Short-term effects of specific humidity and temperature on COVID-19 morbidity in select US cities

The effects of meteorological factors on the COVID-19 omicron variant in Bangladesh

The COVID-19 omicron variant is exceptionally complicated and uncertain due to its rapid transmission and volume of infections. This study examines the impact of climatic factors on daily confirmed cases of COVID-19 omicron variant in Bangladesh. The secondary data of daily confirmed cases from 1 January 2022, to 31 March 2022, of eight distinct geographic divisions have been used for the current study. The multivariate generalized linear negative binomial regression model was applied to determine the effects of climatic factors on omicron transmission. The model revealed that the maximum temperature (Odds: 0.67, p < 0.05), sky clearness (Odds: 0.05, p < 0.05), wind speed (Odds: 0.76, p < 0.05), relative humidity (Odds: 1.02, p < 0.05), and air pressure (Odds: 0.27, p < 0.05) significantly impacted COVID-19 omicron transmission in Bangladesh. The study's findings can assist the concerned authorities and decision-makers take necessary measures to control the spread of omicron cases in Bangladesh.

The application of simulation methods during the COVID-19 pandemic: A scoping review

With the outbreak of COVID-19 pandemic, simulation modelling approaches have become effective tools to simulate the potential effects of different intervention measures and predict the dynamic COVID-19 trends. In this scoping review, Studies published between February 2020 and May 2022 that investigated the spread of COVID-19 using four common simulation modeling methods were systematically reported and summarized. Publication trend, characteristics, software, and code availability of included articles were analyzed. Among the included 340 studies, most articles used agent-based model (ABM; n = 258; 75.9 %), followed by the models of system dynamics (n = 42; 12.4 %), discrete event simulation (n = 25; 7.4 %), and hybrid simulation (n = 15; 4.4 %). Furthermore, our review emphasized the purposes and sample time period of included articles. We classified the purpose of the 340 included studies into five categories, most studies mainly analyzed the spread of COVID-19 under policy interventions. For the sample time period analysis, most included studies analyzed the COVID-19 spread in the second wave. Our findings play a crucial role for policymakers to make evidence-based decisions in preventing the spread of COVID-19 pandemic and help in providing scientific decision-makings resilient to similar events and infectious diseases in the future.

Association of Ambient Temperature and Absolute Humidity with the Effective Reproduction Number of COVID-19 in Japan

This study aimed to quantify the exposure-lag-response relationship between short-term changes in ambient temperature and absolute humidity and the transmission dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Japan. The prefecture-specific daily time-series of newly confirmed cases, meteorological variables, retail and recreation mobility, and Government Stringency Index were collected for all 47 prefectures of Japan for the study period from 15 February 2020 to 15 October 2022. Generalized conditional Gamma regression models were formulated with distributed lag nonlinear models by adopting the case-time-series design to assess the independent and interactive effects of ambient temperature and absolute humidity on the relative risk (RR) of the time-varying effective reproductive number (Rt). With reference to 17.8 °C, the corresponding cumulative RRs (95% confidence interval) at a mean ambient temperatures of 5.1 °C and 27.9 °C were 1.027 (1.016-1.038) and 0.982 (0.974-0.989), respectively, whereas those at an absolute humidity of 4.2 m/g3 and 20.6 m/g3 were 1.026 (1.017-1.036) and 0.995 (0.985-1.006), respectively, with reference to 10.6 m/g3. Both extremely hot and humid conditions synergistically and slightly reduced the Rt. Our findings provide a better understanding of how meteorological drivers shape the complex heterogeneous dynamics of SARS-CoV-2 in Japan.

Natural and socio-environmental factors contribute to the transmissibility of COVID-19: evidence from an improved SEIR model

COVID-19 has ravaged Brazil, and its spread showed spatial heterogeneity. Changes in the environment have been implicated as potential factors involved in COVID-19 transmission. However, considerable research efforts have not elucidated the risk of environmental factors on COVID-19 transmission from the perspective of infectious disease dynamics. The aim of this study is to model the influence of the environment on COVID-19 transmission and to analyze how the socio-ecological factors affecting the probability of virus transmission in 10 states dramatically shifted during the early stages of the epidemic in Brazil. First, this study used a Pearson correlation to analyze the interconnection between COVID-19 morbidity and socio-ecological factors and identified factors with significant correlations as the dominant factors affecting COVID-19 transmission. Then, the time-lag effect of dominant factors on the morbidity of COVID-19 was investigated by constructing a distributed lag nonlinear model and standard two-stage meta-analytic model, and the results were considered in the improved SEIR model. Lastly, a machine learning method was introduced to explore the nonlinear relationship between the environmental propagation probability and socio-ecological factors. By analyzing the impact of environmental factors on virus transmission, it can be found that population mobility directly caused by human activities had a greater impact on virus transmission than temperature and humidity. The heterogeneity of meteorological factors can be accounted for by the diverse climate patterns in Brazil. The improved SEIR model was adopted to explore the interconnection of COVID-19 transmission and the environment, which revealed a new strategy to probe the causal links between them.

Jingfang granules ameliorate inflammation and immune disorders in mice exposed to low temperature and high humidity by restoring the dysregulation of gut microbiota and fecal metabolites

The dramatic changes in global climate on human health have been extremely severe. The immune disorder caused by low temperature and high humidity (LTHH) have become a severe public health issue. Clinically, Jingfang granule (JF) has the effect of dispelling cold and eliminating dampness, and is widely used in the treatment of cold caused by wind and cold, autoimmune diseases, and COVID-19 with cold-dampness stagnating in the lung pattern. Our study aims to elucidate the effect of JF on LTHH-induced immune disorders in mice as well as the underlying mechanisms. In this study, JF increased the spleen index, improved fecal character, repaired the intestinal barrier and alleviated intestinal inflammatory responses. Most importantly, JF ameliorated immune disorder in LTHH mice, which was manifested primarily by the significant increase in gdT, CD8+ Tcm, and CD8+ Tem cells, as well as the decrease in TH1, TH17, CD4+ Tem1, CD4+ Tem2, immature NK, mature NK cells, and M1-like macrophages. Interestingly, the JF treatment not only regulated the gut microbiota by decreasing the abundance of harmful bacteria, as well as up-regulating the abundance of beneficial bacteria, but also ameliorated the metabolic disorders by reversing the levels of fecal metabolites to normality. The results of the correlation analysis demonstrated a significant association among gut microbiota, fecal metabolites and immune cells. In addition, JF inhibited the TLR4/NF-κB/NLRP3 pathway in LTHH mice. In conclusion, our results suggested that JF alleviated inflammation and immune disorders in LTHH mice by restoring gut microbiota and fecal metabolism.

Impact of environmental factors on COVID-19 transmission: spatial variations in the world

The COVID-19 pandemic caused enormous destruction to global health and the economy and has surged worldwide with colossal morbidity and mortality. The pattern of the COVID infection varies in diverse regions of the world based on the variations in the geographic environment. The multivariate generalized linear regression models: zero-inflated negative binomial regression, and the zero-inflated Poisson regression model, have been employed to determine the significant meteorological factors responsible for the spread of the pandemic in different continents. Asia experienced a high COVID-19 infection, and death was extreme in Europe. Relative humidity, air pressure, and wind speed are the salient factors significantly impacting the spread of COVID-19 in Africa. Death due to COVID-19 in Asia is influenced by air pressure, temperature, precipitation, and relative humidity. Air pressure and temperature substantially affect the spread of the pandemic in Europe.

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.

Association between meteorological factors and COVID-19 transmission in low- and middle-income countries: A time-stratified case-crossover study

BACKGROUND

Evidence is limited regarding the association between meteorological factors and COVID-19 transmission in low- and middle-income countries (LMICs).

OBJECTIVE

To investigate the independent and interactive effects of temperature, relative humidity (RH), and ultraviolet (UV) radiation on the spread of COVID-19 in LMICs.

METHODS

We collected daily data on COVID-19 confirmed cases, meteorological factors and non-pharmaceutical interventions (NPIs) in 2143 city- and district-level sites from 6 LMICs during 2020. We applied a time-stratified case-crossover design with distributed lag nonlinear model to evaluate the independent and interactive effects of meteorological factors on COVID-19 transmission after controlling NPIs. We generated an overall estimate through pooling site-specific relative risks (RR) using a multivariate meta-regression model.

RESULTS

There was a positive, non-linear, association between temperature and COVID-19 confirmed cases in all study sites, while RH and UV showed negative non-linear associations. RR of the 90th percentile temperature (28.1 °C) was 1.14 [95% confidence interval (CI): 1.02, 1.28] compared with the 50th percentile temperature (24.4 °C). RR of the10th percentile UV was 1.41 (95% CI: 1.29, 1.54). High temperature and high RH were associated with increased risks in temperate climate but decreased risks in tropical climate, while UV exhibited a consistent, negative association across climate zones. Temperature, RH, and UV interacted to affect COVID-19 transmission. Temperature and RH also showed higher risks in low NPIs sites.

CONCLUSION

Temperature, RH, and UV appeared to independently and interactively affect the transmission of COVID-19 in LMICs but such associations varied with climate zones. Our results suggest that more attention should be paid to meteorological variation when the transmission of COVID-19 is still rampant in LMICs.

Lag-time effects of vaccination on SARS-CoV-2 dynamics in German hospitals and intensive-care units

Background

The Efficacy and effectiveness of vaccination against SARS-CoV-2 have clearly been shown by randomized trials and observational studies. Despite these successes on the individual level, vaccination of the population is essential to relieving hospitals and intensive care units. In this context, understanding the effects of vaccination and its lag-time on the population-level dynamics becomes necessary to adapt the vaccination campaigns and prepare for future pandemics.

Methods

This work applied a quasi-Poisson regression with a distributed lag linear model on German data from a scientific data platform to quantify the effects of vaccination and its lag times on the number of hospital and intensive care patients, adjusting for the influences of non-pharmaceutical interventions and their time trends. We separately evaluated the effects of the first, second and third doses administered in Germany.

Results

The results revealed a decrease in the number of hospital and intensive care patients for high vaccine coverage. The vaccination provides a significant protective effect when at least approximately 40% of people are vaccinated, whatever the dose considered. We also found a time-delayed effect of the vaccination. Indeed, the effect on the number of hospital patients is immediate for the first and second doses while for the third dose about 15 days are necessary to have a strong protective effect. Concerning the effect on the number of intensive care patients, a significant protective response was obtained after a lag time of about 15-20 days for the three doses. However, complex time trends, e.g. due to new variants, which are independent of vaccination make the detection of these findings challenging.

Conclusion

Our results provide additional information about the protective effects of vaccines against SARS-CoV-2; they are in line with previous findings and complement the individual-level evidence of clinical trials. Findings from this work could help public health authorities efficiently direct their actions against SARS-CoV-2 and be well-prepared for future pandemics.

Impact of non-pharmaceutical interventions, weather, vaccination, and variants on COVID-19 transmission across departments in France

BACKGROUND

Multiple factors shape the temporal dynamics of the COVID-19 pandemic. Quantifying their relative contributions is key to guide future control strategies. Our objective was to disentangle the individual effects of non-pharmaceutical interventions (NPIs), weather, vaccination, and variants of concern (VOC) on local SARS-CoV-2 transmission.

METHODS

We developed a log-linear model for the weekly reproduction number (R) of hospital admissions in 92 French metropolitan departments. We leveraged (i) the homogeneity in data collection and NPI definitions across departments, (ii) the spatial heterogeneity in the timing of NPIs, and (iii) an extensive observation period (14 months) covering different weather conditions, VOC proportions, and vaccine coverage levels.

FINDINGS

Three lockdowns reduced R by 72.7% (95% CI 71.3-74.1), 70.4% (69.2-71.6) and 60.7% (56.4-64.5), respectively. Curfews implemented at 6/7 pm and 8/9 pm reduced R by 34.3% (27.9-40.2) and 18.9% (12.04-25.3), respectively. School closures reduced R by only 4.9% (2.0-7.8). We estimated that vaccination of the entire population would have reduced R by 71.7% (56.4-81.6), whereas the emergence of VOC (mainly Alpha during the study period) increased transmission by 44.6% (36.1-53.6) compared with the historical variant. Winter weather conditions (lower temperature and absolute humidity) increased R by 42.2% (37.3-47.3) compared to summer weather conditions. Additionally, we explored counterfactual scenarios (absence of VOC or vaccination) to assess their impact on hospital admissions.

INTERPRETATION

Our study demonstrates the strong effectiveness of NPIs and vaccination and quantifies the role of weather while adjusting for other confounders. It highlights the importance of retrospective evaluation of interventions to inform future decision-making.

The non-linear and interactive effects of meteorological factors on the transmission of COVID-19: A panel smooth transition regression model for cities across the globe

The ongoing pandemic created by COVID-19 has co-existed with humans for some time now, thus resulting in unprecedented disease burden. Previous studies have demonstrated the non-linear and single effects of meteorological factors on viral transmission and have a question of how to exclude the influence of unrelated confounding factors on the relationship. However, the interactions involved in such relationships remain unclear under complex weather conditions. Here, we used a panel smooth transition regression (PSTR) model to investigate the non-linear interactive impact of meteorological factors on daily new cases of COVID-19 based on a panel dataset of 58 global cities observed between Jul 1, 2020 and Jan 13, 2022. This new approach offers a possibility of assessing interactive effects of meteorological factors on daily new cases and uses fixed effects to control other unrelated confounding factors in a panel of cities. Our findings revealed that an optimal temperature range (0°C-20 °C) for the spread of COVID-19. The effect of RH (relative humidity) and DTR (diurnal temperature range) on infection became less positive (coefficient: 0.0427 to -0.0142; p < 0.05) and negative (coefficient: -0.0496 to -0.0248; p < 0.05) with increasing average temperature(T). The highest risk of infection occurred when the temperature was -10 °C and RH was >80% or when the temperature was 10 °C and DTR was 1 °C. Our findings highlight useful implications for policymakers and the general public.

Epidemiological geography at work: An exploratory review about the overall findings of spatial analysis applied to the study of CoViD-19 propagation along the first pandemic year

The present work aims to give an overview on the international scientific papers related to the territorial spreading of SARS-CoV-2, with a specific focus upon applied quantitative geography and territorial analysis, to define a general structure for epidemiological geography research. The target publications were based on GIS spatial analysis, both in the sense of topological analysis and descriptive statistics or lato sensu geographical approaches. The first basic purpose was to organize and enhance the vast knowledge developments generated hitherto by the first pandemic that was studied "on-the-fly" all over the world. The consequent target was to investigate to what extent researchers in geography were able to draw scientifically consistent conclusions about the pandemic evolution, as well as whether wider generalizations could be reasonably claimed. This implied an analysis and a comparison of their findings. Finally, we tested what geographic approaches can say about the pandemic and whether a reliable spatial analysis routine for mapping infectious diseases could be extrapolated. We selected papers proposed for publication during 2020 and 209 articles complied with our parameters of query. The articles were divided in seven categories to enhance existing commonalities. In some cases, converging conclusions were extracted, and generalizations were derived. In other cases, contrasting or inconsistent findings were found, and possible explanations were provided. From the results of our survey, we extrapolated a routine for the production of epidemiological geography analyses, we highlighted the different steps of investigation that were attained, and we underlined the most critical nodes of the methodology. Our findings may help to point out what are the most critical conceptual challenges of epidemiological mapping, and where it might improve to engender informed conclusions and aware outcomes.

What are the Effects of Climate Variables on COVID-19 Pandemic? A Systematic Review and Current Update

The climatological parameters can be different in various geographical locations. Moreover, they have possible impacts on COVID-19 incidence. Therefore, the purpose of this systematic review article was to describe the effects of climatic variables on COVID-19 pandemic in different countries. Systematic literature search was performed in Scopus, ISI Web of Science, and PubMed databases using ("Climate" OR "Climate Change" OR "Global Warming" OR "Global Climate Change" OR "Meteorological Parameters" OR "Temperature" OR "Precipitation" OR "Relative Humidity" OR "Wind Speed" OR "Sunshine" OR "Climate Extremes" OR "Weather Extremes") AND ("COVID" OR "Coronavirus disease 2019" OR "COVID-19" OR "SARS-CoV-2" OR "Novel Coronavirus") keywords. From 5229 articles, 424 were screened and 149 were selected for further analysis. The relationship between meteorological parameters is variable in different geographical locations. The results indicate that among the climatic indicators, the temperature is the most significant factor that influences on COVID-19 pandemic in most countries. Some studies were proved that warm and wet climates can decrease COVID-19 incidence; however, the other studies represented that warm location can be a high risk of COVID-19 incidence. It could be suggested that all climate variables such as temperature, humidity, rainfall, precipitation, solar radiation, ultraviolet index, and wind speed could cause spread of COVID-19. Thus, it is recommended that future studies will survey the role of all meteorological variables and interaction between them on COVID-19 spread in specific small areas such as cities of each country and comparison between them.

How do temperature, humidity, and air saturation state affect the COVID-19 transmission risk?

Environmental parameters have a significant impact on the spread of respiratory viral diseases (temperature (T), relative humidity (RH), and air saturation state). T and RH are strongly correlated with viral inactivation in the air, whereas supersaturated air can promote droplet deposition in the respiratory tract. This study introduces a new concept, the dynamic virus deposition ratio (α), that reflects the dynamic changes in viral inactivation and droplet deposition under varying ambient environments. A non-steady-state-modified Wells-Riley model is established to predict the infection risk of shared air space and highlight the high-risk environmental conditions. Findings reveal that a rise in T would significantly reduce the transmission of COVID-19 in the cold season, while the effect is not significant in the hot season. The infection risk under low-T and high-RH conditions, such as the frozen seafood market, is substantially underestimated, which should be taken seriously. The study encourages selected containment measures against high-risk environmental conditions and cross-discipline management in the public health crisis based on meteorology, government, and medical research.

Impact of Selected Meteorological Factors on COVID-19 Incidence in Southern Finland during 2020-2021

We modelled the impact of selected meteorological factors on the daily number of new cases of the coronavirus disease 2019 (COVID-19) at the Hospital District of Helsinki and Uusimaa in southern Finland from August 2020 until May 2021. We applied a DLNM (distributed lag non-linear model) with and without various environmental and non-environmental confounding factors. The relationship between the daily mean temperature or absolute humidity and COVID-19 morbidity shows a non-linear dependency, with increased incidence of COVID-19 at low temperatures between 0 to -10 °C or at low absolute humidity (AH) values below 6 g/m³. However, the outcomes need to be interpreted with caution, because the associations found may be valid only for the study period in 2020-2021. Longer study periods are needed to investigate whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a seasonal pattern similar such as influenza and other viral respiratory infections. The influence of other non-environmental factors such as various mitigation measures are important to consider in future studies. Knowledge about associations between meteorological factors and COVID-19 can be useful information for policy makers and the education and health sector to predict and prepare for epidemic waves in the coming winters.

The effect of temperature, humidity, precipitation and cloud coverage on the risk of COVID-19 infection in temperate regions of the USA—A case-crossover study

Background

Observations based on the spread of SARS-CoV-2 early into the COVID-19 pandemic have suggested a reduced burden in tropical regions leading to the assumption of a dichotomy between cold and dry and wet and warm climates.

Objectives

Analyzing more than a whole year of COVID-19 infection data, this study intents to refine the understanding of meteorological variables (temperature, humidity, precipitation and cloud coverage) on COVID-19 transmission in settings that experience distinct seasonal changes.

Methods and findings

A time stratified case-crossover design was adopted with a conditional Poisson model in combination with a distributed lag nonlinear model to assess the short-term impact of mentioned meteorological factors on COVID-19 infections in five US study sites (New York City (NYC); Marion County, Indiana (MCI); Baltimore and Baltimore County, Maryland (BCM); Franklin County, Ohio (FCO); King County, Washington (KCW)). Higher-than-average temperatures were consistently associated with a decreased relative risk (RR) of COVID-19 infection in four study sites. At 20 degrees Celsius COVID-19 infection was associated with a relative risk of 0.35 (95%CI: 0.20–0.60) in NYC, 1.03 (95%CI:0.57–1.84) in MCI, 0.34 (95%CI: 0.20–0.57) in BCM, 0.52 (95%CI: 0.31–0.87) in FCO and 0.21 (95%CI: 0.10–0.44) in KCW. Higher-than-average humidity levels were associated with an increased relative risk of COVID-19 infection in four study sites. Relative to their respective means, at a humidity level of 15 g/kg (specific humidity) the RR was 5.83 (95%CI: 2.05–16.58) in BCM, at a humidity level of 10 g/kg the RR was 3.44 (95%CI: 1.95–6.01) in KCW.

Conclusions

The results of this study suggest opposed effects for higher-than-average temperature and humidity concerning the risk of COVID-19 infection. While a distinct seasonal pattern of COVID-19 has not yet emerged, warm and humid weather should not be generally regarded as a time of reduced risk of COVID-19 infections.

An assessment of meteorological parameters effects on COVID-19 pandemic in Bangladesh using machine learning models

Coronavirus (COVID-19) is a highly contagious virus (SARS-CoV-2) that has caused a global pandemic since January 2020. Scientists around the world are doing extensive research to control this disease. They are working tirelessly to find out the origin and causes of the disease. Several studies and experiments mentioned that there are some meteorological parameters which are highly correlated with COVID-19 transmission. In this work, we studied the effects of 11 meteorological parameters on the transmission of COVID-19 in Bangladesh. We first applied statistical analysis and observed that there is no significant effect of these parameters. Therefore, we proposed a novel technique to analyze the insight effects of these parameters by using a combination of Random Forest, CART, and Lasso feature selection techniques. We observed that 4 parameters are highly influential for COVID-19 where [Formula: see text] and Cloud have positive association whereas WS and AQ have negative impact. Among them, Cloud has the highest positive impact which is 0.063 and WS has the highest negative association which is [Formula: see text]. Moreover, we have validated our performance using DLNM technique. The result of this investigation can be used to develop an alert system that will assist the policymakers to know the characteristics of COVID-19 against meteorological parameters and can impose different policies based on the weather conditions.

Exposure-lag response of air temperature on COVID-19 incidence in twelve Italian cities: A meta-analysis

The exposure-lag response of air temperature on daily COVID-19 incidence is unclear and there have been concerns regarding the robustness of previous studies. Here we present an analysis of high spatial and temporal resolution using the distributed lag non-linear modelling (DLNM) framework. Utilising nearly two years' worth of data, we fit statistical models to twelve Italian cities to quantify the delayed effect of air temperature on daily COVID-19 incidence, accounting for several categories of potential confounders (meteorological, air quality and non-pharmaceutical interventions). Coefficients and covariance matrices for the temperature term were then synthesised using random effects meta-analysis to yield pooled estimates of the exposure-lag response with effects presented as the relative risk (RR) and cumulative RR (RR_cum). The cumulative exposure response curve was non-linear, with peak risk at 15.1 °C and declining risk at progressively lower and higher temperatures. The lowest RR_cum at 0.2 °C is 0.72 [0.56,0.91] times that of the highest risk. Due to this non-linearity, the shape of the lag response curve necessarily varied by temperature. This work suggests that on a given day, air temperature approximately 15 °C maximises the incidence of COVID-19, with the effects distributed in the subsequent ten days or more.

Lagged meteorological impacts on COVID-19 incidence among high-risk counties in the United States-a spatiotemporal analysis

BACKGROUND

The associations between meteorological factors and coronavirus disease 2019 (COVID-19) have been discussed globally; however, because of short study periods, the lack of considering lagged effects, and different study areas, results from the literature were diverse and even contradictory.

OBJECTIVE

The primary purpose of this study is to conduct more reliable research to evaluate the lagged meteorological impacts on COVID-19 incidence by considering a relatively long study period and diversified high-risk areas in the United States.

METHODS

This study adopted the distributed lagged nonlinear model with a spatial function to analyze COVID-19 incidence predicted by multiple meteorological measures from March to October of 2020 across 203 high-risk counties in the United States. The estimated spatial function was further smoothed within the entire continental United States by the biharmonic spline interpolation.

RESULTS

Our findings suggest that the maximum temperature, minimum relative humidity, and precipitation were the best meteorological predictors. Most significantly positive associations were found from 3 to 11 lagged days in lower levels of each selected meteorological factor. In particular, a significantly positive association appeared in minimum relative humidity higher than 88.36% at 5-day lag. The spatial analysis also shows excessive risks in the north-central United States.

SIGNIFICANCE

The research findings can contribute to the implementation of early warning surveillance of COVID-19 by using weather forecasting for up to two weeks in high-risk counties.

Exposure-lag response of air temperature on COVID-19 incidence in twelve Italian cities: A meta-analysis

The exposure-lag response of air temperature on daily COVID-19 incidence is unclear and there have been concerns regarding the robustness of previous studies. Here we present an analysis of high spatial and temporal resolution using the distributed lag non-linear modelling (DLNM) framework. Utilising nearly two years' worth of data, we fit statistical models to twelve Italian cities to quantify the delayed effect of air temperature on daily COVID-19 incidence, accounting for several categories of potential confounders (meteorological, air quality and non-pharmaceutical interventions). Coefficients and covariance matrices for the temperature term were then synthesised using random effects meta-analysis to yield pooled estimates of the exposure-lag response with effects presented as the relative risk (RR) and cumulative RR (RR_cum). The cumulative exposure response curve was non-linear, with peak risk at 15.1 °C and declining risk at progressively lower and higher temperatures. The lowest RR_cum at 0.2 °C is 0.72 [0.56,0.91] times that of the highest risk. Due to this non-linearity, the shape of the lag response curve necessarily varied by temperature. This work suggests that on a given day, air temperature approximately 15 °C maximises the incidence of COVID-19, with the effects distributed in the subsequent ten days or more.

The role of remote sensing during a global disaster: COVID-19 pandemic as case study

Remotely sensed imagery is used as a tool to aid decision makers and scientists in a variety of fields. A recent world event in which satellite imagery was extensively relied on by a variety of stakeholders was the COVID-19 pandemic. In this article we aim to give an overview of the types of information offered through remote sensing (RS) to help address different issues related to the pandemic. We also discuss about the stakeholders that benefited from the data, and the value added by its availability. The content is presented under four sub-sections; namely (1) the use of RS in real-time decision-making and strategic planning during the pandemic; how RS revealed the (2) environmental changes and (3) social and economic impacts caused by the pandemic. And (4) how RS informed our understanding of the epidemiology of SARS-CoV-2, the pathogen responsible for the pandemic. High resolution optical imagery offered updated on-the-ground data for e.g., humanitarian aid organizations, and informed operational decision making of shipping companies. Change in the intensity of air and water pollution after reduced anthropogenic activities around the world were captured by remote sensing - supplying concrete evidence that can help inform improved environmental policy. Several economic indicators were measured from satellite imagery, showing the spatiotemporal component of economic impacts caused by the global pandemic. Finally, satellite based meteorological data supported epidemiological studies of environmental disease determinants. The varied use of remote sensing during the COVID-19 pandemic affirms the value of this technology to society, especially in times of large-scale disasters.

Analyzing the research trends of COVID-19 using topic modeling approach

Purpose

The COVID-19 pandemic has impacted 222 countries across the globe, with millions of people losing their lives. The threat from the virus may be assessed from the fact that most countries across the world have been forced to order partial or complete shutdown of their economies for a period of time to contain the spread of the virus. The fallout of this action manifested in loss of livelihood, migration of the labor force and severe impact on mental health due to the long duration of confinement to homes or residences.

Design/methodology/approach

The current study identifies the focus areas of the research conducted on the COVID-19 pandemic. Abstracts of papers on the subject were collated from the SCOPUS database for the period December 2019 to June 2020. The collected sample data (after preprocessing) was analyzed using Topic Modeling with Latent Dirichlet Allocation.

Findings

Based on the research papers published within the mentioned timeframe, the study identifies the 10 most prominent topics that formed the area of interest for the COVID-19 pandemic research.

Originality/value

While similar studies exist, no other work has used topic modeling to comprehensively analyze the COVID-19 literature by considering diverse fields and domains.

A review of the impact of environmental factors and pollutants on covid-19 transmission

The coronavirus disease (COVID-19) caused an unprecedented loss of life with colossal social and economic fallout over 237 countries and territories worldwide. Environmental conditions played a significant role in spreading the virus. Despite the availability of literature, the consecutive waves of COVID-19 in all geographical conditions create the necessity of reviewing the impact of environmental factors on it. This study synthesized and reviewed the findings of 110 previously published articles on meteorological factors and COVID-19 transmission. This study aimed to identify the diversified impacts of meteorological factors on the spread of infection and suggests future research. Temperature, rainfall, air quality, sunshine, wind speed, air pollution, and humidity were found as investigated frequently. Correlation and regression analysis have been widely used in previous studies. Most of the literature showed that temperature and humidity have a favorable relationship with the spread of COVID-19. On the other hand, 20 articles stated no relationship with humidity, and nine were revealed the negative effect of temperature. The daily number of COVID-19 confirmed cases increased by 4.86% for every 1 °C increase in temperature. Sunlight was also found as a significant factor in 10 studies. Moreover, increasing COVID-19 incidence appeared to be associated with increased air pollution, particularly PM10, PM2.5, and O₃ concentrations. Studies also indicated a negative relation between the air quality index and the COVID-19 cases. This review determined environmental variables' complex and contradictory effects on COVID-19 transmission. Hence it becomes essential to include environmental parameters into epidemiological models and controlled laboratory experiments to draw more precious results.

Identifying spatiotemporal patterns of COVID-19 transmissions and the drivers of the patterns in Toronto: a Bayesian hierarchical spatiotemporal modelling

Spatiotemporal patterns and trends of COVID-19 at a local spatial scale using Bayesian approaches are hardly observed in literature. Also, studies rarely use satellite-derived long time-series data on the environment to predict COVID-19 risk at a spatial scale. In this study, we modelled the COVID-19 pandemic risk using a Bayesian hierarchical spatiotemporal model that incorporates satellite-derived remote sensing data on land surface temperature (LST) from January 2020 to October 2021 (89 weeks) and several socioeconomic covariates of the 140 neighbourhoods in Toronto. The spatial patterns of risk were heterogeneous in space with multiple high-risk neighbourhoods in Western and Southern Toronto. Higher risk was observed during Spring 2021. The spatiotemporal risk patterns identified 60% of neighbourhoods had a stable, 37% had an increasing, and 2% had a decreasing trend over the study period. LST was positively, and higher education was negatively associated with the COVID-19 incidence. We believe the use of Bayesian spatial modelling and the remote sensing technologies in this study provided a strong versatility and strengthened our analysis in identifying the spatial risk of COVID-19. The findings would help in prevention planning, and the framework of this study may be replicated in other highly transmissible infectious diseases.

Ambient temperature and Covid-19 transmission: An evidence from a region of Iran based on weather station and satellite data

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.

COVID-19 pandemic over 2020 (withlockdowns) and 2021 (with vaccinations): similar effects for seasonality and environmental factors

How is the dynamics of Coronavirus Disease 2019 (COVID-19) in 2020 with an health policy of full lockdowns and in 2021 with a vast campaign of vaccinations? The present study confronts this question here by developing a comparative analysis of the effects of COVID-19 pandemic between April-September 2020 (based upon strong control measures) and April-September 2021 (focused on health policy of vaccinations) in Italy, which was one of the first European countries to experience in 2020 high numbers of COVID-19 related infected individuals and deaths and in 2021 Italy has a high share of people fully vaccinated against COVID-19 (>89% of population aged over 12 years in January 2022). Results suggest that over the period under study, the arithmetic mean of confirmed cases, hospitalizations of people and admissions to Intensive Care Units (ICUs) in 2020 and 2021 is significantly equal (p-value<0.01), except fatality rate. Results suggest in December 2021 lower hospitalizations, admissions to ICUs, and fatality rate of COVID-19 than December 2020, though confirmed cases and mortality rates are in 2021 higher than 2020, and likely converging trends in the first quarter of 2022. These findings reveal that COVID-19 pandemic is driven by seasonality and environmental factors that reduce the negative effects in summer period, regardless control measures and/or vaccination campaigns. These findings here can be of benefit to design health policy responses of crisis management considering the growth of COVID-19 pandemic in winter months having reduced temperatures and low solar radiations ( COVID-19 has a behaviour of influenza-like illness). Hence, findings here suggest that strategies of prevention and control of infectious diseases similar to COVID-19 should be set up in summer months and fully implemented during low-solar-irradiation periods (autumn and winter period).

Data-driven multiscale modelling and analysis of COVID-19 spatiotemporal evolution using explainable AI

To quantificationally identify the optimal control measures for regulators to best minimize COVID-19′s growth (G-rate) and death (D-rate) rates in today's context, this paper develops a top-down multiscale engineering approach which encompasses a series of systematic analyses, namely: (global scale) predictive modelling of G-rate and D-rate due to COVID-19 globally, followed by determining the most effective control factors which can best minimize both parameters over time via explainable Artificial Intelligence (AI) with SHAP (SHapley Additive exPlanations) method; (continental scale) same predictive forecasting of G-rate and D-rate in all continents, followed by performing explainable SHAP analysis to determine the most effective control factors for the respective continents; and (country scale) clustering the different countries (> 150 in total) into 3 main clusters to identify the universal set of effective control measures. By using the historical period between 2 May 2020 and 1 Oct 2021, the average MAPE scores for forecasting G-rate and D-rate are within 10%, or less on average, at the global and continental scales. Systematically, we have quantificationally demonstrated that the top 3 most effective control measures for regulators to best minimize G-rate universally are COVID-CONTACT-TRACING, PUBLIC-GATHERING-RULES, and COVID-STRINGENCY-INDEX, while the control factors relating to D-rate depend on the modelling scenario.

Evaluation of the effects of meteorological factors on COVID-19 prevalence by the distributed lag nonlinear model

Background

Although numerous studies have explored the impact of meteorological factors on the epidemic of COVID-19, their relationship remains controversial and needs to be clarified.

Methods

We assessed the risk effect of various meteorological factors on COVID-19 infection using the distributed lag nonlinear model, based on related data from July 1, 2020, to June 30, 2021, in eight countries, including Portugal, Greece, Egypt, South Africa, Paraguay, Uruguay, South Korea, and Japan, which are in Europe, Africa, South America, and Asia, respectively. We also explored associations between COVID-19 prevalence and individual meteorological factors by the Spearman’s rank correlation test.

Results

There were significant non-linear relationships between both temperature and relative humidity and COVID-19 prevalence. In the countries located in the Northern Hemisphere with similar latitudes, the risk of COVID-19 infection was the highest at temperature below 5 ℃. In the countries located in the Southern Hemisphere with similar latitudes, their highest infection risk occurred at around 15 ℃. Nevertheless, in most countries, high temperature showed no significant association with reduced risk of COVID-19 infection. The effect pattern of relative humidity on COVID-19 depended on the range of its variation in countries. Overall, low relative humidity was correlated with increased risk of COVID-19 infection, while the high risk of infection at extremely high relative humidity could occur in some countries. In addition, relative humidity had a longer lag effect on COVID-19 than temperature.

Conclusions

The effects of meteorological factors on COVID-19 prevalence are nonlinear and hysteretic. Although low temperature and relative humidity may lower the risk of COVID-19, high temperature or relative humidity could also be associated with a high prevalence of COVID-19 in some regions.

The Effects of Non-pharmaceutical Interventions on COVID-19 Mortality: A Generalized Synthetic Control Approach Across 169 Countries

Importance

Governments have introduced non-pharmaceutical interventions (NPIs) in response to the pandemic outbreak of Coronavirus disease (COVID-19). While NPIs aim at preventing fatalities related to COVID-19, the previous literature on their efficacy has focused on infections and on data of the first half of 2020. Still, findings of early NPI studies may be subject to underreporting and missing timeliness of reporting of cases. Moreover, the low variation in treatment timing during the first wave makes identification of robust treatment effects difficult.

Objective

We enhance the literature on the effectiveness of NPIs with respect to the period, the number of countries, and the analytical approach.

Design, Setting, and Participants

To circumvent problems of reporting and treatment variation, we analyse data on daily confirmed COVID-19-related deaths per capita from Our World in Data, and on 10 different NPIs from the Oxford COVID-19 Government Response Tracker (OxCGRT) for 169 countries from 1st July 2020 to 1st September 2021. To identify the causal effects of introducing NPIs on COVID-19-related fatalities, we apply the generalized synthetic control (GSC) method to each NPI, while controlling for the remaining NPIs, weather conditions, vaccinations, and NPI-residualized COVID-19 cases. This mitigates the influence of selection into treatment and allows to model flexible post-treatment trajectories.

Results

We do not find substantial and consistent COVID-19-related fatality-reducing effects of any NPI under investigation. We see a tentative change in the trend of COVID-19-related deaths around 30 days after strict stay-at-home rules and to a slighter extent after workplace closings have been implemented. As a proof of concept, our model is able to identify a fatality-reducing effect of COVID-19 vaccinations. Furthermore, our results are robust with respect to various crucial sensitivity checks.

Conclusion

Our results demonstrate that many implemented NPIs may not have exerted a significant COVID-19-related fatality-reducing effect. However, NPIs might have contributed to mitigate COVID-19-related fatalities by preventing exponential growth in deaths. Moreover, vaccinations were effective in reducing COVID-19-related deaths.

Predicting COVID-19 Cases From Atmospheric Parameters Using Machine Learning Approach

The dynamical nature of COVID-19 cases in different parts of the world requires robust mathematical approaches for prediction and forecasting. In this study, we aim to (a) forecast future COVID-19 cases based on past infections, (b) predict current COVID-19 cases using PM2.5, temperature, and humidity data, using four different machine learning classifiers (Decision Tree, K-nearest neighbor, Support Vector Machine, and Random Forest). Based on RMSE values, k-nearest neighbor and support vector machine algorithms were found to be the best for predicting future incidences of COVID-19 based on past histories. From the RMSE values obtained, temperature was found to be the best predictor for number of COVID-19 cases, followed by relative humidity. Decision tree models was found to perform poorly in the prediction of COVID-19 cases considering particulate matter and atmospheric parameters as predictors. Our results suggests the possibility of predicting virus infection using machine learning. This will guide policy makers in proactive monitoring and control.

Mask wearing behavior in hot urban spaces of Novi Sad during the COVID-19 pandemic

Urban overheating (due to climate change and urbanization) and COVID-19 are two converging crises that must be addressed in tandem. Fine-scale, place-based, people-centric biometeorological and behavioral data are needed to implement context-specific preventative measures such as mask-wearing. This study collected local biometeorological measurements in diverse urban spaces (square, urban park, river quay) in Novi Sad, Serbia on hot sunny summer days (27-30 August 2020) during the COVID-19 pandemic. Observations were supplemented by an online survey asking questions about thermal sensation, comfort, and concurrent protective behavior of the local population. Biometeorological measurements show that the main square in the city center was the most thermally uncomfortable area. According to the survey, it was also perceived as the least safe space to not contract the virus. The urban park was perceived as the most thermally comfortable area in the morning and during midday. It was also considered the safest urban space for outdoor activities. In the evening, the river quay was the most thermally comfortable area in the city. Intra-urban differences in Physiologically Equivalent Temperatures were highest during midday, while differences in air temperatures were highest in the evening. More than 70% of the respondents did not wear face masks when it was hot because of breathing issues and feeling warmer than without mask. Most people wearing a mask felt "slightly warm" in the morning and evening, while the majority of respondents felt "hot" during midday. Only 3% of the respondents felt comfortable while wearing a mask, while 97% experienced some degree of discomfort (from slight discomfort to very uncomfortable). Our study shows that fine scale temporal and spatial urban biometeorological data and population surveys should be included in decision-making processes during the pandemic to develop climate-sensitive health services that are place-based, people-centric, and facilitate planning towards green, resilient, and inclusive cities.

Impact of climate indicators on the COVID-19 pandemic in Saudi Arabia

The novel coronavirus (COVID-19) outbreak has left a major impact on daily lifestyle and human activities. Many recent studies confirmed that the COVID-19 pandemic has human-to-human transmissibility. Additional studies claimed that other factors affect the viability, transmissibility, and propagation range of COVID-19. The effect of weather factors on the spread of COVID-19 has gained much attention among researchers. The current study investigates the relationship between climate indicators and daily detected COVID-19 cases in Saudi Arabia, focusing on the top five cities with confirmed cases. The examined climate indicators were temperature (°F), dew point (°F), humidity (%), wind speed (mph), and pressure (Hg). Using data from Spring 2020 and 2021, we conducted spatio-temporal correlation, regression, and time series analyses. The results provide preliminary evidence that the COVID-19 pandemic spread in most of the considered cities is significantly correlated with temperature (positive correlation) and pressure (negative correlation). The discrepancies in the results from different cites addressed in this study suggest that non-meteorological factors need to be explored in conjunction with weather attributes in a sufficiently long-term analysis to provide meaningful policy measures for the future.

Stochastic analysis of the relationship between atmospheric variables and coronavirus disease (COVID-19) in a hot, arid climate

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.

Model-based ensembles: Lessons learned from retrospective analysis of COVID-19 infection forecasts across 10 countries

Mathematical models of different types and data intensities are highly used by researchers, epidemiologists, and national authorities to explore the inherently unpredictable progression of COVID-19, including the effects of different non-pharmaceutical interventions. Regardless of model complexity, forecasts of future COVID-19 infections, deaths and hospitalization are associated with large uncertainties, and critically depend on the quality of the training data, and in particular how well the recorded national or regional numbers of infections, deaths and recoveries reflect the the actual situation. In turn, this depends on, e.g., local test and abatement strategies, treatment capacities and available technologies. Other influencing factors including temperature and humidity, which are suggested by several authors to affect the spread of COVID-19 in some countries, are generally only considered by the most complex models and further serve to inflate the uncertainty. Here we use comparative and retrospective analyses to illuminate the aggregated effect of these systematic biases on ensemble-based model forecasts. We compare the actual progression of active infections across ten of the most affected countries in the world until late November 2020 with "re-forecasts" produced by two of the most commonly used model types: (i) a compartment-type, susceptible-infected-removed (SIR) model; and (ii) a statistical (Holt-Winters) time series model. We specifically examine the sensitivity of the model parameters, estimated systematically from different subsets of the data and thereby different time windows, to illustrate the associated implications for short- to medium-term forecasting and for probabilistic projections based on (single) model ensembles as inspired by, e.g., weather forecasting and climate research. Our findings portray considerable variations in forecasting skill in between the ten countries and demonstrate that individual model predictions are highly sensitive to parameter assumptions. Significant skill is generally only confirmed for short-term forecasts (up to a few weeks) with some variation across locations and periods.

Using an Eigenvector Spatial Filtering-Based Spatially Varying Coefficient Model to Analyze the Spatial Heterogeneity of COVID-19 and Its Influencing Factors in Mainland China

The COVID-19 pandemic has led to many deaths and economic disruptions across the world. Several studies have examined the effect of corresponding health risk factors in different places, but the problem of spatial heterogeneity has not been adequately addressed. The purpose of this paper was to explore how selected health risk factors are related to the pandemic infection rate within different study extents and to reveal the spatial varying characteristics of certain health risk factors. An eigenvector spatial filtering-based spatially varying coefficient model (ESF-SVC) was developed to find out how the influence of selected health risk factors varies across space and time. The ESF-SVC was able to take good control of over-fitting problems compared with ordinary least square (OLS), eigenvector spatial filtering (ESF) and geographically weighted regression (GWR) models, with a higher adjusted R2 and lower cross validation RMSE. The impact of health risk factors varied as the study extent changed: In Hubei province, only population density and wind speed showed significant spatially constant impact; while in mainland China, other factors including migration score, building density, temperature and altitude showed significant spatially varying impact. The influence of migration score was less contributive and less significant in cities around Wuhan than cities further away, while altitude showed a stronger contribution to the decrease of infection rates in high altitude cities. The temperature showed mixed correlation as time passed, with positive and negative coefficients at 2.42 °C and 8.17 °C, respectively. This study could provide a feasible path to improve the model fit by considering the problem of spatial autocorrelation and heterogeneity that exists in COVID-19 modeling. The yielding ESF-SVC coefficients could also provide an intuitive method for discovering the different impacts of influencing factors across space in large study areas. It is hoped that these findings improve public and governmental awareness of potential health risks and therefore influence epidemic control strategies.

Meteorological parameters and cases of COVID-19 in Brazilian cities: an observational study

Meteorological parameters modulate transmission of the SARS-Cov-2 virus, the causative agent related to coronavirus disease-2019 (COVID-19) development. However, findings across the globe have been inconsistent attributed to several confounding factors. The aim of the present study was to investigate the relationship between reported meteorological parameters from July 1 to October 31, 2020, and the number of confirmed COVID-19 cases in 4 Brazilian cities: São Paulo, the largest city with the highest number of cases in Brazil, and the cities with greater number of cases in the state of Parana during the study period (Curitiba, Londrina and Maringa). The assessment of meteorological factors with confirmed COVID-19 cases included atmospheric pressure, temperature, relative humidity, wind speed, solar irradiation, sunlight, dew point temperature, and total precipitation. The 7- and 15-day moving averages of confirmed COVID-19 cases were obtained for each city. Pearson's correlation coefficients showed significant correlations between COVID-19 cases and all meteorological parameters, except for total precipitation, with the strongest correlation with maximum wind speed (0.717, <0.001) in São Paulo. Regression tree analysis demonstrated that the largest number of confirmed COVID-19 cases was associated with wind speed (between ≥0.3381 and <1.173 m/s), atmospheric pressure (<930.5mb), and solar radiation (<17.98e⁺³). Lower number of cases was observed for wind speed <0.3381 m/s and temperature <23.86°C. Our results encourage the use of meteorological information as a critical component in future risk assessment models.

The impact of meteorological factors on COVID‐19 of California and its lag effect

As of March 30, 2021, COVID‐19 has been circulating globally for more than 1 year, posing a huge threat to the safety of human life and property. Understanding the relationship between meteorological factors and the COVID‐19 can provide positive help for the prevention and control of the global epidemic. We take California as the research object, use Geodetector to screen out the meteorological factors with the strongest explanatory power for the epidemic, then use partial correlation analysis to study the correlation between the two, and finally construct a distributed lag non‐linear model (DLNM) to further explore the relationship between the dominant factor and COVID‐19 and its lag effect. It turns out that temperature has a greater impact on COVID‐19 and the two have a significant negative correlation. When the temperature is lower than 50°F, it has a significant promotion effect on the epidemic, and the relative risk (RR) increases approximately exponentially as the temperature decreases. The delayed effect of the cold effect on the epidemic can be as long as 15 days. This study has shown that more attention should be paid to epidemic prevention and control when the temperature is low, and the delay effect of temperature on the spread of the epidemic cannot be ignored.

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.

Correlating dynamic climate conditions and socioeconomic-governmental factors to spatiotemporal spread of COVID-19 via semantic segmentation deep learning analysis

In this study, we develop a deep learning model to forecast the transmission rate of COVID-19 globally, via a proposed G parameter, as a function of fused data features which encompass selected climate conditions, socioeconomic and restrictive governmental factors. A 2-step optimization process is adopted for the model's data fusion component which systematically performs the following: (Step I) determining the optimal climate feature which can achieve good precision score (> 70%) when predicting the spatial classes distribution of the G parameter on a global scale consisting of 251 countries, followed by (Step II) fusing the optimal climate feature with 11 selected socioeconomic-governmental factors to further improve the model's predictive capability. By far, the obtained results from the model's testing step indicate that land surface temperature day (LSTD) has the strongest correlation with the global G parameter over time by achieving an average precision score of 72%. When coupled with relevant socioeconomic-governmental factors, the model's average precision score improves to 77%. At the local scale analysis for selected countries, our proposed model can provide insights into the relationship between the fused data features and the respective local G parameter by achieving an average accuracy score of 79%.

Interaction between meteorological parameters and COVID-19: an ecological study on 406 authorities of the UK

Understanding the factors affecting COVID-19 transmission is critical in assessing and mitigating the spread of the pandemic. This study investigated the transmissibility and death distribution of COVID-19 and its association with meteorological parameters to study the propagation pattern of COVID-19 in UK regions. We used the reported case and death per capita rate (as of November 13, 2020; before mass vaccination) and long-term meteorological data (temperature, humidity, precipitation, wind speed, and visibility) in 406 UK local authority levels based on publicity available secondary data. We performed correlation and regression analysis between COVID-19 variables and meteorological parameters to find the association between COVID-19 and independent variables. Student's T and Mann-Whitney's tests were used to analyze data. The correlation and regression analyses revealed that temperature, dew point, wind speed, and humidity were the most important factors associated with spread and death of COVID-19 (P <0.05). COVID-19 cases negatively correlated with humidity in areas with high population density, but the inverse in low population density areas. Wind speeds in low visibility areas, which are considered polluted air, may increase the spread of disease (r=0.42, P <0.05) and decrease the spread in high visibility areas (r=-0.16, P <0.05). Among low (T <10°C) and high (T >10°C) temperature areas, the average incidence rates were 2056.86 (95% confidence interval (CI): 1909.49-2204.23) and 1446.76 (95% CI: 1296.71-1596.81). Also, COVID-19 death per capita rates were 81.55 (95% CI: 77.40-85.70) and 69.78 (95% CI: 64.39-75.16) respectively. According to the comprehensive analysis, the spread of disease will be suppressed as the weather warms and humidity and wind speed decrease. Different environmental conditions can increase or decrease spread of the disease due to affecting spread of disease vectors and by altering people's behavior.

Leveraging weather data for forecasting cases-to-mortality rates due to COVID-19

There are several recent publications criticizing the failure of COVID-19 forecasting models, with swinging over predictions and underpredictions, which have made it difficult for decision and policy making. Observing the failures of several COVID-19 forecasting models and the alarming spread of the virus, we seek to use some stable response for forecasting COVID-19, viz., ratios of COVID-19 cases to mortalities, rather than COVID-19 cases or fatalities. A trend of low COVID-19 cases-to-mortality ratios calls for urgent attention: the need for vaccines, for instance. Studies have shown that there are influences of weather parameters on COVID-19; and COVID-19 may have come to stay and could manifest a seasonal outbreak profile similar to other infectious respiratory diseases. In this paper, the influences of some weather, geographical, economic and demographic covariates were evaluated on COVID-19 response based on a series of Granger-causality tests. The effect of four weather parameters, viz., temperature, rainfall, solar irradiation and relative humidity, on daily COVID-19 cases-to-mortality ratios of 36 countries from 5 continents of the world were determined through regression analysis. Regression studies show that these four weather factors impact ratios of COVID-19 cases-to-mortality differently. The most impactful factor is temperature which is positively correlated with COVID-19 cases-to-mortality responses in 24 out of 36 countries. Temperature minimally affects COVID-19 cases-to-mortality ratios in the tropical countries. The most influential weather factor - temperature - was incorporated in training random forest and deep learning models for forecasting the cases-to-mortality rate of COVID-19 in clusters of countries in the world with similar weather conditions. Evaluation of trained forecasting models incorporating temperature features show better performance compared to a similar set of models trained without temperature features. This implies that COVID-19 forecasting models will predict more accurately if temperature features are factored in, especially for temperate countries.

A cross-sectional analysis of meteorological factors and SARS-CoV-2 transmission in 409 cities across 26 countries

There is conflicting evidence on the influence of weather on COVID-19 transmission. Our aim is to estimate weather-dependent signatures in the early phase of the pandemic, while controlling for socio-economic factors and non-pharmaceutical interventions. We identify a modest non-linear association between mean temperature and the effective reproduction number (Re) in 409 cities in 26 countries, with a decrease of 0.087 (95% CI: 0.025; 0.148) for a 10 °C increase. Early interventions have a greater effect on Re with a decrease of 0.285 (95% CI 0.223; 0.347) for a 5th - 95th percentile increase in the government response index. The variation in the effective reproduction number explained by government interventions is 6 times greater than for mean temperature. We find little evidence of meteorological conditions having influenced the early stages of local epidemics and conclude that population behaviour and government interventions are more important drivers of transmission.

Physical distancing implementation, ambient temperature and Covid-19 containment: An observational study in the United States

Governments may relax physical distancing interventions for coronavirus disease 2019 (Covid-19) containment in warm seasons/areas to prevent economic contractions. However, it is not clear whether higher temperature may offset the transmission risk posed by this relaxation. This study aims to investigate the associations of the effective reproductive number (R_t) of Covid-19 with ambient temperature and the implementation of physical distancing interventions in the United States (US). This study included 50 states and one territory of the US with 4,532,650 confirmed cases between 29 January and 31 July 2020. We used an interrupted time-series model with a state-level random intercept for data analysis. An interaction term of 'physical distancing×temperature' was included to examine their interactions. Stratified analyses by temperature and physical distancing implementation were also performed to analyse the modifying effects. The overall median (interquartile range) R_t was 1.2 (1.0-2.3). The implementation of physical distancing was associated with a 12% decrease in the risk of R_t (relative risk [RR]: 0.88, 95% confident interval [CI]: 0.86-0.89), and each 5 °C increase in temperature was associated with a 2% decrease (RR: 0.98, 95%CI: 0.97-0.98). We observed a statistically significant interaction between temperature and physical distancing implementation, but all the RRs were small (close to one). The containing effects of high temperature were attenuated by 5.1% when physical distancing was implemented. The association of COVID-19 R_t with physical distancing implementation was more stable (0.88 vs. 0.89 in days when temperature was low and high, respectively). Increased temperature did not offset the risk of Covid-19 R_t posed by the relaxation of physical distancing implementation. Our study does not recommend relaxing the implementation of physical distancing interventions in warm seasons/areas.

Association between environmental factors and COVID-19 in Shanghai, China

The outbreak of coronavirus disease 2019 (COVID-19) continues to spread worldwide and has led to recession, rising unemployment, and the collapse of the health-care system. The aim of this study was to explore the exposure-response relationship between daily confirmed COVID-19 cases and environmental factors. We used a time-series generalized additive model (GAM) to investigate the short-term association between COVID-19 and environmental factors by using daily meteorological elements, air pollutant concentration, and daily confirmed COVID-19 cases from January 21, 2020, to February 29, 2020, in Shanghai, China. We observed significant negative associations between daily confirmed COVID-19 cases and mean temperature (Tave), temperature humidity index (THI), and index of wind effect (K), whereas air quality index (AQI), PM2.5, PM10 NO2, and SO2 were significantly associated with the increase in daily confirmed COVID-19 cases. A 1 °C increase in Tave, one-unit increase in THI, and 10-unit increase in K (lag 0-7 days) were associated with 4.7, 1.8, and 1.6% decrease in daily confirmed cases, respectively. Daily Tave, THI, K, PM10, and SO2 had significant lag and persistence (lag 0-7 days), whereas the lag and persistence of AQI, PM2.5, and NO2 were significant at both lag 0-7 and 0-14 days. A 10-μg/m3 increase in PM10 and 1-μg/m3 increase in SO2 was associated with 13.9 and 5.7% increase in daily confirmed cases at lag 0-7 days, respectively, whereas a 10-unit increase in AQI and a 10-μg/m3 increase in PM2.5 and NO2 were associated with 7.9, 7.8, and 10.1% increase in daily confirmed cases at lag 0-14 days, respectively. Our findings have important implications for public health in the city of Shanghai.

Health impacts of daily weather fluctuations: Empirical evidence from COVID-19 in U.S. counties

The emergence of the novel coronavirus has necessitated immense research efforts to understand how several non-environmental and environmental factors affect transmission. With the United States leading the path in terms of case incidence, it is important to investigate how weather variables influence the spread of the disease in the country. This paper assembles a detailed and comprehensive dataset comprising COVID-19 cases and climatological variables for all counties in the continental U.S. and uses a developed econometric approach to estimate the causal effect of certain weather factors on the growth rate of infection. The results indicate a non-linear and significant negative relationship between the individual weather measures and the growth rate of COVID-19 in the U.S. Specifically, the paper finds that a 1 °C rise in daily temperature will reduce daily covid growth rate in the U.S. by approximately 6 percent in the following week, while a marginal increase in relative humidity reduces the same outcome by 1 percent over a similar period. In comparison, a 1 m/s increase in daily wind speed will bring about an 8 percent drop in daily growth rate of COVID-19 in the country. These results differ by location and are robust to several sensitivity checks, so large deviations are unexpected.

A Descriptive Analysis of the Scientific Literature on Meteorological and Air Quality Factors and COVID-19

The role of meteorological and air quality factors in moderating the transmission of SARS-CoV-2 and severity of COVID-19 is a critical topic as an opportunity for targeted intervention and relevant public health messaging. Studies conducted in early 2020 suggested that temperature, humidity, ultraviolet radiation, and other meteorological factors have an influence on the transmissibility and viral dynamics of COVID-19. Previous reviews of the literature have found significant heterogeneity in associations but did not examine many factors relating to epidemiological quality of the analyses such as rigor of data collection and statistical analysis, or consideration of potential confounding factors. To provide greater insight into the current state of the literature from an epidemiological standpoint, the authors conducted a rapid descriptive analysis with a strong focus on the characterization of COVID-19 health outcomes and use of controls for confounding social and demographic variables such as population movement and age. We have found that few studies adequately considered the challenges posed by the use of governmental reporting of laboratory testing as a proxy for disease transmission, including timeliness and consistency. In addition, very few studies attempted to control for confounding factors, including timing and implementation of public health interventions and metrics of population compliance with those interventions. Ongoing research should give greater consideration to the measures used to quantify COVID-19 transmission and health outcomes as well as how to control for the confounding influences of public health measures and personal behaviors.

Role of meteorological factors in the transmission of SARS-CoV-2 in the United States

Improved understanding of the effects of meteorological conditions on the transmission of SARS-CoV-2, the causative agent for COVID-19 disease, is needed. Here, we estimate the relationship between air temperature, specific humidity, and ultraviolet radiation and SARS-CoV-2 transmission in 2669 U.S. counties with abundant reported cases from March 15 to December 31, 2020. Specifically, we quantify the associations of daily mean temperature, specific humidity, and ultraviolet radiation with daily estimates of the SARS-CoV-2 reproduction number (Rt) and calculate the fraction of Rt attributable to these meteorological conditions. Lower air temperature (within the 20-40 °C range), lower specific humidity, and lower ultraviolet radiation were significantly associated with increased Rt. The fraction of Rt attributable to temperature, specific humidity, and ultraviolet radiation were 3.73% (95% empirical confidence interval [eCI]: 3.66-3.76%), 9.35% (95% eCI: 9.27-9.39%), and 4.44% (95% eCI: 4.38-4.47%), respectively. In total, 17.5% of Rt was attributable to meteorological factors. The fractions attributable to meteorological factors generally were higher in northern counties than in southern counties. Our findings indicate that cold and dry weather and low levels of ultraviolet radiation are moderately associated with increased SARS-CoV-2 transmissibility, with humidity playing the largest role.

Associations between meteorology and COVID-19 in early studies: Inconsistencies, uncertainties, and recommendations

Meteorological variables, such as the ambient temperature and humidity, play a well-established role in the seasonal transmission of respiratory viruses and influenza in temperate climates. Since the onset of the novel coronavirus disease 2019 (COVID-19) pandemic, a growing body of literature has attempted to characterize the sensitivity of COVID-19 to meteorological factors and thus understand how changes in the weather and seasonality may impede COVID-19 transmission. Here we select a subset of this literature, summarize the diversity in these studies' scopes and methodologies, and show the lack of consensus in their conclusions on the roles of temperature, humidity, and other meteorological factors on COVID-19 transmission dynamics. We discuss how several aspects of studies' methodologies may challenge direct comparisons across studies and inflate the importance of meteorological factors on COVID-19 transmission. We further comment on outstanding challenges for this area of research and how future studies might overcome them by carefully considering robust modeling approaches, adjusting for mediating and covariate effects, and choosing appropriate scales of analysis.

A review of the impact of weather and climate variables to COVID-19: In the absence of public health measures high temperatures cannot probably mitigate outbreaks

The new severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) pandemic was first recognized at the end of 2019 and has caused one of the most serious global public health crises in the last years. In this paper, we review current literature on the effect of weather (temperature, humidity, precipitation, wind, etc.) and climate (temperature as an essential climate variable, solar radiation in the ultraviolet, sunshine duration) variables on SARS-CoV-2 and discuss their impact to the COVID-19 pandemic; the review also refers to respective effect of urban parameters and air pollution. Most studies suggest that a negative correlation exists between ambient temperature and humidity on the one hand and the number of COVID-19 cases on the other, while there have been studies which support the absence of any correlation or even a positive one. The urban environment and specifically the air ventilation rate, as well as air pollution, can probably affect, also, the transmission dynamics and the case fatality rate of COVID-19. Due to the inherent limitations in previously published studies, it remains unclear if the magnitude of the effect of temperature or humidity on COVID-19 is confounded by the public health measures implemented widely during the first pandemic wave. The effect of weather and climate variables, as suggested previously for other viruses, cannot be excluded, however, under the conditions of the first pandemic wave, it might be difficult to be uncovered. The increase in the number of cases observed during summertime in the Northern hemisphere, and especially in countries with high average ambient temperatures, demonstrates that weather and climate variables, in the absence of public health interventions, cannot mitigate the resurgence of COVID-19 outbreaks.

A review of the impact of weather and climate variables to COVID-19: In the absence of public health measures high temperatures cannot probably mitigate outbreaks

The new severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) pandemic was first recognized at the end of 2019 and has caused one of the most serious global public health crises in the last years. In this paper, we review current literature on the effect of weather (temperature, humidity, precipitation, wind, etc.) and climate (temperature as an essential climate variable, solar radiation in the ultraviolet, sunshine duration) variables on SARS-CoV-2 and discuss their impact to the COVID-19 pandemic; the review also refers to respective effect of urban parameters and air pollution. Most studies suggest that a negative correlation exists between ambient temperature and humidity on the one hand and the number of COVID-19 cases on the other, while there have been studies which support the absence of any correlation or even a positive one. The urban environment and specifically the air ventilation rate, as well as air pollution, can probably affect, also, the transmission dynamics and the case fatality rate of COVID-19. Due to the inherent limitations in previously published studies, it remains unclear if the magnitude of the effect of temperature or humidity on COVID-19 is confounded by the public health measures implemented widely during the first pandemic wave. The effect of weather and climate variables, as suggested previously for other viruses, cannot be excluded, however, under the conditions of the first pandemic wave, it might be difficult to be uncovered. The increase in the number of cases observed during summertime in the Northern hemisphere, and especially in countries with high average ambient temperatures, demonstrates that weather and climate variables, in the absence of public health interventions, cannot mitigate the resurgence of COVID-19 outbreaks.