Effects of Environmental Factors on Severity and Mortality of COVID-19

Identification of differences in the magnitude and specificity of SARS-CoV-2 nucleocapsid antibody responses in naturally infected and vaccinated individuals

As there are limited data on B-cell epitopes for the nucleocapsid protein in SARS-CoV-2, we sought to identify the immunodominant regions within the N protein, recognized by patients with varying severity of natural infection with the Wuhan strain (WT), delta, omicron, and in those who received the Sinopharm vaccines, which is an inactivated, whole virus vaccine. Using overlapping peptides representing the N protein, with an in-house ELISA, we mapped the immunodominant regions within the N protein, in seronegative (n = 30), WT infected (n = 30), delta infected (n = 30), omicron infected + vaccinated (n = 20) and Sinopharm (BBIBP-CorV) vaccinees (n = 30). We then investigated the sensitivity and specificity of these immunodominant regions and analyzed their conservation with other SARS-CoV-2 variants of concern, seasonal human coronaviruses, and bat Sarbecoviruses. We identified four immunodominant regions aa 29-52, aa 155-178, aa 274-297, and aa 365-388, which were highly conserved within SARS-CoV-2 and the bat coronaviruses. The magnitude of responses to these regions varied based on the infecting SARS-CoV-2 variants, >80% of individuals gave responses above the positive cut-off threshold to many of the four regions, with some differences with individuals who were infected with different VoCs. These regions were found to be 100% specific, as none of the seronegative individuals gave any responses. As these regions were highly specific with high sensitivity, they have a potential to be used to develop diagnostic assays and to be used in development of vaccines.

Influence of Seasonality and Public-Health Interventions on the COVID-19 Pandemic in Northern Europe

BACKGROUND

Most government efforts to control the COVID-19 pandemic revolved around non-pharmaceutical interventions (NPIs) and vaccination. However, many respiratory diseases show distinctive seasonal trends. In this manuscript, we examined the contribution of these three factors to the progression of the COVID-19 pandemic.

METHODS

Pearson correlation coefficients and time-lagged analysis were used to examine the relationship between NPIs, vaccinations and seasonality (using the average incidence of endemic human beta-coronaviruses in Sweden over a 10-year period as a proxy) and the progression of the COVID-19 pandemic as tracked by deaths; cases; hospitalisations; intensive care unit occupancy and testing positivity rates in six Northern European countries (population 99.12 million) using a population-based, observational, ecological study method.

FINDINGS

The waves of the pandemic correlated well with the seasonality of human beta-coronaviruses (HCoV-OC43 and HCoV-HKU1). In contrast, we could not find clear or consistent evidence that the stringency of NPIs or vaccination reduced the progression of the pandemic. However, these results are correlations and not causations.

IMPLICATIONS

We hypothesise that the apparent influence of NPIs and vaccines might instead be an effect of coronavirus seasonality. We suggest that policymakers consider these results when assessing policy options for future pandemics.

LIMITATIONS

The study is limited to six temperate Northern European countries with spatial and temporal variations in metrics used to track the progression of the COVID-19 pandemic. Caution should be exercised when extrapolating these findings.

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.

Long-term health impacts of COVID-19 among 242,712 adults in England

The COVID-19 pandemic is having a lasting impact on health and well-being. We compare current self-reported health, quality of life and symptom profiles for people with ongoing symptoms following COVID-19 to those who have never tested positive for SARS-CoV-2 infection and those who have recovered from COVID-19. Overall, 276,840/800,000 (34·6%) of invited participants took part. Mental health and health-related quality of life were worse among participants with ongoing persistent symptoms post-COVID compared with those who had never had COVID-19 or had recovered. In this study, median duration of COVID-related symptoms (N = 130,251) was 1·3 weeks (inter-quartile range 6 days to 2 weeks), with 7·5% and 5·2% reporting ongoing symptoms ≥12 weeks and ≥52 weeks respectively. Female sex, ≥1 comorbidity and being infected when Wild-type variant was dominant were associated with higher probability of symptoms lasting ≥12 weeks and longer recovery time in those with persistent symptoms. Although COVID-19 is usually of short duration, some adults experience persistent and burdensome illness.

Dynamics of SARS-CoV-2 infection hospitalisation and infection fatality ratios over 23 months in England

The relationship between prevalence of infection and severe outcomes such as hospitalisation and death changed over the course of the COVID-19 pandemic. Reliable estimates of the infection fatality ratio (IFR) and infection hospitalisation ratio (IHR) along with the time-delay between infection and hospitalisation/death can inform forecasts of the numbers/timing of severe outcomes and allow healthcare services to better prepare for periods of increased demand. The REal-time Assessment of Community Transmission-1 (REACT-1) study estimated swab positivity for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection in England approximately monthly from May 2020 to March 2022. Here, we analyse the changing relationship between prevalence of swab positivity and the IFR and IHR over this period in England, using publicly available data for the daily number of deaths and hospitalisations, REACT-1 swab positivity data, time-delay models, and Bayesian P-spline models. We analyse data for all age groups together, as well as in 2 subgroups: those aged 65 and over and those aged 64 and under. Additionally, we analysed the relationship between swab positivity and daily case numbers to estimate the case ascertainment rate of England's mass testing programme. During 2020, we estimated the IFR to be 0.67% and the IHR to be 2.6%. By late 2021/early 2022, the IFR and IHR had both decreased to 0.097% and 0.76%, respectively. The average case ascertainment rate over the entire duration of the study was estimated to be 36.1%, but there was some significant variation in continuous estimates of the case ascertainment rate. Continuous estimates of the IFR and IHR of the virus were observed to increase during the periods of Alpha and Delta's emergence. During periods of vaccination rollout, and the emergence of the Omicron variant, the IFR and IHR decreased. During 2020, we estimated a time-lag of 19 days between hospitalisation and swab positivity, and 26 days between deaths and swab positivity. By late 2021/early 2022, these time-lags had decreased to 7 days for hospitalisations and 18 days for deaths. Even though many populations have high levels of immunity to SARS-CoV-2 from vaccination and natural infection, waning of immunity and variant emergence will continue to be an upwards pressure on the IHR and IFR. As investments in community surveillance of SARS-CoV-2 infection are scaled back, alternative methods are required to accurately track the ever-changing relationship between infection, hospitalisation, and death and hence provide vital information for healthcare provision and utilisation.

Vaccine-induced or hybrid immunity and COVID-19-associated mortality during the Omicron wave

BACKGROUND

It is not yet entirely clear to what extent vaccine-induced or hybrid immunity protects individuals in Germany from death during the omicron wave of the COVID-19 pandemic.

METHODS

In this retrospective study, we evaluated 470 159 cases over age 59 in the German federal state of Bavaria who tested positive for SARS-CoV-2 between 1 January and 30 June 2022. Cox models were used to estimate adjusted hazard ratios (aHR) for dying within 60 days of the infection, depending on sex, age, time point of infection, and a range of immunity levels.

RESULTS

Over the period of observation, 3836 COVID-19-associated deaths were registered (case fatality rate 0.82 %). Risk of death was significantly lower in cases with a higher immunity level than in unvaccinated cases (aHR for a full primary immunity level, if reached less than six months before the time of the infection: 0.30, 95 %-confidence interval [0.23; 0.39]; if reached more than six months before: aHR 0.46 [0.35; 0.60]). A boosted immunity level lowered risk of death even further (if reached less than three months before the infection: aHR 0.17 [0.15; 0.20]; if reached more than three months before: aHR 0.25 [0.21; 0.29]).

CONCLUSION

Among elderly persons in Bavaria, a higher immunity level was associated with a substantial degree of protection against death during the Omicron wave; the strength of protection may have diminished somewhat over time.

Persistent SARS-CoV-2 Infection, EBV, HHV-6 and Other Factors May Contribute to Inflammation and Autoimmunity in Long COVID

A novel syndrome called long-haul COVID or long COVID is increasingly recognized in a significant percentage of individuals within a few months after infection with SARS-CoV-2. This disorder is characterized by a wide range of persisting, returning or even new but related symptoms that involve different tissues and organs, including respiratory, cardiac, vascular, gastrointestinal, musculo-skeletal, neurological, endocrine and systemic. Some overlapping symptomatologies exist between long COVID and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Very much like with long ME/CFS, infections with herpes family viruses, immune dysregulation, and the persistence of inflammation have been reported as the most common pattern for the development of long COVID. This review describes several factors and determinants of long COVID that have been proposed, elaborating mainly on viral persistence, reactivation of latent viruses such as Epstein-Barr virus and human herpesvirus 6 which are also associated with the pathology of ME/CFS, viral superantigen activation of the immune system, disturbance in the gut microbiome, and multiple tissue damage and autoimmunity. Based on these factors, we propose diagnostic strategies such as the measurement of IgG and IgM antibodies against SARS-CoV-2, EBV, HHV-6, viral superantigens, gut microbiota, and biomarkers of autoimmunity to better understand and manage this multi-factorial disorder that continues to affect millions of people in the world.

Air pollution and meteorology as risk factors for COVID-19 death in a cohort from Southern California

BACKGROUND

Recent evidence links ambient air pollution to COVID-19 incidence, severity, and death, but few studies have analyzed individual-level mortality data with high quality exposure models.

METHODS

We sought to assess whether higher air pollution exposures led to greater risk of death during or after hospitalization in confirmed COVID-19 cases among patients who were members of the Kaiser Permanente Southern California (KPSC) healthcare system (N=21,415 between 06-01-2020 and 01-31-2022 of whom 99.85 % were unvaccinated during the study period). We used 1 km resolution chemical transport models to estimate ambient concentrations of several common air pollutants, including ozone, nitrogen dioxide, and fine particle matter (PM_2.5). We also derived estimates of pollutant exposures from ultra-fine particulate matter (PM_0.1), PM chemical species, and PM sources. We employed Cox proportional hazards models to assess associations between air pollution exposures and death from COVID-19 among hospitalized patients.

FINDINGS

We found significant associations between COVID-19 death and several air pollution exposures, including: PM_2.5 mass, PM_0.1 mass, PM_2.5 nitrates, PM_2.5 elemental carbon, PM_2.5 on-road diesel, and PM_2.5 on-road gasoline. Based on the interquartile (IQR) exposure increment, effect sizes ranged from hazard ratios (HR) = 1.12 for PM_2.5 mass and PM_2.5 nitrate to HR ∼ 1.06-1.07 for other species or source markers. Humidity and temperature in the month of diagnosis were also significant negative predictors of COVID-19 death and negative modifiers of the air pollution effects.

INTERPRETATION

Air pollution exposures and meteorology were associated the risk of COVID-19 death in a cohort of patients from Southern California. These findings have implications for prevention of death from COVID-19 and for future pandemics.

SARS-COV-2/COVID-19: scenario, epidemiology, adaptive mutations, and environmental factors

The coronavirus pandemic of 2019 has already exerted an enormous impact. For over a year, the worldwide pandemic has ravaged the whole globe, with approximately 250 million verified human infection cases and a mortality rate surpassing 4 million. While the genetic makeup of the related pathogen (SARS-CoV-2) was identified, many unknown facets remain a mystery, comprising the virus's origin and evolutionary trend. There were many rumors that SARS-CoV-2 was human-borne and its evolution was predicted many years ago, but scientific investigation proved them wrong and concluded that bats might be the origin of SARS-CoV-2 and pangolins act as intermediary species to transmit the virus from bats to humans. Airborne droplets were found to be the leading cause of human-to-human transmission of this virus, but later studies showed that contaminated surfaces and other environmental factors are also involved in its transmission. The evolution of different SARS-CoV-2 variants worsens the condition and has become a challenge to overcome this pandemic. The emergence of COVID-19 is still a mystery, and scientists are unable to explain the exact origin of SARS-CoV-2. This review sheds light on the possible origin of SARS-CoV-2, its transmission, and the key factors that worsen the situation.

Evolution of the SARS-CoV-2 omicron variants BA.1 to BA.5: Implications for immune escape and transmission

The first dominant SARS-CoV-2 Omicron variant BA.1 harbours 35 mutations in its Spike protein from the original SARS-CoV-2 variant that emerged late 2019. Soon after its discovery, BA.1 rapidly emerged to become the dominant variant worldwide and has since evolved into several variants. Omicron is of major public health concern owing to its high infectivity and antibody evasion. This review article examines the theories that have been proposed on the evolution of Omicron including zoonotic spillage, infection in immunocompromised individuals and cryptic spread in the community without being diagnosed. Added to the complexity of Omicron's evolution are the multiple reports of recombination events occurring between co-circulating variants of Omicron with Delta and other variants such as XE. Current literature suggests that the combination of the novel mutations in Omicron has resulted in the variant having higher infectivity than the original Wuhan-Hu-1 and Delta variant. However, severity is believed to be less owing to the reduced syncytia formation and lower multiplication in the human lung tissue. Perhaps most challenging is that several studies indicate that the efficacy of the available vaccines have been reduced against Omicron variant (8-127 times reduction) as compared to the Wuhan-Hu-1 variant. The administration of booster vaccine, however, compensates with the reduction and improves the efficacy by 12-35 fold. Concerningly though, the broadly neutralising monoclonal antibodies, including those approved by FDA for therapeutic use against previous SARS-CoV-2 variants, are mostly ineffective against Omicron with the exception of Sotrovimab and recent reports suggest that the Omicron BA.2 is also resistant to Sotrovimab. Currently two new Omicron variants BA.4 and BA.5 are emerging and are reported to be more transmissible and resistant to immunity generated by previous variants including Omicron BA.1 and most monoclonal antibodies. As new variants of SARS-CoV-2 will likely continue to emerge it is important that the evolution, and biological consequences of new mutations, in existing variants be well understood.

Low Environmental Temperature Exacerbates Severe Acute Respiratory Syndrome Coronavirus 2 Infection in Golden Syrian Hamsters

BACKGROUND

The effect of low environmental temperature on viral shedding and disease severity of Coronavirus Disease 2019 (COVID-19) is uncertain.

METHODS

We investigated the virological, clinical, pathological, and immunological changes in hamsters housed at room (21°C), low (12-15°C), and high (30-33°C) temperature after challenge by 105 plaque-forming units of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

RESULTS

The nasal turbinate, trachea, and lung viral load and live virus titer were significantly higher (~0.5-log10 gene copies/β-actin, P < .05) in the low-temperature group at 7 days postinfection (dpi). The low-temperature group also demonstrated significantly higher level of tumor necrosis factor-α, interferon-γ (IFN-γ), interleukin-1β, and C-C motif chemokine ligand 3, and lower level of the antiviral IFN-α in lung tissues at 4 dpi than the other 2 groups. Their lungs were grossly and diffusely hemorrhagic, with more severe and diffuse alveolar and peribronchiolar inflammatory infiltration, bronchial epithelial cell death, and significantly higher mean total lung histology scores. By 7 dpi, the low-temperature group still showed persistent and severe alveolar inflammation and hemorrhage, and little alveolar cell proliferative changes of recovery. The viral loads in the oral swabs of the low-temperature group were significantly higher than those of the other two groups from 10 to 17 dpi by about 0.5-1.0 log10 gene copies/β-actin. The mean neutralizing antibody titer of the low-temperature group was significantly (P < .05) lower than that of the room temperature group at 7 dpi and 30 dpi.

CONCLUSIONS

This study provided in vivo evidence that low environmental temperature exacerbated the degree of virus shedding, disease severity, and tissue proinflammatory cytokines/chemokines expression, and suppressed the neutralizing antibody response of SARS-CoV-2-infected hamsters. Keeping warm in winter may reduce the severity of COVID-19.

Modeling the change in European and US COVID-19 death rates

Motivated by several possible differences in Covid-19 virus strains, age demographics, and face mask wearing between continents and countries, we focussed on changes in Covid death rates in 2020. We have extended our Covid-19 multicompartment model (Khan et al., 2020) to fit cumulative case and death data for 49 European countries and 52 US states and territories during the recent pandemic, and found that the case mortality rate had decreased by at least 80% in most of the US and at least 90% in most of Europe. We found that death rate decreases do not have strong correlations to other model parameters (such as contact rate) or other standard state/national metrics such as population density, GDP, and median age. Almost all the decreases occurred between mid-April and mid-June 2020, which corresponds to the time when many state and national lockdowns were relaxed resulting in surges of new cases. We examine here several plausible causes for this drop—improvements in treatment, face mask wearing, new virus strains, testing, potentially changing demographics of infected patients, and changes in data collection and reporting—but none of their effects are as significant as the death rate changes suggest. In conclusion, this work shows that a two death rate model is effective in quantifying the reported drop in death rates.

Modeling Global COVID-19 Dissemination Data After the Emergence of Omicron Variant Using Multipronged Approaches

The COVID-19 pandemic has followed a wave pattern, with an increase in new cases followed by a drop. Several factors influence this pattern, including vaccination efficacy over time, human behavior, infection management measures used, emergence of novel variants of SARS-CoV-2, and the size of the vulnerable population, among others. In this study, we used three statistical approaches to analyze COVID-19 dissemination data collected from 15 November 2021 to 09 January 2022 for the prediction of further spread and to determine the behavior of the pandemic in the top 12 countries by infection incidence at that time, namely Distribution Fitting, Time Series Modeling, and Epidemiological Modeling. We fitted various theoretical distributions to data sets from different countries, yielding the best-fit distribution for the most accurate interpretation and prediction of the disease spread. Several time series models were fitted to the data of the studied countries using the expert modeler to obtain the best fitting models. Finally, we estimated the infection rates (β), recovery rates (γ), and Basic Reproduction Numbers ([Formula: see text]) for the countries using the compartmental model SIR (Susceptible-Infectious-Recovered). Following more research on this, our findings may be validated and interpreted. Therefore, the most refined information may be used to develop the best policies for breaking the disease's chain of transmission by implementing suppressive measures such as vaccination, which will also aid in the prevention of future waves of infection.

Autoantibodies and autoimmune disorders in SARS-CoV-2 infection: pathogenicity and immune regulation

Coronavirus disease 2019 (COVID-19) is an infectious disease associated with the respiratory system caused by the SARS-CoV-2 virus. The aim of this review article is to establish an understanding about the relationship between autoimmune conditions and COVID-19 infections. Although majority of the population have been protected with vaccines against this virus, there is yet a successful curative medication for this disease. The use of autoimmune medications has been widely considered to control the infection, thus postulating possible relationships between COVID-19 and autoimmune diseases. Several studies have suggested the correlation between autoantibodies detected in patients and the severity of the COVID-19 disease. Studies have indicated that the SARS-CoV-2 virus can disrupt the self-tolerance mechanism of the immune system, thus triggering autoimmune conditions. This review discusses the current scenario and future prospects of promising therapeutic strategies that may be employed to regulate such autoimmune conditions.

COVID-19 Vaccine Hesitancy and Trust in Government in Nigeria

INTRODUCTION

COVID-19 has been impacting our lives globally, including in Nigeria. While the COVID-19 vaccine is available free of charge, vaccination coverage remains low. This study evaluates the relationship between trust in government and COVID-19 vaccine hesitancy.

METHODS

We used an Afrobarometer survey for data on trust in government and the COVID-19 National Longitudinal Phone Survey (NLPS) for data on COVID-19 vaccine hesitancy, merged by strata (states and urban/rural). The simple correlation was evaluated using Ordinary Least Squares (OLS) regression.

RESULTS

Distrust in government was strongly associated with COVID-19 vaccine hesitancy as well as with perceptions that the vaccine was not safe, and concerns about side effects were given as reasons for vaccine refusal.

DISCUSSION/CONCLUSION

Distrust of government is an important predictor of vaccine hesitancy in Nigeria. This result is consistent with findings in the literature, especially in developed countries. Vaccine refusers, who distrust the government, refuse vaccines because they think that vaccines do them harm. Policy makers should be cautious when it comes to strategizing for COVID-19 vaccine distribution, especially in places where trust in government is weak.

Excess Mortality Versus COVID-19 Death Rates: A Spatial Analysis of Socioeconomic Disparities and Political Allegiance Across U.S. States

Excess mortality is a more robust measure than the counts of COVID-19 deaths typically used in epidemiological and spatial studies. Measurement issues around excess mortality, considering data quality and comparability both internationally and within the U.S., are surveyed. This paper is the first state-level spatial analysis of cumulative excess mortality for the U.S. in the first full year of the pandemic. There is strong evidence that, given appropriate controls, states with higher Democrat vote shares experienced lower excess mortality (consistent with county-level studies of COVID-19 deaths). Important demographic and socio-economic controls from a broad set tested were racial composition, age structure, population density, poverty, income, temperature, and timing of arrival of the pandemic. Interaction effects suggest the Democrat vote share effect of reducing mortality was even greater in states where the pandemic arrived early. Omitting political allegiance leads to a significant underestimation of the mortality disparities for minority populations.

Analysis of Deaths and Favorable Developments of Patients with SARS-CoV-2 Hospitalized in the Largest Hospital for Infectious Diseases and Pneumo-Phthisiology in the West of the Country

Purpose

Romania is one of the European countries that has been hit the hardest by the severe acute respiratory syndrome caused by the new coronavirus SARS-CoV-2, with over 1.91 million reported cases and over 59,257 deaths. The aim of this study was to identify the main predictors of death in hospitalized patients.

Patients and Methods

In the period from 1 March 2020 to 30 June 2021, an observational, retrospective, randomized, case-control study was conducted, which included a sample of 139 patients who died in hospital and another sample of 275 patients who had been discharged in an improved or healed condition. Confirmation of COVID-19 cases was performed by RT-PCR from nasopharyngeal and oropharyngeal exudates. Statistical data were analyzed by logistic regression, Cox regression and a comparison of survival curves by the log-rank (Mantel-Cox) test.

Results

The most powerful logistic regression model identified the following independent predictors of death: history of coagulopathy HR = 30.73 [1.94–487.09], p = 0.015; high percentage of neutrophils HR = 1.09 [1.01–1.19], p = 0.027; and decreased blood-oxygenation HR = 53881.97 [1762.24–1647489.44], p < 0.001. Cox regression identified the following factors that influenced the evolution of cases: history of coagulopathy HR = 2.44 [1.38–4.35], p = 0.000; O₂ saturation HR = 0.98 [0.96–0.99], p = 0.043; serum creatinine HR = 1.23 [1.08–1.39], p = 0.001; dyspnea on admission HR = 2.99 [1.42–6.30], p = 0.004; hospitalization directly in the ICU HR = 3.803 [1.97–7.33], p < 0.001; heart damage HR = 16.76 [1.49–188.56], p = 0.022; and decreased blood-oxygenation HR = 35.12 [5.92–208.19], p < 0.001.

Conclusion

Knowledge of the predictors of death in hospitalized patients allows for the future optimization of triage and therapeutic case management for COVID-19.

COVID-19 in Africa: rethinking the tools to manage future pandemics

Corona virus disease 2019 (COVID-19) remains an incurable, progressive pneumonia-like illness characterized by fever, dry cough, fatigue, and headache during its early stages. COVID-19 has ultimately resulted in mortality in at least 2 million people worldwide. Millions of people globally have already been affected by this disease, and the numbers are expected to increase, perhaps until an effective cure or vaccine is identified. Although Africa was initially purported by the World Health Organization (WHO) to be severely hit by the pandemic, Africa recorded the least number of cases during the first wave, with lowest rates of infections, compared to Asia, Europe, and the Americas. This statistic might be attributed to the low testing capacity, existing public health awareness and lessons learnt during Ebola epidemic. Nonetheless, the relatively low rate of infection should be an opportunity for Africa to be better prepared to overcome this and future epidemics. In this paper, the authors provide insights into the dynamics and transmission of the severe acute respiratory syndrome corona virus (SARS-CoV-2) during the first wave of the pandemic; possible explanations into the relatively low rates of infection recorded in Africa; with recommendations for Africa to continue to fight Covid-19; and position itself to effectively manage future pandemics.

Accessible data curation and analytics for international-scale citizen science datasets

The Covid Symptom Study, a smartphone-based surveillance study on COVID-19 symptoms in the population, is an exemplar of big data citizen science. As of May 23rd, 2021, over 5 million participants have collectively logged over 360 million self-assessment reports since its introduction in March 2020. The success of the Covid Symptom Study creates significant technical challenges around effective data curation. The primary issue is scale. The size of the dataset means that it can no longer be readily processed using standard Python-based data analytics software such as Pandas on commodity hardware. Alternative technologies exist but carry a higher technical complexity and are less accessible to many researchers. We present ExeTera, a Python-based open source software package designed to provide Pandas-like data analytics on datasets that approach terabyte scales. We present its design and capabilities, and show how it is a critical component of a data curation pipeline that enables reproducible research across an international research group for the Covid Symptom Study.

COVID-19: A Redox Disease-What a Stress Pandemic Can Teach Us About Resilience and What We May Learn from the Reactive Species Interactome About Its Treatment

Significance: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus causing coronavirus disease 2019 (COVID-19), affects every aspect of human life by challenging bodily, socioeconomic, and political systems at unprecedented levels. As vaccines become available, their distribution, safety, and efficacy against emerging variants remain uncertain, and specific treatments are lacking. Recent Advances: Initially affecting the lungs, COVID-19 is a complex multisystems disease that disturbs the whole-body redox balance and can be long-lasting (Long-COVID). Numerous risk factors have been identified, but the reasons for variations in susceptibility to infection, disease severity, and outcome are poorly understood. The reactive species interactome (RSI) was recently introduced as a framework to conceptualize how cells and whole organisms sense, integrate, and accommodate stress. Critical Issues: We here consider COVID-19 as a redox disease, offering a holistic perspective of its effects on the human body, considering the vulnerability of complex interconnected systems with multiorgan/multilevel interdependencies. Host/viral glycan interactions underpin SARS-CoV-2's extraordinary efficiency in gaining cellular access, crossing the epithelial/endothelial barrier to spread along the vascular/lymphatic endothelium, and evading antiviral/antioxidant defences. An inflammation-driven "oxidative storm" alters the redox landscape, eliciting epithelial, endothelial, mitochondrial, metabolic, and immune dysfunction, and coagulopathy. Concomitantly reduced nitric oxide availability renders the sulfur-based redox circuitry vulnerable to oxidation, with eventual catastrophic failure in redox communication/regulation. Host nutrient limitations are crucial determinants of resilience at the individual and population level. Future Directions: While inflicting considerable damage to health and well-being, COVID-19 may provide the ultimate testing ground to improve the diagnosis and treatment of redox-related stress diseases. "Redox phenotyping" of patients to characterize whole-body RSI status as the disease progresses may inform new therapeutic approaches to regain redox balance, reduce mortality in COVID-19 and other redox diseases, and provide opportunities to tackle Long-COVID. Antioxid. Redox Signal. 35, 1226-1268.

Epidemiological Features and Predictors of Mortality in Patients with COVID-19 with and without Underlying Hypertension

BACKGROUNDS

Individuals with hypertension are at higher risk of COVID-19 infection and related mortality. This study was carried out to assess the epidemiological features and predictors of mortality in patients with COVID-19 with hypertension.

METHODS

In this retrospective study, the epidemiological characteristics of two groups of patients with COVID-19 with hypertension (1927) and without hypertension (39030) were compared. Chi-square test was applied to evaluate the differences between qualitative variables in two study groups. Logistic regression was also used to determine predictors of mortality in patients with COVID-19 and in patients with COVID-19 with hypertension.

RESULTS

The prevalence of hypertension in patients with COVID-19 was 4.7%, and 24.37% of COVID-19 related deaths occurred in these individuals. The average age of hypertension and nonhypertension patients was 61 and 37 years, respectively. Fever, cough, headache, anorexia, fatigue, and comorbid diseases, such as cardiovascular disease, chronic lung and kidney disease, diabetes, immunodeficiency disease, and thyroid disease, were significantly more frequent in people with hypertension than those without hypertension. The chances of mortality in patient with COVID-19 were 1.8 times higher in individuals with dyspnea, 1.25 in individuals with fever, 1.33 in individuals with cough, 3.6 in patients with hypertension, 2.21 in diabetics, and 2.2 in individuals with cardiovascular disease. Also, individuals with COVID-19 with hypertension that had dyspnea, immunodeficiency, and cardiovascular disease were at higher risk of mortality.

CONCLUSION

Hypertension is a serious threat to patients with COVID-19. Therefore, in order to control these patients more precisely and reduce mortality in them, it is extremely important to develop prevention and treatment strategies.

AIDS and COVID-19 in southern Africa

By the end of the first year of the COVID-19 pandemic, in February 2021, the numbers of cases and deaths in southern Africa were low in absolute and relative numbers. The BBC ran a story (which was later retracted) headlined "Coronavirus in Africa: Could poverty explain mystery of low death rate?". A heading in the New York Post said: "Scientists can't explain puzzling lack of coronavirus outbreaks in Africa". Journalist Karen Attiah concluded: "It's almost as if they are disappointed that Africans aren't dying en masse and countries are not collapsing". We wondered if the knowledge that southern African countries have acquired in their struggle against AIDS has contributed to a more effective approach against COVID-19. The viral origins of the diseases through zoonotic events are similar; neither has a cure, yet. In both diseases, behaviour change is an important prevention tool, and there are specific groups that are more vulnerable to infection. Equally, there are important differences: most people with COVID-19 will recover relatively quickly, while people living with HIV will need lifelong treatment. COVID-19 is extremely infectious, while HIV is less easily transmitted.

Covid and Cardiovascular Diseases: Direct and Indirect Damages and Future Perspective

SARS-CoV-2 infection determines a disease that predominantly affects lungs. However the cytokines storms, determined by the huge immune response to the infection, could affect also other organs and apparatus such as heart and vessels. Beyond the acute inflammation itself also hypercoagulative status has been linked to SARSCoV-2 infection and this surely relates to the increase seen in prevalence of pulmonary embolism and myocardial infarction. A number of cardiac abnormalities and pathologies have been observed, with special attention to cardiac arrhythmias and myocardial involvement. Furthermore, indirect damages determined by the reduction in acute and chronic cardiovascular care, results in a strong mortality and morbidity outcomes in cardiological patients. In this review we will summarise current knowledge on both direct and indirect cardiovascular damages determined by the SARS-CoV-2 pandemia.

Prevalence and characteristics of myocardial injury during COVID-19 pandemic: A new role for high-sensitive troponin

BACKGROUND

Coronavirus disease 2019 (COVID-19) is a pandemic disease that is causing a public health emergency. Characteristics and clinical significance of myocardial injury remain unclear.

METHODS

This retrospective single-center study analyzed 189 patients who received a COVID-19 diagnosis out of all 758 subjects with a high sensitive troponin I (Hs-TnI) measurement within the first 24 h of admission at the Policlinico A.Gemelli (Rome, Italy) between February 20th 2020 to April 09th 2020.

RESULTS

The prevalence of myocardial injury in our COVID-19 population is of 16%. The patients with cardiac injury were older, had a greater number of cardiovascular comorbidities and higher values of acute phase and inflammatory markers and leucocytes. They required more frequently hospitalization in Intensive Care Unit (10 [32.3%] vs 18 [11.4%]; p = .003) and the mortality rate was significantly higher (17 [54.8%] vs. 15 [9.5%], p < .001). Among patients in ICU, the subjects with myocardial injury showed an increase need of endotracheal intubation (8 out of 9 [88%] vs 7 out of 19[37%], p = .042). Multivariate analyses showed that hs-TnI can significantly predict the degree of COVID-19 disease, the intubation need and in-hospital mortality.

CONCLUSIONS

In this study we demonstrate that hs-Tn can significantly predict disease severity, intubation need and in-hospital death. Therefore, it may be reasonable to use Hs-Tn as a clinical tool in COVID-19 patients in order to triage them into different risk groups and can play a pivotal role in the detection of subjects at high risk of cardiac impairment during both the early and recovery stage.

Social Distancing Associations with COVID-19 Infection and Mortality Are Modified by Crowding and Socioeconomic Status

The SARS-CoV-2 virus is a public health emergency. Social distancing is a key approach to slowing disease transmission. However, more evidence is needed on its efficacy, and little is known on the types of areas where it is more or less effective. We obtained county-level data on COVID-19 incidence and mortality during the first wave, smartphone-based average social distancing (0–5, where higher numbers indicate more social distancing), and census data on demographics and socioeconomic status. Using generalized linear mixed models with a Poisson distribution, we modeled associations between social distancing and COVID-19 incidence and mortality, and multiplicative interaction terms to assess effect modification. In multivariable models, each unit increase in social distancing was associated with a 26% decrease (p < 0.0001) in COVID-19 incidence and a 31% decrease (p < 0.0001) in COVID-19 mortality. Percent crowding, minority population, and median household income were all statistically significant effect modifiers. County-level increases in social distancing led to reductions in COVID-19 incidence and mortality but were most effective in counties with lower percentages of black residents, higher median household incomes, and with lower levels of household crowding.

AC2: An Efficient Protein Sequence Compression Tool Using Artificial Neural Networks and Cache-Hash Models

Recently, the scientific community has witnessed a substantial increase in the generation of protein sequence data, triggering emergent challenges of increasing importance, namely efficient storage and improved data analysis. For both applications, data compression is a straightforward solution. However, in the literature, the number of specific protein sequence compressors is relatively low. Moreover, these specialized compressors marginally improve the compression ratio over the best general-purpose compressors. In this paper, we present AC2, a new lossless data compressor for protein (or amino acid) sequences. AC2 uses a neural network to mix experts with a stacked generalization approach and individual cache-hash memory models to the highest-context orders. Compared to the previous compressor (AC), we show gains of 2–9% and 6–7% in reference-free and reference-based modes, respectively. These gains come at the cost of three times slower computations. AC2 also improves memory usage against AC, with requirements about seven times lower, without being affected by the sequences’ input size. As an analysis application, we use AC2 to measure the similarity between each SARS-CoV-2 protein sequence with each viral protein sequence from the whole UniProt database. The results consistently show higher similarity to the pangolin coronavirus, followed by the bat and human coronaviruses, contributing with critical results to a current controversial subject. AC2 is available for free download under GPLv3 license.

Genomic Surveillance of Circulating SARS-CoV-2 in South East Italy: A One-Year Retrospective Genetic Study

In order to provide insights into the evolutionary and epidemiological viral dynamics during the current COVID-19 pandemic in South Eastern Italy, a total of 298 genomes of SARS-CoV-2 strains collected in the Apulia and Basilicata regions, between March 2020 and January 2021, were sequenced. The genomic analysis performed on the draft genomes allowed us to assign the genetic clades and lineages of belonging to each sample and provide an overview of the main circulating viral variants. Our data showed the spread in Apulia and Basilicata of SARS-CoV-2 variants which have emerged during the second wave of infections and are being currently monitored worldwide for their increased transmission rate and their possible impact on vaccines and therapies. These results emphasize the importance of genome sequencing for the epidemiological surveillance of the new SARS-CoV-2 variants' spread.

Inferring the Association between the Risk of COVID-19 Case Fatality and N501Y Substitution in SARS-CoV-2

As COVID-19 is posing a serious threat to global health, the emerging mutation in SARS-CoV-2 genomes, for example, N501Y substitution, is one of the major challenges against control of the pandemic. Characterizing the relationship between mutation activities and the risk of severe clinical outcomes is of public health importance for informing the healthcare decision-making process. Using a likelihood-based approach, we developed a statistical framework to reconstruct a time-varying and variant-specific case fatality ratio (CFR), and to estimate changes in CFR associated with a single mutation empirically. For illustration, the statistical framework is implemented to the COVID-19 surveillance data in the United Kingdom (UK). The reconstructed instantaneous CFR gradually increased from 1.0% in September to 2.2% in November 2020 and stabilized at this level thereafter, which monitors the mortality risk of COVID-19 on a real-time basis. We identified a link between the SARS-CoV-2 mutation activity at molecular scale and COVID-19 mortality risk at population scale, and found that the 501Y variants may slightly but not significantly increase 18% of fatality risk than the preceding 501N variants. We found no statistically significant evidence of change in COVID-19 mortality risk associated with 501Y variants, and highlighted the real-time estimating potentials of the modelling framework.

Higher Temperatures, Higher Solar Radiation, and Less Humidity Is Associated With Poor Clinical and Laboratory Outcomes in COVID-19 Patients

Background: The COVID-19 pandemic varies between countries, with suggestions that weather might contribute to the transmission mode, disease presentation, severity, and clinical outcomes. Yet the exact link between climate and COVID-19 is still not well-explored. Objectives: This study aimed to evaluate the effect of hot geographical region weather [like United Arab Emirates (UAE)] on COVID-19 clinical profile and outcomes. Temperature, wind speed, cloud cover, precipitation, and other weather-related variables were studied concerning COVID-19 patients outcomes and laboratory results. Methodology: A total of 434 COVID-19 positive patients admitted between January and June 2020, were recruited from Al Kuwait Hospital, Dubai, UAE. Temperature, wind speed, cloud cover, and precipitation rate were retrieved from history+ for the day when COVID-19 patients presented to the hospital. These weather parameters were correlated with COVID-19 clinical and laboratory parameters. Results: Our results showed that patients needed admission in days with higher temperatures, higher solar radiation, and less humidity were associated with higher deaths. This association can be linked to the association of these weather parameters with age at diagnosis; higher C-reactive protein (CRP), neutrophil count, white cell count (WCC), aspartate aminotransferase (AST), and alkaline phosphatase (ALP); and lower lymphocyte count, estimated glomerular filtration rate (eGFR), hemoglobin (Hb), Na, and albumin, all of which are considered poor prognostic factors for COVID-19. Conclusion: Our study highlighted the importance of weather-related variables on the dynamics of mortality and clinical outcomes of COVID-19. The hot weather might makes some people, especially those with comorbidities or older ages, develop aggressive inflammation that ends up with complications and mortality.

The Importance of Glycosylation in COVID-19 Infection

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently one of the major health problems worldwide. SARS-CoV-2 survival and virulence are shown to be impacted by glycans, covalently attached to proteins in a process of glycosylation, making glycans an area of interest in SARS-CoV-2 biology and COVID-19 infection. The SARS-CoV-2 uses its highly glycosylated spike (S) glycoproteins to bind to the cell surface receptor angiotensin-converting enzyme 2 (ACE2) glycoprotein and facilitate host cell entry. Viral glycosylation has wide-ranging roles in viral pathobiology, including mediating protein folding and stability, immune evasion, host receptor attachment, and cell entry. Modification of SARS-CoV-2 envelope membrane with glycans is important in host immune recognition and interaction between S and ACE2 glycoproteins. On the other hand, immunoglobulin G, a key molecule in immune response, shows a distinct glycosylation profile in COVID-19 infection and with increased disease severity. Hence, further studies on the role of glycosylation in SARS-CoV-2 infectivity and COVID-19 infection are needed for its successful prevention and treatment. This chapter focuses on recent findings on the importance of glycosylation in COVID-19 infection.