruses as well as temperature variation and humidity may also be important factors affecting the COVID-19 mortality (Ma et al, 2020) Coccia M. (2020) Two mechanisms for accelerated diffusion of COVID-19 outbreaks in regions with high intensity of population and polluting industrialization: the air pollution-to-human and human-to-human transmission dynamics socioeconomic system. Some studies done in the past show the causality of the reduction of air pollution on health benefits.…”

Section: Figure 4 -Reduction Nitrogen Dioxide Before and After Lockdomentioning

confidence: 99%

Two mechanisms for accelerated diffusion of COVID-19 outbreaks in regions with high intensity of population and polluting industrialization: the air pollution-to-human and human-to-human transmission dynamics

Coccia

2020

Preprint

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What is COVID-19? Coronavirus disease 2019 (COVID-19) is viral infection that generates a severe acute respiratory syndrome with serious pneumonia that may result in progressive respiratory failure and death. What are the goals of this investigation? This study explains the geo-environmental determinants of the accelerated diffusion of COVID-19 in Italy that is generating a high level of deaths and suggests general lessons learned for a strategy to cope with future epidemics similar to COVID-19 to reduce viral infectivity and negative impacts in economic systems and society. What are the results of this study? The main results are: o The accelerate and vast diffusion of COVID-19 in North Italy has a high association with air pollution. o Hinterland cities have average days of exceeding the limits set for PM10 (particulate matter 10 micrometers or less in diameter) equal to 80 days, and an average number of infected more than 2,000 individuals as of April 1st, 2020, coastal cities have days of exceeding the limits set for PM10 equal to 60 days and have about 700 infected in average. o Cities that average number of 125 days exceeding the limits set for PM10, last year, they have an average number of infected individual higher than 3,200 units, whereas cities having less than 100 days (average number of 48 days) exceeding the limits set for PM10, they have an average number of about 900 infected individuals. o The results reveal that accelerated transmission dynamics of COVID-19 in specific environments is due to two mechanisms given by: air pollution-to-human transmission and human-to-human transmission; in particular, the mechanisms of air pollution-to-human transmission play a critical role rather than human-to-human transmission. o The finding here suggests that to minimize future epidemic similar to COVID-19, the max number of days per year in which cities can exceed the limits set for PM10 or for ozone, considering their meteorological condition, is less than 50 days. After this critical threshold, the analytical output here suggests that environmental inconsistencies because of the combination between air pollution and meteorological conditions (with high moisture%, low wind speed and fog) trigger a take-off of viral infectivity (accelerated epidemic diffusion) with damages for health of population, economy and society. What is a socioeconomic strategy to prevent future epidemics similar to COVID-19? Considering the complex interaction between air pollution, meteorological conditions and biological characteristics of viral infectivity, lessons learned for COVID-19 have to be applied for a proactive socioeconomic strategy to cope with future epidemics, especially an environmental policy based on reduction of air pollution mainly in hinterland zones of countries, having low wind speed, high percentage of moisture and fog that create an environment that can damage immune system of people and foster a fast transmission of viral infectivity similar to the COVID-19. This study must conclude that a strategy to prevent future epidemics similar to COVID 19 has also to be designed in environmental and sustainability science and not only in terms of biology.

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“…Some studies show that in addition to human-to-human contact, ambient temperature is an important factor in the transmission and survival of coronaviruses (Zhu et al, 2020) as well as temperature variation and humidity may also be important factors affecting the COVID-19 mortality(Ma et al, 2020) Journal Pre-proof…”

mentioning

confidence: 99%

Factors determining the diffusion of COVID-19 and suggested strategy to prevent future accelerated viral infectivity similar to COVID

Coccia

2020

Science of The Total Environment

529

552

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This study has two goals. The first is to explain the geo-environmental determinants of the accelerated diffusion of COVID-19 in Italy that is generating a high level of deaths. The second is to suggest a strategy to cope with future epidemic threats having accelerated viral infectivity in society. Using data on N=55 Italian province capitals, and data of infected individuals at as of April 7 th , 2020, results reveal that the accelerate and vast diffusion of COVID-19 in North Italy has a high association with air pollution of cities measured with days exceeding the limits set for PM10 (particulate matter 10 micrometers or less in diameter) or ozone in previous years. In particular, hinterland cities with average higher number of days exceeding the limits set for PM10 (and a low intensity of wind speed) have a very high number of infected people on 7 th April, 2020 (arithmetic mean about 2,200 infected, with average polluted days greater than 80), than coastal cities also having days of exceeding the limits set for PM10 or ozone but with high intensity of wind speed (arithmetic mean about 944.70 infected individuals, with about 60 average polluted days); moreover, cities having more than 100 days of air pollution (exceeding the limits set for PM10), they have a very high average number of infected people (about 3,350 infected individuals, 7 th April 2020), whereas cities having less than 100 days of air pollution, they have a lower average number of infected individuals (about 1014). The findings here also suggest that to minimize the impact of future epidemics similar to COVID-19, the max number of days per year in which Italian provincial capitals can exceed the limits set for PM10 or for ozone, considering their meteorological conditions, is about 48 days. Moreover, results here reveal that the explanatory variable of air pollution in cities under study seems to be a more important predictor in the initial phase of diffusion (on 17 th March 2020, b1 = 1.27, p<0.001) than interpersonal contacts (b2 = 0.31, p<0.05). In the second phase of maturity of the transmission dynamics of COVID-19, air pollution reduces intensity (on 7 th April, 2020 with b'1 = .81, p<0.001) also because of indirect effect of lockdown, whereas coefficient of transmission by interpersonal contacts has stability (b'2 = 0.31, p<0.01). This result reveals that accelerated transmissions dynamics of COVID-19 is due to mainly to the mechanism of "air pollution-to-human transmission" rather than "human-to-human transmission". Overall, then, transmission dynamics of viral infectivity, such as COVID-19, is due to systemic causes: general factors that are the same for all regions (e.g., biological characteristics of virus, incubation period, etc.) and specific factors which are different for each region (e.g., complex interaction between air pollution, meteorological conditions and biological characteristics of viral infectivity) and health level of individuals (habits, immune system, age, sex, etc.). Lessons learned for COVID-19 in the case study o...

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Effects of temperature variation and humidity on the death of COVID-19 in Wuhan, China

Cited by 798 publications

References 38 publications

GIS-based spatial modeling of COVID-19 incidence rate in the continental United States

GIS-based spatial modeling of COVID-19 incidence rate in the continental United States

Two mechanisms for accelerated diffusion of COVID-19 outbreaks in regions with high intensity of population and polluting industrialization: the air pollution-to-human and human-to-human transmission dynamics

Factors determining the diffusion of COVID-19 and suggested strategy to prevent future accelerated viral infectivity similar to COVID

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