Meteorological conditions are heterogeneous factors for COVID-19 risk in China

Geospatial analysis in the United States reveals the changing roles of temperature on COVID-19 transmission

Environmental factors are known to affect outbreak patterns of infectious disease, but their impacts on the spread of COVID-19 along with the evolution of this relationship over time intervals and in different regions are unclear. This study utilized 3 years of data on COVID-19 cases in the continental United States from 2020 to 2022 and the corresponding weather data. We used regression analysis to investigate weather impacts on COVID-19 spread in the mainland United States and estimate the changes of these impacts over space and time. Temperature exhibited a significant and moderately strong negative correlation for most of the US while relative humidity and precipitation experienced mixed relationships. By regressing temperature factors with the spreading rate of waves, we found temperature change can explain over 20% of the spatial-temporal variation in the COVID-19 spreading, with a significant and negative response between temperature change and spreading rate. The pandemic in the continental United States during 2020-2022 was characterized by seven waves, with different transmission rates and wave peaks concentrated in seven time periods. When repeating the analysis for waves in the seven periods and nine climate zones, we found temperature impacts evolve over time and space, possibly due to virus mutation, changes in population susceptibility, social behavior, and control measures. Temperature impacts became weaker in 6 of 9 climate zones from the beginning of the epidemic to the end of 2022, suggesting that COVID-19 has increasingly adapted to wider weather conditions.

Peculiar weather patterns effects on air pollution and COVID-19 spread in Tokyo metropolis

As a pandemic hotspot in Japan, between March 1, 2020-October 1, 2022, Tokyo metropolis experienced seven COVID-19 waves. Motivated by the high rate of COVID-19 incidence and mortality during the seventh wave, and environmental/health challenges we conducted a time-series analysis to investigate the long-term interaction of air quality and climate variability with viral pandemic in Tokyo. Through daily time series geospatial and observational air pollution/climate data, and COVID-19 incidence and death cases, this study compared the environmental conditions during COVID-19 multiwaves. In spite of five State of Emergency (SOEs) restrictions associated with COVID-19 pandemic, during (2020-2022) period air quality recorded low improvements relative to (2015-2019) average annual values, namely: Aerosol Optical Depth increased by 9.13% in 2020 year, and declined by 6.64% in 2021, and 12.03% in 2022; particulate matter PM2.5 and PM10 decreased during 2020, 2021, and 2022 years by 10.22%, 62.26%, 0.39%, and respectively by 4.42%, 3.95%, 5.76%. For (2021-2022) period the average ratio of PM2.5/PM10 was (0.319 ± 0.1640), showing a higher contribution to aerosol loading of traffic-related coarse particles in comparison with fine particles. The highest rates of the daily recorded COVID-19 incidence and death cases in Tokyo during the seventh COVID-19 wave (1 July 2022-1 October 2022) may be attributed to accumulation near the ground of high levels of air pollutants and viral pathogens due to: 1) peculiar persistent atmospheric anticyclonic circulation with strong positive anomalies of geopotential height at 500 hPa; 2) lower levels of Planetary Boundary Layer (PBL) heights; 3) high daily maximum air temperature and land surface temperature due to the prolonged heat waves (HWs) in summer 2022; 4) no imposed restrictions. Such findings can guide public decision-makers to design proper strategies to curb pandemics under persistent stable anticyclonic weather conditions and summer HWs in large metropolitan areas.

The influence of meteorological factors on COVID-19 spread in Italy during the first and second wave

The relation between meteorological factors and COVID-19 spread remains uncertain, particularly with regard to the role of temperature, relative humidity and solar ultraviolet (UV) radiation. To assess this relation, we investigated disease spread within Italy during 2020. The pandemic had a large and early impact in Italy, and during 2020 the effects of vaccination and viral variants had not yet complicated the dynamics. We used non-linear, spline-based Poisson regression of modeled temperature, UV and relative humidity, adjusting for mobility patterns and additional confounders, to estimate daily rates of COVID-19 new cases, hospital and intensive care unit admissions, and deaths during the two waves of the pandemic in Italy during 2020. We found little association between relative humidity and COVID-19 endpoints in both waves, whereas UV radiation above 40 kJ/m² showed a weak inverse association with hospital and ICU admissions in the first wave, and a stronger relation with all COVID-19 endpoints in the second wave. Temperature above 283 K (10 °C/50 °F) showed a strong non-linear negative relation with COVID-19 endpoints, with inconsistent relations below this cutpoint in the two waves. Given the biological plausibility of a relation between temperature and COVID-19, these data add support to the proposition that temperature above 283 K, and possibly high levels of solar UV radiation, reduced COVID-19 spread.

Evolution of the COVID-19 Pandemic after the Introduction of School Referral Nurses in the Province of Granada—A Descriptive Study

The aim of this research is to describe the evolution of the pandemic in a school context, following the introduction of school nurses into the educational setting. Background: The first wave of COVID-19 in Spain prevented social interaction by imposing lockdowns on the population. All non-essential activities, including face-to-face education, were interrupted, affecting the school-aged population during the second and third trimester of the 2019/2020 school year. Schools are places where prevention, identification and case management measures should be rapidly implemented. Methods: This is a prospective and descriptive study using a quantitative method to study the school population of Granada and its province during the school year 2020/2021, from September 2020 to May 2021. The study participants were all schools under the jurisdiction of the Territorial Delegation of Education of Granada, whether public, private, state-subsidised or charter schools, including all educational stages. Results: The confirmation rate in Granada city (11.2%), in contrast to the Andalusian average (6.9%), places Granada as the province with the highest incidence rate. The infection rates among teachers show the same confirmation rate as the general population of Granada (9%); however, among students this rate is lower (7%). There is a higher incidence of outbreaks taking place outside school and a lower incidence of outbreaks occurring within the school environment. Both partial and total outbreaks are more frequent in early childhood education. Conclusion: The early identification and management of reported suspected cases of COVID-19 in schools is proving effective in preventing infection in the school population, achieving good pandemic control in schools, and reducing the number of outbreaks and people affected. Schools have been confirmed to be safe. Establishing screening for asymptomatic schoolchildren could be a tool to improve control in schools.

Effects of meteorological factors and air pollutants on the incidence of COVID-19 in South Korea

Air pollution and meteorological factors can exacerbate susceptibility to respiratory viral infections. To establish appropriate prevention and intervention strategies, it is important to determine whether these factors affect the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Therefore, this study examined the effects of sunshine, temperature, wind, and air pollutants including sulfur dioxide (SO₂), carbon monoxide (CO), ozone (O₃), nitrogen dioxide (NO₂), particulate matter ≤2.5 μm (PM_2.5), and particulate matter ≤10 μm (PM₁₀) on the age-standardized incidence ratio of coronavirus disease (COVID-19) in South Korea between January 2020 and April 2020. Propensity score weighting was used to randomly select observations into groups according to whether the case was cluster-related, to reduce selection bias. Multivariable logistic regression analyses were used to identify factors associated with COVID-19 incidence. Age 60 years or over (odds ratio [OR], 1.29; 95% CI, 1.24-1.35), exposure to ambient air pollutants, especially SO₂ (OR, 5.19; 95% CI, 1.13-23.9) and CO (OR, 1.17; 95% CI, 1.07-1.27), and non-cluster infection (OR, 1.28; 95% CI, 1.24-1.32) were associated with SARS-CoV-2 infection. To manage and control COVID-19 effectively, further studies are warranted to confirm these findings and to develop appropriate guidelines to minimize SARS-CoV-2 transmission.

The COVID-19 pandemic: Virus transmission and risk assessment

The coronaviruses are the largest known RNA viruses of which SASR-CoV-2 has been spreading continuously due to its repeated mutation triggered by several environmental factors. Multiple human interventions and lessons learned from the SARS 2002 outbreak helped reduce its spread considerably, and thus, the virus was contained but the emerging mutations burdened the medical facility leading to many deaths in the world. As per the world health organization (WHO) droplet mode transmission is the most common mode of SASR-CoV-2 transmission to which environmental factors including temperature and humidity play a major role. This article highlights the responsibility of environmental causes that would affect the distribution and fate of the virus. Recent development in the risk assessment models is also covered in this article.

A methodological proposal to evaluate the health hazard scenario from COVID-19 in Italy

2019 Coronavirus disease (COVID-19) had a big impact in Italy, mainly concentrated in the northern part of the Country. All this was mainly due to similarities of this area with Wuhan in Hubei Province, according to geographical, environmental and socio-economic points of view. The basic hypothesis of this research was that the presence of atmospheric pollutants can generate stress on health conditions of the population and determine pre-conditions for the development of diseases of the respiratory system and complications related to them. In most cases the attention on environmental aspects is mainly concentrated on pollution, neglecting issues such as land management which, in some way, can contribute to reducing the impact of pollution. The reduction of land take and the decrease in the loss of ecosystem services can represent an important aspect in improving environmental quality. In order to integrate policies for environmental change and human health, the main factors analyzed in this paper can be summarized in environmental, climatic and land management. The main aim of this paper was to produce three different hazard scenarios respectively related to environmental, climatic and land management-related factors. A Spatial Analytical Hierarchy Process (AHP) method has been applied over thirteen informative layers grouped in aggregation classes of environmental, climatic and land management. The results of the health hazard maps show a disparity in the distribution of territorial responses to the pandemic in Italy. The environmental components play an extremely relevant role in the definition of the red zones of hazard, with a consequent urgent need to renew sustainable development strategies. The comparison of hazard maps related to different scenarios provides decision makers with tools to orient policy choices with a different degree of priority according to a place-based approach. In particular, the geospatial representation of risks could be a tool for legitimizing the measures chosen by decision-makers, proposing a renewed approach that highlights and takes account of the differences between the spatial contexts to be considered - Regions, Provinces, Municipalities - also in terms of climatic and environmental variables.

Impact analysis of environmental and social factors on early-stage COVID-19 transmission in China by machine learning

As a highly contagious disease, COVID-19 caused a worldwide pandemic and it is still ongoing. However, the infection in China has been successfully controlled although its initial transmission was also nationwide and has caused a serious public health crisis. The analysis on the early-stage COVID-19 transmission in China is worth investigating for its guiding significance on prevention to other countries and regions. In this study, we conducted the experiments from the perspectives of COVID-19 occurrence and intensity. We eliminated unimportant factors from 113 variables and applied four machine learning-based classification and regression models to predict COVID-19 occurrence and intensity, respectively. The influence of each important factor was analysed when applicable. Our optimal model on COVID-19 occurrence prediction presented an accuracy of 91.91% and the best R2 of intensity prediction reached 0.778. Linear regression-based model was identified as unable to fit and predict the intensity, and thus only the variable influence on COVID-19 occurrence can be explained. We found that (1) CO VID-19 was more likely to occur in prosperous cities closer to the epicentre and located on higher altitudes, (2) and the occurrence was higher under extreme weather and high minimum relative humidity. (3) Most air pollutants increased the risk of COVID-19 occurrence except NO2 and O3, and there existed a lag effect of 6-7 days. (4) NPIs (non-pharmaceutical interventions) did not show apparent effect until two weeks after.

COVID-19 seasonality in temperate countries

INTRODUCTION

While the beneficial effect of vaccination, restrictive measures, and social distancing in reducing mortality due to SARS-CoV-2 is intuitive and taken for granted, seasonality (predictable fluctuation or pattern that recurs or repeats over a one-year period) is still poorly understood and insufficiently taken into consideration. We aimed to examine SARS-CoV-2 seasonality in countries with temperate climate.

METHODS

We identified countries with temperate climate and extracted average country temperature data from the National Center for Environmental information and from the Climate Change Knowledge Portal. We obtained mortality and vaccination rates from an open access database. We used the stringency index derived from the Oxford COVID-19 Government Response Tracker to quantify restriction policies. We used Spearman's and rank-correlation non-parametric test coefficients to investigate the association between COVID-19 mortality and temperature values. We employed multivariate regression models to analyze how containment measures, vaccinations, and monthly temperatures affected COVID-19 mortality rates.

RESULTS

The time series for daily deaths per million inhabitants and average monthly temperatures of European countries and US states with a temperate climate had a negative correlation (p < 0.0001 for all countries, 0.40 < R < 0.86). When running multivariate regression models with country fixed effects, we noted that mortality rates were significantly lower when temperature were higher. Interestingly, when adding an interaction term between monthly temperatures and vaccination rates, we found that as monthly temperatures dropped, the effect of the vaccination campaign on mortality was larger than at higher temperatures.

DISCUSSION

Deaths attributed to SARS-CoV-2 decreased during the summer period in temperate countries. We found that the effect of vaccination rates on mortality was stronger when temperatures were lower. Stakeholders should consider seasonality in managing SARS-CoV-2 and future pandemics to minimize mortality, limit the pressure on hospitals and intensive care units while maintaining economic and social activities.

Assessing the impact of air pollution and climate seasonality on COVID-19 multiwaves in Madrid, Spain

While the COVID-19 pandemic is still in progress, being under the fifth COVID-19 wave in Madrid, over more than one year, Spain experienced a four wave pattern. The transmission of SARS-CoV-2 pathogens in Madrid metropolitan region was investigated from an urban context associated with seasonal variability of climate and air pollution drivers. Based on descriptive statistics and regression methods of in-situ and geospatial daily time series data, this study provides a comparative analysis between COVID-19 waves incidence and mortality cases in Madrid under different air quality and climate conditions. During analyzed period 1 January 2020-1 July 2021, for each of the four COVID-19 waves in Madrid were recorded anomalous anticyclonic synoptic meteorological patterns in the mid-troposphere and favorable stability conditions for COVID-19 disease fast spreading. As airborne microbial temporal pattern is most affected by seasonal changes, this paper found: 1) a significant negative correlation of air temperature, Planetary Boundary Layer height, and surface solar irradiance with daily new COVID-19 incidence and deaths; 2) a similar mutual seasonality with climate variables of the first and the fourth COVID-waves from spring seasons of 2020 and 2021 years. Such information may help the health decision makers and public plan for the future.

Challenges in the control of COVID-19 outbreaks caused by the delta variant during periods of low humidity: an observational study in Sydney, Australia

Background

Since the appearance of severe acute respiratory coronavirus 2 (SARS-CoV-2) and the coronavirus disease 2019 (COVID-19) pandemic, a growing body of evidence has suggested that weather factors, particularly temperature and humidity, influence transmission. This relationship might differ for the recently emerged B.1.617.2 (delta) variant of SARS-CoV-2. Here we use data from an outbreak in Sydney, Australia that commenced in winter and time-series analysis to investigate the association between reported cases and temperature and relative humidity.

Methods

Between 16 June and 10 September 2021, the peak of the outbreak, there were 31,662 locally-acquired cases reported in five local health districts of Sydney, Australia. The associations between daily 9:00 am and 3:00 pm temperature (°C), relative humidity (%) and their difference, and a time series of reported daily cases were assessed using univariable and multivariable generalized additive models and a 14-day exponential moving average. Akaike information criterion (AIC) and the likelihood ratio statistic were used to compare different models and determine the best fitting model. A sensitivity analysis was performed by modifying the exponential moving average.

Results

During the 87-day time-series, relative humidity ranged widely (< 30–98%) and temperatures were mild (approximately 11–17 °C). The best-fitting (AIC: 1,119.64) generalized additive model included 14-day exponential moving averages of 9:00 am temperature (P < 0.001) and 9:00 am relative humidity (P < 0.001), and the interaction between these two weather variables (P < 0.001). Humidity was negatively associated with cases no matter whether temperature was high or low. The effect of lower relative humidity on increased cases was more pronounced below relative humidity of about 70%; below this threshold, not only were the effects of humidity pronounced but also the relationship between temperature and cases of the delta variant becomes apparent.

Conclusions

We suggest that the control of COVID-19 outbreaks, specifically those due to the delta variant, is particularly challenging during periods of the year with lower relative humidity and warmer temperatures. In addition to vaccination, stronger implementation of other interventions such as mask-wearing and social distancing might need to be considered during these higher risk periods.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s40249-021-00926-0.