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1.
PLoS One ; 17(3): e0263265, 2022.
Article in English | MEDLINE | ID: covidwho-1765533

ABSTRACT

In the last century, the increase in traffic, human activities and industrial production have led to a diffuse presence of air pollution, which causes an increase of risk of several health conditions such as respiratory diseases. In Europe, air pollution is a serious concern that affects several areas, one of the worst ones being northern Italy, and in particular the Po Valley, an area characterized by low air quality due to a combination of high population density, industrial activity, geographical factors and weather conditions. Public health authorities and local administrations are aware of this problem, and periodically intervene with temporary traffic limitations and other regulations, often insufficient to solve the problem. In February 2020, this area was the first in Europe to be severely hit by the SARS-CoV-2 virus causing the COVID-19 disease, to which the Italian government reacted with the establishment of a drastic lockdown. This situation created the condition to study how significant is the impact of car traffic and industrial activity on the pollution in the area, as these factors were strongly reduced during the lockdown. Differently from some areas in the world, a drastic decrease in pollution measured in terms of particulate matter (PM) was not observed in the Po Valley during the lockdown, suggesting that several external factors can play a role in determining the severity of pollution. In this study, we report the case study of the city of Pavia, where data coming from 23 air quality sensors were analyzed to compare the levels measured during the lockdown with the ones coming from the same period in 2019. Our results show that, on a global scale, there was a statistically significant reduction in terms of PM levels taking into account meteorological variables that can influence pollution such as wind, temperature, humidity, rain and solar radiation. Differences can be noticed analyzing daily pollution trends too, as-compared to the study period in 2019-during the study period in 2020 pollution was higher in the morning and lower in the remaining hours.


Subject(s)
COVID-19/prevention & control , Cities/statistics & numerical data , Particulate Matter/analysis , Quarantine , COVID-19/epidemiology , Cities/epidemiology , Data Mining , Humans , Italy/epidemiology , Quarantine/statistics & numerical data , Traffic-Related Pollution/statistics & numerical data , Weather
2.
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
3.
Environ Res ; 206: 112272, 2022 04 15.
Article in English | MEDLINE | ID: covidwho-1719714

ABSTRACT

Studying the influence of weather conditions on the COVID-19 epidemic is an emerging field. However, existing studies in this area tend to utilize time-series data, which have certain limitations and fail to consider individual, social, and economic factors. Therefore, this study aimed to fill this gap. In this paper, we explored the influence of weather conditions on the COVID-19 epidemic using COVID-19-related prefecture-daily panel data collected in mainland China between January 1, 2020, and February 19, 2020. A two-way fixed effect model was applied taking into account factors including public health measures, effective distance to Wuhan, population density, economic development level, health, and medical conditions. We also used a piecewise linear regression to determine the relationship in detail. We found that there is a conditional negative relationship between weather conditions and the epidemic. Each 1 °C rise in mean temperature led to a 0.49% increase in the confirmed cases growth rate when mean temperature was above -7 °C. Similarly, when the relative humidity was greater than 46%, it was negatively correlated with the epidemic, where a 1% increase in relative humidity decreased the rate of confirmed cases by 0.19%. Furthermore, prefecture-level administrative regions, such as Chifeng (included as "warning cities") have more days of "dangerous weather", which is favorable for outbreaks. In addition, we found that the impact of mean temperature is greatest in the east, the influence of relative humidity is most pronounced in the central region, and the significance of weather conditions is more important in the coastal region. Finally, we found that rising diurnal temperatures decreased the negative impact of weather conditions on the spread of COVID-19. We also observed that strict public health measures and high social concern can mitigate the adverse effects of cold and dry weather on the spread of the epidemic. To the best of our knowledge, this is the first study which applies the two-way fixed effect model to investigate the influence of weather conditions on the COVID-19 epidemic, takes into account socio-economic factors and draws new conclusions.


Subject(s)
COVID-19 , China/epidemiology , Humans , SARS-CoV-2 , Temperature , Weather
4.
Int J Health Serv ; 52(2): 189-200, 2022 04.
Article in English | MEDLINE | ID: covidwho-1714524

ABSTRACT

Extreme weather events (EWEs) affected health in every world region during 2021, placing the planet in "uncharted territory." Portraying the human impacts of EWEs is part of a health frame that suggests public knowledge of these risks will spur support for needed policy change. The health frame has gained traction since the Paris COP21 (United Nations Climate Change Conference) and arguably helped to achieve modest progress at the Glasgow COP26. However, reporting rarely covers the full picture of health impacts from EWEs, instead focusing on cost of damages, mortality, and displacement. This review summarizes data for 30 major EWEs of 2021 and, based on the epidemiological literature, discusses morbidity-related exposures for four hazards that marked the year: wildfire smoke; extreme cold and power outages; extreme, precipitation-related flooding; and drought. A very large likely burden of morbidity was found, with particularly widespread exposure to risk of respiratory outcomes (including interactions with COVID-19) and mental illnesses. There is need for a well-disseminated global annual report on EWE morbidity, including affected population estimates and evolving science. In this way, the public health frame may be harnessed to bolster evidence for the broader and promising frame of "urgency and agency" for climate change action.


Subject(s)
COVID-19 , Extreme Weather , Animals , COVID-19/epidemiology , Climate Change , Female , Humans , Morbidity , SARS-CoV-2 , Sheep , Weather
5.
Environ Res ; 208: 112484, 2022 May 15.
Article in English | MEDLINE | ID: covidwho-1693480

ABSTRACT

This paper investigates at the world level the influence of climate on the transmission of the SARS-CoV-2 virus. For that purpose, panel regressions of the number of cases and deaths from 134 countries are run on a set of explanatory variables (air temperature, relative humidity, precipitation, and wind) along with control variables (government interventions and population size and density). The analysis is completed with a panel threshold regression to check for potential non-linearities of the weather variables on virus transmission. The main findings support the role of climate in the circulation of the virus across countries. The detailed analysis reveals that relative humidity reduces the number of cases and deaths in both low and high regimes, while temperature and wind reduce the number of deaths.


Subject(s)
COVID-19 , Climate , Communicable Disease Control , Pandemics , COVID-19/epidemiology , COVID-19/prevention & control , Government , Humans , Humidity , Pandemics/prevention & control , SARS-CoV-2 , Temperature , Weather
6.
Pol Arch Intern Med ; 132(1)2022 01 28.
Article in English | MEDLINE | ID: covidwho-1675433

ABSTRACT

INTRODUCTION: COVID­19 is an infectious disease caused by SARS-CoV-2. Little is known on the impact of weather conditions on the transmission of COVID­19. OBJECTIVES: We aimed to assess correlations between 6 different meteorologic parameters and the transmission dynamics of the COVID­19 pandemic in 16 administrative regions (voivodeships) of Poland. PATIENTS AND METHODS: Data for analysis were obtained from epidemiologic reports of the Polish Ministry of Health. For each voivodeship, one synoptic station was selected to provide meteorologic data on daily maximum and minimum temperatures, variability of daily temperature, sunshine duration, relative humidity, and wind speed. The periods with significant weather impact were determined using multiple linear regression. Cross­correlation function (CCF) and random forest models were used to assess correlations between meteorologic parameters and the incidence of COVID­19 as well as the number of hospitalizations for COVID­19. RESULTS: In all voivodeships, the incidence of new COVID­19 cases correlated with relative humidity (CCF = 0.41), daily maximum temperature (CCF = -0.41), variability of daily temperature (CCF = -0.40), and sunshine duration (CCF = 0.35). For all parameters, a similar time lag of 10 to 14 days was noted. There were no significant correlations for wind speed in most voivodeships. The risk of hospitalization for COVID­19 correlated with daily maximum temperature (CCF = -0.48; time lag, 10 days) and sunshine duration (CCF = -0.45; time lag, 10 days). CONCLUSIONS: The delayed effects of the meteorologic factors on the incidence of COVID­19 and the risk of hospitalization for COVID­19 were observed. In each voivodeship, the dynamics of COVID­19 transmission was most strongly affected by relative humidity and daily maximum temperature.


Subject(s)
COVID-19 , Humans , Humidity , Pandemics , Poland/epidemiology , SARS-CoV-2 , Weather
7.
Environ Res ; 209: 112887, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1664913

ABSTRACT

BACKGROUND: The SARS-CoV-2 virus pandemic is primarily transmitted by direct contact between infected and uninfected people, though, there are still many unknown factors influencing the survival and transmission of the virus. Air temperature is one of the main susceptible factors. This study aimed to explore the impact of air and land surface temperatures on Covid-19 transmission in a region of Iran. METHOD: Daily Land Surface Temperature (LST) measured by satellite and Air Temperature measured by weather station were used as the predictors of Covid-19 transmission. The data were obtained from February 2020 to April 2021. Spatio-temporal kriging was used in order to predict LST in some days in which no image was recorded by the satellite. The validity of the predicted values was assessed by Bland-Altman technique. The impact of the predictors was analyzed by Distributed Lag Non-linear Model (DLNM). In addition to main effect of temperature, its linear as well as non-linear interaction effect with relative humidity were considered using Generalized Additive Model (GAM) and a bivariate response surface model. Sensitivity analyses were done to select models' parameters, autocorrelation model and function of associations. RESULTS: The dose-response curve revealed that the impact of both predictors was not obvious, though, the risk of transmission tended to be positive due to low values of temperatures. Although the linear interaction effect was not statistically significant, but joint patterns showed that the impact of both LST and AT tended to be different when humidity values were changed. CONCLUSION: However the findings suggested that both LST and AT were not statistically important predictors, but they tended to predict the Covid-19 transmission in some lags. Because of local based evidence, the wide confidence intervals and then non-significant values should be cautiously interpreted.


Subject(s)
COVID-19 , COVID-19/epidemiology , Humans , Humidity , Iran/epidemiology , SARS-CoV-2 , Temperature , Weather
8.
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
9.
Lancet Planet Health ; 5(10): e671-e680, 2021 10.
Article in English | MEDLINE | ID: covidwho-1639201

ABSTRACT

BACKGROUND: Understanding how environmental factors affect SARS-CoV-2 transmission could inform global containment efforts. Despite high scientific and public interest and multiple research reports, there is currently no consensus on the association of environmental factors and SARS-CoV-2 transmission. To address this research gap, we aimed to assess the relative risk of transmission associated with weather conditions and ambient air pollution. METHODS: In this global analysis, we adjusted for the delay between infection and detection, estimated the daily reproduction number at 3739 global locations during the COVID-19 pandemic up until late April, 2020, and investigated its associations with daily local weather conditions (ie, temperature, humidity, precipitation, snowfall, moon illumination, sunlight hours, ultraviolet index, cloud cover, wind speed and direction, and pressure data) and ambient air pollution (ie, PM2·5, nitrogen dioxide, ozone, and sulphur dioxide). To account for other confounding factors, we included both location-specific fixed effects and trends, controlling for between-location differences and heterogeneities in locations' responses over time. We built confidence in our estimations through synthetic data, robustness, and sensitivity analyses, and provided year-round global projections for weather-related risk of global SARS-CoV-2 transmission. FINDINGS: Our dataset included data collected between Dec 12, 2019, and April 22, 2020. Several weather variables and ambient air pollution were associated with the spread of SARS-CoV-2 across 3739 global locations. We found a moderate, negative relationship between the estimated reproduction number and temperatures warmer than 25°C (a decrease of 3·7% [95% CI 1·9-5·4] per additional degree), a U-shaped relationship with outdoor ultraviolet exposure, and weaker positive associations with air pressure, wind speed, precipitation, diurnal temperature, sulphur dioxide, and ozone. Results were robust to multiple assumptions. Independent research building on our estimates provides strong support for the resulting projections across nations. INTERPRETATION: Warmer temperature and moderate outdoor ultraviolet exposure result in a slight reduction in the transmission of SARS-CoV-2; however, changes in weather or air pollution alone are not enough to contain the spread of SARS-CoV-2 with other factors having greater effects. FUNDING: None.


Subject(s)
Air Pollution , COVID-19 , Global Health , Weather , Air Pollution/adverse effects , COVID-19/epidemiology , COVID-19/transmission , Global Health/statistics & numerical data , Humans , Pandemics , SARS-CoV-2
10.
Eur Rev Med Pharmacol Sci ; 26(1): 305-311, 2022 01.
Article in English | MEDLINE | ID: covidwho-1638619

ABSTRACT

The pandemic of COVID-19 started spreading more exponentially across Pakistan since the end of February 2020. Numerous models and factors have been used to estimate predictions of the prevalence and severity of COVID-19 infections around the globe. While many factors play a role in the spread of COVID-19, climate and weather conditions are considered key elements in the transmission of COVID-19. Many researchers believe that recent increases in COVID-19 cases correlate strongly with local temperatures and factors (such as humidity, weather conditions, etc.) related to it. In this manuscript we test the hypothesis that SARS-CoV-2 spread is temperature-dependent by using the available data derived from Pakistan. The present review focuses on the relationship between temperature and COVID-19, examining the virus's viability and infectivity under various conditions. Our findings indicate that the trough and crest of the COVID-19 wave observed in 2020 are likely to repeat in the summer and winter of 2021, respectively. In Pakistan, temperatures, and humidity significantly affect the COVID-19 transmission and incidence. Like other types of beta-coronaviruses (ß-CoVs), the spread of COVID-19 may depend upon a great deal on temperature.


Subject(s)
COVID-19/epidemiology , COVID-19/transmission , Weather , Humans , Humidity , Incidence , Pakistan/epidemiology , Pandemics , SARS-CoV-2 , Seasons , Temperature
11.
J Environ Manage ; 302(Pt B): 114085, 2022 Jan 15.
Article in English | MEDLINE | ID: covidwho-1587288

ABSTRACT

The coronavirus disease 2019 (COVID-19) has been first reported in December 2019 and rapidly spread worldwide. As other severe acute respiratory syndromes, it is a widely discussed topic whether seasonality affects the COVID-19 infection spreading. This study presents two different approaches to analyse the impact of social activity factors and weather variables on daily COVID-19 cases at county level over the Continental U.S. (CONUS). The first one is a traditional statistical method, i.e., Pearson correlation coefficient, whereas the second one is a machine learning algorithm, i.e., random forest regression model. The Pearson correlation is analysed to roughly test the relationship between COVID-19 cases and the weather variables or the social activity factor (i.e. social distance index). The random forest regression model investigates the feasibility of estimating the number of county-level daily confirmed COVID-19 cases by using different combinations of eight factors (county population, county population density, county social distance index, air temperature, specific humidity, shortwave radiation, precipitation, and wind speed). Results show that the number of daily confirmed COVID-19 cases is weakly correlated with the social distance index, air temperature and specific humidity through the Pearson correlation method. The random forest model shows that the estimation of COVID-19 cases is more accurate with adding weather variables as input data. Specifically, the most important factors for estimating daily COVID-19 cases are the population and population density, followed by the social distance index and the five weather variables, with temperature and specific humidity being more critical than shortwave radiation, wind speed, and precipitation. The validation process shows that the general values of correlation coefficients between the daily COVID-19 cases estimated by the random forest model and the observed ones are around 0.85.


Subject(s)
COVID-19 , Humans , Humidity , SARS-CoV-2 , Temperature , United States , Weather
12.
Biomed Environ Sci ; 34(11): 871-880, 2021 Nov 20.
Article in English | MEDLINE | ID: covidwho-1580280

ABSTRACT

Objective: Previous studies have shown that meteorological factors may increase COVID-19 mortality, likely due to the increased transmission of the virus. However, this could also be related to an increased infection fatality rate (IFR). We investigated the association between meteorological factors (temperature, humidity, solar irradiance, pressure, wind, precipitation, cloud coverage) and IFR across Spanish provinces ( n = 52) during the first wave of the pandemic (weeks 10-16 of 2020). Methods: We estimated IFR as excess deaths (the gap between observed and expected deaths, considering COVID-19-unrelated deaths prevented by lockdown measures) divided by the number of infections (SARS-CoV-2 seropositive individuals plus excess deaths) and conducted Spearman correlations between meteorological factors and IFR across the provinces. Results: We estimated 2,418,250 infections and 43,237 deaths. The IFR was 0.03% in < 50-year-old, 0.22% in 50-59-year-old, 0.9% in 60-69-year-old, 3.3% in 70-79-year-old, 12.6% in 80-89-year-old, and 26.5% in ≥ 90-year-old. We did not find statistically significant relationships between meteorological factors and adjusted IFR. However, we found strong relationships between low temperature and unadjusted IFR, likely due to Spain's colder provinces' aging population. Conclusion: The association between meteorological factors and adjusted COVID-19 IFR is unclear. Neglecting age differences or ignoring COVID-19-unrelated deaths may severely bias COVID-19 epidemiological analyses.


Subject(s)
COVID-19/epidemiology , Pandemics/statistics & numerical data , Weather , Adult , Aged , Aged, 80 and over , COVID-19/virology , Humans , Meteorological Concepts , Middle Aged , SARS-CoV-2/physiology , Spain/epidemiology , Young Adult
13.
J Appl Microbiol ; 132(4): 2673-2682, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1546347

ABSTRACT

AIM: During several local COVID-19 outbreaks in China in 2020, SARS-CoV-2 or its RNA was isolated or detected from frozen food or packages, revealing the lack of effective disinfection measures in the frozen food chain and risk of transmission. We explored the possibility that disinfectant plus antifreeze could be delivered as thermal fog to realize effective disinfection at subzero temperatures. METHODS AND RESULTS: We selected two disinfectant-antifreeze combinations, didecyl dimethyl ammonium bromide (DDAB) - propylene glycol (PPG) and peracetic acid (PAA) - triethylene glycol (TEG), and each combination is used with a custom-optimized thermal fogging machine. The two fogs were tested in -20°C freezer warehouses for their disinfection efficacy against a coronavirus porcine epidemic diarrhoea virus (PEDV) field strain, a swine influenza virus (SIV) field strain, and three indicator bacteria, Escherichia coli, Staphylococcus aureus and Bacillus subtilis endospores. At -20°C, the DDAB-PPG or PAA-TEG thermal fogs settle within 3.5 to 4.5 h and effectively inactivated PEDV with median tissue culture infective dose of 10-3.5 0.1 ml-1 and SIV-H1N1 with hemagglutination titre of 26  ml-1 within 15-60 min. DDAB-PPG could inactivate S. aureus and E. coli vegetative cells (106  cfu ml-1 ) within 15-60 min but not effective on B. subtilis spores, while PAA-TEG could disinfect B. subtilis spores more effectively than for S. aureus and E. coli. CONCLUSIONS: We showed that a practical subzero temperature disinfection technology was effective in killing enveloped viruses and vegetative bacteria or bacterial spores. DDAB-PPG or PAA-TEG thermal fogging may be a practical technology for cold-chain disinfection. SIGNIFICANCE AND IMPACT OF THE STUDY: This subzero temperature disinfection technology could help to meet the urgent public health need of environmental disinfection in frozen food logistics against pandemic and other potential pathogens and to enhance national and international biosecurity.


Subject(s)
COVID-19 , Disinfectants , Influenza A Virus, H1N1 Subtype , Animals , Bacillus subtilis , Disinfectants/pharmacology , Disinfection/methods , Escherichia coli , Peracetic Acid/pharmacology , SARS-CoV-2 , Staphylococcus aureus , Swine , Weather
14.
Sensors (Basel) ; 21(21)2021 Oct 27.
Article in English | MEDLINE | ID: covidwho-1512560

ABSTRACT

Providing a stable, low-price, and safe supply of energy to end-users is a challenging task. The energy service providers are affected by several events such as weather, volatility, and special events. As such, the prediction of these events and having a time window for taking preventive measures are crucial for service providers. Electrical load forecasting can be modeled as a time series prediction problem. One solution is to capture spatial correlations, spatial-temporal relations, and time-dependency of such temporal networks in the time series. Previously, different machine learning methods have been used for time series prediction tasks; however, there is still a need for new research to improve the performance of short-term load forecasting models. In this article, we propose a novel deep learning model to predict electric load consumption using Dual-Stage Attention-Based Recurrent Neural Networks in which the attention mechanism is used in both encoder and decoder stages. The encoder attention layer identifies important features from the input vector, whereas the decoder attention layer is used to overcome the limitations of using a fixed context vector and provides a much longer memory capacity. The proposed model improves the performance for short-term load forecasting (STLF) in terms of the Mean Absolute Error (MAE) and Root Mean Squared Errors (RMSE) scores. To evaluate the predictive performance of the proposed model, the UCI household electric power consumption (HEPC) dataset has been used during the experiments. Experimental results demonstrate that the proposed approach outperforms the previously adopted techniques.


Subject(s)
Machine Learning , Neural Networks, Computer , Forecasting , Time , Weather
15.
Elife ; 92020 06 08.
Article in English | MEDLINE | ID: covidwho-1497819

ABSTRACT

SARS-CoV-2 presents an unprecedented international challenge, but it will not be the last such threat. Here, we argue that the world needs to be much better prepared to rapidly detect, define and defeat future pandemics. We propose that a Global Immunological Observatory and associated developments in systems immunology, therapeutics and vaccine design should be at the heart of this enterprise.


Subject(s)
Communicable Disease Control/organization & administration , Communicable Diseases, Emerging/prevention & control , Coronavirus Infections/epidemiology , Disaster Planning/organization & administration , Global Health , International Cooperation , Pandemics/prevention & control , Pneumonia, Viral/epidemiology , Population Surveillance , Animals , Anti-Infective Agents , COVID-19 , Climate Change , Cohort Studies , Communicable Disease Control/methods , Communicable Diseases, Emerging/diagnosis , Communicable Diseases, Emerging/epidemiology , Communicable Diseases, Emerging/immunology , Drug Development , Forecasting , Global Health/trends , Humans , Interdisciplinary Communication , Mass Screening/organization & administration , Models, Animal , Population Surveillance/methods , Serologic Tests , Vaccines , Weather , Zoonoses
16.
J Infect Public Health ; 14(10): 1320-1327, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1492289

ABSTRACT

BACKGROUND: World Health Organization has reported fifty countries have now detected the new coronavirus (B.1.1.7 variant) since a couple of months ago. In Indonesia, the B.1.1.7 cases have been found in several provinces since January 2021, although they are still in a lower number than the old variant of COVID-19. Therefore, this study aims to create a forecast analysis regarding the occasions of COVID-19 and B.1.1.7 cases based on data from the 1st January to 18th March 2021, and also analyze the association between meteorological factors with B.1.1.7 incidences in three different provinces of Indonesia such as the West Java, South Sumatra and East Kalimantan. METHODS: We used the Autoregressive Moving Average Models (ARIMA) to forecast the number of cases in the upcoming 14 days and the Spearman correlation analysis to analyze the relationship between B.1.1.7 cases and meteorological variables such as temperature, humidity, rainfall, sunshine, and wind speed. RESULTS: The results of the study showed the fitted ARIMA models forecasted there was an increase in the daily cases in three provinces. The total cases in three provinces would increase by 36% (West Java), 13.5% (South Sumatra), and 30% (East Kalimantan) as compared with actual cases until the end of 14 days later. The temperature, rainfall and sunshine factors were the main contributors for B.1.1.7 cases with each correlation coefficients; r = -0.230; p < 0.05, r = 0.211; p < 0.05 and r = -0.418; p < 0.01, respectively. CONCLUSIONS: We recapitulated that this investigation was the first preliminary study to analyze a short-term forecast regarding COVID-19 and B.1.1.7 cases as well as to determine the associated meteorological factors that become primary contributors to the virus spread.


Subject(s)
COVID-19 , SARS-CoV-2 , Weather , COVID-19/epidemiology , COVID-19/virology , Humans , Humidity , Indonesia/epidemiology , Meteorological Concepts
17.
J Occup Environ Med ; 63(6): 462-468, 2021 06 01.
Article in English | MEDLINE | ID: covidwho-1494061

ABSTRACT

OBJECTIVE: To estimate the association between weather and COVID-19 fatality rates during US stay-at-home orders. METHODS: With a county-level longitudinal design, this study analyzed COVID-19 deaths from public health departments' daily reports and considered exposure as the 18 to 22 day-period before death. Models included state-level social distancing measures, Census Bureau demographics, daily weather information, and daily air pollution. The primary measures included minimum and maximum daily temperature, precipitation, ozone concentration, PM2.5 concentrations, and U.V. light index. RESULTS: A 1 °F increase in the minimum temperature was associated with 1.9% (95% CI, 0.2% to 3.6%) increase in deaths 20 days later. An ozone concentration increase of 1 ppb (part per billion) decreased daily deaths by 2.0% (95% CI, 0.1% to 3.6%); ozone levels below 38 ppb negatively correlated with deaths. CONCLUSIONS: Increased mobility may drive the observed association of minimum daily temperature on COVID-19 deaths.


Subject(s)
COVID-19/mortality , Weather , Air Pollutants/analysis , COVID-19/prevention & control , Humans , Models, Theoretical , Ozone/analysis , Physical Distancing , SARS-CoV-2 , Temperature , United States/epidemiology
18.
Clin Infect Dis ; 73(2): e513-e522, 2021 07 15.
Article in English | MEDLINE | ID: covidwho-1493765

ABSTRACT

BACKGROUND: For pediatric pneumonia, the meteorological and air pollution indicators have been frequently investigated for their association with viral circulation but not for their impact on disease severity. METHODS: We performed a 10-year prospective, observational study in 1 hospital in Chongqing, China, to recruit children with pneumonia. Eight commonly seen respiratory viruses were tested. Autoregressive distributed lag (ADL) and random forest (RF) models were used to fit monthly detection rates of each virus at the population level and to predict the possibility of severe pneumonia at the individual level, respectively. RESULTS: Between 2009 and 2018, 6611 pediatric pneumonia patients were included, and 4846 (73.3%) tested positive for at least 1 respiratory virus. The patient median age was 9 months (interquartile range, 4‒20). ADL models demonstrated a decent fitting of detection rates of R2 > 0.7 for respiratory syncytial virus, human rhinovirus, parainfluenza virus, and human metapneumovirus. Based on the RF models, the area under the curve for host-related factors alone was 0.88 (95% confidence interval [CI], .87‒.89) and 0.86 (95% CI, .85‒.88) for meteorological and air pollution indicators alone and 0.62 (95% CI, .60‒.63) for viral infections alone. The final model indicated that 9 weather and air pollution indicators were important determinants of severe pneumonia, with a relative contribution of 62.53%, which is significantly higher than respiratory viral infections (7.36%). CONCLUSIONS: Meteorological and air pollution predictors contributed more to severe pneumonia in children than did respiratory viruses. These meteorological data could help predict times when children would be at increased risk for severe pneumonia and when interventions, such as reducing outdoor activities, may be warranted.


Subject(s)
Air Pollution , Pneumonia , Respiratory Syncytial Virus, Human , Respiratory Tract Infections , Virus Diseases , Air Pollution/adverse effects , Air Pollution/analysis , Child , China/epidemiology , Humans , Infant , Pneumonia/epidemiology , Pneumonia/etiology , Prospective Studies , Weather
19.
BMC Infect Dis ; 21(1): 1101, 2021 Oct 26.
Article in English | MEDLINE | ID: covidwho-1486063

ABSTRACT

BACKGROUND: The survival of coronaviruses are influenced by weather conditions and seasonal coronaviruses are more common in winter months. We examine the seasonality of respiratory infections in England and Wales and the associations between weather parameters and seasonal coronavirus cases. METHODS: Respiratory virus disease data for England and Wales between 1989 and 2019 was extracted from the Second-Generation Surveillance System (SGSS) database used for routine surveillance. Seasonal coronaviruses from 2012 to 2019 were compared to daily average weather parameters for the period before the patient's specimen date with a range of lag periods. RESULTS: The seasonal distribution of 985,524 viral infections in England and Wales (1989-2019) showed coronavirus infections had a similar seasonal distribution to influenza A and bocavirus, with a winter peak between weeks 2 to 8. Ninety percent of infections occurred where the daily mean ambient temperatures were below 10 °C; where daily average global radiation exceeded 500 kJ/m2/h; where sunshine was less than 5 h per day; or where relative humidity was above 80%. Coronavirus infections were significantly more common where daily average global radiation was under 300 kJ/m2/h (OR 4.3; CI 3.9-4.6; p < 0.001); where average relative humidity was over 84% (OR 1.9; CI 3.9-4.6; p < 0.001); where average air temperature was below 10 °C (OR 6.7; CI 6.1-7.3; p < 0.001) or where sunshine was below 4 h (OR 2.4; CI 2.2-2.6; p < 0.001) when compared to the distribution of weather values for the same time period. Seasonal coronavirus infections in children under 3 years old were more frequent at the start of an annual epidemic than at the end, suggesting that the size of the susceptible child population may be important in the annual cycle. CONCLUSIONS: The dynamics of seasonal coronaviruses reflect immunological, weather, social and travel drivers of infection. Evidence from studies on different coronaviruses suggest that low temperature and low radiation/sunlight favour survival. This implies a seasonal increase in SARS-CoV-2 may occur in the UK and countries with a similar climate as a result of an increase in the R0 associated with reduced temperatures and solar radiation. Increased measures to reduce transmission will need to be introduced in winter months for COVID-19.


Subject(s)
COVID-19 , Respiratory Tract Infections , Child , Child, Preschool , Humans , Respiratory Tract Infections/epidemiology , SARS-CoV-2 , Seasons , Weather
20.
Proc Natl Acad Sci U S A ; 118(43)2021 10 26.
Article in English | MEDLINE | ID: covidwho-1475568

ABSTRACT

Fire is a common ecosystem process in forests and grasslands worldwide. Increasingly, ignitions are controlled by human activities either through suppression of wildfires or intentional ignition of prescribed fires. The southeastern United States leads the nation in prescribed fire, burning ca. 80% of the country's extent annually. The COVID-19 pandemic radically changed human behavior as workplaces implemented social-distancing guidelines and provided an opportunity to evaluate relationships between humans and fire as fire management plans were postponed or cancelled. Using active fire data from satellite-based observations, we found that in the southeastern United States, COVID-19 led to a 21% reduction in fire activity compared to the 2003 to 2019 average. The reduction was more pronounced for federally managed lands, up to 41% below average compared to the past 20 y (38% below average compared to the past decade). Declines in fire activity were partly affected by an unusually wet February before the COVID-19 shutdown began in mid-March 2020. Despite the wet spring, the predicted number of active fire detections was still lower than expected, confirming a COVID-19 signal on ignitions. In addition, prescribed fire management statistics reported by US federal agencies confirmed the satellite observations and showed that, following the wet February and before the mid-March COVID-19 shutdown, cumulative burned area was approaching record highs across the region. With fire return intervals in the southeastern United States as frequent as 1 to 2 y, COVID-19 fire impacts will contribute to an increasing backlog in necessary fire management activities, affecting biodiversity and future fire danger.


Subject(s)
COVID-19/prevention & control , Pandemics , Physical Distancing , SARS-CoV-2 , Wildfires/prevention & control , Biodiversity , COVID-19/epidemiology , Droughts/statistics & numerical data , Ecosystem , Forests , Human Activities , Humans , Models, Statistical , Pandemics/prevention & control , Southeastern United States/epidemiology , Weather , Wildfires/statistics & numerical data
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