Ambient temperature and subsequent COVID-19 mortality in the OECD countries and individual United States

Association of air pollution and weather conditions during infection course with COVID-19 case fatality rate in the United Kingdom

Although the relationship between the environmental factors, such as weather conditions and air pollution, and COVID-19 case fatality rate (CFR) has been found, the impacts of these factors to which infected cases are exposed at different infectious stages (e.g., virus exposure time, incubation period, and at or after symptom onset) are still unknown. Understanding this link can help reduce mortality rates. During the first wave of COVID-19 in the United Kingdom (UK), the CFR varied widely between and among the four countries of the UK, allowing such differential impacts to be assessed. We developed a generalized linear mixed-effect model combined with distributed lag nonlinear models to estimate the odds ratio of the weather factors (i.e., temperature, sunlight, relative humidity, and rainfall) and air pollution (i.e., ozone, [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text]) using data between March 26, 2020 and September 15, 2020 in the UK. After retrospectively time adjusted CFR was estimated using back-projection technique, the stepwise model selection method was used to choose the best model based on Akaike information criteria and the closeness between the predicted and observed values of CFR. The risk of death reached its maximum level when the low temperature (6 °C) occurred 1 day before (OR 1.59; 95% CI 1.52-1.63), prolonged sunlight duration (11-14 h) 3 days after (OR 1.24; 95% CI 1.18-1.30) and increased [Formula: see text] (19 μg/m3) 1 day after the onset of symptom (OR 1.12; 95% CI 1.09-1.16). After reopening, many COVID-19 cases will be identified after their symptoms appear. The findings highlight the importance of designing different preventive measures against severe illness or death considering the time before and after symptom onset.

Covid-19 outbreak associated with demographic-meteorological factors in the arid and semi-arid region Iran: case study Gonabad city, 2020-2021

Since the (Covid-19) pandemic outbreak, questioning regarding climate and incident of Covid-19 infection rates has been debated, while there is no clear research evidence until now in Iran. This study has focused on investigating the association between Covid-19 cases and demographic -meteorological factors in arid and semi-arid zones of Iran (from March 1, 2020, to January 31, 2022) by analyzing with Via Poisson and negative binomial regression. As a result, the incidence rate of both Covid-19 hospitalization and mortality cases reached peaks in the summer followed by the autumn. Interestingly, Covid-19 hospitalization cases are associated with humidity, temperature, and wind factors seasonally and monthly, but mortality cases are just associated with wind. In conclusion, the result demonstrated that demographicand meteorological factorsare positively and negatively associated with Covid-19 cases. Therefore, identifying the environmental factors contributing to the excess Covid-19 can help to prevent future pandemic waves in Iranian arid and semi-arid zone.

Relationship between meteorological factors and mortality in patients with coronavirus disease 2019: A cross-sectional study

Background

Recent studies on COVID-19 have demonstrated that poverty, comorbidities, race/ethnicity, population density, mobility, hygiene and use of masks are some of the important correlates of COVID-19 outcomes. In fact, weather conditions also play an important role in enhancing or eradicating health issues. Based on Chinese experience, the development of SARS and COVID-19 is partially associated with alterations in climate that align with the seasonal shifts of the "24 solar terms." However, the applicability of this pattern to other countries, particularly the United States, which has the highest global incidence and mortality rates, remains subject to ongoing investigation. We need to find more evidence to in the U.S. states verify the relationship between meteorological factors and COVID-19 outcomes to provide epidemiological and environmental support for the COVID-19 pandemic prevention and resource preservation.

Objective

To evaluate the relationship between meteorological factors and Coronavirus Disease 2019 (COVID-19) mortality.

Methods

We conducted an ecological cross-sectional study to evaluate the relationship between meteorological factors (maximum temperature, minimum temperature, humidity, wind speed, precipitation, atmospheric pressure) and COVID-19 mortality. This retrospective observational study examines mortality rates among COVID-19 patients in the three US states, California, Texas, and New York, with the highest fatality numbers, between March 7, 2020 and March 7, 2021. The study draws upon data sourced from the publicly accessible Dryad database. The daily corresponding meteorological conditions were retrieved from the National Oceanic and Atmospheric Administration Global Meteorological website (https://www.ncei.noaa.gov/maps/hourly/). This study employed multivariate linear regression analysis to assess the correlation between six meteorological factors and COVID-19 mortality. Gaussian distribution models were utilized to generate smooth curves for examining the linear association between maximum or minimum temperature and mortality. Additionally, breakpoint analysis was conducted to evaluate the threshold effect of temperature.

Results

We found that the death toll of patients with COVID-19 decreased with an increase in the highest and lowest ambient temperatures (p < 0.001). In our study, we observed a seasonal difference in mortality rates, with a higher number of deaths occurring during winter months, particularly in January and February. However, mortality rates decreased significantly in March. Notably, we found no statistically significant correlation between relative humidity, average precipitation, and average wind speed with COVID-19 mortality (all p > 0.05). Daily COVID-19 death was negatively correlated with the maximum temperature (β = -22, 95% CI, -26.2 to -17.79 -, p < 0.01), while the maximum temperature was below 30 °C. Similarly, the number of deaths was negatively correlated with the minimum temperature (β = -27.46, 95% CI, -31.48 to -23.45, p < 0.01), when the minimum temperature was below 8 °C. Our study found a significant association between temperature and COVID-19 mortality, with every 1 °C increase in maximum or minimum temperature resulting in a decrease of 22 and 27 deceased cases, respectively. The relationship between atmospheric pressure and COVID-19 mortality was not fully elucidated due to its complex interaction with maximum temperature.

Conclusions

This empirical study adds to the existing body of research on the impact of climate factors on COVID-19 prevention and resource allocation. Policymakers and health scientists may find these findings useful in conjunction with other social factors when making decisions related to COVID-19 prevention and resource allocation.

Differential COVID-19 preventive behaviors among Asian subgroups in the United States

BACKGROUND

Given the observed within-Asian disparity in COVID-19 incidence, we aimed to explore the differential preventive behaviors among Asian subgroups in the United States.

METHODS

Based on data from the Asian subsample (N = 982) of the 2020 Health, Ethnicity, and Pandemic survey, we estimated the weighted proportion of noncompliance with Centers for Disease Control and Prevention (CDC) guidelines on preventive behaviors and COVID-19 testing by Asian subgroups (Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, Other Asian). We examined these subgroup differences after adjusting for demographic factors and state-level clustering.

RESULTS

Filipinos demonstrated the lowest rate of noncompliance for mask-wearing, social distancing, and handwashing. As compared with the Filipinos, our logistic models showed that the Chinese and the 'other Asians' subgroup had significantly higher risk of noncompliance with mask-wearing, while the Japanese, the Vietnamese, and other Asians were significantly more likely to report noncompliance with social distancing.

CONCLUSIONS

The significant variation of preventive behavior across Asian subgroups signals the necessity of data disaggregation when it comes to understanding the health behavior of Asian Americans, which is critical for future pandemic preparedness. The excess behavioral risk among certain Asian subgroups (especially those 'other Asians') warrants further investigation and interventions about the driving forces behind these disparities.

Insight into vaccination and meteorological factors on daily COVID-19 cases and mortality in Bangladesh

The ongoing COVID-19 contagious disease caused by SARS-CoV-2 has disrupted global public health, businesses, and economies due to widespread infection, with 676.41 million confirmed cases and 6.77 million deaths in 231 countries as of February 07, 2023. To control the rapid spread of SARS-CoV-2, it is crucial to determine the potential determinants such as meteorological factors and their roles. This study examines how COVID-19 cases and deaths changed over time while assessing meteorological characteristics that could impact these disparities from the onset of the pandemic. We used data spanning two years across all eight administrative divisions, this is the first of its kind--showing a connection between meteorological conditions, vaccination, and COVID-19 incidences in Bangladesh. We further employed several techniques including Simple Exponential Smoothing (SES), Auto-Regressive Integrated Moving Average (ARIMA), Auto-Regressive Integrated Moving Average with explanatory variables (ARIMAX), and Automatic forecasting time-series model (Prophet). We further analyzed the effects of COVID-19 vaccination on daily cases and deaths. Data on COVID-19 cases collected include eight administrative divisions of Bangladesh spanning March 8, 2020, to January 31, 2023, from available online servers. The meteorological data include rainfall (mm), relative humidity (%), average temperature (°C), surface pressure (kPa), dew point (°C), and maximum wind speed (m/s). The observed wind speed and surface pressure show a significant negative impact on COVID-19 cases (-0.89, 95% confidence interval (CI): 1.62 to -0.21) and (-1.31, 95%CI: 2.32 to -0.29), respectively. Similarly, the observed wind speed and surface pressure show a significant negative impact on COVID-19 deaths (-0.87, 95% CI: 1.54 to -0.21) and (-3.11, 95%CI: 4.44 to -1.25), respectively. The impact of meteorological factors is almost similar when vaccination information is included in the model. However, the impact of vaccination in both cases and deaths model is significantly negative (for cases: 1.19, 95%CI: 2.35 to -0.38 and for deaths: 1.55, 95%CI: 2.88 to -0.43). Accordingly, vaccination effectively reduces the number of new COVID-19 cases and fatalities in Bangladesh. Thus, these results could assist future researchers and policymakers in the assessment of pandemics, by making thorough efforts that account for COVID-19 vaccinations and meteorological conditions.

Influence of meteorological factors on the severity of COVID-19 in Spain: Observational ecological study

Objectives

Evaluating whether meteorological and geographical variables could be associated with the severity of COVID-19 in Spain.

Methods

An ecological study was performed to analyze the influence of meteorological and geographical factors in hospital admissions and deaths due to COVID-19 in the 52 provinces of Spain (24 coastal and 28 inland regions), during the first three pandemic waves. Medical and mortality data were collected from the Carlos III Health Institute (ISCIII) and meteorological variables were requested to the Spanish State Meteorological Agency (AEMET).

Results

Regarding the diagnosed cases it is remarkable that the percentage of patients hospitalized for COVID-19 was lower in the coastal provinces than in the inland ones (8.7 ± 2.6% vs. 11.5 ± 2.6%; p = 9.9 × 10^-5). Furthermore, coastal regions registered a lower percentage of mortality than inland regions (2.0 ± 0.6% vs. 3.1 ± 0.8%; p = 1.7 × 10^-5). Mean air temperature was inversely correlated both with COVID-19 hospitalizations (Rho: -0.59; p = 3.0 × 10^-6) and mortality (Rho: -0.70; p = 5.3 × 10^-9). In those provinces with a mean air temperature <10 °C mortality by COVID-19 was twice that of those with >16 °C. Finally, we found an association between mortality and the location of the province (coastal/inland), altitude, patient age and the average air temperature; the latter was inversely and independently correlated with mortality (non standardised B coeff.: -0.24; IC 95%: -0.31 to -0.16; p = 2.38 × 10^-8).

Conclusions

The average air temperature was inversely associated with COVID-19 mortality in our country during the first three waves of the pandemic.

Warmer ambient air temperatures reduce nasal turbinate and brain infection, but increase lung inflammation in the K18-hACE2 mouse model of COVID-19

Warmer climatic conditions have been associated with fewer COVID-19 cases. Herein we infected K18-hACE2 mice housed at the standard animal house temperature of ∼22 °C, or at ∼31 °C, which is considered to be thermoneutral for mice. On day 2 post infection, RNA-Seq analyses showed no significant differential gene expression lung in lungs of mice housed at the two temperatures, with almost identical viral loads and type I interferon responses. There was also no significant difference in viral loads in lungs on day 5, but RNA-Seq and histology analyses showed clearly elevated inflammatory signatures and infiltrates. Thermoneutrality thus promoted lung inflammation. On day 2 post infection mice housed at 31 °C showed reduced viral loads in nasal turbinates, consistent with increased mucociliary clearance at the warmer ambient temperature. These mice also had reduced virus levels in the brain, and an ensuing amelioration of weight loss and a delay in mortality. Warmer air temperatures may thus reduce infection of the upper respiratory track and the olfactory epithelium, resulting in reduced brain infection. Potential relevance for anosmia and neurological sequelae in COVID-19 patients is discussed.

Long-term air pollution exposure and COVID-19 case-severity: An analysis of individual-level data from Switzerland

Several studies are pointing out that exposure to elevated air pollutants could contribute to increased COVID-19 mortality. However, literature on the associations between air pollution exposure and COVID-19 severe morbidity is rather sparse. In addition, the majority of the studies used an ecological study design and were applied in regions with rather high air pollution levels. Here, we study the differential effects of long-term exposure to air pollution on severe morbidity and mortality risks from COVID-19 in various population subgroups in Switzerland, a country known for clean air. We perform individual-level analyses using data covering the first two major waves of COVID-19 between February 2020 and May 2021. High-resolution maps of particulate matter (PM_2.5) and nitrogen dioxide (NO₂) concentrations were produced for the 6 years preceding the pandemic using Bayesian geostatistical models. Air pollution exposure for each patient was measured by the long-term average concentration across the municipality of residence. The models were adjusted for the effects of individual characteristics, socio-economic, health-system, and climatic factors. The variables with an important association to COVID-19 case-severity were identified using Bayesian spatial variable selection. The results have shown that the individual-level characteristics are important factors related to COVID-19 morbidity and mortality in all the models. Long-term exposure to air pollution appears to influence the severity of the disease only when analyzing data during the first wave; this effect is attenuated upon adjustment for health-system related factors during the entire study period. Our findings suggest that the burden of air pollution increased the risks of COVID-19 in Switzerland during the first wave of the pandemic, but not during the second wave, when the national health system was better prepared.

Epidemiological Context and Risk Factors Associated with the Evolution of the Coronavirus Disease (COVID-19): A Retrospective Cohort Study

Since its initial appearance in December 2019, COVID-19 has posed a serious challenge to healthcare authorities worldwide. The purpose of the current study was to identify the epidemiological context associated with the respiratory illness propagated by the spread of COVID-19 and outline various risk factors related to its evolution in the province of Debila (Southeastern Algeria). A retrospective analysis was carried out for a cohort of 612 COVID-19 patients admitted to hospitals between March 2020 and February 2022. The results were analyzed using descriptive statistics. Further, logistic regression analysis was employed to perform the odds ratio. In gendered comparison, males were found to have a higher rate of incidence and mortality compared to females. In terms of age, individuals with advanced ages of 60 years or over were typically correlated with higher rates of incidence and mortality in comparison toindividuals below this age. Furthermore, the current research indicated that peri-urban areas were less affected that the urban regions, which had relatively significant incidence and mortality rates. The summer season was marked with the highest incidence and mortality rate in comparison with other seasons. Patients who were hospitalized, were the age of 60 or over, or characterized by comorbidity, were mainly associated with death evolution (odds ratio [OR] = 8.695; p = 0.000), (OR = 6.192; p = 0.000), and (OR = 2.538; p = 0.000), respectively. The study identifies an important relationship between the sanitary status of patients, hospitalization, over-age categories, and the case severity of the COVID-19 patient.

Nature's contributions in coping with a pandemic in the 21st century: A narrative review of evidence during COVID-19

While COVID-19 lockdowns have slowed coronavirus transmission, such structural measures also have unintended consequences on mental and physical health. Growing evidence shows that exposure to the natural environment (e.g., blue-green spaces) can improve human health and wellbeing. In this narrative review, we synthesized the evidence about nature's contributions to health and wellbeing during the first two years of the COVID-19 pandemic. We found that during the pandemic, people experienced multiple types of nature, including both outdoors and indoors. Frequency of visits to outdoor natural areas (i.e., public parks) depended on lockdown severity and socio-cultural contexts. Other forms of nature exposure, such as spending time in private gardens and viewing outdoor greenery from windows, may have increased. The majority of the evidence suggests nature exposure during COVID-19 pandemic was associated with less depression, anxiety, stress, and more happiness and life satisfaction. Additionally, nature exposure was correlated with less physical inactivity and fewer sleep disturbances. Evidence was mixed regarding associations between nature exposure and COVID-related health outcomes, while nature visits might be associated with greater rates of COVID-19 transmission and mortality when proper social distancing measures were not maintained. Findings on whether nature exposure during lockdowns helped ameliorate health inequities by impacting the health of lower-socioeconomic populations more than their higher-socioeconomic counterparts for example were mixed. Based on these findings, we argue that nature exposure may have buffered the negative mental and behavioral impacts of lockdowns during the COVID-19 pandemic. Recovery and resilience during the current crises and future public health crises might be improved with nature-based infrastructure, interventions, designs, and governance.

Nature's contributions in coping with a pandemic in the 21st century: A narrative review of evidence during COVID-19

While COVID-19 lockdowns have slowed coronavirus transmission, such structural measures also have unintended consequences on mental and physical health. Growing evidence shows that exposure to the natural environment (e.g., blue-green spaces) can improve human health and wellbeing. In this narrative review, we synthesized the evidence about nature's contributions to health and wellbeing during the first two years of the COVID-19 pandemic. We found that during the pandemic, people experienced multiple types of nature, including both outdoors and indoors. Frequency of visits to outdoor natural areas (i.e., public parks) depended on lockdown severity and socio-cultural contexts. Other forms of nature exposure, such as spending time in private gardens and viewing outdoor greenery from windows, may have increased. The majority of the evidence suggests nature exposure during COVID-19 pandemic was associated with less depression, anxiety, stress, and more happiness and life satisfaction. Additionally, nature exposure was correlated with less physical inactivity and fewer sleep disturbances. Evidence was mixed regarding associations between nature exposure and COVID-related health outcomes, while nature visits might be associated with greater rates of COVID-19 transmission and mortality when proper social distancing measures were not maintained. Findings on whether nature exposure during lockdowns helped ameliorate health inequities by impacting the health of lower-socioeconomic populations more than their higher-socioeconomic counterparts for example were mixed. Based on these findings, we argue that nature exposure may have buffered the negative mental and behavioral impacts of lockdowns during the COVID-19 pandemic. Recovery and resilience during the current crises and future public health crises might be improved with nature-based infrastructure, interventions, designs, and governance.

Assessing the impact of long-term exposure to nine outdoor air pollutants on COVID-19 spatial spread and related mortality in 107 Italian provinces

This paper investigates the air quality in 107 Italian provinces in the period 2014-2019 and the association between exposure to nine outdoor air pollutants and the COVID-19 spread and related mortality in the same areas. The methods used were negative binomial (NB) regression, ordinary least squares (OLS) model, and spatial autoregressive (SAR) model. The results showed that (i) common air pollutants-nitrogen dioxide (NO₂), ozone (O₃), and particulate matter (PM_2.5 and PM₁₀)-were highly and positively correlated with large firms, energy and gas consumption, public transports, and livestock sector; (ii) long-term exposure to NO₂, PM_2.5, PM₁₀, benzene, benzo[a]pyrene (BaP), and cadmium (Cd) was positively and significantly correlated with the spread of COVID-19; and (iii) long-term exposure to NO_2, O₃, PM_2.5, PM₁₀, and arsenic (As) was positively and significantly correlated with COVID-19 related mortality. Specifically, particulate matter and Cd showed the most adverse effect on COVID-19 prevalence; while particulate matter and As showed the largest dangerous impact on excess mortality rate. The results were confirmed even after controlling for eighteen covariates and spatial effects. This outcome seems of interest because benzene, BaP, and heavy metals (As and Cd) have not been considered at all in recent literature. It also suggests the need for a national strategy to drive down air pollutant concentrations to cope better with potential future pandemics.

Role of meteorological factors on SARS-CoV-2 infection incidence in Italy and Spain before the vaccination campaign. A multi-city time series study

Numerous studies have been conducted worldwide to investigate if an association exists between meteorological factors and the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection incidence. Although research studies provide conflicting results, which can be partially explained by different methods used, some clear trends emerge on the role of weather conditions and SARS-CoV-2 infection, especially for temperature and humidity. This study sheds more light on the relationship between meteorological factors and SARS-CoV-2 infection incidence in 23 Italian and 52 Spanish cities. For the purposes of this study, daily air temperature, absolute and relative humidity, wind speed, ultraviolet radiation, and rainfall are considered exposure variables. We conducted a two-stage meta-regression. In the first stage, we estimated the exposure-response association through time series regression analysis at the municipal level. In the second stage, we pooled the association parameters using a meta-analytic model. The study demonstrates an association between meteorological factors and SARS-CoV-2 infection incidence. Specifically, low levels of ambient temperatures and absolute humidity were associated with an increased relative risk. On the other hand, low and high levels of relative humidity and ultraviolet radiation were associated with a decreased relative risk. Concerning wind speed and rainfall, higher values contributed to the reduction of the risk of infection. Overall, our results contribute to a better understanding of how the meteorological factors influence the spread of the SARS-CoV-2 and should be considered in a wider context of existing robust literature that highlight the importance of measures such as social distancing, improved hygiene, face masks and vaccination campaign.

The effect of COVID certificates on vaccine uptake, health outcomes, and the economy

In the COVID-19 pandemic many countries required COVID certificates, proving vaccination, recovery, or a recent negative test, to access public and private venues. We estimate their effect on vaccine uptake for France, Germany, and Italy using counterfactuals constructed via innovation diffusion theory. The announcement of COVID certificates during summer 2021 were associated - although causality cannot be directly inferred - with increased vaccine uptake in France of 13.0 (95% CI 9.7-14.9) percentage points (p.p.) of the total population until the end of the year, in Germany 6.2 (2.6-6.9) p.p., and in Italy 9.7 (5.4-12.3) p.p. Based on these estimates, an additional 3979 (3453-4298) deaths in France, 1133 (-312-1358) in Germany, and 1331 (502-1794) in Italy were averted; and gross domestic product (GDP) losses of €6.0 (5.9-6.1) billion in France, €1.4 (1.3-1.5) billion in Germany, and €2.1 (2.0-2.2) billion in Italy were prevented. Notably, in France, the application of COVID certificates averted high intensive care unit occupancy levels where prior lockdowns were instated.

From 15 Minutes to 15 Seconds: How the Delta Variant Changed the Risk of Exposure to COVID-19. A Comparative Epidemiological Investigation Using Community Mobility Data From the Metropolitan Area of Genoa, Italy

The Delta variant became dominant during the second wave of the Covid-19 pandemic due to its competitive advantage, the ability to reduce close contact duration from minutes to seconds, and, consequently, increase the risk of exposure to COVID-19. We used game theory to model the most effective public health response to this new threat. We compared the absolute and relative risk of exposure to COVID-19 before and after the emergence of the Delta variant. The absolute risk of exposure was defined as the product of crowding (people within a six feet distance) and visit duration. Our epidemiological investigation used aggregated and anonymized mobility data from Google Maps to estimate the visit duration for 808 premises in the metropolitan area of Genoa, Italy, in June 2021. The relative risk of exposure was obtained by dividing the risk of exposure of each activity by the lowest value (gas stations = 1). The median absolute risk of exposure to COVID-19 increased by sixty-fold in the first semester of 2021, while the relative risk did not significantly differ from the risk of exposure to the ancestral form of Covid-19 (5.9 in 2021 vs. 2.5 in 2021). The Delta variant represents an evolution of the game against COVID-19, but it is not a game-changer. The best response is to commit to our original strategy based on population-wide vaccination and social distancing. Unilateral deviations from the dominant strategy could offer COVID-19 a fighting chance against humanity.

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.

An analysis of the dynamic spatial spread of COVID-19 across South Korea

The first case of coronavirus disease 2019 (COVID-19) in South Korea was confirmed on January 20, 2020, approximately three weeks after the report of the first COVID-19 case in Wuhan, China. By September 15, 2021, the number of cases in South Korea had increased to 277,989. Thus, it is important to better understand geographical transmission and design effective local-level pandemic plans across the country over the long term. We conducted a spatiotemporal analysis of weekly COVID-19 cases in South Korea from February 1, 2020, to May 30, 2021, in each administrative region. For the spatial domain, we first covered the entire country and then focused on metropolitan areas, including Seoul, Gyeonggi-do, and Incheon. Moran's I and spatial scan statistics were used for spatial analysis. The temporal variation and dynamics of COVID-19 cases were investigated with various statistical visualization methods. We found time-varying clusters of COVID-19 in South Korea using a range of statistical methods. In the early stage, the spatial hotspots were focused in Daegu and Gyeongsangbuk-do. Then, metropolitan areas were detected as hotspots in December 2020. In our study, we conducted a time-varying spatial analysis of COVID-19 across the entirety of South Korea over a long-term period and found a powerful approach to demonstrating the current dynamics of spatial clustering and understanding the dynamic effects of policies on COVID-19 across South Korea. Additionally, the proposed spatiotemporal methods are very useful for understanding the spatial dynamics of COVID-19 in South Korea.

Overlapping Delta and Omicron Outbreaks During the COVID-19 Pandemic: Dynamic Panel Data Estimates

BACKGROUND

The Omicron variant of SARS-CoV-2 is more transmissible than prior variants of concern (VOCs). It has caused the largest outbreaks in the pandemic, with increases in mortality and hospitalizations. Early data on the spread of Omicron were captured in countries with relatively low case counts, so it was unclear how the arrival of Omicron would impact the trajectory of the pandemic in countries already experiencing high levels of community transmission of Delta.

OBJECTIVE

The objective of this study is to quantify and explain the impact of Omicron on pandemic trajectories and how they differ between countries that were or were not in a Delta outbreak at the time Omicron occurred.

METHODS

We used SARS-CoV-2 surveillance and genetic sequence data to classify countries into 2 groups: those that were in a Delta outbreak (defined by at least 10 novel daily transmissions per 100,000 population) when Omicron was first sequenced in the country and those that were not. We used trend analysis, survival curves, and dynamic panel regression models to compare outbreaks in the 2 groups over the period from November 1, 2021, to February 11, 2022. We summarized the outbreaks in terms of their peak rate of SARS-CoV-2 infections and the duration of time the outbreaks took to reach the peak rate.

RESULTS

Countries that were already in an outbreak with predominantly Delta lineages when Omicron arrived took longer to reach their peak rate and saw greater than a twofold increase (2.04) in the average apex of the Omicron outbreak compared to countries that were not yet in an outbreak.

CONCLUSIONS

These results suggest that high community transmission of Delta at the time of the first detection of Omicron was not protective, but rather preluded larger outbreaks in those countries. Outbreak status may reflect a generally susceptible population, due to overlapping factors, including climate, policy, and individual behavior. In the absence of strong mitigation measures, arrival of a new, more transmissible variant in these countries is therefore more likely to lead to larger outbreaks. Alternately, countries with enhanced surveillance programs and incentives may be more likely to both exist in an outbreak status and detect more cases during an outbreak, resulting in a spurious relationship. Either way, these data argue against herd immunity mitigating future outbreaks with variants that have undergone significant antigenic shifts.

The impact of three progressively introduced interventions on second wave daily COVID-19 case numbers in Melbourne, Australia

BACKGROUND

The city of Melbourne, Australia experienced two waves of the COVID-19 epidemic peaking, the first in March and a more substantial wave in July 2020. During the second wave, a series of control measure were progressively introduced that initially slowed the growth of the epidemic then resulted in decreasing cases until there was no detectable local transmission.

METHODS

To determine the relative efficacy of the progressively introduced intervention measures, we modelled the second wave as a series of exponential growth and decay curves. We used a linear regression of the log of daily cases vs time, using a four-segment linear spline model corresponding to implementation of the three successive major public health measures. The primary model used all reported cases between 14 June and 15 September 2020 then compared the projection of the model with observed cases predicting future case trajectory up until the 31 October 2020 to assess the use of exponential models in projecting the future course and planning future interventions. The main outcome measures were the exponential daily growth constants, analysis of residuals and estimates of the 95% confidence intervals for the expected case distributions, comparison of predicted daily cases.

RESULTS

The exponential growth/decay constants in the primary analysis were: 0.122 (s.e. 0.004), 0.035 (s.e. 0.005), - 0.037 (s.e. 0.011), and - 0.069 (s.e. 0.003) for the initial growth rate, Stage 3, Stage 3 + compulsory masks and Stage 4, respectively. Extrapolation of the regression model from the 14 September to the 31 October matched the decline in observed cases over this period.

CONCLUSIONS

The four-segment exponential model provided an excellent fit of the observed reported case data and predicted the day-to-day range of expected cases. The extrapolated regression accurately predicted the decline leading to epidemic control in Melbourne.

A narrative review on the role of temperature and humidity in COVID-19: Transmission, persistence, and epidemiological evidence

Since December 2019, the 2019 coronavirus disease (COVID-19) outbreak has become a global pandemic. Understanding the role of environmental conditions is important in impeding the spread of COVID-19. Given that airborne spread and contact transmission are considered the main pathways for the spread of COVID-19, this narrative review first summarized the role of temperature and humidity in the airborne trajectory of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Meanwhile, we reviewed the persistence of the virus in aerosols and on inert surfaces and summarized how the persistence of SARS-CoV-2 is affected by temperature and humidity. We also examined the existing epidemiological evidence and addressed the limitations of these epidemiological studies. Although uncertainty remains, more evidence may support the idea that high temperature is slightly and negatively associated with COVID-19 growth, while the conclusion for humidity is still conflicting. Nonetheless, the spread of COVID-19 appears to have been controlled primarily by government interventions rather than environmental factors.

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).

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.

The Impact of COVID-19 on Mortality in Italy: Retrospective Analysis of Epidemiological Trends

BACKGROUND

Despite the available evidence on its severity, COVID-19 has often been compared with seasonal flu by some conspirators and even scientists. Various public discussions arose about the noncausal correlation between COVID-19 and the observed deaths during the pandemic period in Italy.

OBJECTIVE

This paper aimed to search for endogenous reasons for the mortality increase recorded in Italy during 2020 to test this controversial hypothesis. Furthermore, we provide a framework for epidemiological analyses of time series.

METHODS

We analyzed deaths by age, sex, region, and cause of death in Italy from 2011 to 2019. Ordinary least squares (OLS) linear regression analyses and autoregressive integrated moving average (ARIMA) were used to predict the best value for 2020. A Grubbs 1-sided test was used to assess the significance of the difference between predicted and observed 2020 deaths/mortality. Finally, a 1-sample t test was used to compare the population of regional excess deaths to a null mean. The relationship between mortality and predictive variables was assessed using OLS multiple regression models. Since there is no uniform opinion on multicomparison adjustment and false negatives imply great epidemiological risk, the less-conservative Siegel approach and more-conservative Holm-Bonferroni approach were employed. By doing so, we provided the reader with the means to carry out an independent analysis.

RESULTS

Both ARIMA and OLS linear regression models predicted the number of deaths in Italy during 2020 to be between 640,000 and 660,000 (range of 95% CIs: 620,000-695,000) against the observed value of above 750,000. We found strong evidence supporting that the death increase in all regions (average excess=12.2%) was not due to chance (t21=7.2; adjusted P<.001). Male and female national mortality excesses were 18.4% (P<.001; adjusted P=.006) and 14.1% (P=.005; adjusted P=.12), respectively. However, we found limited significance when comparing male and female mortality residuals' using the Mann-Whitney U test (P=.27; adjusted P=.99). Finally, mortality was strongly and positively correlated with latitude (R=0.82; adjusted P<.001). In this regard, the significance of the mortality increases during 2020 varied greatly from region to region. Lombardy recorded the highest mortality increase (38% for men, adjusted P<.001; 31% for women, P<.001; adjusted P=.006).

CONCLUSIONS

Our findings support the absence of historical endogenous reasons capable of justifying the mortality increase observed in Italy during 2020. Together with the current knowledge on SARS-CoV-2, these results provide decisive evidence on the devastating impact of COVID-19. We suggest that this research be leveraged by government, health, and information authorities to furnish proof against conspiracy hypotheses that minimize COVID-19-related risks. Finally, given the marked concordance between ARIMA and OLS regression, we suggest that these models be exploited for public health surveillance. Specifically, meaningful information can be deduced by comparing predicted and observed epidemiological trends.

The effects of super spreading events and movement control measures on the COVID-19 pandemic in Malaysia

This paper aims to develop an automated web application to generate validated daily effective reproduction numbers (Rt) which can be used to examine the effects of super-spreading events due to mass gatherings and the effectiveness of the various Movement Control Order (MCO) stringency levels on the outbreak progression of COVID-19 in Malaysia. The effective reproduction number, Rt, was estimated by adopting and modifying an Rt estimation algorithm using a validated distribution mean of 3.96 and standard deviation of 4.75 with a seven-day sliding window. The Rt values generated were validated using thea moving window SEIR model with a negative binomial likelihood fitted using methods from the Bayesian inferential framework. A Pearson’s correlation between the Rt values estimated by the algorithm and the SEIR model was r = 0.70, p < 0.001 and r = 0.81, p < 0.001 during the validation period The Rt increased to reach the highest values at 3.40 (95% CI 1.47, 6.14) and 1.72 (95% CI 1.54, 1.90) due to the Sri Petaling and Sabah electoral process during the second and third waves of COVID-19 respectively. The MCOs was able to reduce the Rt values by 63.2 to 77.1% and 37.0 to 47.0% during the second and third waves of COVID-19, respectively. Mass gathering events were one of the important drivers of the COVID-19 outbreak in Malaysia. However, COVID-19 transmission can be fuelled by noncompliance to Standard Operating Procedure, population mobility, ventilation and environmental factors.

Flight attendant occupational nutrition and lifestyle factors associated with COVID-19 incidence

In the era of COVID-19, essential workers are plagued with unforeseen and obfuscated challenges. Flight attendants are a unique subgroup of essential workers who face a multitude of health risks attributed to occupational exposures that are accentuated by the COVID-19 pandemic. Such risks can be ameliorated with strategies that target factors which enhance COVID-19 risk, including modifiable factors of diet and lifestyle. The aim of this cross-sectional study is to detect occupational dietary and lifestyle factors which could increase COVID-19 incidence amongst flight attendants. To identify potential risk factors, a questionnaire was administered to eighty-four flight attendants and examined the participants’ diet and lifestyle, and COVID-19 incidence. Descriptive statistics and logistic regression indicated that the participants’ perceived dietary quality at work (p = 0.003), sleep disruptions which impacted their consumption of a healthy diet (p = 0.013), job tenure (OR: 0.67, 95% CI: 0.46:0.98) and frequency of reported cold/flu (OR: 1.49, 95% CI: 1.014–2.189) were all factors associated with confirmed/suspected COVID-19 incidence. This study also revealed that a lack of infrastructure for food storage and time limitations are considerable occupational barriers for flight attendants to consume healthy foods. Additional investigation can further elucidate these relationships and related solutions to mitigate COVID-19 risk in the future.

The impact of temperature on the transmissibility potential and virulence of COVID-19 in Tokyo, Japan

Assessing the impact of temperature on COVID-19 epidemiology is critical for implementing non-pharmaceutical interventions. However, few studies have accounted for the nature of contagious diseases, i.e., their dependent happenings. We aimed to quantify the impact of temperature on the transmissibility and virulence of COVID-19 in Tokyo, Japan, employing two epidemiological measurements of transmissibility and severity: the effective reproduction number (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_{t}$$\end{document}Rt) and case fatality risk (CFR). We estimated the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_{t}$$\end{document}Rt and time-delay adjusted CFR and to subsequently assess the nonlinear and delayed effect of temperature on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_{t}$$\end{document}Rt and time-delay adjusted CFR. For \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_{t}$$\end{document}Rt at low temperatures, the cumulative relative risk (RR) at the first temperature percentile (3.3 °C) was 1.3 (95% confidence interval (CI): 1.1–1.7). As for the virulence to humans, moderate cold temperatures were associated with higher CFR, and CFR also increased as the temperature rose. The cumulative RR at the 10th and 99th percentiles of temperature (5.8 °C and 30.8 °C) for CFR were 3.5 (95% CI: 1.3–10.0) and 6.4 (95% CI: 4.1–10.1). Our results suggest the importance to take precautions to avoid infection in both cold and warm seasons to avoid severe cases of COVID-19. The results and our proposed approach will also help in assessing the possible seasonal course of COVID-19 in the future.

The role of European health system characteristics in affecting Covid 19 lethality during the early days of the pandemic

This article examines the main factors affecting COVID-19 lethality across 16 European Countries with a focus on the role of health system characteristics during the first phase of the diffusion of the virus. Specifically, we investigate the leading causes of lethality at 10, 20, 30, 40 days in the first hit of the pandemic. Using a random forest regression (ML), with lethality as outcome variable, we show that the percentage of people older than 65 years (with two or more chronic diseases) is the main predictor variable of lethality by COVID-19, followed by the number of hospital intensive care unit beds, investments in healthcare spending compared to GDP, number of nurses and doctors. Moreover, the variable of general practitioners has little but significant predicting quality. These findings contribute to provide evidence for the prediction of lethality caused by COVID-19 in Europe and open the discussion on health policy and management of health care and ICU beds during a severe epidemic.