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1.
Epidemiology ; 32(1): 134-135, 2021 01.
Article in English | MEDLINE | ID: covidwho-1956605
2.
Int J Environ Res Public Health ; 19(10)2022 05 18.
Article in English | MEDLINE | ID: covidwho-1862786

ABSTRACT

Since the COVID-19 epidemic outbreak at the end of 2019, many studies regarding the impact of meteorological factors on the attack have been carried out, and inconsistent conclusions have been reached, indicating the issue's complexity. To more accurately identify the effects and patterns of meteorological factors on the epidemic, we used a combination of logistic regression (LgR) and partial least squares regression (PLSR) modeling to investigate the possible effects of common meteorological factors, including air temperature, relative humidity, wind speed, and surface pressure, on the transmission of the COVID-19 epidemic. Our analysis shows that: (1) Different countries and regions show spatial heterogeneity in the number of diagnosed patients of the epidemic, but this can be roughly classified into three types: "continuous growth", "staged shock", and "finished"; (2) Air temperature is the most significant meteorological factor influencing the transmission of the COVID-19 epidemic. Except for a few areas, regional air temperature changes and the transmission of the epidemic show a significant positive correlation, i.e., an increase in air temperature is conducive to the spread of the epidemic; (3) In different countries and regions studied, wind speed, relative humidity, and surface pressure show inconsistent correlation (and significance) with the number of diagnosed cases but show some regularity.


Subject(s)
COVID-19 , Epidemics , COVID-19/epidemiology , Humans , Meteorological Concepts , Meteorology , Wind
3.
Sci Rep ; 11(1): 22027, 2021 11 11.
Article in English | MEDLINE | ID: covidwho-1758313

ABSTRACT

Rising temperature levels during spring and summer are often argued to enable lifting of strict containment measures even in the absence of herd immunity. Despite broad scholarly interest in the relationship between weather and coronavirus spread, previous studies come to very mixed results. To contribute to this puzzle, the paper examines the impact of weather on the COVID-19 pandemic using a unique granular dataset of over 1.2 million daily observations covering over 3700 counties in nine countries for all seasons of 2020. Our results show that temperature and wind speed have a robust negative effect on virus spread after controlling for a range of potential confounding factors. These effects, however, are substantially larger during mealtimes, as well as in periods of high mobility and low containment, suggesting an important role for social behaviour.


Subject(s)
COVID-19/epidemiology , Humans , Humidity , Pandemics , Risk Factors , SARS-CoV-2/isolation & purification , Seasons , Social Behavior , Temperature , Weather , Wind
4.
Environ Sci Pollut Res Int ; 29(15): 21811-21825, 2022 Mar.
Article in English | MEDLINE | ID: covidwho-1750802

ABSTRACT

The COVID-19 pandemic affected the world through its ability to cause widespread infection. The Middle East including the Kingdom of Saudi Arabia (KSA) has also been hit by the COVID-19 pandemic like the rest of the world. This study aims to examine the relationships between meteorological factors and COVID-19 case counts in three cities of the KSA. The distribution of the COVID-19 case counts was observed for all three cities followed by cross-correlation analysis which was carried out to estimate the lag effects of meteorological factors on COVID-19 case counts. Moreover, the Poisson model and negative binomial (NB) model with their zero-inflated versions (i.e., ZIP and ZINB) were fitted to estimate city-specific impacts of weather variables on confirmed case counts, and the best model is evaluated by comparative analysis for each city. We found significant associations between meteorological factors and COVID-19 case counts in three cities of KSA. We also perceived that the ZINB model was the best fitted for COVID-19 case counts. In this case study, temperature, humidity, and wind speed were the factors that affected COVID-19 case counts. The results can be used to make policies to overcome this pandemic situation in the future such as deploying more resources through testing and tracking in such areas where we observe significantly higher wind speed or higher humidity. Moreover, the selected models can be used for predicting the probability of COVID-19 incidence across various regions.


Subject(s)
COVID-19 , Meteorological Concepts , Pandemics , COVID-19/epidemiology , Cities/epidemiology , Humans , Humidity , Saudi Arabia/epidemiology , Temperature , Wind
5.
Epidemiol Infect ; 150: e38, 2022 01 21.
Article in English | MEDLINE | ID: covidwho-1641805

ABSTRACT

In this study, we analysed the relationship between meteorological factors and the number of patients with coronavirus disease 2019 (COVID-19). The study period was from 12 April 2020 to 13 October 2020, and daily meteorological data and the daily number of patients with COVID-19 in each state of the United States were collected. Based on the number of COVID-19 patients in each state of the United States, we selected four states (California, Florida, New York, Texas) for analysis. One-way analysis of variance ( ANOVA), scatter plot analysis, correlation analysis and distributed lag nonlinear model (DLNM) analysis were used to analyse the relationship between meteorological factors and the number of patients with COVID-19. We found that the significant influencing factors of the number of COVID-19 cases differed among the four states. Specifically, the number of COVID-19 confirmed cases in California and New York was negatively correlated with AWMD (P < 0.01) and positively correlated with AQI, PM2.5 and TAVG (P < 0.01) but not significantly correlated with other factors. Florida was significantly correlated with TAVG (positive) (P < 0.01) but not significantly correlated with other factors. The number of COVID-19 cases in Texas was only significantly negatively associated with AWND (P < 0.01). The influence of temperature and PM2.5 on the spread of COVID-19 is not obvious. This study shows that when the wind speed was 2 m/s, it had a significant positive correlation with COVID-19 cases. The impact of meteorological factors on COVID-19 may be very complicated. It is necessary to further explore the relationship between meteorological factors and COVID-19. By exploring the influence of meteorological factors on COVID-19, we can help people to establish a more accurate early warning system.


Subject(s)
COVID-19/epidemiology , Particulate Matter , Weather , Air Pollution , Analysis of Variance , COVID-19/transmission , California/epidemiology , Florida/epidemiology , Humans , New York/epidemiology , Nonlinear Dynamics , SARS-CoV-2 , Temperature , Texas/epidemiology , Wind
6.
Sci Rep ; 11(1): 23378, 2021 12 16.
Article in English | MEDLINE | ID: covidwho-1585808

ABSTRACT

Emissions of black carbon (BC) particles from anthropogenic and natural sources contribute to climate change and human health impacts. Therefore, they need to be accurately quantified to develop an effective mitigation strategy. Although the spread of the emission flux estimates for China have recently narrowed under the constraints of atmospheric observations, consensus has not been reached regarding the dominant emission sector. Here, we quantified the contribution of the residential sector, as 64% (44-82%) in 2019, using the response of the observed atmospheric concentration in the outflowing air during Feb-Mar 2020, with the prevalence of the COVID-19 pandemic and restricted human activities over China. In detail, the BC emission fluxes, estimated after removing effects from meteorological variability, dropped only slightly (- 18%) during Feb-Mar 2020 from the levels in the previous year for selected air masses of Chinese origin, suggesting the contributions from the transport and industry sectors (36%) were smaller than the rest from the residential sector (64%). Carbon monoxide (CO) behaved differently, with larger emission reductions (- 35%) in the period Feb-Mar 2020, suggesting dominance of non-residential (i.e., transport and industry) sectors, which contributed 70% (48-100%) emission during 2019. The estimated BC/CO emission ratio for these sectors will help to further constrain bottom-up emission inventories. We comprehensively provide a clear scientific evidence supporting mitigation policies targeting reduction in residential BC emissions from China by demonstrating the economic feasibility using marginal abatement cost curves.


Subject(s)
Air Pollutants/analysis , Air Pollution/analysis , COVID-19/prevention & control , Particulate Matter/analysis , SARS-CoV-2/isolation & purification , Soot/analysis , Algorithms , Atmosphere/analysis , COVID-19/epidemiology , COVID-19/virology , China , Climate Change , Environmental Monitoring/methods , Environmental Monitoring/statistics & numerical data , Geography , Human Activities , Humans , Models, Theoretical , Pandemics , Residence Characteristics , SARS-CoV-2/physiology , Seasons , Wind
7.
BMC Infect Dis ; 21(1): 1194, 2021 Nov 27.
Article in English | MEDLINE | ID: covidwho-1538060

ABSTRACT

BACKGROUND: To examine whether outdoor transmission may contribute to the COVID-19 epidemic, we hypothesized that slower outdoor wind speed is associated with increased risk of transmission when individuals socialize outside. METHODS: Daily COVID-19 incidence reported in Suffolk County, NY, between March 16th and December 31st, 2020, was the outcome. Average wind speed and maximal daily temperature were collated by the National Oceanic and Atmospheric Administration. Negative binomial regression was used to model incidence rates while adjusting for susceptible population size. RESULTS: Cases were very high in the initial wave but diminished once lockdown procedures were enacted. Most days between May 1st, 2020, and October 24th, 2020, had temperatures 16-28 °C and wind speed diminished slowly over the year and began to increase again in December 2020. Unadjusted and multivariable-adjusted analyses revealed that days with temperatures ranging between 16 and 28 °C where wind speed was < 8.85 km per hour (KPH) had increased COVID-19 incidence (aIRR = 1.45, 95% C.I. = [1.28-1.64], P < 0.001) as compared to days with average wind speed ≥ 8.85 KPH. CONCLUSION: Throughout the U.S. epidemic, the role of outdoor shared spaces such as parks and beaches has been a topic of considerable interest. This study suggests that outdoor transmission of COVID-19 may occur by noting that the risk of transmission of COVID-19 in the summer was higher on days with low wind speed. Outdoor use of increased physical distance between individuals, improved air circulation, and use of masks may be helpful in some outdoor environments where airflow is limited.


Subject(s)
COVID-19 , Wind , Communicable Disease Control , Humans , SARS-CoV-2 , Temperature
8.
Environ Sci Pollut Res Int ; 29(15): 21811-21825, 2022 Mar.
Article in English | MEDLINE | ID: covidwho-1509301

ABSTRACT

The COVID-19 pandemic affected the world through its ability to cause widespread infection. The Middle East including the Kingdom of Saudi Arabia (KSA) has also been hit by the COVID-19 pandemic like the rest of the world. This study aims to examine the relationships between meteorological factors and COVID-19 case counts in three cities of the KSA. The distribution of the COVID-19 case counts was observed for all three cities followed by cross-correlation analysis which was carried out to estimate the lag effects of meteorological factors on COVID-19 case counts. Moreover, the Poisson model and negative binomial (NB) model with their zero-inflated versions (i.e., ZIP and ZINB) were fitted to estimate city-specific impacts of weather variables on confirmed case counts, and the best model is evaluated by comparative analysis for each city. We found significant associations between meteorological factors and COVID-19 case counts in three cities of KSA. We also perceived that the ZINB model was the best fitted for COVID-19 case counts. In this case study, temperature, humidity, and wind speed were the factors that affected COVID-19 case counts. The results can be used to make policies to overcome this pandemic situation in the future such as deploying more resources through testing and tracking in such areas where we observe significantly higher wind speed or higher humidity. Moreover, the selected models can be used for predicting the probability of COVID-19 incidence across various regions.


Subject(s)
COVID-19 , Meteorological Concepts , Pandemics , COVID-19/epidemiology , Cities/epidemiology , Humans , Humidity , Saudi Arabia/epidemiology , Temperature , Wind
9.
J Environ Manage ; 302(Pt A): 113994, 2022 Jan 15.
Article in English | MEDLINE | ID: covidwho-1506490

ABSTRACT

53,000 tonnes of blade waste from on-shore wind farms will potentially be generated in Ireland by 2040. The recycling of blades, which are made from composite material, is costly and thus far no high volume recycling solution exists. Repurposing blades into second life structures is an alternative which is gaining in popularity, but has many challenges. Green Public Procurement has the potential to help drive demand for blade products in Irish public works. The Re-Wind project has generated a Design Atlas with 47 blade product concepts and these are screened for their ability to overcome repurposing challenges. Three Irish scenarios are developed based on this ranking, maximal utilization of the blade, and on the end customer. Life Cycle Assessment is used to determine the marginal environmental impacts of the raw material substitution provided by the use of blade material. Focusing on greenhouse gas emissions, an estimated 342 kg CO2 e can be saved for every tonne of blade waste used in these scenarios. Blade substitution of steel products was found to provide the most impact, followed by substitution of concrete products. Although repurposing is unlikely to offer an end-of-life solution for all Irish blade waste, the use of 20% of this material annually would divert 315 tonnes of blade waste from landfill, as well as avoiding emissions of 71,820 kg CO2 e. Green procurement has the potential to create a demand for repurposed blade products, which in turn could create jobs in high unemployment areas. Utilization of repurposed, local material could contribute to creating resiliency in supply chains. Both job creation and supply chain resiliency are essential for a post-Covid recovery in Ireland.


Subject(s)
COVID-19 , Energy-Generating Resources , Animals , Humans , Life Cycle Stages , SARS-CoV-2 , Wind
11.
Int J Environ Res Public Health ; 18(15)2021 08 02.
Article in English | MEDLINE | ID: covidwho-1335081

ABSTRACT

The unprecedented COVID-19 pandemic has caused a traffic tie-up across the world. In addition to home quarantine orders and travel bans, the social distance guideline of about six feet was enacted to reduce the risk of contagion. However, with recent life gradually returning to normal, the crisis is not over. In this research, a moving train test and a Gaussian puff model were employed to investigate the impact of wind raised by a train running on the transmission and dispersion of SARS-CoV-2 from infected individuals. Our findings suggest that the 2 m social distance guideline may not be enough; under train-induced wind action, human respiratory disease-carrier droplets may travel to unexpected places. However, there are deficiencies in passenger safety guidelines and it is necessary to improve the quantitative research in the relationship between train-induced wind and virus transmission. All these findings could provide a fresh insight to contain the spread of COVID-19 and provide a basis for preventing and controlling the pandemic virus, and probe into strategies for control of the disease in the future.


Subject(s)
COVID-19 , Pandemics , Humans , Quarantine , SARS-CoV-2 , Wind
12.
Chemosphere ; 286(Pt 1): 131634, 2022 Jan.
Article in English | MEDLINE | ID: covidwho-1322021

ABSTRACT

One contemporary issue is how environmental pollution and climate can affect the dissemination and severity of COVID-19 in humans. We documented the first case of association between particulate matter ≤2.5 µm (PM2.5) and COVID-19 mortality rates that involved rural and medium-sized municipalities in northwestern Mexico, where direct air quality monitoring is absent. Alternatively, anthropogenic PM2.5 emissions were used to estimate the PM2.5 exposure in each municipality using two scenarios: 1) considering the fraction derived from combustion of vehicle fuel; and 2) the one derived from modeled anthropogenic sources. This study provides insights to better understand and face future pandemics by examining the relation between PM2.5 pollution and COVID-19 mortality considering the population density and the wind speed. The main findings are: (i) municipalities with high PM2.5 emissions and high population density have a higher COVID-19 mortality rate; (ii) the exceptionally high COVID-19 mortality rates of the rural municipalities could be associated to dust events, which are common in these regions where soils without vegetation are dominant; and (iii) the influence of wind speed on COVID-19 mortality rate was evidenced only in municipalities with <100 inhabitants per km2. These results confirm the suggestion that high levels of air pollutants associated with high population density and an elevated frequency of dust events may promote an extended prevalence and severity of viral particles in the polluted air of urban, suburban, and rural communities. This supports an additional means of dissemination of the coronavirus SARS-CoV-2, in addition to the direct human-to-human transmission.


Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Air Pollutants/analysis , Air Pollution/adverse effects , Air Pollution/analysis , Cities , Dust/analysis , Environmental Monitoring , Humans , Particulate Matter/analysis , Population Density , Rural Population , SARS-CoV-2 , Wind
13.
Emerg Med J ; 38(9): 673-678, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1287247

ABSTRACT

AIM: Cardiopulmonary resuscitation (CPR) is an emergency procedure where interpersonal distance cannot be maintained. There are and will always be outbreaks of infection from airborne diseases. Our objective was to assess the potential risk of airborne virus transmission during CPR in open-air conditions. METHODS: We performed advanced high-fidelity three-dimensional modelling and simulations to predict airborne transmission during out-of-hospital hands-only CPR. The computational model considers complex fluid dynamics and heat transfer phenomena such as aerosol evaporation, breakup, coalescence, turbulence, and local interactions between the aerosol and the surrounding fluid. Furthermore, we incorporated the effects of the wind speed/direction, the air temperature and relative humidity on the transport of contaminated saliva particles emitted from a victim during a resuscitation process based on an Airborne Infection Risk (AIR) Index. RESULTS: The results reveal low-risk conditions that include wind direction and high relative humidity and temperature. High-risk situations include wind directed to the rescuer, low humidity and temperature. Combinations of other conditions have an intermediate AIR Index and risk for the rescue team. CONCLUSIONS: The fluid dynamics, simulation-based AIR Index provides a classification of the risk of contagion by victim's aerosol in the case of hands-only CPR considering environmental factors such as wind speed and direction, relative humidity and temperature. Therefore, we recommend that rescuers perform a quick assessment of their airborne infectious risk before starting CPR in the open air and positioning themselves to avoid wind directed to their faces.


Subject(s)
COVID-19/transmission , Cardiopulmonary Resuscitation/adverse effects , Models, Biological , Out-of-Hospital Cardiac Arrest/therapy , SARS-CoV-2/pathogenicity , Aerosols/adverse effects , COVID-19/complications , COVID-19/virology , Cardiopulmonary Resuscitation/standards , Computer Simulation , Guidelines as Topic , Humans , Humidity , Hydrodynamics , Out-of-Hospital Cardiac Arrest/complications , Personal Protective Equipment/standards , Risk Assessment/methods , Risk Assessment/statistics & numerical data , Temperature , Wind
14.
Integr Environ Assess Manag ; 18(2): 500-516, 2022 Mar.
Article in English | MEDLINE | ID: covidwho-1279366

ABSTRACT

The rapid outbreak of the coronavirus disease (COVID-19) has affected millions of people all over the world and killed hundreds of thousands. Atmospheric conditions can play a fundamental role in the transmission of a virus. The relationship between several atmospheric variables and the transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are therefore investigated in this study, in which the State of Kuwait, which has a hot, arid climate, is considered during free movement (without restriction), partial lockdown (partial restrictions), and full lockdown (full restriction). The relationship between the infection rate, growth rate, and doubling time for SARS-CoV-2 and atmospheric variables are also investigated in this study. Daily data describing the number of COVID-19 cases and atmospheric variables, such as temperature, relative humidity, wind speed, visibility, and solar radiation, were collected for the period February 24 to May 30, 2020. Stochastic models were employed to analyze how atmospheric variables can affect the transmission of SARS-CoV-2. The normal and lognormal probability and cumulative density functions (PDF and CDF) were applied to analyze the relationship between atmospheric variables and COVID-19 cases. The Spearman's rank correlation test and multiple regression model were used to investigate the correlation of the studied variables with the transmission of SARS-CoV-2 and to confirm the findings obtained from the stochastic models. The results indicate that relative humidity had a significant negative correlation with the number of COVID-19 cases, whereas positive correlations were observed for cases of infection and temperature, wind speed, and visibility. The infection rate for SARS-CoV-2 is directly proportional to the air temperature, wind speed, and visibility, whereas inversely related to the humidity. The lowest growth rate and longest doubling time of the COVID-19 infection occurred during the full lockdown period. The results in this study may help the World Health Organization (WHO) make specific recommendations about the outbreak of COVID-19 for decision-makers around the world. Integr Environ Assess Manag 2022;18:500-516. © 2021 SETAC.


Subject(s)
COVID-19 , Climate , COVID-19/epidemiology , Communicable Disease Control , Hot Temperature , Humans , Humidity , SARS-CoV-2 , Wind
15.
Sci Rep ; 11(1): 10746, 2021 05 24.
Article in English | MEDLINE | ID: covidwho-1242047

ABSTRACT

This study presents a systematic review and meta-analysis over the findings of significance of correlations between weather parameters (temperature, humidity, rainfall, ultra violet radiation, wind speed) and COVID-19. The meta-analysis was performed by using 'meta' package in R studio. We found significant correlation between temperature (0.11 [95% CI 0.01-0.22], 0.22 [95% CI, 0.16-0.28] for fixed effect death rate and incidence, respectively), humidity (0.14 [95% CI 0.07-0.20] for fixed effect incidence) and wind speed (0.58 [95% CI 0.49-0.66] for fixed effect incidence) with the death rate and incidence of COVID-19 (p < 0.01). The study included 11 articles that carried extensive research work on more than 110 country-wise data set. Thus, we can show that weather can be considered as an important element regarding the correlation with COVID-19.


Subject(s)
COVID-19/epidemiology , COVID-19/mortality , COVID-19/pathology , COVID-19/virology , Humans , Humidity , Incidence , Risk Factors , SARS-CoV-2/isolation & purification , Temperature , Wind
16.
J Infect Dev Ctries ; 15(2): 230-236, 2021 03 07.
Article in English | MEDLINE | ID: covidwho-1125225

ABSTRACT

INTRODUCTION: The spatiotemporal patterns of Corona Virus Disease 2019 (COVID-19) is detected in the United States, which shows temperature difference (TD) with cumulative hysteresis effect significantly changes the daily new confirmed cases after eliminating the interference of population density. METHODOLOGY: The nonlinear feature of updated cases is captured through Generalized Additive Mixed Model (GAMM) with threshold points; Exposure-response curve suggests that daily confirmed cases is changed at the different stages of TD according to the threshold points of piecewise function, which traces out the rule of updated cases under different meteorological condition. RESULTS: Our results show that the confirmed cases decreased by 0.390% (95% CI: -0.478 ~ -0.302) for increasing each one degree of TD if TD is less than 11.5°C; It will increase by 0.302% (95% CI: 0.215 ~ 0.388) for every 1°C increase in the TD (lag0-4) at the interval [11.5, 16]; Meanwhile the number of newly confirmed COVID-19 cases will increase by 0.321% (95% CI: 0.142 ~ 0.499) for every 1°C increase in the TD (lag0-4) when the TD (lag0-4) is over 16°C, and the most fluctuation occurred on Sunday. The results of the sensitivity analysis confirmed our model robust. CONCLUSIONS: In US, this interval effect of TD reminds us that it is urgent to control the spread and infection of COVID-19 when TD becomes greater in autumn and the ongoing winter.


Subject(s)
COVID-19/epidemiology , Nonlinear Dynamics , Atmospheric Pressure , Humans , Humidity , Meteorological Concepts , Population Density , Rain , Spatio-Temporal Analysis , Temperature , United States/epidemiology , Wind
17.
Diabetes Metab Syndr ; 14(6): 1735-1742, 2020.
Article in English | MEDLINE | ID: covidwho-1059526

ABSTRACT

BACKGROUND AND AIMS: Meteorological parameters play a major role in the transmission of infectious diseases such as COVID-19. In this study, we aim to analyze the correlation between meteorological parameters and COVID-19 pandemic in the financial capital of India, Mumbai. METHODS: In this research, we collected data from April 27 till July 25, 2020 (90 days). A Spearman rank correlation test along with two-tailed p test and an Artificial Neural Network (ANN) technique have been used to predict the associations of COVID-19 with meteorological parameters. RESULTS: A significant correlation of COVID-19 was found with temperature (Tmin), dew point (DPmax), relative humidity (RHmax, RHavg, RHmin) and surface pressure (Pmax, Pavg, Pmin). The parameters which showed significant correlation were then taken for the modeling and prediction of COVID-19 infections using Artificial Neural Network technique. CONCLUSIONS: It was found that the relative humidity and pressure parameters had the most influencing effect out of all other significant parameters (obtained from Spearman's method) on the active number of COVID-19 cases. The finding in this study might be useful for the public, local authorities, and the Ministry of Health, Govt. of India to combat COVID-19.


Subject(s)
COVID-19/epidemiology , Meteorological Concepts , Atmospheric Pressure , Humans , Humidity , India/epidemiology , Neural Networks, Computer , SARS-CoV-2 , Temperature , Wind
18.
J Occup Environ Med ; 63(1): 69-73, 2021 01 01.
Article in English | MEDLINE | ID: covidwho-1050212

ABSTRACT

OBJECTIVES: To assess the effect of ambient temperature, humidity and wind speed on disease occurrence in Delhi, India. DATA AND METHODS: Data regarding daily corona cases, temperature, humidity, wind speed, doubling time and basic reproduction number (R0) was retrieved from online sources. Pearson's coefficient was used to assess the correlation between daily as well as weekly corona cases and various environmental factors. RESULTS: During the study period of 97 days, there was a steady rise in number of corona cases with median (interquartile range) cases per day being 224 (58 to 635). The doubling time demonstrated a strong positive correlation with temperature while R0 had strong negative correlation with temperature (correlation coefficients 0.814 and -0.78, respectively). No significant correlation with humidity or wind speed was observed. CONCLUSION: Increasing temperature decreases COVID-19 infectivity; however, actual role of environmental factors in expansion of pandemic needs further evaluation globally.


Subject(s)
COVID-19/diagnosis , COVID-19/epidemiology , Humidity , Temperature , Wind , COVID-19/transmission , Correlation of Data , Humans , India/epidemiology
19.
Life Sci ; 269: 119093, 2021 Mar 15.
Article in English | MEDLINE | ID: covidwho-1032432

ABSTRACT

BACKGROUND: Coronavirus disease 2019 (COVID-19) has become a severe public health problem around the globe. Various epidemiological, statistical, and laboratory-based studies have shown that the role of temperature and other environmental factors has important influence in the transmission of coronaviruses. Scientific research is needed to answer the questions about the spread and transmission of the infection, whether people could be avoided from being infected with COVID-19 in next summer. AIM: We aim to investigate the association of daily average temperature, daily average dew point, daily average humidity, daily average wind speed, and daily average pressure with the infection caused by this novel coronavirus in Pakistan. KEY FINDINGS: First, we check the correlation between environmental factors and daily infected cases of COVID-19; among them, temperature and dew point have positive linear relationship with daily infected cases of COVID-19. The thought-provoking findings of the present study suggested that higher temperature and dew point can contribute to a rise in COVID-19 disease in four provinces of Pakistan, possible to genome modifications and viral resistance to harsh environment. Moreover, it is also observed that humidity in Punjab and Sindh, and wind speed in Balochistan and Khyber Pakhtunkhwa have influenced the spreading of daily infected COVID-19 cases. SIGNIFICANCE: Current study will serve as a guideline to develop understanding of environmental factors that influence COVID-19 spread, helping policymakers to prepare and handle a catastrophe resulting from this pandemic.


Subject(s)
COVID-19/epidemiology , Temperature , Weather , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/transmission , Child , Data Interpretation, Statistical , Female , Humans , Humidity , Male , Middle Aged , Pakistan/epidemiology , Wind , Young Adult
20.
Int J Environ Res Public Health ; 18(2)2021 01 09.
Article in English | MEDLINE | ID: covidwho-1016178

ABSTRACT

The purpose of this study is to investigate whether the relationship between meteorological factors (i.e., daily maximum temperature, minimum temperature, average temperature, temperature range, relative humidity, average wind speed and total precipitation) and COVID-19 transmission is affected by season and geographical location during the period of community-based pandemic prevention and control. COVID-19 infected case records and meteorological data in four cities (Wuhan, Beijing, Urumqi and Dalian) in China were collected. Then, the best-fitting model of COVID-19 infected cases was selected from four statistic models (Gaussian, logistic, lognormal distribution and allometric models), and the relationship between meteorological factors and COVID-19 infected cases was analyzed using multiple stepwise regression and Pearson correlation. The results showed that the lognormal distribution model was well adapted to describing the change of COVID-19 infected cases compared with other models (R2 > 0.78; p-values < 0.001). Under the condition of implementing community-based pandemic prevention and control, relationship between COVID-19 infected cases and meteorological factors differed among the four cities. Temperature and relative humidity were mainly the driving factors on COVID-19 transmission, but their relations obviously varied with season and geographical location. In summer, the increase in relative humidity and the decrease in maximum temperature facilitate COVID-19 transmission in arid inland cities, while at this point the decrease in relative humidity is good for the spread of COVID-19 in coastal cities. For the humid cities, the reduction of relative humidity and the lowest temperature in the winter promote COVID-19 transmission.


Subject(s)
COVID-19/epidemiology , COVID-19/transmission , Meteorological Concepts , Seasons , Beijing , China/epidemiology , Cities , Humans , Humidity , Temperature , Wind
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