Winter Is Coming: A Southern Hemisphere Perspective of the Environmental Drivers of SARS-CoV-2 and the Potential Seasonality of COVID-19

On the SARS-CoV-2 Variants

The evolutionary dynamics of viruses, particularly exemplified by SARS-CoV-2 during the ongoing COVID-19 pandemic, underscore the intricate interplay between genetics, host adaptation, and viral spread. This paper delves into the genetic evolution of SARS-CoV-2, emphasizing the implications of viral variants on global health. Initially emerging from the Wuhan-Hu-1 lineage, SARS-CoV-2 rapidly diversified into numerous variants, each characterized by distinct mutations in the spike protein and other genomic regions. Notable variants such as B.1.1.7 (α), B.1.351 (β), P.1 (γ), B.1.617.2 (δ), and the Omicron variant have garnered significant attention due to their heightened transmissibility and immune evasion capabilities. In particular, the Omicron variant has presented a myriad of subvariants, raising concerns about its potential impact on public health. Despite the emergence of numerous variants, the vast majority have exhibited limited expansion capabilities and have not posed significant threats akin to early pandemic strains. Continued genomic surveillance is imperative to identify emerging variants of concern promptly. While genetic adaptation is intrinsic to viral evolution, effective public health responses must be grounded in empirical evidence to navigate the evolving landscape of the pandemic with resilience and precision.

Exploring the interplay between climate, population immunity and SARS-CoV-2 transmission dynamics in Mediterranean countries

The relationship between SARS-CoV-2 transmission and environmental factors has been analyzed in numerous studies since the outbreak of the pandemic, resulting in heterogeneous results and conclusions. This may be due to differences in methodology, considered variables, confounding factors, studied periods and/or lack of adequate data. Furthermore, previous works have reported that the lack of population immunity is the fundamental driver in transmission dynamics and can mask the potential impact of environmental variables. In this study, we aimed to investigate the association between climate variables and COVID-19 transmission considering the influence of population immunity. We analyzed two different periods characterized by the absence of vaccination (low population immunity) and a high degree of vaccination (high level of population immunity), respectively. Although this study has some limitations, such us the restriction to a specific climatic zone and the omission of other environmental factors, our results indicate that transmission of SARS-CoV-2 may increase independently of temperature and specific humidity in periods with low levels of population immunity while a negative association is found under conditions with higher levels of population immunity in the analyzed regions.

Climate and human health: a review of publication trends in the International Journal of Biometeorology

The climate-health nexus is well documented in the field of biometeorology. Since its inception, Biometeorology has in many ways become the umbrella under which much of this collaborative research has been conducted. Whilst a range of review papers have considered the development of biometeorological research and its coverage in this journal, and a few have reviewed the literature on specific diseases, none have focused on the sub-field of climate and health as a whole. Since its first issue in 1957, the International Journal of Biometeorology has published a total of 2183 papers that broadly consider human health and its relationship with climate. In this review, we identify a total of 180 (8.3%, n = 2183) of these papers that specifically focus on the intersection between meteorological variables and specific, named diagnosable diseases, and explore the publication trends thereof. The number of publications on climate and health in the journal increases considerably since 2011. The largest number of publications on the topic was in 2017 (18) followed by 2021 (17). Of the 180 studies conducted, respiratory diseases accounted for 37.2% of the publications, cardiovascular disease 17%, and cerebrovascular disease 11.1%. The literature on climate and health in the journal is dominated by studies from the global North, with a particular focus on Asia and Europe. Only 2.2% and 8.3% of these studies explore empirical evidence from the African continent and South America respectively. These findings highlight the importance of continued research on climate and human health, especially in low- and lower-middle-income countries, the populations of which are more vulnerable to climate-sensitive illnesses.

Spatio-temporal multidisciplinary analysis of socio-environmental conditions to explore the COVID-19 early evolution in urban sites in South America

This study aimed to analyse how socio-environmental conditions affected the early evolution of COVID-19 in 14 urban sites in South America based on a spatio-temporal multidisciplinary approach. The daily incidence rate of new COVID-19 cases with symptoms as the dependent variable and meteorological-climatic data (mean, maximum, and minimum temperature, precipitation, and relative humidity) as the independent variables were analysed. The study period was from March to November of 2020. We inquired associations of these variables with COVID-19 data using Spearman's non-parametric correlation test, and a principal component analysis considering socio economic and demographic variables, new cases, and rates of COVID-19 new cases. Finally, an analysis using non-metric multidimensional scale ordering by the Bray-Curtis similarity matrix of meteorological data, socio economic and demographic variables, and COVID-19 was performed. Our findings revealed that the average, maximum, and minimum temperatures and relative humidity were significantly associated with rates of COVID-19 new cases in most of the sites, while precipitation was significantly associated only in four sites. Additionally, demographic variables such as the number of inhabitants, the percentage of the population aged 60 years and above, the masculinity index, and the GINI index showed a significant correlation with COVID-19 cases. Due to the rapid evolution of the COVID-19 pandemic, these findings provide strong evidence that biomedical, social, and physical sciences should join forces in truly multidisciplinary research that is critically needed in the current state of our region.

Psychological impacts of transition to distance learning due to COVID-19 on nursing students

The purpose of this study was to assess the psychological impacts of the abrupt transition to distance learning due to COVID-19 and associated factors among Jordanian nursing students. A cross-sectional online survey with a sample of 224 nursing students in three universities was carried out. The survey measured students' depression, anxiety, stress, post-traumatic stress disorder (PTSD), academic self-efficacy, and demographic and distance learning-related data. Students have moderate levels of depression, anxiety, and stress. Depression, anxiety, stress, and PTSD were significantly associated with various demographic, disease-related, and distance learning-related variables. Further work is required to prepare professional psychological help for nursing students and develop future teaching plans that take into consideration factors underlying students' negative symptoms.

Effects of hydrometeorological and other factors on SARS-CoV-2 reproduction number in three contiguous countries of tropical Andean South America: a spatiotemporally disaggregated time series analysis

Background

The COVID-19 pandemic has caused societal disruption globally, and South America has been hit harder than other lower-income regions. This study modeled the effects of six weather variables on district-level SARS-CoV-2 reproduction numbers (Rt ) in three contiguous countries of tropical Andean South America (Colombia, Ecuador, and Peru), adjusting for environmental, policy, healthcare infrastructural and other factors.

Methods

Daily time-series data on SARS-CoV-2 infections were sourced from the health authorities of the three countries at the smallest available administrative level. Rt values were calculated and merged by date and unit ID with variables from a unified COVID-19 dataset and other publicly available sources for May-December, 2020. Generalized additive models were fitted.

Findings

Relative humidity and solar radiation were inversely associated with SARS-CoV-2 Rt . Days with radiation above 1000 kJ/m2 saw a 1.3% reduction in Rt , and those with humidity above 50% recorded a 0.9% reduction in Rt . Transmission was highest in densely populated districts, and lowest in districts with poor healthcare access and on days with lowest population mobility. Wind speed, temperature, region, aggregate government policy response, and population age structure had little impact. The fully adjusted model explained 4.3% of Rt variance.

Interpretation

Dry atmospheric conditions of low humidity increase district-level SARS-CoV-2 reproduction numbers, while higher levels of solar radiation decrease district-level SARS-CoV-2 reproduction numbers - effects that are comparable in magnitude to population factors like lockdown compliance. Weather monitoring could be incorporated into disease surveillance and early warning systems in conjunction with more established risk indicators and surveillance measures.

Funding

NASA's Group on Earth Observations Work Programme (16-GEO16-0047).

The ecosyndemic framework of the global environmental change and the COVID-19 pandemic

The ecosyndemic theory combines the concept of 'synergy' with 'epidemic' and the term "eco" implies the role of the environmental changes. Each of the conditions enhances the negative impacts of the other in an additive way making our society more vulnerable and heightening individual risk factors. In this study, we analyze the mutually reinforcing links between the environment and health from the complexity angle of the ecosyndemic theory and propose the characterization of the COVID-19 pandemic as ecosyndemic. We use the term 'ecosyndemic' because the global environmental change contributes to local-scale, regional-scale and global-scale alterations of the Earth's systems. These changes have their root causes in the way that people interact with the physical, chemical, and biotic factors of the environment. These interactions disturb nature and the consequences have feedbacks in every living organism.

Defining the South African Acute Respiratory Infectious Disease Season

The acute respiratory infectious disease season, or colloquially the "flu season", is defined as the annually recurring period characterized by the prevalence of an outbreak of acute respiratory infectious diseases. It has been widely agreed that this season spans the winter period globally, but the precise timing or intensity of the season onset in South Africa is not well defined. This limits the efficacy of the public health sector to vaccinate for influenza timeously and for health facilities to synchronize efficiently for an increase in cases. This study explores the statistical intensity thresholds in defining this season to determine the start and finish date of the acute respiratory infectious disease season in South Africa. Two sets of data were utilized: public-sector hospitalization data that included laboratory-tested RSV and influenza cases and private-sector medical insurance claims under ICD 10 codes J111, J118, J110, and J00. Using the intensity threshold methodology proposed by the US CDC in 2017, various thresholds were tested for alignment with the nineteen-week flu season as proposed by the South African NICD. This resulted in varying thresholds for each province. The respiratory disease season commences in May and ends in September. These findings were seen in hospitalization cases and medical insurance claim cases, particularly with influenza-positive cases in Baragwanath hospital for the year 2019. These statistically determined intensity thresholds and timing of the acute respiratory infectious disease season allow for improved surveillance and preparedness among the public and private healthcare.

The emergence of SARS-CoV-2 Omicron subvariants: current situation and future trends

The SARS-CoV-2 Omicron variant (B.1.1.529) has been the most recent variant of concern (VOC) established by the World Health Organization (WHO). Because of its greater infectivity and immune evasion, this variant quickly became the dominant type of circulating SARS-CoV-2 worldwide. Our literature review thoroughly explains the current state of Omicron emergence, particularly by comparing different omicron subvariants, including BA.2, BA.1, and BA.3. Such elaboration would be based on structural variations, mutations, clinical manifestation, transmissibility, pathogenicity, and vaccination effectiveness. The most notable difference between the three subvariants is the insufficiency of deletion (Δ69-70) in the spike protein, which results in a lower detection rate of the spike (S) gene target known as (S) gene target failure (SGTF). Furthermore, BA.2 had a stronger affinity to the human Angiotensin-converting Enzyme (hACE2) receptor than other Omicron sub-lineages. Regarding the number of mutations, BA.1.1 has the most (40), followed by BA.1, BA.3, and BA.3 with 39, 34, and 31 mutations, respectively. In addition, BA.2 and BA.3 have greater transmissibility than other sub-lineages (BA.1 and BA.1.1). These characteristics are primarily responsible for Omicron's vast geographical spread and high contagiousness rates, particularly BA.2 sub-lineages.

What will the ambitions of primary healthcare be in the 21st century?

This article attempts to analyze the problem of the modernity of primary healthcare, as well as the reforms to be implemented in a new context characterized by COVID-19. This article offers another vision to follow, to build a modern primary healthcare system. It is a descriptive and analytical study, which addresses the failure of the health system in general, as it proposes the reforms necessary to provide equitable, efficient and modern primary healthcare. The results clearly show that we can no longer act unilaterally; multi-sector efforts at the national level should be encouraged: we must act, in a preventive way, on the causes of the disease; international agreements on the environment, prices of basic foods and medicines, etc. are much needed to improve the health status of middle and low income countries, the case of the majority of African countries. So, different solutions can be conveyed by primary healthcare, in order to improve the whole health system.

The Relative Contribution of Climatic, Demographic Factors, Disease Control Measures and Spatiotemporal Heterogeneity to Variation of Global COVID-19 Transmission

Despite a substantial number of COVID-19 related research papers published, it remains unclear as to which factors are associated with the observed variation in global transmission and what are their relative levels of importance. This study applies a rigorous statistical framework to provide robust estimations of the factor effects for a global and integrated perspective on this issue. We developed a mixed effect model exploring the relative importance of potential factors driving COVID-19 transmission while incorporating spatial and temporal heterogeneity of spread. We use an integrated data set for 87 countries across six continents for model specification and fitting. The best model accounts for 70.4% of the variance in the data analyzed: 10 fixed effect factors explain 20.5% of the variance, random temporal and spatial effects account for 50% of the variance. The fixed effect factors are classified into climatic, demographic and disease control groups. The explained variance in global transmission by the three groups are 0.6%, 1.1%, and 4.4% respectively. The high proportion of variance accounted for by random effects indicated striking differences in temporal transmission trajectories and effects of population mobility among the countries. In particular, the country-specific mobility-transmission relationship turns out to be the most important factor in explaining the observed global variation of transmission in the early phase of COVID-19 pandemic.

Assessing the Impact of Meteorological Factors on COVID-19 Seasonality in Metropolitan Chennai, India

Meteorological factors may influence coronavirus disease 2019 (COVID-19) transmission. Due to the small number of time series studies, the relative importance of seasonality and meteorological factors is still being debated. From March 2020 to April 2021, we evaluated the impact of meteorological factors on the transmission of COVID-19 in Chennai, India. Understanding how the COVID-19 pandemic spreads over the year is critical to developing public health strategies. Correlation models were used to examine the influence of meteorological factors on the transmission of COVID-19. The results revealed seasonal variations in the number of COVID-19-infected people. COVID-19 transmission was greatly aggravated by temperature, wind speed, nitric oxide (NO) and barometric pressure (BP) during summer seasons, whereas wind speed and BP aggravated COVID-19 transmission during rainy seasons. Furthermore, PM 2.5, NO and BP aggravated COVID-19 transmission during winter seasons. However, their relationships fluctuated seasonally. Our research shows that seasonal influences must be considered when developing effective interventions.

Spatial differentiation and determinants of COVID-19 in Indonesia

Background

The spread of the coronavirus disease 2019 (COVID-19) has increasingly agonized daily lives worldwide. As an archipelagic country, Indonesia has various physical and social environments, which implies that each region has a different response to the pandemic. This study aims to analyze the spatial differentiation of COVID-19 in Indonesia and its interactions with socioenvironmental factors.

Methods

The socioenvironmental factors include seven variables, namely, the internet development index, literacy index, average temperature, urban index, poverty rate, population density (PD) and commuter worker (CW) rate. The multiple linear regression (MLR) and geographically weighted regression (GWR) models are used to analyze the impact of the socioenvironmental factors on COVID-19 cases. COVID-19 data is obtained from the Indonesian Ministry of Health until November 30th 2020.

Results

Results show that the COVID-19 cases in Indonesia are concentrated in Java, which is a densely populated area with high urbanization and industrialization. The other provinces with numerous confirmed COVID-19 cases include South Sulawesi, Bali, and North Sumatra. This study shows that the socioenvironmental factors, simultaneously, influence the increasing of confirmed COVID-19 cases in the 34 provinces of Indonesia. Spatial interactions between the variables in the GWR model are relatively better than those between the variables in the MLR model. The highest spatial tendency is observed outside Java, such as in East Nusa Tenggara, West Nusa Tenggara, and Bali.

Conclusion

Priority for mitigation and outbreak management should be high in areas with high PD, urbanized spaces, and CW.

Meteorological factors' effects on COVID-19 show seasonality and spatiality in Brazil

The meteorological conditions may affect COVID-19 transmission. However, the roles of seasonality and macro-climate are still contentious due to the limited time series for early-stage studies. We studied meteorological factors' effects on COVID-19 transmission in Brazil from February 25 to November 15, 2020. We aimed to explore whether this impact showed seasonal characteristics and spatial variations related to the macro-climate. We applied two-way fixed-effect models to identify the effects of meteorological factors on COVID-19 transmission and used spatial analysis to explore their spatial-temporal characteristics with a relatively long-time span. The results showed that cold, dry and windless conditions aggravated COVID-19 transmission. The daily average temperature, humidity, and wind speed negatively affected the daily new cases. Humidity and temperature played a dominant role in this process. For the time series, the influences of meteorological conditions on COVID-19 had a periodic fluctuation of 3-4 months (in line with the seasons in Brazil). The turning points of this fluctuation occurred at the turn of seasons. Spatially, the negative effects of temperature and humidity on COVID-19 transmission clustered in the northeastern and central parts of Brazil. This is consistent with the range of arid climate types. Overall, the seasonality and similar climate types should be considered to estimate the spatial-temporal COVID-19 patterns. Winter is a critical time to be alert for COVID-19, especially in the northern part of Brazil.

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).

The demographic and geographic impact of the COVID pandemic in Bulgaria and Eastern Europe in 2020

The COVID-19 pandemic followed a unique trajectory in Eastern Europe compared to other heavily affected regions, with most countries there only experiencing a major surge of cases and deaths towards the end of 2020 after a relatively uneventful first half of the year. However, the consequences of that surge have not received as much attention as the situation in Western countries. Bulgaria, even though it has been one of the most heavily affected countries, has been one of those neglected cases. We use mortality and mobility data from Eurostat, official governmental and other sources to examine the development and impact of the COVID-19 pandemic in Bulgaria and other European countries. We find a very high level of excess mortality in Eastern European countries measured by several metrics including excess mortality rate (EMR), P-scores, potential years of life lost (PYLL) and its age standardised version (ASYR), and working years of life lost (WYLL). By the last three metrics Eastern Europe emerges as the hardest hit region by the pandemic in Europe in 2020. With a record EMR at ~0.27% and a strikingly large and mostly unique to it mortality rate in the working age (15–64 years) demographics, Bulgaria emerges as one of the most affected countries in Eastern Europe. The high excess mortality in Bulgaria correlates with insufficient intensity of testing, with delayed imposition of “lockdown” measures, and with high prevalence of cardiovascular diseases. We also find major geographic and demographic disparities within the country, with considerably lower mortality observed in major cities relative to more remote areas (likely due to disparities in the availability of medical resources). Analysis of the course of the epidemic revealed that individual mobility measures were predictive of the eventual decline in cases and deaths. However, while mobility declined as a result of the imposition of a lockdown, it already trended downwards before such measures were introduced, which resulted in a reduction of deaths independent of the effect of restrictions. Large excess mortality and high numbers of potential years of life lost are observed as a result of the COVID pandemic in Bulgaria, as well as in several other countries in Eastern Europe. Significant delays in the imposition of stringent mobility-reducing measures combined with a lack of medical resources likely caused a substantial loss of life, including in the working age population.

Population Genomics Approaches for Genetic Characterization of SARS-CoV-2 Lineages

The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19), has been sequenced at an unprecedented scale leading to a tremendous amount of viral genome sequencing data. To assist in tracing infection pathways and design preventive strategies, a deep understanding of the viral genetic diversity landscape is needed. We present here a set of genomic surveillance tools from population genetics which can be used to better understand the evolution of this virus in humans. To illustrate the utility of this toolbox, we detail an in depth analysis of the genetic diversity of SARS-CoV-2 in first year of the COVID-19 pandemic. We analyzed 329,854 high-quality consensus sequences published in the GISAID database during the pre-vaccination phase. We demonstrate that, compared to standard phylogenetic approaches, haplotype networks can be computed efficiently on much larger datasets. This approach enables real-time lineage identification, a clear description of the relationship between variants of concern, and efficient detection of recurrent mutations. Furthermore, time series change of Tajima's D by haplotype provides a powerful metric of lineage expansion. Finally, principal component analysis (PCA) highlights key steps in variant emergence and facilitates the visualization of genomic variation in the context of SARS-CoV-2 diversity. The computational framework presented here is simple to implement and insightful for real-time genomic surveillance of SARS-CoV-2 and could be applied to any pathogen that threatens the health of populations of humans and other organisms.

Spatiotemporal Characteristics and Patterns of the COVID-19 Pandemic in China: An Empirical Study Based on 413 Cities or Regions

The global economy was stagnant and even regressed since the outbreak of COVID-19. Exploring the spatiotemporal characteristics and patterns of COVID-19 pandemic spread may contribute to more scientific and effective pandemic prevention and control. This paper attempts to investigate the spatiotemporal characteristics in cumulative confirmed COVID-19 cases, mortality, and cure rate in 413 Chinese cities or regions using the data officially disclosed by the government. The results showed that: (1) The pandemic development can be divided into five stages: early stage (sustained growth), early mid-stage (accelerated growth), mid-stage (rapid growth), late mid-stage (slow growth), and late-stage (stable disappearance); (2) the cumulative number of confirmed COVID-19 cases remained constant in Wuhan, whilst the mortality tended to rise faster from the early stage to the late-stage and the cure rate moved from the southeast to the northwest; (3) the three indicators mentioned above showed significant and positive spatial correlation. Moran’s I curve demonstrated an inverted “V” trend in cumulative confirmed COVID-19 cases; the mortality curve was generally flat; the cure rate curve tended to rise. There are apparent differences in the local spatial autocorrelation pattern of the three primary indicators.

Does social capital enforce social distancing? The role of bridging and bonding social capital in the evolution of the pandemic

By shaping the way people look at members of their networks as well as strangers, social capital affects the behavior of a population during a pandemic. Over the course of 2020, various countries implemented non-pharmaceutical interventions (NPI), imposing restrictions that were difficult to enforce (due to the scale of the policies) in order to protect the public from the threat of COVID-19. This is an interesting quasi-experimental setting in which to test the compliance of populations with different levels of social capital with government suggestions and prescriptions. With the help of European Social Survey data, and the John Hopkins University dataset on the spread of COVID-19 around the world, the present work aims to test the impact within a sample of European countries with different social capital stocks on the spread of coronavirus. The results show that countries with higher social capital have fewer COVID-19 cases, ceteris paribus for NPI. This is especially true if this capital is of the bonding kind.

COVID-19 severity determinants inferred through ecological and epidemiological modeling

Understanding variations in the severity of infectious diseases is essential for planning proper mitigation strategies. Determinants of COVID-19 clinical severity are commonly assessed by transverse or longitudinal studies of the fatality counts. However, the fatality counts depend both on disease clinical severity and transmissibility, as more infected also lead to more deaths. Instead, we use epidemiological modeling to propose a disease severity measure that accounts for the underlying disease dynamics. The measure corresponds to the ratio of population-averaged mortality and recovery rates (m/r), is independent of the disease transmission dynamics (i.e., the basic reproduction number), and has a direct mechanistic interpretation. We use this measure to assess demographic, medical, meteorological, and environmental factors associated with the disease severity. For this, we employ an ecological regression study design and analyze different US states during the first disease outbreak. Principal Component Analysis, followed by univariate, and multivariate analyses based on machine learning techniques, is used for selecting important predictors. The usefulness of the introduced severity measure and the validity of the approach are confirmed by the fact that, without using prior knowledge from clinical studies, we recover the main significant predictors known to influence disease severity, in particular age, chronic diseases, and racial factors. Additionally, we identify long-term pollution exposure and population density as not widely recognized (though for the pollution previously hypothesized) significant predictors. The proposed measure is applicable for inferring severity determinants not only of COVID-19 but also of other infectious diseases, and the obtained results may aid a better understanding of the present and future epidemics. Our holistic, systematic investigation of disease severity at the human-environment intersection by epidemiological dynamical modeling and machine learning ecological regressions is aligned with the One Health approach. The obtained results emphasize a syndemic nature of COVID-19 risks.

The seasonal behaviour of COVID-19 and its galectin-like culprit of the viral spike

Seasonal behaviour is an attribute of many viral diseases. Like other 'winter' RNA viruses, infections caused by the causative agent of COVID-19, SARS-CoV-2, appear to exhibit significant seasonal changes. Here we discuss the seasonal behaviour of COVID-19, emerging viral phenotypes, viral evolution, and how the mutational landscape of the virus affects the seasonal attributes of the disease. We propose that the multiple seasonal drivers behind infectious disease spread (and the spread of COVID-19 specifically) are in 'trade-off' relationships and can be better described within a framework of a 'triangle of viral persistence' modulated by the environment, physiology, and behaviour. This 'trade-off' exists as one trait cannot increase without a decrease in another. We also propose that molecular components of the virus can act as sensors of environment and physiology, and could represent molecular culprits of seasonality. We searched for flexible protein structures capable of being modulated by the environment and identified a galectin-like fold within the N-terminal domain of the spike protein of SARS-CoV-2 as a potential candidate. Tracking the prevalence of mutations in this structure resulted in the identification of a hemisphere-dependent seasonal pattern driven by mutational bursts. We propose that the galectin-like structure is a frequent target of mutations because it helps the virus evade or modulate the physiological responses of the host to further its spread and survival. The flexible regions of the N-terminal domain should now become a focus for mitigation through vaccines and therapeutics and for prediction and informed public health decision making.

SARS-CoV-2 antibodies in employees working in non-medical contact-intensive professions in the Netherlands: Baseline data from the prospective COco-study

COVID-19 has made a global impact since early 2020, requiring characterization of the SARS-CoV-2 virus, including transmission risk. The COco-study aims to evaluate the risk for COVID-19 infections in two non-medical contact-intensive professions. COco is a prospective cohort study evaluating SARS-CoV-2 antibodies in hairdressers and hospitality personnel in the province of North-Brabant in the Netherlands, using a total antibody enzyme-linked immunosorbent assay. Baseline data from June/July 2020 were analyzed. Participants filled out a questionnaire, providing information on demographics, health, work situation, and risk factors for COVID-19. Odds ratios (OR) and 95% confidence intervals (95% CI) were calculated using logistic regression. In June/July 2020, 497 participants were enrolled: 236 hairdressers, 259 hospitality employees, and two participants worked in both industries. Hospitality staff was more frequently seropositive than hairdressers (14.2% versus 8.0%, respectively; OR 1.9, 95% CI 1.1-3.4). Furthermore, a high education level (OR 3.0, 95% CI: 1.7-5.6) and increased alcohol use (OR, 7 glasses per week increment: 1.3, 95% CI: 1.1-1.5) were associated with seropositivity. Of the 56 seropositive participants, 18 (32%) had not experienced any COVID-19 symptoms. The symptoms anosmia/ageusia differed most evidently between seropositive and seronegative participants (53.6% versus 5.7%, respectively; P < 0.001 (chi-squared test)). In conclusion, four months after the first identified COVID-19 patient in the Netherlands, employees in the hospitality industry had significantly more frequently detectable SARS-CoV-2 antibodies than hairdressers.

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.

Factors associated with the difference between the incidence and case-fatality ratio of coronavirus disease 2019 by country

Coronavirus disease (COVID-19) has been spreading all over the world; however, its incidence and case-fatality ratio differ greatly between countries and between continents. We investigated factors associated with international variation in COVID-19 incidence and case-fatality ratio (CFR) across 107 northern hemisphere countries, using publicly available COVID-19 outcome data as of 14 September 2020. We included country-specific geographic, demographic, socio-economic features, global health security index (GHSI), healthcare capacity, and major health behavior indexes in multivariate models to explain this variation. Multiple linear regression highlighted that incidence was associated with ethnic region (p < 0.05), global health security index 4 (GHSI4) (beta coefficient [β] 0.50, 95% Confidence Interval [CI] 0.14-0.87), population density (β 0.35, 95% CI 0.10-0.60), and water safety level (β 0.51, 95% CI 0.19-0.84). The CFR was associated with ethnic region (p < 0.05), GHSI4 (β 0.53, 95% CI 0.14-0.92), proportion of population over 65 (β 0.71, 95% CI 0.19-1.24), international tourism receipt level (β - 0.23, 95% CI - 0.43 to - 0.03), and the number of physicians (β - 0.37, 95% CI - 0.69 to - 0.06). Ethnic region was the most influential factor for both COVID-19 incidence (partial [Formula: see text] = 0.545) and CFR (partial [Formula: see text] = 0.372), even after adjusting for various confounding factors.

Investigating the roles of meteorological factors in COVID-19 transmission in Northern Italy

The novel COVID-19 is a highly invasive, pathogenic, and transmittable disease that has stressed the health care sector and hampered global development. Information of other viral respiratory diseases indicates that COVID-19 transmission could be affected by varying weather conditions; however, the impact of meteorological factors on the COVID-19 death counts remains unexplored. By investigating the impact of meteorological factors (absolute humidity, relative humidity, and temperature), this study will contribute both theoretically and practically to the concerned domain of pandemic management to be better prepared to control the spread of the disease. For this study, data is collected from 23 February to 31 March 2020 for Milan, Northern Italy, one of the badly hit regions by COVID-19. The generalized additive model (GAM) is applied, and a nonlinear relationship is examined with penalized spline methods. A sensitivity analysis is conducted for the verification of model results. The results reveal that temperature, relative humidity, and absolute humidity have a significant but negative relationship with the COVID-19 mortality rate. Therefore, it is possible to postulate that cool and dry environmental conditions promote virus transmission, leading to an increase in COVID-19 death counts. The results may facilitate health care policymakers in developing and implementing effective control measures in a timely and efficient way.

Assessment of effective mitigation and prediction of the spread of SARS-CoV-2 in Germany using demographic information and spatial resolution

Non-pharmaceutical interventions (NPIs) are important to mitigate the spread of infectious diseases as long as no vaccination or outstanding medical treatments are available. We assess the effectiveness of the sets of non-pharmaceutical interventions that were in place during the course of the Coronavirus disease 2019 (Covid-19) pandemic in Germany. Our results are based on hybrid models, combining SIR-type models on local scales with spatial resolution. In order to account for the age-dependence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we include realistic prepandemic and recently recorded contact patterns between age groups. The implementation of non-pharmaceutical interventions will occur on changed contact patterns, improved isolation, or reduced infectiousness when, e.g., wearing masks. In order to account for spatial heterogeneity, we use a graph approach and we include high-quality information on commuting activities combined with traveling information from social networks. The remaining uncertainty will be accounted for by a large number of randomized simulation runs. Based on the derived factors for the effectiveness of different non-pharmaceutical interventions over the past months, we provide different forecast scenarios for the upcoming time.

COVID-19 Shuts Doors to Flu but Keeps Them Open to Rhinoviruses

Simple Summary

Ten years have passed since the beginning of the H1N1pdm09 flu pandemic. No sooner had humanity recovered from its consequences than a new attack came—the COVID-19 pandemic. What happens to other respiratory infectious diseases during a global disaster such as the COVID-19 pandemic? The pandemic brought about by the novel SARS-CoV-2 virus has disrupted many well-established epidemiological and pathogenetic relationships, as well as mechanisms affecting infections with other respiratory viruses. The level of circulation of many respiratory pathogens has changed significantly. For instance, global influenza activity is at much lower levels than expected. In many regions, the influenza season has not started. Intriguingly, the COVID-19 pandemic did not substantially affect the spread of human rhinoviruses. In this review, the main properties of epidemiologically significant respiratory viruses such as SARS-CoV-2, influenza virus, and human rhinovirus are described.

Abstract

It is well known that rhinoviruses are distributed across the globe and are the most common cause of the common cold in all age groups. Rhinoviruses are widely considered to be harmless because they are generally perceived as respiratory viruses only capable of causing mild disease. However, they may also infect the lower respiratory tract, inducing chronic obstructive pulmonary disease and exacerbations of asthma, bronchiolitis, etc. The role of rhinoviruses in pathogenesis and the epidemiological process is underestimated, and they need to be intensively studied. In the light of recent data, it is now known that rhinoviruses could be one of the key epidemiological barriers that may influence the spread of influenza and novel coronaviruses. It has been reported that endemic human rhinoviruses delayed the development of the H1N1pdm09 influenza pandemic through viral interference. Moreover, human rhinoviruses have been suggested to block SARS-CoV-2 replication in the airways by triggering an interferon response. In this review, we summarized the main biological characteristics of genetically distinct viruses such as rhinoviruses, influenza viruses, and SARS-CoV-2 in an attempt to illuminate their main discrepancies and similarities. We hope that this comparative analysis will help us to better understand in which direction research in this area should move.

COVID-19 and the clinical course of rheumatic manifestations

The manifestations of COVID-19 have been evolving over time. Various post-COVID-19 syndromes are being recognised. Various viruses have been implicated in the pathogenesis of autoimmune diseases, and we expect a similar outcome with the severe acute respiratory syndrome-associated coronavirus-2 (SARS-CoV-2). The SARS-CoV-2 virus penetrates various tissues and organs and has a predisposition to lead to endotheliitis that may cause vascular manifestations including thrombosis. SARS-CoV-2 has been shown to activate Toll-like receptors and the complement system. It perpetuates NETosis and leads to autoantibody formation. These predispose to systemic autoimmunity. Both reactive arthritis and connective tissue disorders such as lupus and inflammatory myositis have been reported after COVID-19. Other reported autoimmune disorders include haemolytic anaemia, immune thrombocytopenia, cutaneous vasculitis, and Guillain Barré-like acute demyelinating disorders. The multi-system inflammatory syndrome in children and its adult counterpart are another post-COVID-19 entity that presents as an admixture of Kawasaki disease and staphylococcal toxic shock syndrome. Patients with preexisting rheumatic diseases may flare during the SARS-CoV-2 infection. They may develop novel autoimmune features also. The immune-suppressants used during the acute COVID-19 illness may confound the outcomes whereas comorbidities present in patients with rheumatic diseases may mask them. There is an urgent need to follow-up patients recovering from COVID and monitor autoantibody production in the context of rheumatic manifestations. Key Points • COVID-19 is associated with both innate and acquired immune reactions and production of various autoantibodies. • Various immune-mediated manifestations such as arthritis, myositis, haemolytic anaemia, thrombocytopenia, and acute demyelination may develop after COVID-19. • Longitudinal cohort data are warranted to describe, predict, and test prevent various rheumatic manifestations in post-COVID-19 subjects.

Temperature and population density influence SARS-CoV-2 transmission in the absence of nonpharmaceutical interventions

As COVID-19 continues to spread across the world, it is increasingly important to understand the factors that influence its transmission. Seasonal variation driven by responses to changing environment has been shown to affect the transmission intensity of several coronaviruses. However, the impact of the environment on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains largely unknown, and thus seasonal variation remains a source of uncertainty in forecasts of SARS-CoV-2 transmission. Here we address this issue by assessing the association of temperature, humidity, ultraviolet radiation, and population density with estimates of transmission rate (R). Using data from the United States, we explore correlates of transmission across US states using comparative regression and integrative epidemiological modeling. We find that policy intervention ("lockdown") and reductions in individuals' mobility are the major predictors of SARS-CoV-2 transmission rates, but, in their absence, lower temperatures and higher population densities are correlated with increased SARS-CoV-2 transmission. Our results show that summer weather cannot be considered a substitute for mitigation policies, but that lower autumn and winter temperatures may lead to an increase in transmission intensity in the absence of policy interventions or behavioral changes. We outline how this information may improve the forecasting of COVID-19, reveal its future seasonal dynamics, and inform intervention policies.

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.

Test for Covid-19 seasonality and the risk of second waves

Ten months into the Covid-19 pandemic it remains unclear whether transmission of SARS-CoV-2 is affected by climate factors. Using a dynamic epidemiological model with Covid-19 climate sensitivity in the likely range, we demonstrate why attempts to detect a climate signal in Covid-19 have thus far been inconclusive. Then we formulate a novel methodology based on susceptible-infected time trajectories that can be used to test for seasonal climate sensitivity in observed Covid-19 infection data. We show that if the disease does have a substantial seasonal dependence, and herd immunity is not established during the first peak season of the outbreak (or a vaccine does not become available), there is likely to be a seasonality-sensitive second wave of infections about one year after the initial outbreak. In regions where non-pharmaceutical control has contained the disease in the first year of outbreak and thus kept a large portion of the population susceptible, the second wave may be substantially larger in amplitude than the first if control measures are relaxed. This is simply because it develops under the favorable conditions of a full autumn to winter period and from a larger pool of infected individuals.

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.

The 2020 WMO Symposium on Climatological, Meteorological and Environmental factors in the COVID-19 pandemic: A special issue from symposium presentations

The COVID-19 pandemic has become one of the great historical events of the modern era, presenting a generational challenge to the world. Questions about the role of weather on SARS-CoV-2 transmission led to the gathering of scientists at an online event, the "International Virtual Symposium on Climatological, Meteorological and Environmental factors in the COVID-19 pandemic," convened on 4-6 August 2020 under the auspices of the World Meteorological Organization. This collection of papers arise from the Symposium.

The Long-Term Presence of SARS-CoV-2 on Cold-Chain Food Packaging Surfaces Indicates a New COVID-19 Winter Outbreak: A Mini Review

Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is a highly infectious virus that is transmitted primarily through droplets or by coming in close contact with an infected person. In 2020, there was a global outbreak of COVID-19, resulting in an unprecedented global burden of disease, health care costs, and had a significant economic impact. Recently, SARS-CoV-2 was detected on the outer packaging of imported cold chain items in China and has led to virus transmission events, causing great concern. This paper analyses the factors of SARS-CoV-2 survival and transmission in different places and environments, especially the characteristics of low temperatures and object surfaces. It was found that SARS-CoV-2 could survive on surfaces of cold and moist objects in the cold chain for more than 3 weeks, potentially causing COVID-19 transmission. We believe that the low-temperature environment in winter may accelerate the spread of the outbreak and new outbreaks may occur. Overall, SARS-CoV-2 transmission that is susceptible to low winter temperatures is critical for predicting winter pandemics, allowing for the appropriate action to be taken in advance.

SARS-COV-2: SIR Model Limitations and Predictive Constraints

Background: The main purpose of this research is to describe the mathematical asymmetric patterns of susceptible, infectious, or recovered (SIR) model equation application in the light of coronavirus disease 2019 (COVID-19) skewness patterns worldwide. Methods: The research modeled severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) spreading and dissemination patterns sensitivity by redesigning time series data extraction of daily new cases in terms of deviation consistency concerning variables that sustain COVID-19 transmission. The approach opened a new scenario where seasonality forcing behavior was introduced to understand SARS-COV-2 non-linear dynamics due to heterogeneity and confounding epidemics scenarios. Results: The main research results are the elucidation of three birth- and death-forced seasonality persistence phases that can explain COVID-19 skew patterns worldwide. They are presented in the following order: (1) the environmental variables (Earth seasons and atmospheric conditions); (2) health policies and adult learning education (HPALE) interventions; (3) urban spaces (local indoor and outdoor spaces for transit and social-cultural interactions, public or private, with natural physical features (river, lake, terrain). Conclusions: Three forced seasonality phases (positive to negative skew) phases were pointed out as a theoretical framework to explain uncertainty found in the predictive SIR model equations that might diverge in outcomes expected to express the disease’s behaviour.

The future of sustainability in the context of COVID-19

The COVID-19 pandemic is a global crisis emanating both from a virus (SARS-CoV-2) and from the drastic actions to contain it. Here, we reflect on the immediate responses of most world powers amid the pandemic chaos: totalitarian surveillance and nationalist isolation. Drawing on published literature, we consider measures such as wildlife-use bans, lockdowns and travel restrictions, along with their reverberations for people, economies and the planet. Our synthesis highlights significant shortfalls of applying command-and-control tactics in emergencies. For one, heavy-handed bans risk enormous unintended consequences and tend to fail if they lack legitimacy or clash with people's values. Furthermore, reactive and myopic strategies typically view the pandemic as a stand-alone crisis, rather than unravelling the complex interplay of nature-society interactions through which zoonotic diseases originate. A return to adaptive management approaches that recognise root causes and foster socio-ecological resilience will be essential to improve human and planetary health and mitigate future pandemics.

Can SARS-CoV-2 Global Seasonality be Determined After One Year of Pandemic?

Supplemental Digital Content is available in the text.

Meteorological factors and incidence of COVID-19 during the first wave of the pandemic in Catalonia (Spain): A multi-county study

The transmission of coronaviruses can be affected by several factors, including the climate. Due to the rapid spread of COVID-19 and the urgent need for rapid responses to contain the pandemic, it is essential to understand the role that weather conditions on the transmission of SARS-CoV-2. We evaluate the influence of meteorological factors on the incidence of COVID-19 during the first wave of the epidemic in Catalonia. We conducted a geographical analysis at the county level to evaluate the association between mean temperature, absolute humidity, solar radiation, and the cumulative incidence of COVID-19. Next, we used a time-series design to assess the short-term effects of meteorological factors on the daily incidence of COVID-19. We found a geographical association between meteorological factors and the cumulative incidence of COVID-19, from the end of March to June 2020, and a lesser extent in the short-term on the daily incidence during the first wave of the epidemic in Spain. Our findings suggest that warm and wet climates may reduce the incidence of COVID-19 in Catalonia. However, policy makers must interpret with caution any COVID-19 risk predictions based on climate information alone.

Using demographic data to understand the distribution of H1N1 and COVID-19 pandemics cases among federal entities and municipalities of Mexico

Background

The novel coronavirus disease (COVID-19) pandemic is the second global health emergency the world has faced in less than two decades, after the H1N1 Influenza pandemic in 2009–2010. Spread of pandemics is frequently associated with increased population size and population density. The geographical scales (national, regional or local scale) are key elements in determining the correlation between demographic factors and the spread of outbreaks. The aims of this study were: (a) to collect the Mexican data related to the two pandemics; (b) to create thematic maps using federal and municipal geographic scales; (c) to investigate the correlations between the pandemics indicators (numbers of contagious and deaths) and demographic patterns (population size and density).

Methods

The demographic patterns of all Mexican Federal Entities and all municipalities were taken from the database of “Instituto Nacional de Estadística y Geografía” (INEGI). The data of “Centro Nacional de Programas Preventivos y Control de Enfermedades” (CENAPRECE) and the geoportal of Mexico Government were also used in our analysis. The results are presented by means of tables, graphs and thematic maps. A Spearman correlation was used to assess the associations between the pandemics indicators and the demographic patterns. Correlations with a p value < 0.05 were considered significant.

Results

The confirmed cases (ccH1N1) and deaths (dH1N1) registered during the H1N1 Influenza pandemic were 72.4 thousand and 1.2 thousand respectively. Mexico City (CDMX) was the most affected area by the pandemic with 8,502 ccH1N1 and 152 dH1N1. The ccH1N1 and dH1N1 were positively correlated to demographic patterns; p-values higher than the level of marginal significance were found analyzing the % ccH1N1 and the % dH1N1 vs the population density. The COVID-19 pandemic data indicated 75.0 million confirmed cases (ccCOVID-19) and 1.6 million deaths (dCOVID-19) worldwide, as of date. The CDMX, where 264,330 infections were recorded, is the national epicenter of the pandemic. The federal scale did not allow to observe the correlation between demographic data and pandemic indicators; hence the next step was to choose a more detailed geographical scale (municipal basis). The ccCOVID-19 and dCOVID-19 (municipal basis) were highly correlated with demographic patterns; also the % ccCOVID-19 and % dCOVID-19 were moderately correlated with demographic patterns.

Conclusion

The magnitude of COVID-19 pandemic is much greater than the H1N1 Influenza pandemic. The CDMX was the national epicenter in both pandemics. The federal scale did not allow to evaluate the correlation between exanimated demographic variables and the spread of infections, but the municipal basis allowed the identification of local variations and “red zones” such as the delegation of Iztapalapa and Gustavo A. Madero in CDMX.

Did anomalous atmospheric circulation favor the spread of COVID-19 in Europe?

The current pandemic of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is having negative health, social and economic consequences worldwide. In Europe, the pandemic started to develop strongly at the end of February and beginning of March 2020. Subsequently, it spread over the continent, with special virulence in northern Italy and inland Spain. In this study we show that an unusual persistent anticyclonic situation prevailing in southwestern Europe during February 2020 (i.e. anomalously strong positive phase of the North Atlantic and Arctic Oscillations) could have resulted in favorable conditions, e.g., in terms of air temperature and humidity among other factors, in Italy and Spain for a quicker spread of the virus compared with the rest of the European countries. It seems plausible that the strong atmospheric stability and associated dry conditions that dominated in these regions may have favored the virus propagation, both outdoors and especially indoors, by short-range droplet and aerosol (airborne) transmission, or/and by changing social contact patterns. Later recent atmospheric circulation conditions in Europe (July 2020) and the U.S. (October 2020) seem to support our hypothesis, although further research is needed in order to evaluate other confounding variables. Interestingly, the atmospheric conditions during the Spanish flu pandemic in 1918 seem to have resembled at some stage with the current COVID-19 pandemic.

Did anomalous atmospheric circulation favor the spread of COVID-19 in Europe?

The current pandemic of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is having negative health, social and economic consequences worldwide. In Europe, the pandemic started to develop strongly at the end of February and beginning of March 2020. Subsequently, it spread over the continent, with special virulence in northern Italy and inland Spain. In this study we show that an unusual persistent anticyclonic situation prevailing in southwestern Europe during February 2020 (i.e. anomalously strong positive phase of the North Atlantic and Arctic Oscillations) could have resulted in favorable conditions, e.g., in terms of air temperature and humidity among other factors, in Italy and Spain for a quicker spread of the virus compared with the rest of the European countries. It seems plausible that the strong atmospheric stability and associated dry conditions that dominated in these regions may have favored the virus propagation, both outdoors and especially indoors, by short-range droplet and aerosol (airborne) transmission, or/and by changing social contact patterns. Later recent atmospheric circulation conditions in Europe (July 2020) and the U.S. (October 2020) seem to support our hypothesis, although further research is needed in order to evaluate other confounding variables. Interestingly, the atmospheric conditions during the Spanish flu pandemic in 1918 seem to have resembled at some stage with the current COVID-19 pandemic.

Assessing the influence of climate on wintertime SARS-CoV-2 outbreaks

High susceptibility has limited the role of climate in the SARS-CoV-2 pandemic to date. However, understanding a possible future effect of climate, as susceptibility declines and the northern-hemisphere winter approaches, is an important open question. Here we use an epidemiological model, constrained by observations, to assess the sensitivity of future SARS-CoV-2 disease trajectories to local climate conditions. We find this sensitivity depends on both the susceptibility of the population and the efficacy of non-pharmaceutical interventions (NPIs) in reducing transmission. Assuming high susceptibility, more stringent NPIs may be required to minimize outbreak risk in the winter months. Our results suggest that the strength of NPIs remain the greatest determinant of future pre-vaccination outbreak size. While we find a small role for meteorological forecasts in projecting outbreak severity, reducing uncertainty in epidemiological parameters will likely have a more substantial impact on generating accurate predictions.

Non-linear correlation between daily new cases of COVID-19 and meteorological factors in 127 countries

Meteorological parameters are the critical factors of affecting respiratory infectious disease such as Middle East Respiratory Syndrome (MERS), Severe Acute Respiratory Syndrome (SARS) and influenza, however, the effect of meteorological parameters on coronavirus disease 2019 (COVID-19) remains controversial. This study investigated the effects of meteorological factors on daily new cases of COVID-19 in 127 countries, as of August 31 2020. The log-linear generalized additive model (GAM) was used to analyze the effect of meteorological variables on daily new cases of COVID-19. Our findings revealed that temperature, relative humidity, and wind speed are nonlinearly correlated with daily new cases, and they may be negatively correlated with the daily new cases of COVID-19 over 127 countries when temperature, relative humidity and wind speed were below 20°C, 70% and 7 m/s respectively. Temperature(>20°C) was positively correlated with daily new cases. Wind speed (when>7 m/s) and relative humidity (>70%) was not statistically associated with transmission of COVID-19. The results of this research will be a useful supplement to help healthcare policymakers in the Belt and Road countries, the Centers for Disease Control (CDC) and the World Health Organization (WHO) to develop strategies to combat COVID-19.

Diversity of SARS-CoV-2 isolates driven by pressure and health index

One of the main concerns about the fast spreading coronavirus disease 2019 (Covid-19) pandemic is how to intervene. We analysed severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) isolates data using the multifractal approach and found a rich in viral genome diversity, which could be one of the root causes of the fast Covid-19 pandemic and is strongly affected by pressure and health index of the hosts inhabited regions. The calculated mutation rate (mr) is observed to be maximum at a particular pressure, beyond which mr maintains diversity. Hurst exponent and fractal dimension are found to be optimal at a critical pressure (Pm), whereas, for P > Pm and P < Pm, we found rich genome diversity relating to complicated genome organisation and virulence of the virus. The values of these complexity measurement parameters are found to be increased linearly with health index values.

Climate effects on the COVID-19 outbreak: a comparative analysis between the UAE and Switzerland

The main aim of the present study is to disclose the similarities or differences of the climate effects on the COVID-19 outbreak in two countries, which have different climatic conditions. Using the correlation modeling, the results revealed that some climatic factors, such as the ULR, temperature, and CH₄ in the UAE and aerosol index and NO₂ in Switzerland have positive lagged correlations with the outburst of COVID-19 by intensifying role within − 9, − 7, and − 2 days. The mitigating role was also observed for ozone/solar radiation and temperature/long-wave radiation in the UAE and Switzerland, respectively. The initial hypotheses of the research have confirmed the correlations between new cases of COVID-19 and ULR and aerosol indices in the UAE and Switzerland. However, the main finding revealed that the climate effects on the COVID-19 outbreak show different roles in the different countries, locating in dissimilar climatic zones. Accordingly, the COVID-19 can be intensified by increases of the ULR and temperature in an arid region, while it can be exactly mitigated by increases of these factors in a temperate area. This finding may be useful for future researches for identifying the essential influencing factors for the mitigating COVID-19 outbreak.

CSF3 Is a Potential Drug Target for the Treatment of COVID-19

Coronavirus Disease 2019 (COVID-19) is an acute respiratory infectious disease that appeared at the end of 2019. As of July 2020, the cumulative number of infections and deaths have exceeded 15 million and 630,000, respectively. And new cases are increasing. There are still many difficulties surrounding research on the mechanism and development of therapeutic vaccines. It is urgent to explore the pathogenic mechanism of viruses to help prevent and treat COVID-19. In our study, we downloaded two datasets related to COVID-19 (GSE150819 and GSE147507). By analyzing the high-throughput expression matrix of uninfected human bronchial organoids and infected human bronchial organoids in the GSE150819, 456 differentially expressed genes (DEGs) were identified, which were mainly enriched in the cytokine-cytokine receptor interaction pathway and so on. We also constructed the protein-protein interaction (PPI) network of DEGs to identify the hub genes. Then we analyzed GSE147507, which contained lung adenocarcinoma cell lines (A549 and Calu3) and the primary bronchial epithelial cell line (NHBE), obtaining 799, 460, and 46 DEGs, respectively. The results showed that in human bronchial organoids, A549, Calu3, and NHBE samples infected with SARS-CoV-2, only one upregulated gene CSF3 was identified. Interestingly, CSF3 is one of the hub genes we previously screened in GSE150819, suggesting that CSF3 may be a potential drug target. Further, we screened potential drugs targeting CSF3 by MOE; the top 50 drugs were screened by flexible docking and rigid docking, with 37 intersections. Two antiviral drugs (Elbasvir and Ritonavir) were included; Elbasvir and Ritonavir formed van der Waals (VDW) interactions with surrounding residues to bind with CSF3, and Elbasvir and Ritonavir significantly inhibited CSF3 protein expression.

Weather Variability and COVID-19 Transmission: A Review of Recent Research

Weather and climate play a significant role in infectious disease transmission, through changes to transmission dynamics, host susceptibility and virus survival in the environment. Exploring the association of weather variables and COVID-19 transmission is vital in understanding the potential for seasonality and future outbreaks and developing early warning systems. Previous research examined the effects of weather on COVID-19, but the findings appeared inconsistent. This review aims to summarize the currently available literature on the association between weather and COVID-19 incidence and provide possible suggestions for developing weather-based early warning system for COVID-19 transmission. Studies eligible for inclusion used ecological methods to evaluate associations between weather (i.e., temperature, humidity, wind speed and rainfall) and COVID-19 transmission. The review showed that temperature was reported as significant in the greatest number of studies, with COVID-19 incidence increasing as temperature decreased and the highest incidence reported in the temperature range of 0–17 °C. Humidity was also significantly associated with COVID-19 incidence, though the reported results were mixed, with studies reporting positive and negative correlation. A significant interaction between humidity and temperature was also reported. Wind speed and rainfall results were not consistent across studies. Weather variables including temperature and humidity can contribute to increased transmission of COVID-19, particularly in winter conditions through increased host susceptibility and viability of the virus. While there is less indication of an association with wind speed and rainfall, these may contribute to behavioral changes that decrease exposure and risk of infection. Understanding the implications of associations with weather variables and seasonal variations for monitoring and control of future outbreaks is essential for early warning systems.