What the data say about asymptomatic COVID infections

Dual-strain dynamics of COVID-19 variants in India: Modeling, analysis, and implications for pandemic control

This study introduces a detailed compartmental model developed to understand the complex dynamics of COVID-19 transmission, focusing on the Delta and Omicron variants in India. The model tracks disease progression through different population compartments, considering factors like vaccination, time-dependent transmission, economic burden and COVID-19 death rates, loss of vaccine-induced immunity, and the transition of asymptomatic cases to recovery. The model is validated against established epidemiological knowledge and real-world data, emphasizing dynamic parameterization and accurate representation of immunity dynamics. The basic reproduction number for both variants is calculated, and sensitivity analysis for various parameters is conducted. Time-dependent parameters are estimated using the discrete inverse method. The study also explores the economic burden, impact of different types of masks, vaccine efficacy, and vaccine-induced immunity through numerical analysis.

Mathematical model for understanding the relationship between diabetes and novel coronavirus

A new model is proposed to explore interactions between diabetes and novel coronavirus. The model accounted for both the omicron variant and variants varying from omicron. The model investigated compartments such as hospitalization, diabetes, co-infection, omicron variant, and quarantine. Additionally, the impact of different vaccination doses is assessed. Sensitivity analysis is carried out to determine disease prevalence and control options, emphasizing the significance of knowing epidemics and their characteristics. The model is validated using actual data from Japan. The parameters are fitted with the help of "Least Square Curve Fitting" method to describe the dynamic behavior of the proposed model. Simulation results and theoretical findings demonstrate the dynamic behavior of novel coronavirus and diabetes mellitus (DM). Biological illustrations that illustrate impact of model parameters are evaluated. Furthermore, effect of vaccine efficacy and vaccination rates for the vaccine's first, second, and booster doses is conducted. The impact of various preventive measures, such as hospitalization rate, quarantine or self-isolation rate, vaccine dose-1, dose-2, and booster dose, is considered for diabetic individuals in contact with symptomatic or asymptomatic COVID-19 infectious people in the proposed model. The findings demonstrate the significance of vaccine doses on people with diabetes and individuals infectious with omicron variant. The proposed work helps with subsequent prevention efforts and the design of a vaccination policy to mitigate the effect of the novel coronavirus.

Evaluating the spike in the symptomatic proportion of SARS-CoV-2 in China in 2022 with variolation effects: a modeling analysis

Despite most COVID-19 infections being asymptomatic, mainland China had a high increase in symptomatic cases at the end of 2022. In this study, we examine China's sudden COVID-19 symptomatic surge using a conceptual SIR-based model. Our model considers the epidemiological characteristics of SARS-CoV-2, particularly variolation, from non-pharmaceutical intervention (facial masking and social distance), demography, and disease mortality in mainland China. The increase in symptomatic proportions in China may be attributable to (1) higher sensitivity and vulnerability during winter and (2) enhanced viral inhalation due to spikes in SARS-CoV-2 infections (high transmissibility). These two reasons could explain China's high symptomatic proportion of COVID-19 in December 2022. Our study, therefore, can serve as a decision-support tool to enhance SARS-CoV-2 prevention and control efforts. Thus, we highlight that facemask-induced variolation could potentially reduces transmissibility rather than severity in infected individuals. However, further investigation is required to understand the variolation effect on disease severity.

Optimal social distancing in epidemic control: cost prioritization, adherence and insights into preparedness principles

The COVID-19 pandemic experience has highlighted the importance of developing general control principles to inform future pandemic preparedness based on the tension between the different control options, ranging from elimination to mitigation, and related costs. Similarly, during the COVID-19 pandemic, social distancing has been confirmed to be the critical response tool until vaccines become available. Open-loop optimal control of a transmission model for COVID-19 in one of its most aggressive outbreaks is used to identify the best social distancing policies aimed at balancing the direct epidemiological costs of a threatening epidemic with its indirect (i.e., societal level) costs arising from enduring control measures. In particular, we analyse how optimal social distancing varies according to three key policy factors, namely, the degree of prioritization of indirect costs, the adherence to control measures, and the timeliness of intervention. As the prioritization of indirect costs increases, (i) the corresponding optimal distancing policy suddenly switches from elimination to suppression and, finally, to mitigation; (ii) the "effective" mitigation region-where hospitals' overwhelming is prevented-is dramatically narrow and shows multiple control waves; and (iii) a delicate balance emerges, whereby low adherence and lack of timeliness inevitably force ineffective mitigation as the only accessible policy option. The present results show the importance of open-loop optimal control, which is traditionally absent in public health preparedness, for studying the suppression-mitigation trade-off and supplying robust preparedness guidelines.

The effectiveness and efficiency of asymptomatic SARS-CoV-2 testing strategies for patient and healthcare workers within acute NHS hospitals during an omicron-like period

BACKGROUND

Asymptomatic SARS-CoV-2 testing of hospitalised patients began in April-2020, with twice weekly healthcare worker (HCW) testing introduced in November-2020. Guidance recommending asymptomatic testing was withdrawn in August-2022. Assessing the impact of this decision from data alone is challenging due to concurrent changes in infection prevention and control practices, community transmission rates, and a reduction in ascertainment rate from reduced testing. Computational modelling is an effective tool for estimating the impact of this change.

METHODS

Using a computational model of SARS-CoV-2 transmission in an English hospital we estimate the effectiveness of several asymptomatic testing strategies, namely; (1) Symptomatic testing of patients and HCWs, (2) testing of all patients on admission with/without repeat testing on days 3 and 5-7, and (3) symptomatic testing plus twice weekly asymptomatic HCW testing with 70% compliance. We estimate the number of patient and HCW infections, HCW absences, number of tests, and tests per case averted or absence avoided, with differing community prevalence rates over a 12-week period.

RESULTS

Testing asymptomatic patients on admission reduces the rate of nosocomial SARS-CoV-2 infection by 8.1-21.5%. Additional testing at days 3 and 5-7 post admission does not significantly reduce infection rates. Twice weekly asymptomatic HCW testing can reduce the proportion of HCWs infected by 1.0-4.4% and monthly absences by 0.4-0.8%. Testing asymptomatic patients repeatedly requires up to 5.5 million patient tests over the period, and twice weekly asymptomatic HCW testing increases the total tests to almost 30 million. The most efficient patient testing strategy (in terms of tests required to prevent a single patient infection) was testing asymptomatic patients on admission across all prevalence levels. The least efficient was repeated testing of patients with twice weekly asymptomatic HCW testing in a low prevalence scenario, and in all other prevalence levels symptomatic patient testing with regular HCW testing was least efficient.

CONCLUSIONS

Testing patients on admission can reduce the rate of nosocomial SARS-CoV-2 infection but there is little benefit of additional post-admission testing. Asymptomatic HCW testing has little incremental benefit for reducing patient cases at low prevalence but has a potential role at higher prevalence or with low community transmission. A full health-economic evaluation is required to determine the cost-effectiveness of these strategies.

Comparison of plasma mitochondrial DNA copy number in asymptomatic and symptomatic COVID-19 patients

Introduction

Since the beginning of the COVID-19 pandemic, a wide clinical spectrum, from asymptomatic infection to mild or severe disease and death, have been reported in COVID-19 patients. Studies have suggested several possible factors, which may affect the clinical outcome of COVID-19. A pro-inflammatory state and impaired antiviral response have been suggested as major contributing factors in severe COVID-19. Considering that mitochondria have an important role in regulating the immune responses to pathogens, pro-inflammatory signaling, and cell death, it has received much attention in SARS-CoV-2 infection. Recent studies have demonstrated that high levels of cell-free mitochondrial DNA (cf-mtDNA) are associated with an increased risk of COVID-19 intensive care unit (ICU) admission and mortality. However, there have been few studies on cf-mtDNA in SARS-CoV-2 infection, mainly focusing on critically ill COVID-19 cases. In the present study, we investigated cf-mtDNA copy number in COVID-19 patients and compared between asymptomatic and symptomatic cases, and assessed the clinical values. We also determined the cf-nuclear DNA (cf-nDNA) copy number and mitochondrial transcription factor A (TFAM) mRNA level in the studied groups.

Materials and methods

Plasma and buffy coat samples were collected from 37 COVID-19 patients and 33 controls. Briefly, after total DNA extraction, plasma cf-mtDNA, and cf-nDNA copy numbers were measured by absolute qPCR using a standard curve method. Furthermore, after total RNA extraction from buffy coat and cDNA synthesis, TFAM mRNA levels were evaluated by qPCR.

Results

The results showed that cf-mtDNA levels in asymptomatic COVID-19 patients were statistically significantly higher than in symptomatic cases (p value = 0.01). However, cf-nDNA levels were higher in symptomatic patients than in asymptomatic cases (p value = 0.00). There was no significant difference between TFAM levels in the buffy coat of these two groups (p value > 0.05). Also, cf-mtDNA levels showed good diagnostic potential in COVID-19 subgroups.

Conclusion

cf-mtDNA is probably important in the outcome of SARS-CoV-2 infection due to its role in inflammation and immune response. It can also be a promising candidate biomarker for the diagnosis of COVID-19 subgroups. Further investigation will help understanding the COVID-19 pathophysiology and effective diagnostic and therapeutic strategies.

Oligosymptomatic long-term carriers of SARS-CoV-2 display impaired innate resistance but increased high-affinity anti-spike antibodies

The vast spectrum of clinical features of COVID-19 keeps challenging scientists and clinicians. Low resistance to infection might result in long-term viral persistence, but the underlying mechanisms remain unclear. Here, we studied the immune response of immunocompetent COVID-19 patients with prolonged SARS-CoV-2 infection by immunophenotyping, cytokine and serological analysis. Despite viral loads and symptoms comparable to regular mildly symptomatic patients, long-term carriers displayed weaker systemic IFN-I responses and fewer circulating pDCs and NK cells at disease onset. Type 1 cytokines remained low, while type-3 cytokines were in turn enhanced. Of interest, we observed no defects in antigen-specific cytotoxic T cell responses, and circulating antibodies displayed higher affinity against different variants of SARS-CoV-2 Spike protein in these patients. The identification of distinct immune responses in long-term carriers adds up to our understanding of essential host protective mechanisms to ensure tissue damage control despite prolonged viral infection.

Network-augmented compartmental models to track asymptomatic disease spread

Summary

A major challenge in understanding the spread of certain newly emerging viruses is the presence of asymptomatic cases. Their prevalence is hard to measure in the absence of testing tools, and yet the information is critical for tracking disease spread and shaping public health policies. Here, we introduce a framework that combines classic compartmental models with travel networks and we use it to estimate asymptomatic rates. Our platform, traSIR ("tracer"), is an augmented susceptible-infectious-recovered (SIR) model that incorporates multiple locations and the flow of people between them; it has a compartment model for each location and estimates of commuting traffic between compartments. TraSIR models both asymptomatic and symptomatic infections, as well as the dampening effect symptomatic infections have on traffic between locations. We derive analytical formulae to express the asymptomatic rate as a function of other key model parameters. Next, we use simulations to show that empirical data fitting yields excellent agreement with actual asymptomatic rates using only information about the number of symptomatic infections over time and compartments. Finally, we apply our model to COVID-19 data consisting of reported daily infections in the New York metropolitan area and estimate asymptomatic rates of COVID-19 to be ∼34%, which is within the 30-40% interval derived from widespread testing. Overall, our work demonstrates that traSIR is a powerful approach to express viral propagation dynamics over geographical networks and estimate key parameters relevant to virus transmission.

Availability and implementation

No public repository.

Forecasting and what-if analysis of new positive COVID-19 cases during the first three waves in Italy

The joint exploitation of data related to epidemiological, mobility, and restriction aspects of COVID-19 with machine learning algorithms can support the development of predictive models that can be used to forecast new positive cases and study the impact of more or less severe restrictions. In this work, we integrate heterogeneous data from several sources and solve a multivariate time series forecasting task, specifically targeting the Italian case at both national and regional levels, during the first three waves of the pandemic. The goal is to build a robust predictive model to predict the number of new cases over a given time horizon so that any restrictive actions can be better planned. In addition, we perform a what-if analysis based on the best-identified predictive models to evaluate the impact of specific restrictions on the trend of positive cases. Our focus on the first three waves is motivated by the fact that it represents a typical emergency scenario (when no stable cure or vaccine is available) that may occur when a new pandemic spreads. Our experimental results prove that exploiting the considered heterogeneous data leads to accurate predictive models, reaching a WAPE of 5.75% at the national level. Furthermore, in the subsequent what-if analysis, we observed that strong all-in-one initiatives, such as total lockdowns, may not be adequate, while more specific and targeted solutions should be adopted. The developed models can help policy and decision-makers better plan intervention strategies and retrospectively analyze the effects of the decisions made at different scales. Joint exploitation of data on epidemiological, mobility, and restriction aspects of COVID-19 with machine learning algorithms to learn predictive models to forecast new positive cases.

Relationships between COVID-19 and disaster risk in Costa Rican municipalities

The COVID-19 pandemic has had far-reaching impacts on every aspect of human life since the first confirmed case in December 2019. Costa Rica reported its first case of COVID-19 in March 2020, coinciding with a notable correlation between the occurrence of disaster events at the municipal scale over the past five decades. In Costa Rica, over 90% of disasters are hydrometeorological in nature, while geological disasters have caused significant economic and human losses throughout the country's history. To analyze the relationship between COVID-19 cases and disaster events in Costa Rica, two Generalized Linear Models (GLMs) were used to statistically evaluate the influence of socio-environmental parameters such as population density, social development index, road density, and non-forested areas. The results showed that population and road density are the most critical factors in explaining the spread of COVID-19, while population density and social development index can provide insights into disaster events at the municipal level in Costa Rica. This study provides valuable information for understanding municipal vulnerability and exposure to disasters in Costa Rica and can serve as a model for other countries to assess disaster risk.

Factors Associated with Reported COVID-like Symptoms and Seroprevalence Data Matched with COVID-like Symptoms in Slums and Non-Slums of Two Major Cities in Bangladesh

OBJECTIVES

To examine the levels and socio-demographic differentials of: (a) reported COVID-like symptoms; and (b) seroprevalence data matched with COVID-like symptoms.

METHODS

Survey data of reported COVID-like symptoms and seroprevalence were assessed by Roche Elecsys^® Anti-SARS-CoV-2 immunoassay. Survey data of 10,050 individuals for COVID-like symptoms and seroprevalence data of 3205 individuals matched with COVID-like symptoms were analyzed using bivariate and multivariate logistic analysis.

RESULTS

The odds of COVID-like symptoms were significantly higher for Chattogram city, for non-slum, people having longer years of schooling, working class, income-affected households, while for households with higher income had lower odd. The odds of matched seroprevalence and COVID-like symptoms were higher for non-slum, people having longer years of schooling, and for working class. Out of the seropositive cases, 37.77% were symptomatic-seropositive, and 62.23% were asymptomatic, while out of seronegative cases, 68.96% had no COVID-like symptoms.

CONCLUSIONS

Collecting community-based seroprevalence data is important to assess the extent of exposure and to initiate mitigation and awareness programs to reduce COVID-19 burden.

Intermediate levels of asymptomatic transmission can lead to the highest epidemic fatalities

Asymptomatic infections have hampered the ability to characterize and prevent the transmission of SARS-CoV-2 throughout the pandemic. Although asymptomatic infections reduce severity at the individual level, they can make population-level outcomes worse if asymptomatic individuals-unaware they are infected-transmit more than symptomatic individuals. Using an epidemic model, we show that intermediate levels of asymptomatic infection lead to the highest levels of epidemic fatalities when the decrease in symptomatic transmission, due either to individual behavior or mitigation efforts, is strong. We generalize this result to include presymptomatic transmission, showing that intermediate levels of nonsymptomatic transmission lead to the highest levels of fatalities. Finally, we extend our framework to illustrate how the intersection of asymptomatic spread and immunity profiles determine epidemic trajectories, including population-level severity, of future variants. In particular, when immunity provides protection against symptoms, but not against infections or deaths, epidemic trajectories can have faster growth rates and higher peaks, leading to more total deaths. Conversely, even modest levels of protection against infection can mitigate the population-level effects of asymptomatic spread.

Filter Questions in Symptom Assessment Affect the Prevalence of (A)Symptomatic COVID-19 Cases

BACKGROUND

It has been reported that a substantial number of COVID-19 infections are asymptomatic, with both symptomatic and asymptomatic infections contributing to transmission dynamics. Yet, the share of asymptomatic cases varies greatly across studies. One reason for this could be the measurement of symptoms in medical studies and surveys.

DESIGN

In 2 experimental survey studies (total N > 3,000) with participants from Germany and the United Kingdom, respectively, we varied the inclusion of a filter question on whether participants who tested positive for COVID-19 had experienced symptoms prior to presenting a checklist of symptoms. We measured the reporting of asymptomatic (versus symptomatic) COVID-19 infections.

RESULTS

The inclusion of a filter question increased the reporting of asymptomatic (versus symptomatic) COVID-19 infections. Particularly mild symptoms were underreported when using a filter question.

CONCLUSIONS AND IMPLICATIONS

Filter questions affect the reporting of (a)symptomatic COVID-19 cases. To account for such differences in the estimation of population infection rates, future studies should transparently report the applied question format.

HIGHLIGHTS

Both symptomatic and asymptomatic infections are important for COVID-19 transmission dynamics.In previous research, symptoms have been assessed either with or without a filter question prior to presenting a symptom list.We show that filter questions reduce the reporting of asymptomatic infections.Particularly mild symptoms are underreported when using a filter question.

Uncertainty quantification in mechanistic epidemic models via cross-entropy approximate Bayesian computation

This paper proposes a data-driven approximate Bayesian computation framework for parameter estimation and uncertainty quantification of epidemic models, which incorporates two novelties: (i) the identification of the initial conditions by using plausible dynamic states that are compatible with observational data; (ii) learning of an informative prior distribution for the model parameters via the cross-entropy method. The new methodology's effectiveness is illustrated with the aid of actual data from the COVID-19 epidemic in Rio de Janeiro city in Brazil, employing an ordinary differential equation-based model with a generalized SEIR mechanistic structure that includes time-dependent transmission rate, asymptomatics, and hospitalizations. A minimization problem with two cost terms (number of hospitalizations and deaths) is formulated, and twelve parameters are identified. The calibrated model provides a consistent description of the available data, able to extrapolate forecasts over a few weeks, making the proposed methodology very appealing for real-time epidemic modeling.

Zebrafish models of COVID-19

Although COVID-19 has only recently appeared, research studies have already developed and implemented many animal models for deciphering the secrets of the disease and provided insights into the biology of SARS-CoV-2. However, there are several major factors that complicate the study of this virus in model organisms, such as the poor infectivity of clinical isolates of SARS-CoV-2 in some model species, and the absence of persistent infection, immunopathology, severe acute respiratory distress syndrome, and, in general, all the systemic complications which characterize COVID-19 clinically. Another important limitation is that SARS-CoV-2 mainly causes severe COVID-19 in older people with comorbidities, which represents a serious problem when attempting to use young and immunologically naïve laboratory animals in COVID-19 testing. We review here the main animal models developed so far to study COVID-19 and the unique advantages of the zebrafish model that may help to contribute to understand this disease, in particular to the identification and repurposing of drugs to treat COVID-19, to reveal the mechanism of action and side-effects of Spike-based vaccines, and to decipher the high susceptibility of aged people to COVID-19.

SARS-CoV-2 Transmission among Students after the Full Reopening of Schools

Objective Closing of schools within the scope of the pandemic measures and switching to online education have negatively affected the mental and physical health of children as well as their education. The effect of complete reopening of schools on the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is not fully known. In the 2021–2022 academic year, the Ministry of National Education of Türkiye has decided to continue face-to-face education at all levels and in all private and public schools, by following a series of measures. There are no studies on school transmission reported from Türkiye since then. This study aimed to assess the dynamics of coronavirus diseases 2019 (COVID-19) transmission in schools by evaluating the data of the school contact screening outpatient clinic of a tertiary children's hospital.

Methods According to the Ministry of National Education guidelines in Türkiye, all students in a class with two polymerase chain reaction (PCR)–confirmed COVID-19 cases are sent to their homes and have a SARS-CoV-2 PCR test on the fifth day. While the students with negative test results return to school, students who test positive continue to stay at home until the 10th day. The current study retrospectively analyzed the screening results of primary, middle, and high school students who presented at the school contact screening polyclinic during the first semester of the 2021–2022 academic season.

Discussion There were a total of 11,608 presentations to the school contact screening polyclinic, and 1,107 children tested positive with SARS-CoV-2 PCR (9.5%). The median weekly positivity rate was 9.7% in primary school, 9.4% in middle school, and 5.6% in high school. The weekly positivity rate increased from the 16th week at all school levels; the positivity rates ran in parallel before this time but significantly increased from the 16th week to the half-term break.

Conclusion The curve of the positivity rates in schools was similar to Türkiye's pandemic curve. In this respect, the low SARS-CoV-2 transmission in schools correlated with the low number of cases in the community.

"I wouldn't survive it, as simple as that": Syndemic vulnerability among people living with chronic non-communicable disease during the COVID-19 pandemic

The co-occurrence of COVID-19, non-communicable diseases and socioeconomic disadvantage has been identified as creating a syndemic: a state of synergistic epidemics, occurring when co-occurring health conditions interact with social conditions to amplify the burden of disease. In this study, we use the concept of illness management work to explore the impact of the COVID-19 pandemic on the lives of people living with, often multiple, chronic health conditions in a range of social circumstances. In-depth interviews were conducted between May and July 2020 with 29 participants living in a city in North East England. Qualitative data provide unique insights for those seeking to better understand the consequences for human life and wellbeing of the interacting social, physical and psychological factors that create syndemic risks in people's lives. Among this group of people at increased vulnerability to harm from COVID-19, we find that the pandemic public health response increased the work required for condition management. Mental distress was amplified by fear of infection and by the requirements of social isolation and distancing that removed participants' usual sources of support. Social conditions, such as poor housing, low incomes and the requirement to earn a living, further amplified the work of managing everyday life and risked worsening existing mental ill health. As evidenced by the experiences reported here, the era of pandemics will require a renewed focus on the connection between health and social justice if stubborn, and worsening health and social inequalities are to be addressed or, at the very least, not increased.

A serological investigation in Southern Italy: was SARS-CoV-2 circulating in late 2019?

In March 2020, the first pandemic caused by a coronavirus was declared by the World Health Organization. Italy was one of the first and most severely affected countries, particularly the northern part of the country. The latest evidence suggests that the virus could have been circulating, at least in Italy, before the first autochthonous SARS-COV-2 case was detected in February 2020. The present study aimed to investigate the presence of antibodies against SARS-CoV-2 in human serum samples collected in the last months of 2019 (September–December) in the Apulia region, Southern Italy. Eight of 455 samples tested proved positive on in-house receptor-binding-domain-based ELISA. Given the month of collection of the positive samples, these findings may indicate early circulation of SARS-CoV-2 in Apulia region in the autumn of 2019. However, it cannot be completely ruled out that the observed sero-reactivity could be an unknown antigen specificity in another virus to which subjects were exposed containing an epitope adventitiously cross-reactive with an epitope of SARS-CoV-2.

Perception about Health Applications (Apps) in Smartphones towards Telemedicine during COVID-19: A Cross-Sectional Study

BACKGROUND

The use of health applications (apps) in smartphones increased exponentially during COVID-19. This study was conducted the with the aim to understand the factors that determine the consumer's perception of health apps in smartphones towards telemedicine during COVID-19 and to test any relation between these factors and consumers towards Telemedicine in India.

METHODS

This questionnaire-based cross-sectional study was conducted from July 2021 to December 2021 in India. Out of 600 selected participants, 594 responded and in that 535 valid questionnaires were measured. The questionnaire consists of close-ended responses, with the first part consisting of demographic information, the second part consisting of questions associated with consumers' perceptions and the third part kept for suggestions and complaints. The questionnaire was distributed using digital platforms via WhatsApp or email. A 5-point Likert scale, ranging from strongly agree' (5) to strongly disagree (1) was used to record responses.

RESULTS

Results revealed a high response rate of 90%. The highest score was obtained for the question assessing the satisfaction of the users towards health apps [1175 = 500 (agree-4) + 675 (Strongly agree-5)]. The interface of the app scored very low, showing disagreement (514) with app functionality, and was the most common disadvantage as perceived by patients. The mean scores of reliabilities and vicinity of health services; efficacy and comprehensive health information; development and improvement of health apps and telemedicine (3.24, 3.18, 3.62, 3.49), respectively, show the difference in attraction existing between groups. There is a strong positive correlation between the variables except for efficacy and comprehensive information about health and Telemedicine (-0.249), development and improvement of health apps, and reliability and vicinity of health services (-0.344) which have a negative correlation.

CONCLUSIONS

The findings of this survey reveal a positive outlook of health apps toward telemedicine. This research also found a strong forecaster of the consumer's perception of health apps in smartphones towards telemedicine. In the broad spectrum, the future of health app affiliates for telemedicine is better affected by the consumer's perception of health app efficacy. This study suggests that health app marketers develop more innovative apps to increase usage and help consumers.

Impact of the eHealth literacy, knowledge and attitudes on COVID-19 prevention behavior among residents in the second year of the COVID-19 pandemic: A cross-sectional study in Anhui Province, China

Objective

The pandemic of COVID-19 continues to challenge people's health worldwide. In the second year of the pandemic, adherence to COVID-19 preventive behavior is key to continuing efforts to overcome the epidemic. This study aims to assess the COVID-19-related knowledge, attitude, and prevention behavior (KAP) and electronic health literacy (eHealth literacy) among Anhui residents in China.

Methods

From January 30 to March 27, 2021, the cross-sectional study was performed among Anhui residents in China, including 16 cities. An online survey was adopted to assess KAP regarding COVID-19, and eHealth, involving a total of 2,122 citizens. Following informed consent, residents were recruited by convenience sampling. Frequencies and proportions were calculated. Additionally, Mann-Whitney U tests were used to analyze the variables. Independent predictors of preventive behavior of COVID-19 were ascertained using a multivariable logistic regression model.

Result

Residents demonstrated good knowledge, positive attitudes, acceptable practices, and good eHealth literacy. Online news and WeChat are the main health information resources. Citizens who had good knowledge, a positive attitude, good eHealth, and did not participate in the online lectures or training COVID-19 were more likely to take preventive measures. Those with poor health, who were male, did not have family members working in health care facilities, and did not work in a face-to-face environment were less likely to take precautions. Compared with a master's degree and above, participants with middle school education level and below took preventive behavior sometimes. Residents who browse the COVID-19 webpage <15 min weekly seldom took preventive actions.

Conclusion

The study showed that in the second year of the COVID-19 pandemic, Chinese residents had adequate knowledge of COVID-19, positive attitudes, appropriate preventive practices, and basic eHealth literacy. To prevent the rebound of the COVID-19 epidemic, the government and health agencies should inform citizens concerning which information channels or websites to use and assist the underprivileged population who lacks basic infrastructure. In addition, increasing the level of knowledge and attitude, enhancing eHealth literacy and the Health Belief Model (HBM), and implementing the Health Code were seen as ways to reinforce adherence to preventive behavior. Targeting men, implementing public awareness campaigns, community engagement strategies, and health education programs are recommended.

CRISPR-based systems for sensitive and rapid on-site COVID-19 diagnostics

The COVID-19 pandemic has strained healthcare systems. Sensitive, specific, and timely COVID-19 diagnosis is crucial for effective medical intervention and transmission control. RT-PCR is the most sensitive/specific, but requires costly equipment and trained personnel in centralized laboratories, which are inaccessible to resource-limited areas. Antigen rapid tests enable point-of-care (POC) detection but are significantly less sensitive/specific. CRISPR-Cas systems are compatible with isothermal amplification and dipstick readout, enabling sensitive/specific on-site testing. However, improvements in sensitivity and workflow complexity are needed to spur clinical adoption. We outline the mechanisms/strategies of major CRISPR-Cas systems, evaluate their on-site diagnostic capabilities, and discuss future research directions.

Estimate of the rate of unreported COVID-19 cases during the first outbreak in Rio de Janeiro

In this work we fit an epidemiological model SEIAQR (Susceptible - Exposed - Infectious - Asymptomatic - Quarantined - Removed) to the data of the first COVID-19 outbreak in Rio de Janeiro, Brazil. Particular emphasis is given to the unreported rate, that is, the proportion of infected individuals that is not detected by the health system. The evaluation of the parameters of the model is based on a combination of error-weighted least squares method and appropriate B-splines. The structural and practical identifiability is analyzed to support the feasibility and robustness of the parameters' estimation. We use the Bootstrap method to quantify the uncertainty of the estimates. For the outbreak of March-July 2020 in Rio de Janeiro, we estimate about 90% of unreported cases, with a 95% confidence interval (85%, 93%).

Keeping up with the COVID's-Could siRNA-based antivirals be a part of the answer?

Coronavirus disease 2019 (COVID-19) is a highly contagious viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This deadly infection has resulted in more than 5.2 million deaths worldwide. The global rollout of COVID-19 vaccines has without doubt saved countless lives by reducing the severity of symptoms for patients. However, as the virus continues to evolve, there is a risk that the vaccines and antiviral designed to target the infection will no longer be therapeutically viable. Furthermore, there remain fears over both the short and long-term side effects of repeat exposure to currently available vaccines. In this review, we discuss the pros and cons of the vaccine rollout and promote the idea of a COVID medicinal toolbox made up of different antiviral treatment modalities, and present some of the latest therapeutic strategies that are being explored in this respect to try to combat the COVID-19 virus and other COVID viruses that are predicted to follow. Lastly, we review current literature on the use of siRNA therapeutics as a way to remain adaptable and in tune with the ever-evolving mutation rate of the COVID-19 virus.

Heterogeneous adaptive behavioral responses may increase epidemic burden

Non-pharmaceutical interventions (NPIs) constitute the front-line responses against epidemics. Yet, the interdependence of control measures and individual microeconomics, beliefs, perceptions and health incentives, is not well understood. Epidemics constitute complex adaptive systems where individual behavioral decisions drive and are driven by, among other things, the risk of infection. To study the impact of heterogeneous behavioral responses on the epidemic burden, we formulate a two risk-groups mathematical model that incorporates individual behavioral decisions driven by risk perceptions. Our results show a trade-off between the efforts to avoid infection by the risk-evader population, and the proportion of risk-taker individuals with relaxed infection risk perceptions. We show that, in a structured population, privately computed optimal behavioral responses may lead to an increase in the final size of the epidemic, when compared to the homogeneous behavior scenario. Moreover, we find that uncertain information on the individuals' true health state may lead to worse epidemic outcomes, ultimately depending on the population's risk-group composition. Finally, we find there is a set of specific optimal planning horizons minimizing the final epidemic size, which depend on the population structure.

Burden of COVID-19: DALY and productivity loss for Karnataka, India

Background

COVID-19 is a pandemic that is devastating the world right now quelling over 2.5 million people worldwide. Similarly, in India and its largest southern state Karnataka, the coronavirus is responsible for around 161,000 and 12,449 deaths, respectively. These numbers capture the havoc caused by this novel coronavirus, but fail to discern the complete picture.

Objectives

Broadly, this study aimed to study the mortality, morbidity, and the economic issues inflicted by the COVID-19 in the state of Karnataka. Specifically, the study used publically available epidemiological data to study both mortality and morbidity by means of disability-adjusted life years (DALYs). Furthermore, the study aimed at estimating the permanent losses to the state gross domestic product (SGDP) due to the pandemic.

Materials and Methods

Publicly available epidemiological data are used from selected sources and DALYs are computed. The permanent loss to the SGDP is estimated using the human capital approach.

Results

The total DALYs for Karnataka are computed to be 22,506 of which 22,041 correspond to mortality and remaining correspond to morbidity. Financially, Karnataka lost around 208 years of productive years of lives costing around ₹590 million rupees to the SGDP.

Conclusions

It is found that major burden of COVID-19 during study period is due to mortality. Morbidity accounts for around 2% of the total DALYs. Males are the most affected by the mortality and also the morbidity. With respect to loss in productivity, the losses due to premature mortality of COVID-19 amounted to ₹590 million.

A Simulation-Based Cost-Effectiveness Analysis of Severe Acute Respiratory Syndrome Coronavirus 2 Infection Prevention Strategies for Visitors of Healthcare Institutions

OBJECTIVES

The aim is to quantitatively evaluate different infection prevention strategies in the context of hospital visitor management during pandemics and to provide a decision support system for strategic and operational decisions based on this evaluation.

METHODS

A simulation-based cost-effectiveness analysis is applied to the data of a university hospital in Southern Germany and published COVID-19 research. The performance of different hospital visitor management strategies is evaluated by several decision-theoretic methods with varying objective functions.

RESULTS

Appropriate visitor restrictions and infection prevention measures can reduce additional infections and costs caused by visitors of healthcare institutions by >90%. The risk of transmission of severe acute respiratory syndrome coronavirus 2 by visitors of terminal care (ie, palliative care) patients can be reduced almost to 0 if appropriate infection prevention measures are implemented. Antigen tests do not seem to be beneficial from both a cost and an effectiveness perspective.

CONCLUSIONS

Hospital visitor management is crucial and effectively prevents infections while maintaining cost-effectiveness. For terminal care patients, visitor restrictions can be omitted if appropriate infection prevention measures are taken. Antigen testing plays a subordinate role, except in the case of a pure focus on additional infections caused by visitors of healthcare institutions. We provide decision support to authorities and hospital visitor managers to identify appropriate visitor restriction and infection prevention strategies for specific local conditions, incidence rates, and objectives.

Knowledge and practices related to COVID-19 among mothers of under-2 children and adult males: a cross-sectional study in Bangladesh

OBJECTIVES

To assess the knowledge and practices related to COVID-19 among Bangladeshi mothers with children aged 2 years or less and adult males.

DESIGN

We conducted a cross-sectional study to assess the knowledge and practices using a multistage cluster sampling technique.

SETTINGS

Six districts with high COVID-19 infection rates in Bangladesh.

PARTICIPANTS

2185 mothers of under-2 children and 657 adult males were surveyed in December 2020.

MAIN OUTCOME MEASURES

We constructed weighted composite knowledge and practice scores and examined associations between composite scores and background characteristics using linear regression models.

RESULTS

Knowledge on possible routes of transmission of the novel coronavirus and the critical handwashing and mask-wearing etiquettes was poor. On a scale of 100, the mean composite knowledge scores of mothers and adult males were respectively 33.5 (SD=15; 95% CI 32.9 to 34.1) and 38.2 (SD=14.8; 95% CI 37.1 to 39.4). In contrast to knowledge, adult males obtained lower practice scores than mothers, primarily due to poor physical distancing practices. The mean practice scores of mothers and adult males were 63.0 (SD=18.1; 95% CI 62.3 to 63.8) and 53.4 (SD=17.5; 95% CI 52.0 to 54.7). Moreover, education, household income and access to television and the internet are significantly associated with knowledge. People residing proximal to a city revealed higher knowledge than the relatively distant ones. This was also the case for practice scores; however, the other factors associated with knowledge did not have a significant association with practices.

CONCLUSIONS

In general, both mothers and adult males presented with poor knowledge and practices related to COVID-19. While local, national and international institutions should design and implement educational interventions to help improve knowledge, our research shows that mere knowledge may not be enough to ensure practice. Hence, authorities could reinforce positive social norms by setting benchmarks and introducing rewards or sanctions to improve practices.

The new normal: Covid-19 risk perceptions and support for continuing restrictions past vaccinations

I test the possibility that over-estimating negative consequences of COVID-19 (e.g., hospitalizations, deaths, and threats to children) will be associated with stronger support the 'new normal' (i.e., continuation of restrictions for an undefined period starting with wide-spread access to vaccines and completed vaccinations of vulnerable people). The new normal was assessed by endorsing practices such as vaccine passports, travel restrictions, mandatory masking, continuing contact tracing, and pursuing elimination. Results are based on five samples (N = 1,233 from April 2021 and N = 264 from January 2022) and suggest that people over-estimate COVID-19 risks to children and healthy people, as evidenced by median estimates that 5% of all global deaths were children, 29% were generally healthy people under 65, and that a healthy person under the age of 65 has 5% chance of dying from COVID-19. Over-estimates observed in this study align with those based on representative samples, and they were consistently related to stronger support for the new normal. This relationship emerged when participants estimated risks with percentages (core indicators) and indicated the extent to which risk-based statements are true/supported with evidence or false/unsupported (alternative indicators). People were notably more likely to support continuing restrictions if they believed that COVID-19 risk and risk mitigation tactics are true, even when they are not (e.g., children need to be prioritized for boosters). These relationships persisted when considering competing explanations (political ideology, statistics literacy, belief in conspiracy theories). I trace these effects to well-meaning efforts to prevent under-estimation. Public policy and people's perceptions of risks are intertwined, where even inaccurate judgments may influence decisions. Failure to combat all misinformation with equal rigor may jeopardize the restoration of the social and economic life essential for building adaptive post-pandemic societies.

An exploration of the political, social, economic and cultural factors affecting how different global regions initially reacted to the COVID-19 pandemic

Responses to the early (February-July 2020) COVID-19 pandemic varied widely, globally. Reasons for this are multiple but likely relate to the healthcare and financial resources then available, and the degree of trust in, and economic support provided by, national governments. Cultural factors also affected how different populations reacted to the various pandemic restrictions, like masking, social distancing and self-isolation or self-quarantine. The degree of compliance with these measures depended on how much individuals valued their needs and liberties over those of their society. Thus, several themes may be relevant when comparing pandemic responses across different regions. East and Southeast Asian populations tended to be more collectivist and self-sacrificing, responding quickly to early signs of the pandemic and readily complied with most restrictions to control its spread. Australasian, Eastern European, Scandinavian, some Middle Eastern, African and South American countries also responded promptly by imposing restrictions of varying severity, due to concerns for their wider society, including for some, the fragility of their healthcare systems. Western European and North American countries, with well-resourced healthcare systems, initially reacted more slowly, partly in an effort to maintain their economies but also to delay imposing pandemic restrictions that limited the personal freedoms of their citizens.

The Seroprevalence of SARS-CoV-2 Antibodies among HealthCare Workers in University Hospital in Krakow before the Era of Vaccination

Background: Knowledge of occupational health is crucial to the safety of healthcare workers in the pandemic period. The aim of our study was the rating of SARS-CoV-2 seroprevalence in connection with selected demographic, social, and organizational factors, as well as the identification of key elements determining the safety of HCWs and patients of the University Hospital in Krakow. Methods: This was a non-interventional, uncontrolled, open, single-center, cross-sectional online survey on the preparedness for the COVID-19 epidemic and the seroprevalence of medical and non-medical HCWs and students. Serum specimens from 1221 persons were tested using an immunoassay analyzer based on the ECLIA technique for the anti-SARS-CoV-2 antibodies IgM + IgG. Results: The total seroprevalence was 42.7%. In medical students it was 25.2%, while in physicians it was 43.4% and in nurses/midwives it was 48.1%. Of those who tested positive, 21.5% did not know their serological status. The use of personal protective equipment did not have any significant impact on the result of testing for anti-SARS-CoV-2 antibodies. The risk of developing the disease was not influenced by sex, professional work experience, workplace, or intensity of contact with the patient. Among the studied elements, only care of COVID-19 patients significantly increased the risk. The protective factor was starting work between the waves of the epidemic (June–September 2020). Conclusions: PPE is only one element of infection prevention and control—without other components, such as hand hygiene, it can be dangerous and contribute to self-infection. It is also very important to test healthcare workers. Not being aware of the COVID-19 status of HCWs poses a threat to other staff members, as well as patients and the family and friends of the infected. Thus, extreme caution should be applied when employing respirators with exhalation valves during the COVID-19 pandemic.

Residential Mobility of a Cohort of Homeless People in Times of Crisis: COVID-19 Pandemic in a European Metropolis

Most vulnerable individuals are particularly affected by the COVID-19 pandemic. This study takes place in a large city in France. The aim of this study is to describe the mobility of the homeless population at the beginning of the health crisis and to analyze its impact in terms of COVID-19 prevalence. From June to August 2020 and September to December 2020, 1272 homeless people were invited to be tested for SARS-CoV-2 antibodies and virus and complete questionnaires. Our data show that homeless populations are sociologically different depending on where they live. We show that people that were living on the street were most likely to be relocated to emergency shelters than other inhabitants. Some neighborhoods are points of attraction for homeless people in the city while others emptied during the health crisis, which had consequences for virus circulation. People with a greater number of different dwellings reported became more infected. This first study of the mobility and epidemiology of homeless people in the time of the pandemic provides unique information about mobility mapping, sociological factors of this mobility, mobility at different scales, and epidemiological consequences. We suggest that homeless policies need to be radically transformed since the actual model exposes people to infection in emergency.

Incidence of COVID-19 infection in hospital workers from March 1, 2020 to May 31, 2021 routinely tested, before and after vaccination with BNT162B2

To evaluate the incidence of COVID-19 infection in health care workers from the start of the COVID-19 pandemic in NE Italy, vaccination with BNT162b2. This was a retrospective cohort study. Healthcare workers were routinely tested for SARS-CoV-2 infection using real-time polymerase chain reaction tests in nasopharyngeal swabs. Logistic regression was used to calculate the incidence rate ratios (IRRs) of the factors associated with COVID-19. A total of 4251 workers were followed up, and the prevalence of COVID-19 was 13.6%. In March 2021 the incidence of infection was 4.88 and 103.55 cases for 100,000 person-days in vaccinated and non-vaccinated workers, respectively, with an adjusted IRRs of 0.05 (95% CI 0.02–0.08). Our study evaluated the monthly incidence in health care workers in Trieste hospitals before and after vaccination, finding an estimated vaccine effectiveness of 95% in health care workers routinely tested.

Social Media Efficacy in Crisis Management: Effectiveness of Non-pharmaceutical Interventions to Manage COVID-19 Challenges

The new identified virus COVID-19 has become one of the most contagious diseases in human history. The ongoing coronavirus has created severe threats to global mental health, which have resulted in crisis management challenges and international concerns related to health issues. As of September 9, 2021, there were over 223.4 million patients with COVID-19, including 4.6 million deaths and over 200 million recovered patients reported worldwide, which has made the COVID-19 outbreak one of the deadliest pandemics in human history. The aggressive public health implementations endorsed various precautionary safety and preventive strategies to suppress and minimize COVID-19 disease transmission. The second, third, and fourth waves of COVID-19 continue to pose global challenges to crisis management, as its evolution and implications are still unfolding. This study posits that examining the strategic ripostes and pandemic experiences sheds light on combatting this global emergency. This study recommends two model strategies that help reduce the adverse effects of the pandemic on the immune systems of the general population. This present paper recommends NPI interventions (non-pharmaceutical intervention) to combine various measures, such as the suppression strategy (lockdown and restrictions) and mitigation model to decrease the burden on health systems. The current COVID-19 health crisis has influenced all vital economic sectors and developed crisis management problems. The global supply of vaccines is still not sufficient to manage this global health emergency. In this crisis, NPIs are helpful to manage the spillover impacts of the pandemic. It articulates the prominence of resilience and economic and strategic agility to resume economic activities and resolve healthcare issues. This study primarily focuses on the role of social media to tackle challenges and crises posed by COVID-19 on economies, business activities, healthcare burdens, and government support for societies to resume businesses, and implications for global economic and healthcare provision disruptions. This study suggests that intervention strategies can control the rapid spread of COVID-19 with hands-on crisis management measures, and the healthcare system will resume normal conditions quickly. Global economies will revitalize scientific contributions and collaborations, including social science and business industries, through government support.

The anxiety of not knowing: Diagnosis uncertainty about COVID-19

The emergence and swift global spread of COVID-19 brought increased anxiety worldwide (Santabárbara et al. (Progress in Neuro-Psychopharmacology & Biological Psychiatry, 109, 110207, 2021)). Research regarding the COVID-19 outbreak addressed factors that contribute to anxiety people experienced as they tried to handle the changes in their lives associated with COVID-19 (Holmes et al. (The Lancet Psychiatry, 7(6), 547-560, 2020)). This paper focuses on diagnosis uncertainty as a particular source of anxiety. We use self-reported anxiety measures to understand how different stressors, and particularly how being sick or being unsure if one or one's close friends or relatives are sick, relate to overall anxiety levels. Five-hundred and thirty-three participants from a country with a stringent COVID-19 testing policy were surveyed in the spring of 2020 on various aspects of their anxiety and risk for depression, as well as on whether they or their friends or family had COVID-19. Analysis of survey results found that anxiety related to uncertainty regarding whether the survey responder or their friends or family were carrying COVID-19 may be even greater than fear of the virus itself. This paper discusses directional issues related to this finding and offers policy implications for decreasing anxiety during pandemics for certain types of communities. In addition to the main findings regarding diagnosis uncertainty and anxiety, this paper's results also indicate the importance of providing participants with an option for "not sure" in closed questions and imply the increased knowledge that can be gained by analyzing an unsure response independently of "yes" or "no".

Asymptomatic and pre-symptomatic infection in Coronavirus Disease 2019 pandemic

With the presence of Coronavirus Disease 2019 (COVID-19) asymptomatic infections detected, their proportion, transmission potential, and other aspects such as immunity and related emerging challenges have attracted people's attention. We have found that based on high-quality research, asymptomatic infections account for at least one-third of the total cases, whereas based on systematic review and meta-analysis, the proportion is about one-fifth. Evaluating the true transmission potential of asymptomatic cases is difficult but critical, since it may affect national policies in response to COVID-19. We have summarized the current evidence and found, compared with symptomatic cases, the transmission capacity of asymptomatic individuals is weaker, even though they have similar viral load and relatively short virus shedding duration. As the outbreak progresses, asymptomatic infections have also been found to develop long COVID-19. In addition, the role of asymptomatic infection in COVID-19 remains to be further revealed as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants continue to emerge. Nevertheless, as asymptomatic infections transmit the SARS-CoV-2 virus silently, they still pose a substantial threat to public health. Therefore, it is essential to conduct screening to obtain more knowledge about the asymptomatic infections and to detect them as soon as possible; meanwhile, management of them is also a key point in the fight against COVID-19 community transmission. The different management of asymptomatic infections in various countries are compared and the experience in China is displayed in detail.

Serious role of non-quarantined COVID-19 patients for random walk simulations

The infectious disease (COVID-19) causes serious damages and outbreaks. A large number of infected people have been reported in the world. However, such a number only represents those who have been tested; e.g. PCR test. We focus on the infected individuals who are not checked by inspections. The susceptible-infected-recovered (SIR) model is modified: infected people are divided into quarantined (Q) and non-quarantined (N) agents. Since N-agents behave like uninfected people, they can move around in a stochastic simulation. Both theory of well-mixed population and simulation of random-walk reveal that the total population size of Q-agents decrease in spite of increasing the number of tests. Such a paradox appears, when the ratio of Q exceeds a critical value. Random-walk simulations indicate that the infection hardly spreads, if the movement of all people is prohibited ("lockdown"). In this case the infected people are clustered and locally distributed within narrow spots. The similar result can be obtained, even when only non-infected people move around. However, when both N-agents and uninfected people move around, the infection spreads everywhere. Hence, it may be important to promote the inspections even for asymptomatic people, because most of N-agents are mild or asymptomatic.

COVID-19 Pandemic and Periodontal Practice: The Immunological, Clinical, and Economic Points of View

The recent global health problem, COVID-19, has had far-reaching impacts on lifestyles. Although many effective WHO-approved vaccines have been produced that have reduced the spread and severity of the disease, it appears to persist in humans for a long time and possibly forever as everyday it turns out to have new mutations. COVID-19 involves the lungs and other organs primarily through cytokine storms, which have been implicated in many other inflammatory disorders, including periodontal diseases. COVID-19 is in a close association with dental and periodontal practice from two respects: first, repeated mandatory lockdowns have reduced patient referrals to dentists and limited the dental and periodontal procedures to emergency treatments, whereas it is important to recognize the oral manifestations of COVID-19 as well as the influence of oral and periodontal disease on the severity of COVID-19. Second, dentistry is one of the high-risk professions in terms of close contact with unmasked individuals, necessitating redefining the principles of infection control. The pressures of the economic recession on patients as well as dentists add to the difficulty of resuming elective dental services. Therefore, this study is divided into two parts corresponding to what mentioned above: the first part examines the clinical and immunological associations between COVID-19 and periodontal and oral diseases, and the second part delineates the measures needed to control the disease transmission in dental clinics as well as the economic impact of the pandemic era on dental services.

Predictors of mask-wearing during the advent of the COVID-19 pandemic: Evidence from South Africa

Background

In the absence of a vaccine, the global spread of COVID-19 during 2020 has necessitated non-pharmaceutical interventions to curb the rise of cases.

Purpose

The article uses the health belief model and a novel rapid mobile survey to examine correlates of reported mask-wearing as a non-pharmaceutical intervention in South Africa between May and August 2020.

Methods

Two-way tabulations and multivariable analysis via logistic regression modeling describe correlations between reported mask-wearing and factors of interest among a sample of 7074 adults in a two-period national longitudinal survey, the National Income Dynamics Study-Coronavirus Rapid Mobile Survey (NIDS-CRAM).

Results

In line with the health belief model, results showed that self-efficacy, the prevalence of others’ mask-wearing in the same district, and affluence were positively associated with reported mask-wearing. Those who reported staying at home were significantly less likely to report wearing a mask. There was little evidence that the expected severity of the disease if contracted, affects these decisions. Hypertension, obesity, or being overweight (measured three years earlier) did not have a significant association with mask-wearing. The prevalence of mask-wearing increased significantly from May to August 2020 as COVID-19 cases increased and lockdown restrictions were eased. Contrary to the health belief model, we found that despite having a higher mortality risk, the elderly had significantly lower odds of mask-wearing.

Conclusion

In South Africa, the mask-wearing adherence has increased rapidly. It is concerning that the elderly had lower odds of mask-wearing. This should be examined further in future research.

The Prediction Model of Risk Factors for COVID-19 Developing into Severe Illness Based on 1046 Patients with COVID-19

This study analyzed the risk factors for patients with COVID-19 developing severe illnesses and explored the value of applying the logistic model combined with ROC curve analysis to predict the risk of severe illnesses at COVID-19 patients' admissions. The clinical data of 1046 COVID-19 patients admitted to a designated hospital in a certain city from July to September 2020 were retrospectively analyzed, the clinical characteristics of the patients were collected, and a multivariate unconditional logistic regression analysis was used to determine the risk factors for severe illnesses in COVID-19 patients during hospitalization. Based on the analysis results, a prediction model for severe conditions and the ROC curve were constructed, and the predictive value of the model was assessed. Logistic regression analysis showed that age (OR = 3.257, 95% CI 10.466–18.584), complications with chronic obstructive pulmonary disease (OR = 7.337, 95% CI 0.227–87.021), cough (OR = 5517, 95% CI 0.258–65.024), and venous thrombosis (OR = 7322, 95% CI 0.278–95.020) were risk factors for COVID-19 patients developing severe conditions during hospitalization. When complications were not taken into consideration, COVID-19 patients' ages, number of diseases, and underlying diseases were risk factors influencing the development of severe illnesses. The ROC curve analysis results showed that the AUC that predicted the severity of COVID-19 patients at admission was 0.943, the optimal threshold was −3.24, and the specificity was 0.824, while the sensitivity was 0.827. The changes in the condition of severe COVID-19 patients are related to many factors such as age, clinical symptoms, and underlying diseases. This study has a certain value in predicting COVID-19 patients that develop from mild to severe conditions, and this prediction model is a useful tool in the quick prediction of the changes in patients' conditions and providing early intervention for those with risk factors.

Lung segmentation and automatic detection of COVID-19 using radiomic features from chest CT images

This paper aims to develop an automatic method to segment pulmonary parenchyma in chest CT images and analyze texture features from the segmented pulmonary parenchyma regions to assist radiologists in COVID-19 diagnosis. A new segmentation method, which integrates a three-dimensional (3D) V-Net with a shape deformation module implemented using a spatial transform network (STN), was proposed to segment pulmonary parenchyma in chest CT images. The 3D V-Net was adopted to perform an end-to-end lung extraction while the deformation module was utilized to refine the V-Net output according to the prior shape knowledge. The proposed segmentation method was validated against the manual annotation generated by experienced operators. The radiomic features measured from our segmentation results were further analyzed by sophisticated statistical models with high interpretability to discover significant independent features and detect COVID-19 infection. Experimental results demonstrated that compared with the manual annotation, the proposed segmentation method achieved a Dice similarity coefficient of 0.9796, a sensitivity of 0.9840, a specificity of 0.9954, and a mean surface distance error of 0.0318 mm. Furthermore, our COVID-19 classification model achieved an area under curve (AUC) of 0.9470, a sensitivity of 0.9670, and a specificity of 0.9270 when discriminating lung infection with COVID-19 from community-acquired pneumonia and healthy controls using statistically significant radiomic features. The significant features measured from our segmentation results agreed well with those from the manual annotation. Our approach has great promise for clinical use in facilitating automatic diagnosis of COVID-19 infection on chest CT images.

Cytidine deamination-induced perpetual immunity to SAR-CoV-2 infection is a potential new therapeutic target

As the world is racing to develop perpetual immunity to the SARS-CoV-2 virus. The emergence of new viral strains, together with vaccination and reinfections, are all contributing to a long-term immunity against the deadly virus that has taken over the world since its introduction to humans in late December 2019. The discovery that more than 95 percent of people who recovered from COVID-19 had long-lasting immunity and that asymptomatic people have a different immune response to SARS-CoV-2 than symptomatic people has shifted attention to how our immune system initiates such diverse responses. These findings have provided reason to believe that SARS-CoV-2 days are numbered. Hundreds of research papers have been published on the causes of long-lasting immune responses and variations in the numbers of different immune cell types in COVID 19 survivors, but the main reason of these differences has still not been adequately identified. In this article, we focus on the activation-induced cytidine deaminase (AID), which initiates molecular processes that allow our immune system to generate antibodies against SARS-CoV-2. To establish lasting immunity to SARS-CoV-2, we suggest that AID could be the key to unlocking it.

Asymptomatic individuals can increase the final epidemic size under adaptive human behavior

Infections produced by non-symptomatic (pre-symptomatic and asymptomatic) individuals have been identified as major drivers of COVID-19 transmission. Non-symptomatic individuals, unaware of the infection risk they pose to others, may perceive themselves-and be perceived by others-as not presenting a risk of infection. Yet, many epidemiological models currently in use do not include a behavioral component, and do not address the potential consequences of risk misperception. To study the impact of behavioral adaptations to the perceived infection risk, we use a mathematical model that incorporates the behavioral decisions of individuals, based on a projection of the system's future state over a finite planning horizon. We found that individuals' risk misperception in the presence of non-symptomatic individuals may increase or reduce the final epidemic size. Moreover, under behavioral response the impact of non-symptomatic infections is modulated by symptomatic individuals' behavior. Finally, we found that there is an optimal planning horizon that minimizes the final epidemic size.

Reduced subgenomic RNA expression is a molecular indicator of asymptomatic SARS-CoV-2 infection

Background

It is estimated that up to 80% of infections caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are asymptomatic and asymptomatic patients can still effectively transmit the virus and cause disease. While much of the effort has been placed on decoding single nucleotide variation in SARS-CoV-2 genomes, considerably less is known about their transcript variation and any correlation with clinical severity in human hosts, as defined here by the presence or absence of symptoms.

Methods

To assess viral genomic signatures of disease severity, we conducted a systematic characterization of SARS-CoV-2 transcripts and genetic variants in 81 clinical specimens collected from symptomatic and asymptomatic individuals using multi-scale transcriptomic analyses including amplicon-seq, short-read metatranscriptome and long-read Iso-seq.

Results

Here we show a highly coordinated and consistent pattern of sgRNA expression from individuals with robust SARS-CoV-2 symptomatic infection and their expression is significantly repressed in the asymptomatic infections. We also observe widespread inter- and intra-patient variants in viral RNAs, known as quasispecies frequently found in many RNA viruses. We identify unique sets of deletions preferentially found primarily in symptomatic individuals, with many likely to confer changes in SARS-CoV-2 virulence and host responses. Moreover, these frequently occurring structural variants in SARS-CoV-2 genomes serve as a mechanism to further induce SARS-CoV-2 proteome complexity.

Conclusions

Our results indicate that differential sgRNA expression and structural mutational burden are highly correlated with the clinical severity of SARS-CoV-2 infection. Longitudinally monitoring sgRNA expression and structural diversity could further guide treatment responses, testing strategies, and vaccine development.

Freedom Without Responsibility: the Promise of Bolsonaro’s COVID-19 Denial

Jair Bolsonaro, the current President of Brazil, has made himself into one of the most influent advocates of COVID-19 denial. His health policy and his political doctrine are partly based on an implicit moral claim, which is neglected by contemporary political theory. Bolsonarism’s rhetoric raises a moral claim to freedom without responsibility, which relieves its followers from the burdens that emerge from liberal accounts of liberty or from basic goods accepted in a political community. In opposition to liberal or communitarian accounts, Bolsonarism endorses a Hobbesian concept of freedom that describes it as the absence of ‘impediments to motion’. Nonetheless, it differs from Hobbes because it treats this liberty as endowed with moral value and non-negotiable through a social contract.

Science's Response to CoVID-19

CoVID-19 is a multi-symptomatic disease which has made a global impact due to its ability to spread rapidly, and its relatively high mortality rate. Beyond the heroic efforts to develop vaccines, which we do not discuss herein, the response of scientists and clinicians to this complex problem has reflected the need to detect CoVID-19 rapidly, to diagnose patients likely to show adverse symptoms, and to treat severe and critical CoVID-19. Here we aim to encapsulate these varied and sometimes conflicting approaches and the resulting data in terms of chemistry and biology. In the process we highlight emerging concepts, and potential future applications that may arise out of this immense effort.

SARS-CoV-2 seroprevalence among blood donors in Québec, and analysis of symptoms associated with seropositivity: a nested case-control study

OBJECTIVES

A substantial proportion of individuals infected with SARS-CoV-2 do not experience noticeable symptoms typical of COVID-19. Our objectives were to evaluate the impact of the first wave of the pandemic in Québec by measuring SARS-CoV-2 antibody seroprevalence in a convenience sample of healthy blood donors and to study the association between seropositivity and the occurrence of COVID-19 symptoms.

METHODS

The study design was a cross-sectional serological survey with a nested case-control study. Residual blood samples from donations collected between May 25 and July 9, 2020 (well before vaccination rollout) in the province of Québec were tested for anti-Spike RBD antibodies by ELISA. Seropositive donors and a control group of seronegative donors were questioned about prior COVID-19 symptoms. All qualified blood donors were eligible for participation.

RESULTS

A total of 7691 blood donors were included in the study. After adjustments, the seroprevalence rate was 2.2% (95% CI 1.9-2.6). Seropositive donors reported one or more symptoms in a proportion of 52.2% (95% CI 44.2-60.1); this proportion was 19.1% (95% CI 13.4-26.1) among seronegative donors, suggesting that approximately 50-66% of all infections were asymptomatic. Univariate analysis of associations between symptoms and seropositivity revealed that except for rhinorrhea, all symptoms were significantly associated with seropositivity.

CONCLUSION

Assuming that blood donors are fairly representative of the general adult population, this study shows that less than 3% of 18-69-year-olds have been infected during the first wave of the pandemic in the province of Québec. Our data also confirm that many infections escaped detection, including a substantial proportion that were asymptomatic.

Country transition index based on hierarchical clustering to predict next COVID-19 waves

COVID-19 has widely spread around the world, impacting the health systems of several countries in addition to the collateral damage that societies will face in the next years. Although the comparison between countries is essential for controlling this disease, the main challenge is the fact of countries are not simultaneously affected by the virus. Therefore, from the COVID-19 dataset by the Johns Hopkins University Center for Systems Science and Engineering, we present a temporal analysis on the number of new cases and deaths among countries using artificial intelligence. Our approach incrementally models the cases using a hierarchical clustering that emphasizes country transitions between infection groups over time. Then, one can compare the current situation of a country against others that have already faced previous waves. By using our approach, we designed a transition index to estimate the most probable countries' movements between infectious groups to predict next wave trends. We draw two important conclusions: (1) we show the historical infection path taken by specific countries and emphasize changing points that occur when countries move between clusters with small, medium, or large number of cases; (2) we estimate new waves for specific countries using the transition index.