Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020

The burden and dynamics of hospital-acquired SARS-CoV-2 in England

Hospital-based transmission had a dominant role in Middle East respiratory syndrome coronavirus (MERS-CoV) and severe acute respiratory syndrome coronavirus (SARS-CoV) epidemics1,2, but large-scale studies of its role in the SARS-CoV-2 pandemic are lacking. Such transmission risks spreading the virus to the most vulnerable individuals and can have wider-scale impacts through hospital-community interactions. Using data from acute hospitals in England, we quantify within-hospital transmission, evaluate likely pathways of spread and factors associated with heightened transmission risk, and explore the wider dynamical consequences. We estimate that between June 2020 and March 2021 between 95,000 and 167,000 inpatients acquired SARS-CoV-2 in hospitals (1% to 2% of all hospital admissions in this period). Analysis of time series data provided evidence that patients who themselves acquired SARS-CoV-2 infection in hospital were the main sources of transmission to other patients. Increased transmission to inpatients was associated with hospitals having fewer single rooms and lower heated volume per bed. Moreover, we show that reducing hospital transmission could substantially enhance the efficiency of punctuated lockdown measures in suppressing community transmission. These findings reveal the previously unrecognized scale of hospital transmission, have direct implications for targeting of hospital control measures and highlight the need to design hospitals better equipped to limit the transmission of future high-consequence pathogens.

A data-driven semi-parametric model of SARS-CoV-2 transmission in the United States

To support decision-making and policy for managing epidemics of emerging pathogens, we present a model for inference and scenario analysis of SARS-CoV-2 transmission in the USA. The stochastic SEIR-type model includes compartments for latent, asymptomatic, detected and undetected symptomatic individuals, and hospitalized cases, and features realistic interval distributions for presymptomatic and symptomatic periods, time varying rates of case detection, diagnosis, and mortality. The model accounts for the effects on transmission of human mobility using anonymized mobility data collected from cellular devices, and of difficult to quantify environmental and behavioral factors using a latent process. The baseline transmission rate is the product of a human mobility metric obtained from data and this fitted latent process. We fit the model to incident case and death reports for each state in the USA and Washington D.C., using likelihood Maximization by Iterated particle Filtering (MIF). Observations (daily case and death reports) are modeled as arising from a negative binomial reporting process. We estimate time-varying transmission rate, parameters of a sigmoidal time-varying fraction of hospitalized cases that result in death, extra-demographic process noise, two dispersion parameters of the observation process, and the initial sizes of the latent, asymptomatic, and symptomatic classes. In a retrospective analysis covering March-December 2020, we show how mobility and transmission strength became decoupled across two distinct phases of the pandemic. The decoupling demonstrates the need for flexible, semi-parametric approaches for modeling infectious disease dynamics in real-time.

Coronavirus disease 2019 related parosmia: an exploratory survey of demographics and treatment strategies

OBJECTIVE

To investigate the clinical features, therapeutic efficacy and symptom time course of post-coronavirus disease 2019 parosmia.

METHODS

A 22-item online questionnaire was distributed to AbScent research group and Facebook coronavirus disease 2019 anosmia group adult members to assess clinical features, interventions and their subjective efficacy for parosmia.

RESULTS

A total of 209 participants (86 per cent females) reported: smell loss on average 3 days after coronavirus symptoms, recovery 4 weeks later, and first parosmia symptoms 12 weeks post infection. Respondents reported 10 per cent body weight loss, and listed onion and garlic as significant parosmia triggers. Regarding quality of life, depression was the most cited item (54 per cent). Smell training was trialled by 74 per cent of participants, followed by nasal corticosteroid spray (49 per cent). Stellate ganglion block, trialled by 16 per cent of respondents, had the highest reported improvement (45 per cent), with 21 per cent reporting a sustained benefit - the highest rate amongst registered treatment options.

CONCLUSION

Post-coronavirus parosmia has a significant impact and remains challenging to treat. Stellate ganglion block appears to be successful relative to other reported treatments. Further research into the pathophysiology, efficacy and mechanism of stellate ganglion block effect is warranted.

Layered Screening and Contact-Limiting Interventions Are Necessary to Reduce SARS-Cov-2 Outbreak Risks in Large Urban Jails

Highly transmissible infections with short serial intervals, such as SARS-Cov-2 and influenza, can quickly overwhelm healthcare resources in institutional settings such as jails. We assessed the impact of intake screening measures on the risk of SARS-CoV-2 outbreaks in this setting. We identified which elements of the intake process created the largest reductions in caseload. We implemented an individual-based simulation representative of SARS-Cov-2 transmission in a large urban jail utilizing testing at entry, quarantine, and post-quarantine testing to protect its general population from mass infection. We tracked the caseload under each scenario and quantified the impact of screening steps by varying quarantine duration, removing testing, and using a range of test sensitivities. We repeated the simulations under a range of transmissibility and community prevalence levels to evaluate the sensitivity of our results. We found that brief quarantine of newly incarcerated individuals separate from the existing population of the jail to permit pre-quarantine and end-of-quarantine tests reduced SARS-CoV-2 caseload 30-70% depending on test sensitivity. These results were robust to variation in the transmissibility. Further quarantine (up to 14 days) on average created only a 5% further reduction in caseload. A multilayered intake process is necessary to limit the spread of highly transmissible pathogens with short serial intervals. The pre-symptomatic phase means that no single strategy can be effective. We also show that shorter durations of quarantine combined with testing can be nearly as effective at preventing spread as longer-duration quarantine up to 14 days.

COVID-19 as a polymorphic inflammatory spectrum of diseases: a review with focus on the brain

There appear to be huge variations and aberrations in the reported data in COVID-19 2 years now into the pandemic. Conflicting data exist at almost every level and also in the reported epidemiological statistics across different regions. It is becoming clear that COVID-19 is a polymorphic inflammatory spectrum of diseases, and there is a wide range of inflammation-related pathology and symptoms in those infected with the virus. The host's inflammatory response to COVID-19 appears to be determined by genetics, age, immune status, health status and stage of disease. The interplay of these factors may decide the magnitude, duration, types of pathology, symptoms and prognosis in the spectrum of COVID-19 disorders, and whether neuropsychiatric disorders continue to be significant. Early and successful management of inflammation reduces morbidity and mortality in all stages of COVID-19.

Markov modeling and performance analysis of infectious diseases with asymptomatic patients

After over three years of COVID-19, it has become clear that infectious diseases are difficult to eradicate, and humans remain vulnerable under their influence in a long period. The presence of presymptomatic and asymptomatic patients is a significant obstacle to preventing and eliminating infectious diseases. However, the long-term transmission of infectious diseases involving asymptomatic patients still remains unclear. To address this issue, this paper develops a novel Markov process for infectious diseases with asymptomatic patients by means of a continuous-time level-dependent quasi-birth-and-death (QBD) process. The model accurately captures the transmission of infectious diseases by specifying several key parameters (or factors). To analyze the role of asymptomatic and symptomatic patients in the infectious disease transmission process, a simple sufficient condition for the stability of the Markov process of infectious diseases is derived using the mean drift technique. Then, the stationary probability vector of the QBD process is obtained by using RG-factorizations. A method of using the stationary probability vector is provided to obtain important performance measures of the model. Finally, some numerical experiments are presented to demonstrate the model's feasibility through analyzing COVID-19 as an example. The impact of key parameters on the system performance evaluation and the infectious disease transmission process are analyzed. The methodology and results of this paper can provide theoretical and technical support for the scientific control of the long-term transmission of infectious diseases, and we believe that they can serve as a foundation for developing more general models of infectious disease transmission.

Estimating age-stratified transmission and reproduction numbers during the early exponential phase of an epidemic: A case study with COVID-19 data

In a pending pandemic, early knowledge of age-specific disease parameters, e.g., susceptibility, infectivity, and the clinical fraction (the fraction of infections coming to clinical attention), supports targeted public health responses like school closures or sequestration of the elderly. The earlier the knowledge, the more useful it is, so the present article examines an early phase of many epidemics, exponential growth. Using age-stratified COVID-19 case counts collected in Canada, China, Israel, Italy, the Netherlands, and the United Kingdom before April 23, 2020, we present a linear analysis of the exponential phase that attempts to estimate the age-specific disease parameters given above. Some combinations of the parameters can be estimated by requiring that they change smoothly with age. The estimation yielded: (1) the case susceptibility, defined for each age-group as the product of susceptibility to infection and the clinical fraction; (2) the mean number of transmissions of infection per contact within each age-group; and (3) the reproduction number of infection within each age-group, i.e., the diagonal of the age-stratified next-generation matrix. Our restriction to data from the exponential phase indicates the combinations of epidemic parameters that are intrinsically easiest to estimate with early age-stratified case counts. For example, conclusions concerning the age-dependence of case susceptibility appeared more robust than corresponding conclusions about infectivity. Generally, the analysis produced some results consistent with conclusions confirmed much later in the COVID-19 pandemic. Notably, our analysis showed that in some countries, the reproduction number of infection within the half-decade 70-75 was unusually large compared to other half-decades. Our analysis therefore could have anticipated that without countermeasures, COVID-19 would spread rapidly once seeded in homes for the elderly.

Making waves: Integrating wastewater surveillance with dynamic modeling to track and predict viral outbreaks

Wastewater surveillance has proved to be a valuable tool to track the COVID-19 pandemic. However, most studies using wastewater surveillance data revolve around establishing correlations and lead time relative to reported case data. In this perspective, we advocate for the integration of wastewater surveillance data with dynamic within-host and between-host models to better understand, monitor, and predict viral disease outbreaks. Dynamic models overcome emblematic difficulties of using wastewater surveillance data such as establishing the temporal viral shedding profile. Complementarily, wastewater surveillance data bypasses the issues of time lag and underreporting in clinical case report data, thus enhancing the utility and applicability of dynamic models. The integration of wastewater surveillance data with dynamic models can enhance real-time tracking and prevalence estimation, forecast viral transmission and intervention effectiveness, and most importantly, provide a mechanistic understanding of infectious disease dynamics and the driving factors. Dynamic modeling of wastewater surveillance data will advance the development of a predictive and responsive monitoring system to improve pandemic preparedness and population health.

Quantifying individual-level heterogeneity in infectiousness and susceptibility through household studies

The spread of SARS-CoV-2, like that of many other pathogens, is governed by heterogeneity. "Superspreading," or "over-dispersion," is an important factor in transmission, yet it is hard to quantify. Estimates from contact tracing data are prone to potential biases due to the increased likelihood of detecting large clusters of cases, and may reflect variation in contact behavior more than biological heterogeneity. In contrast, the average number of secondary infections per contact is routinely estimated from household surveys, and these studies can minimize biases by testing all members of a household. However, the models used to analyze household transmission data typically assume that infectiousness and susceptibility are the same for all individuals or vary only with predetermined traits such as age. Here we develop and apply a combined forward simulation and inference method to quantify the degree of inter-individual variation in both infectiousness and susceptibility from observations of the distribution of infections in household surveys. First, analyzing simulated data, we show our method can reliably ascertain the presence, type, and amount of these heterogeneities given data from a sufficiently large sample of households. We then analyze a collection of household studies of COVID-19 from diverse settings around the world, and find strong evidence for large heterogeneity in both the infectiousness and susceptibility of individuals. Our results also provide a framework to improve the design of studies to evaluate household interventions in the presence of realistic heterogeneity between individuals.

Application of wastewater-based surveillance and copula time-series model for COVID-19 forecasts

The objective of this study was to develop a novel copula-based time series (CTS) model to forecast COVID-19 cases and trends based on wastewater SARS-CoV-2 viral load and clinical variables. Wastewater samples were collected from wastewater pumping stations in five sewersheds in the City of Chesapeake VA. Wastewater SARS-CoV-2 viral load was measured using reverse transcription droplet digital PCR (RT-ddPCR). The clinical dataset included daily COVID-19 reported cases, hospitalization cases, and death cases. The CTS model development included two steps: an autoregressive moving average (ARMA) model for time series analysis (step I), and an integration of ARMA and a copula function for marginal regression analysis (step II). Poisson and negative binomial marginal probability densities for copula functions were used to determine the forecasting capacity of the CTS model for COVID-19 forecasts in the same geographical area. The dynamic trends predicted by the CTS model were well suited to the trend of the reported cases as the forecasted cases from the CTS model fell within the 99 % confidence interval of the reported cases. Wastewater SARS CoV-2 viral load served as a reliable predictor for forecasting COVID-19 cases. The CTS model provided robust modeling to predict COVID-19 cases.

Vaccine hesitancy promotes emergence of new SARS-CoV-2 variants

The successive emergence of SARS-CoV-2 mutations has led to an unprecedented increase in COVID-19 incidence worldwide. Currently, vaccination is considered to be the best available solution to control the ongoing COVID-19 pandemic. However, public opposition to vaccination persists in many countries, which can lead to increased COVID-19 caseloads and hence greater opportunities for vaccine-evasive mutant strains to arise. To determine the extent that public opinion regarding vaccination can induce or hamper the emergence of new variants, we develop a model that couples a compartmental disease transmission framework featuring two strains of SARS-CoV-2 with game theoretical dynamics on whether or not to vaccinate. We combine semi-stochastic and deterministic simulations to explore the effect of mutation probability, perceived cost of receiving vaccines, and perceived risks of infection on the emergence and spread of mutant SARS-CoV-2 strains. We find that decreasing the perceived costs of being vaccinated and increasing the perceived risks of infection (that is, decreasing vaccine hesitation) will decrease the possibility of vaccine-resistant mutant strains becoming established by about fourfold for intermediate mutation rates. Conversely, we find increasing vaccine hesitation to cause both higher probability of mutant strains emerging and more wild-type cases after the mutant strain has appeared. We also find that once a new variant has emerged, perceived risk of being infected by the original variant plays a much larger role than perceptions of the new variant in determining future outbreak characteristics. Furthermore, we find that rapid vaccination under non-pharmaceutical interventions is a highly effective strategy for preventing new variant emergence, due to interaction effects between non-pharmaceutical interventions and public support for vaccination. Our findings indicate that policies that combine combating vaccine-related misinformation with non-pharmaceutical interventions (such as reducing social contact) will be the most effective for avoiding the establishment of harmful new variants.

Clinical Characteristics of 6102 Asymptomatic and Mild Cases for Patients with COVID-19 in Indonesia

BACKGROUND

The COVID-19 pandemic has led to a rise in confirmed cases, making epidemiological studies crucial for identifying the source of transmission and developing effective treatment methods. We conducted a study on the clinical characteristics of patients with asymptomatic and mild symptoms of COVID-19 at a rescue hospital in Indonesia.

METHODS

This is an epidemiological study involving 6102 patients who were admitted to the Indrapura forefront hospital in Surabaya from May 2020 to February 2021. We described demographic data, clinical signs and symptoms, laboratory data, therapy, and clinical outcomes.

RESULTS

A total of 6102 patients were involved in this study, with 3664 (60.04%) being male and 2438 (39.95%) being female. The age range of 21-30 years was the most prevalent, accounting for 31.1% (1898 patients). The population had 1476 patients (24.2%) with comorbid conditions. The most prevalent comorbidity observed among these patients was hypertension, affecting 1015 individuals (16.6%). Out of the total 6006 patients observed, 40.7% (n = 2486) were asymptomatic, 54.6% (n = 3329) had mild symptoms, and 3.1% (n = 191) had moderate symptoms. All patients were administered supportive therapy without the use of antiviral medication. Out of the 6102 patients included in the study, 5923 patients (97.1%) achieved a cure, 36 patients (0.6%) are currently undergoing treatment, 142 patients (2.3%) were referred for desaturation indications (SpO2 < 94%), and one patient died due to a suspected cardiovascular event. Out of the total number of patients, 74.5% (4529 patients) had an average length of stay (LOS) of less than 10 days, while 25.6% (1563 patients) had an average length of stay of more than 10 days.

CONCLUSION

The clinical presentation of asymptomatic and mild COVID-19 patients at a rescue hospital varies significantly based on the age and sex of patients. Cough and hyposmia are commonly observed symptoms. Supportive therapy is effective, and strict implementation of social distancing is crucial in preventing the spread of this disease from individuals who are asymptomatic or have mild symptoms.

Immunologic and Autoimmune-Related Sequelae of Severe Acute Respiratory Syndrome Coronavirus 2 Infection: Clinical Symptoms and Mechanisms of Disease

The COVID-19 pandemic has resulted in a significant number of people developing long-term health effects of postacute sequelae SARS-CoV-2 infection (PASC). Both acute COVID-19 and PASC are now recognized as multiorgan diseases with multiple symptoms and disease causes. The development of immune dysregulation during acute COVID-19 and PASC is of high epidemiologic concern. Both conditions may also be influenced by comorbid conditions such as pulmonary dysfunction, cardiovascular disease, neuropsychiatric conditions, prior autoimmune conditions and cancer. This review discusses the clinical symptoms, pathophysiology, and risk factors that affect both acute COVID-19 and PASC.

Identification of Asymptomatic Severe Acute Respiratory Syndrome Coronavirus 2 Infections Among Healthcare Workers at Sultan Qaboos University Hospital, Oman

Objectives

This study aimed to describe the incidence and features of asymptomatic COVID-19 infections among healthcare workers (HCWs) at a tertiary hospital in Oman.

Methods

This cross-sectional study was conducted between August 2020 and February 2021 among HCWs with no history of COVID-19 infection. An online questionnaire collected sociodemographic and clinical data. COVID-19 infection was diagnosed using nasopharyngeal/throat swabs, which were tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Analyses were performed using the Chi-squared test, Fisher's exact test or univariate ordinary least squares regression, as appropriate.

Results

A total of 583 HCWs participated in the study, most of whom were female (56.6%), and the mean age was 35 ± 8 years. Only 9.6% (95% confidence interval [CI]: 7.3-12.3%) of the HCWs were at high exposure risk as they were directly involved in the care of COVID-19-infected patients. Overall, 4.1% (95% CI: 2.7-6.1%) of the HCWs screened positive for SARS-CoV-2, of which 20.8% developed symptoms within two weeks. The frequency of SARS-CoV-2 positivity among HCWs working in high-, intermediate-, low- and miscellaneous-risk areas was 1.8% (95% CI: <0.1-9.6%), 2.6% (95% CI: <0.1-6.5%), 5.3% (95% CI: 0.3-9.3%) and 4.8% (95% CI: <0.1-69.3%), respectively. Working in high-risk areas was associated with increased compliance with various infection control strategies (P <0.001).

Conclusion

There was a greater frequency of SARS-CoV-2 positivity among HCWs working in low-risk areas, whereas HCWs who worked in high-risk areas were significantly more likely to report increased compliance with infection control strategies.

The spread of coronavirus disease 2019 (COVID-19) via staff work and household networks in residential aged-care services in Victoria, Australia, May-October 2020

OBJECTIVE

Morbidity and mortality from coronavirus disease 2019 (COVID-19) have been significant among elderly residents of residential aged-care services (RACS). To prevent incursions of COVID-19 in RACS in Australia, visitors were banned and aged-care workers were encouraged to work at a single site. We conducted a review of case notes and a social network analysis to understand how workplace and social networks enabled the spread of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) among RACS.

DESIGN

Retrospective outbreak review.

SETTING AND PARTICIPANTS

Staff involved in COVID-19 outbreaks in RACS in Victoria, Australia, May-October 2020.

METHODS

The Victorian Department of Health COVID-19 case and contact data were reviewed to construct 2 social networks: (1) a work network connecting RACS through workers and (2) a household network connecting to RACS through households. Probable index cases were reviewed to estimate the number and size (number of resident cases and deaths) of outbreaks likely initiated by multisite work versus transmission via households.

RESULTS

Among 2,033 cases linked to an outbreak as staff, 91 (4.5%) were multisite staff cases. Forty-three outbreaks were attributed to multisite work and 35 were deemed potentially preventable had staff worked at a single site. In addition, 99 staff cases were linked to another RACS outbreak through their household contacts, and 21 outbreaks were attributed to staff-household transmission.

CONCLUSIONS

Limiting worker mobility through single-site policies could reduce the chances of SARS-CoV-2 spreading from one RACS to another. However, initiatives that reduce the chance of transmission via household networks would also be needed.

Molecular Determinants, Clinical Manifestations and Effects of Immunization on Cardiovascular Health During COVID-19 Pandemic Era - A Review

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has enveloped the world into an unprecedented pandemic since 2019. Significant damage to multiple organs, such as the lungs and heart, has been extensively reported. Cardiovascular injury by ACE2 downregulation, hypoxia-induced myocardial injury, and systemic inflammatory responses complicate the disease. This virus causes multisystem inflammatory syndrome in children with similar symptoms to adult SARS-CoV-2-induced myocarditis. While several treatment strategies and immunization programs have been implemented to control the menace of this disease, the risk of long-term cardiovascular damage associated with the disease has not been adequately assessed. In this review, we surveyed and summarized all the available information on the effects of COVID-19 on cardiovascular health as well as comorbidities. We also examined several case reports on post-immunization cardiovascular complications.

Identify hidden spreaders of pandemic over contact tracing networks

The COVID-19 infection cases have surged globally, causing devastations to both the society and economy. A key factor contributing to the sustained spreading is the presence of a large number of asymptomatic or hidden spreaders, who mix among the susceptible population without being detected or quarantined. Due to the continuous emergence of new virus variants, even if vaccines have been widely used, the detection of asymptomatic infected persons is still important in the epidemic control. Based on the unique characteristics of COVID-19 spreading dynamics, here we propose a theoretical framework capturing the transition probabilities among different infectious states in a network, and extend it to an efficient algorithm to identify asymptotic individuals. We find that using pure physical spreading equations, the hidden spreaders of COVID-19 can be identified with remarkable accuracy, even with incomplete information of the contract-tracing networks. Furthermore, our framework can be useful for other epidemic diseases that also feature asymptomatic spreading.

Epidemiological analysis of coronavirus disease (COVID-19) patients on ships arriving at Busan port in Korea, 2020

Quarantine played an important role in preventing the spread of infectious diseases between countries in the early stages of the COVID-19 outbreak. In particular, in ports, infection during transit can cause a large number of patients on board ships and can flow into the community. In this study investigated cause of the cause of transmission in ships and suggested the way of preventing secondary transmission by analyzing clinical and epidemiological characteristics of COVID-19 patients identified at Busan Port (South Korea) in 2020. During the study period, out of 19,396 ships that arrived at Busan Port, 50 ships had COVID-19 confirmed cases. Among the 50 ships, type of deep-sea fishing vessels (24 ships, 48.0%), ships weighing less than 5,000 tons (31 ships, 62.0%), and ships from Russia (41 ships, 82.0%) had the highest positivity rates. Total 283 of the 25,450 arrivals tested positive for COVID-19 (a positivity rate of 1.1%), and 270 (95.4%) were asymptomatic. Moreover, the number of COVID-19 patients increased with the duration of the waiting period between arrival and sample collection (12.7% to 37.4%), and the positivity rate was significantly higher for those working as stewards (64.3%). These results indicate secondary transmission was active on board ships and that infection among stewards importantly contributed to group outbreaks. In addition, onboard residence time after arrival significantly elevated to COVID-19 positivity rates, indicating that rapid isolation, as determined using various screening techniques, might be effective at preventing onboard transmission and subsequent community outbreaks.

Association of epidemiological and clinical features with PCR cycle threshold values of SARS-CoV-2 infection: a cross-sectional study

The cycle threshold (Ct) in quantitative real-time reverse-transcriptase polymerase chain reaction (qRT-PCR) is inversely correlated to the amount of viral nucleic acid or viral load and can be regarded as an indicator of infectivity. We examined the association of socio-demographic and clinical characteristics of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) polymerase chain reaction (PCR) positive cases with PCR cycle threshold (Ct) values at the time of diagnosis. SARS-CoV-2 cases reported between 12 October 2020 and 24 January 2021 in Regensburg were analyzed employing bivariate and multivariable methods. We included 3,029 SARS-CoV-2 cases (31% asymptomatic at diagnosis) and analyzed the association of case characteristics with Ct values in 2,606 cases. Among symptomatic patients, cough (38.0%), rhinitis (32.4%), headache (32.0), and fever/chills (29.9%) were the most frequent complaints. Ct values ≤20 were more frequent in symptomatic cases (20.9% vs. 11.3%), whereas Ct values >30 were more common in asymptomatic cases (32.6% vs. 18.0%). Ct values >20 and ≤30 were most common in symptomatic and asymptomatic cases (48.0% vs 40.7%). We observed lower median Ct values of E and N gene in symptomatic cases. In a random forest model, the total number of symptoms, respiratory symptoms, and age were most strongly associated with low Ct values. In conclusion, certain symptoms and age were associated with lower Ct values. Ct values can be used as a pragmatic approach in estimating infectivity at the first notification of a case and, thus, in guiding containment measures.

Oxidative Biomarkers Associated with the Pulmonary Manifestation of Post-COVID-19 Complications

INTRODUCTION

The role of mitochondria in post coronavirus disease 2019 (post-COVID-19) complications is unclear, especially in the long-term pulmonary complications. This study aims to investigate the association between post-COVID-19 pulmonary complications and mitochondrial regulatory proteins in the context of oxidative stress.

METHODOLOGY

Patients who had recovered from COVID-19 were enrolled. According to the evidence of persistent interstitial lung lesions on computed tomography (CT), patients were divided into a long-term pulmonary complications group (P(+)) and a control group without long-term pulmonary complications (P(-)). We randomly selected 80 patients for investigation (40 subjects for each group). Biomarkers levels were determined by enzyme-linked immunosorbent assay (ELISA).

RESULTS

The serum concentrations of mitochondrial regulatory proteins were significantly higher in the P(+) group, including PTEN-induced kinase 1 (PINK1): 1.62 [1.02-2.29] ng/mL vs. 1.34 [0.94-1.74] ng/mL (p = 0.046); Dynamin-1-like protein (DNM1L): 1.6 [0.9-2.4] ng/mL IQR vs. 0.9 [0.5-1.6] ng/mL (p = 0.004); and Mitofusin-2 (MFN2): 0.3 [0.2-0.5] ng/mL vs. 0.2 [0.1-0.3] ng/mL IQR (p = 0.001). Patients from the P(+) group also had higher serum levels of chemokine ligand 18 (PARC, CCL18), IL-6, and tumour necrosis factor-alpha (TNF-α) cytokines than the P(-) group. The concentration of interferon alpha (IFN-α) was decreased in the P(+) group. Furthermore, we observed statistically significant correlations between the advanced glycation end product (sRAGE) and TNF-α (Pearson's factor R = 0.637; p < 0.001) and between serum levels of DNM1L and IFN-α (Pearson's factor R = 0.501; p = 0.002) in P(+) patients.

CONCLUSIONS

Elevated concentrations of mitochondrial biomarkers in post-COVID-19 patients with long-term pulmonary complications indicate their possible role in the pathobiology of COVID-19 pulmonary sequelae. Oxidative stress is associated with the immune response and inflammation after COVID-19. TNF-α could be a promising biomarker for predicting pulmonary complications and may be a potential target for therapeutic intervention in patients with post-COVID-19 complications.

[Factors associated with the risk of hospitalization and death related to SARS-CoV-2 infection.]

OBJECTIVE

COVID-19 has tried out global health causing high mortality. There are some risk factors that associate greater severity and mortality from COVID-19; but their individual impact is unknown yet. There are also no fixed criteria for hospital admission. For this reason, this study aimed to analyze the factors associated with the severity of COVID-19 and create predictive models for the risk of hospitalization and death due to COVID-19.

METHODS

A descriptive retrospective cohort study was made in Talavera de la Reina (Toledo, Spain). Data were collected through computerized records of Primary Care, Emergencies and Hospitalization. The sample consisted of 275 patients over eighteen years old diagnosed with COVID-19 in a centralized laboratory from March 1st to May 31st, 2020. Analysis was carried on using SPSS, creating two predictive models for the risk of hospitalization and death using linear regression.

RESULTS

The probability of hospitalization increased independently with polypharmacy (OR 1.086; CI95% 1.009-1.169), the Charlson index (OR 1.613; CI95% 1.158-2.247), the history of acute myocardial infarction (AMI) (OR 4.358; 95% CI 1.114-17.051) and the presence of COVID symptoms (OR 7.001; 95% CI 2.805-17.475). The probability of death was independently associated with age, increasing 8.1% (OD 1.081; 95% CI 1.054- 1.110) for each year of the patient.

CONCLUSIONS

Comorbidity, polypharmacy, history of AMI and the presence of COVID-19 symptoms predict the risk of hospitalization. The age of individuals predicts the risk of death. Detecting patients at high risk of hospitalization and death allows us to define the target population and define measures to implement.

A spectrum of pathological changes induced by SARS-COV-2: An observational study in a cohort of pregnant women from Mizoram, India

Background

Maternal infection by SARS-CoV-2 may lead to adverse pregnancy outcomes and causes pathological changes in the placenta. However, consensus regarding characteristic pathological features is lacking. Researchof the placental histopathology in a cohort of women from Mizoram, India, was conducted to relate the SARS-CoV-2 infection's effectswith pregnancy and its outcome.

Materials and methods

The characteristics of 72 pregnant women diagnosed positive for SARS-CoV-2 who eventually delivered at Zoram Medical College Hospital, Mizoram, neonates' well-being, and histopathological features of placentas were studied.

Results

Of 72 women in this study, 59 (81.9%) gave birth at full term. Among these births, 5 were normal vaginal deliveries, while the remaining 67 (93.1%) were delivered via cesarean section. The reasons for cesarean delivery were either related to SARS-CoV-2 infection (n ​= ​49), existing obstetric problems (n ​= ​15) or fetal-distress (n ​= ​5). All deliveries resulted in live births of COVID-negative babies, with 80.6% (n ​= ​58) of the newborns having a birth weight of over 2.5 ​kg. APGAR scores ranged from 4 to 6 in 61 (84.7%) of the babies, and 10 neonates required resuscitation, of which 8 were managed in the neonatal intensive care unit (NICU). The placental histopathology showed increased fibrin thrombi in 8 cases (11.1%), while 20 cases (28%) showed focal infarction, microcalcification levels were elevated in 16 cases (22.2%), and a small percentage of cases (1.4%) exhibited small fibrotic villi and inter-villus agglutination. Placental chorioangiosis was detected in 28 (38.9%) of the cases, while avascular villi were seen in 6 cases. Meconium-stained liquor was observed in a single case. Intervillous hemorrhage was found in 42 cases, whileintervillous inflammation and increased syncytial knots were present in 14 and 5 cases, respectively. The placenta pathology of 10 neonates who required resuscitation/NICU admission was not significantly different from that of the 62 neonates who did not require it. However, a higher proportion of placenta from the asymptomatic group showed no abnormality compared to the symptomatic group (p ​= ​0.046).

Conclusion

SARS-CoV-2 infection causes a range of morphological changes and lesions in the placenta, including chorangiosis, villositis, chorioamnionitis, fetal vascular malperfusion/thrombosis, fibrin-deposition, increased syncytial-knotting, increased microcalcification, increased villous agglutination, focal infarct, intervilloushemorrhage as well as inflammation. Placental histopathological findings from this study can provide additional information to the existing literature on the subject.

Uncertain Malmquist productivity index: An application to evaluate healthcare systems during COVID-19 pandemic

Evaluation of healthcare systems, as a key organization providing different health services, is essential. This issue becomes more crucial when occurring crises such as a pandemic. They need to keep track of their success in the face of the crisis to assess the effects of policy changes and their capability to respond to new challenges. The Malmquist Productivity Index (MPI) is measured to analyze the causes of productivity change between two periods of time. The estimation of the traditional MPI requires reliable and detailed information on the inputs and outputs of decision-making units. However, there are a lot of situations where input and/or output may be imprecise. It is not manageable to reliably measure certain measurement indices, such as quality of treatment or system flexibility. For such cases, experts are invited to model their opinion. Uncertainty theory is a mathematical branch rationally dealing with belief degrees. The primary objective of this study is to apply MPI concept in the nonparametric approach of data envelopment analysis to calculate the efficiency of systems over different periods of time under uncertain conditions. Accordingly, we consider the MPI when inputs and outputs are belief degrees of experts. Furthermore, the sensitivity of the model is analyzed to determine the reliability of the results to the variation of variables. Finally, as an illustrative example, we explore longitudinal efficiency of healthcare systems during COVID-19 pandemic. According to the results of our model, the majority of the countries have improved in the second period which can be the result of efforts to improve pandemic preparedness. The decomposition of MPI into efficiency changes and technical changes indicates that the rise in productivity is entirely related to the progressive change of the production frontier related to policymaking. This application attempts to demonstrate how crucial it is to take uncertainties into account when comparing the performance of different systems over periods of time. The developed model enables us to consider the uncertainty existing in COVID-19 pandemic. The proposed model can handle more accurately the uncertainty during the pandemic. Thus, the result could be more reliable, which can benefit decision-makers in regard to performance improvement.

Exploring possible strategies for treating SARS-CoV-2 in sewage wastewater: A review of current research and future directions

The advent of acute respiratory coronavirus disease (COVID-19) is convoyed by the shedding of the virus in stool. Although inhalation from person-to-person and aerosol/droplet transmission are the main modes of SARS-Coronavirus-2 (SARS-CoV-2) transmission, currently available evidence indicates the presence of viral RNA in the sewerage wastewater, which highlights the need for more effective corona virus treatment options. In the existing COVID-19 pandemic, a substantial percentage of cases shed SARS-CoV-2 viral RNA in their faeces. Hence the treating this sewerage wastewater with proper surveillance is essential to contain this deadly pathogen from further transmission. Since, the viral disinfectants will not be very effective on sewerage waste as organic matter, and suspended solids in water can protect viruses that adsorb to these particles. More effective methods and measures are needed to prevent this virus from spreading. This review will explore some potential methods to treat the SARS-CoV-2 infected sewerage wastewater, current research and future directions.

Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020

As of January 2021, Australia had effectively controlled local transmission of COVID-19 despite a steady influx of imported cases and several local, but contained, outbreaks in 2020. Throughout 2020, state and territory public health responses were informed by weekly situational reports that included an ensemble forecast of daily COVID-19 cases for each jurisdiction. We present here an analysis of one forecasting model included in this ensemble across the variety of scenarios experienced by each jurisdiction from May to October 2020. We examine how successfully the forecasts characterised future case incidence, subject to variations in data timeliness and completeness, showcase how we adapted these forecasts to support decisions of public health priority in rapidly-evolving situations, evaluate the impact of key model features on forecast skill, and demonstrate how to assess forecast skill in real-time before the ground truth is known. Conditioning the model on the most recent, but incomplete, data improved the forecast skill, emphasising the importance of developing strong quantitative models of surveillance system characteristics, such as ascertainment delay distributions. Forecast skill was highest when there were at least 10 reported cases per day, the circumstances in which authorities were most in need of forecasts to aid in planning and response.

SARS-CoV-2 testing, infection and places of contamination in France, a national cross-sectional study, December 2021

BACKGROUND

This study aimed to describe the use of diagnostic testing for SARS-CoV-2 in France until December 2021, the characteristics of people infected, and places of contamination.

METHODS

Data were collected from the national 2021 Health Barometer cross-sectional study, which was conducted between February and December 2021 and included French-speaking individuals aged 18-85 years old selected through randomly generated landline and mobile phone numbers. Participants were interviewed about COVID-19-like symptoms in the previous 12 months, diagnostic testing for SARS-CoV-2, positive diagnosis for SARS-CoV-2, and the place(s) of contamination. Determinants of diagnostic testing and of infection were studied using univariate and multivariate Poisson regressions.

RESULTS

A total of 24,514 persons participated in the study. We estimated that 66.4% [65.0-67.7] of persons had been tested for SARS-CoV-2 the last time they experienced COVID-19-like symptoms, and that 9.8% [9.3-10.3] of the population in France - with or without symptoms - had been tested positive. Diagnostic testing was less frequent in men, unemployed persons, and people living alone; it was also less frequent during the first months of the pandemic. The estimated proportion of the population infected was higher in healthcare professionals (PRa: 1.5 [1.3-1.7]), those living in large cities ( > = 200 000 inhabitants, and Paris area) (1.4 [1.2-1.6]), and in households comprising > 3 persons (1.7 [1.5-2.0]). It was lower in retired persons (0.8 [0.6-0.97]) and those over 65 years old (0.6 [0.4-0.9]). Almost two-thirds (65.7%) of infected persons declared they knew where they were contaminated; 5.8% [4.5-7.4] reported being contaminated outdoors, 47.9% [44.8-51.0] in unventilated indoor environments, and 43.4% [40.3-46.6] in ventilated indoor environments. Specifically, 51.1% [48.0-54.2] declared they were contaminated at home or in a family of friend's house, 29.1% [26.4-31.9] at their workplace, 13.9% [11.9-16.1] in a healthcare structure, and 9.0% [7.4-10.8] in a public eating place (e.g., cafeteria, bar, restaurant).

CONCLUSIONS

To limit viral spread, preventive actions should preferentially target persons tested least frequently and those at a higher risk of infection. They should also target contamination in households, healthcare structures, and public eating places. Importantly, contamination is most frequent in places where prevention measures are most difficult to implement.

SARS-CoV-2 IgG seroprevalence surveys in blood donors before the vaccination campaign, France 2020-2021

We conducted a cross-sectional study for SARS-CoV-2 anti-S1 IgG prevalence in French blood donors (n = 32605), from March-2020 to January-2021. A mathematical model combined seroprevalence with a daily number of hospital admissions to estimate the probability of hospitalization upon infection and determine the number of infections while correcting for antibody decay. There was an overall seroprevalence increase over the study period and we estimate that ∼15% of the French population had been infected by SARS-CoV-2 by January-2021. The infection/hospitalization ratio increased with age, from 0.31% (18-30yo) to 4.5% (61-70yo). Half of the IgG-S1 positive individuals had no detectable antibodies 4 to 5 months after infection. The seroprevalence in group O donors (7.43%) was lower (p = 0.003) than in A, B, and AB donors (8.90%). We conclude, based on seroprevalence data and mathematical modeling, that a large proportion of the French population was unprotected against severe disease prior to the vaccination campaign.

Study of optimal vaccination strategies for early COVID-19 pandemic using an age-structured mathematical model: A case study of the USA

In this paper we study different vaccination strategies that could have been implemented for the early COVID-19 pandemic. We use a demographic epidemiological mathematical model based on differential equations in order to investigate the efficacy of a variety of vaccination strategies under limited vaccine supply. We use the number of deaths as the metric to measure the efficacy of each of these strategies. Finding the optimal strategy for the vaccination programs is a complex problem due to the large number of variables that affect the outcomes. The constructed mathematical model takes into account demographic risk factors such as age, comorbidity status and social contacts of the population. We perform simulations to assess the performance of more than three million vaccination strategies which vary depending on the vaccine priority of each group. This study focuses on the scenario corresponding to the early vaccination period in the USA, but can be extended to other countries. The results of this study show the importance of designing an optimal vaccination strategy in order to save human lives. The problem is extremely complex due to the large amount of factors, high dimensionality and nonlinearities. We found that for low/moderate transmission rates the optimal strategy prioritizes high transmission groups, but for high transmission rates, the optimal strategy focuses on groups with high CFRs. The results provide valuable information for the design of optimal vaccination programs. Moreover, the results help to design scientific vaccination guidelines for future pandemics.

Perspectives of (/memorandum for) systems thinking on COVID-19 pandemic and pathology

Is data-driven analysis sufficient for understanding the COVID-19 pandemic and for justifying public health regulations? In this paper, we argue that such analysis is insufficient. Rather what is needed is the identification and implementation of over-arching hypothesis-related and/or theory-based rationales to conduct effective SARS-CoV2/COVID-19 (Corona) research. To that end, we analyse and compare several published recommendations for conceptual and methodological frameworks in medical research (e.g., public health, preventive medicine and health promotion) to current research approaches in medical Corona research. Although there were several efforts published in the literature to develop integrative conceptual frameworks before the COVID-19 pandemic, such as social ecology for public health issues and systems thinking in health care, only a few attempts to utilize these concepts can be found in medical Corona research. For this reason, we propose nested and integrative systemic modelling approaches to understand Corona pandemic and Corona pathology. We conclude that institutional efforts for knowledge integration and systemic thinking, but also for integrated science, are urgently needed to avoid or mitigate future pandemics and to resolve infection pathology.

How Asymptomatic Transmission Influences Mitigation and Suppression Strategies during a Pandemic

Asymptomatic transmission complicates any public health strategies to combat a pandemic, which proved especially accurate in the case of COVID-19. Although asymptomatic cases are not unique to COVID-19, the high asymptomatic case rate raised many problems for developing effective public health interventions. The current modeling effort explored how asymptomatic transmission might impact pandemic responses in four key areas: isolation procedures, changes in reproduction rate, the potential for reduced transmission from asymptomatic cases, and social adherence to public health measures. A high rate of asymptomatic cases effectively requires large-scale public health suppression and mitigation procedures given that quarantine procedures alone could not prevent an outbreak for a virus such as SARS-CoV-2. This problem only becomes worse without lowering the effective reproduction rate, and even assuming the potential for reduced transmission, any virus with a high degree of asymptomatic transmission will likely produce a pandemic. Finally, there is a concern that asymptomatic individuals will also refuse to adhere to public health guidance. Analyses indicate that, given certain assumptions, even half of the population adhering to public health guidance could reduce the peak and flatten the curve by over 90%. Taken together, these analyses highlight the importance of taking asymptomatic cases into account when modeling viral spread and developing public health intervention strategies.

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.

[Research on infectious diseases in the French Armed Forces Health Service: one hundred years after Alphonse Laveran]

The army has always been particularly exposed to the risk of infection, which Alphonse Laveran already analyzed in 1875 in his Traité des maladies et épidémies des armées. Nowadays, the risk of infection is still present, which is why the Armed Forces Health Service (SSA) employs modern research resources in this area structured around the Armed Forces Biomedical Research Institute (IRBA) supported by the Military Training Hospitals (HIA), the Armed Forces Epidemiology and Public Health Center (CESPA), and the Val-de-Grâce School.These resources meet current research needs in infectious and tropical diseases and are preparing to respond to future emergences.Recently, the SSA research has stood out in several epidemics and emergences that have affected the French Armed Forces and the national population.

Optimal two-stage spatial sampling design for estimating critical parameters of SARS-CoV-2 epidemic: Efficiency versus feasibility

The COVID-19 pandemic presents an unprecedented clinical and healthcare challenge for the many medical researchers who are attempting to prevent its worldwide spread. It also presents a challenge for statisticians involved in designing appropriate sampling plans to estimate the crucial parameters of the pandemic. These plans are necessary for monitoring and surveillance of the phenomenon and evaluating health policies. In this respect, we can use spatial information and aggregate data regarding the number of verified infections (either hospitalized or in compulsory quarantine) to improve the standard two-stage sampling design broadly adopted for studying human populations. We present an optimal spatial sampling design based on spatially balanced sampling techniques. We prove its relative performance analytically in comparison to other competing sampling plans, and we also study its properties through a series of Monte Carlo experiments. Considering the optimal theoretical properties of the proposed sampling plan and its feasibility, we discuss suboptimal designs that approximate well optimality and are more readily applicable.

A mathematical model for COVID-19 considering waning immunity, vaccination and control measures

In this work we define a modified SEIR model that accounts for the spread of infection during the latent period, infections from asymptomatic or pauci-symptomatic infected individuals, potential loss of acquired immunity, people's increasing awareness of social distancing and the use of vaccination as well as non-pharmaceutical interventions like social confinement. We estimate model parameters in three different scenarios-in Italy, where there is a growing number of cases and re-emergence of the epidemic, in India, where there are significant number of cases post confinement period and in Victoria, Australia where a re-emergence has been controlled with severe social confinement program. Our result shows the benefit of long term confinement of 50% or above population and extensive testing. With respect to loss of acquired immunity, our model suggests higher impact for Italy. We also show that a reasonably effective vaccine with mass vaccination program are successful measures in significantly controlling the size of infected population. We show that for a country like India, a reduction in contact rate by 50% compared to a reduction of 10% reduces death from 0.0268 to 0.0141% of population. Similarly, for a country like Italy we show that reducing contact rate by half can reduce a potential peak infection of 15% population to less than 1.5% of population, and potential deaths from 0.48 to 0.04%. With respect to vaccination, we show that even a 75% efficient vaccine administered to 50% population can reduce the peak number of infected population by nearly 50% in Italy. Similarly, for India, a 0.056% of population would die without vaccination, while 93.75% efficient vaccine given to 30% population would bring this down to 0.036% of population, and 93.75% efficient vaccine given to 70% population would bring this down to 0.034%.

High Rate of SARS-CoV-2 Transmission Due to Choir Practice in France at the Beginning of the COVID-19 Pandemic

OBJECTIVE

To document the case of a high transmission rate of SARS-CoV-2 generating a cluster linked to an indoor choir rehearsal hold at the beginning of the COVID-19 pandemic in France.

METHOD

Case study. Data were obtained via a questionnaire.

RESULTS

Twenty-seven participants, including 25 singers, 1 conductor and 1 accompanist attended a choir practice on March 12, 2020. The practice was indoor and took place in a non ventilated space of 45 m². No choir member reported having been symptomatic for COVID-19 between March 2 and March 12.The mean age of the participants was 66.9 (range 35-86) years. The secondary attack rate was 70%: 19/27 participants were diagnosed with COVID-19 from 1 to 12 days after the rehearsal with a median of 5.1 days. Thirty-six percent of the cases needed a hospitalization (7/19), and 21% (4/19) were admitted to an ICU. The index cases were asymptomatic and possibly multiple.

CONCLUSION

In the absence of valid barrier measures to prevent COVID-19 transmission, indoor choir practice should be suspended during the SARSCoV-2 surging phases. Transmission of the virus among gatherings from asymptomatic cases is a crucial issue and a main challenge to COVID-19 control.

Comparative effectiveness of contact tracing interventions in the context of the COVID-19 pandemic: a systematic review

Contact tracing is a non-pharmaceutical intervention (NPI) widely used in the control of the COVID-19 pandemic. Its effectiveness may depend on a number of factors including the proportion of contacts traced, delays in tracing, the mode of contact tracing (e.g. forward, backward or bidirectional contact training), the types of contacts who are traced (e.g. contacts of index cases or contacts of contacts of index cases), or the setting where contacts are traced (e.g. the household or the workplace). We performed a systematic review of the evidence regarding the comparative effectiveness of contact tracing interventions. 78 studies were included in the review, 12 observational (ten ecological studies, one retrospective cohort study and one pre-post study with two patient cohorts) and 66 mathematical modelling studies. Based on the results from six of the 12 observational studies, contact tracing can be effective at controlling COVID-19. Two high quality ecological studies showed the incremental effectiveness of adding digital contact tracing to manual contact tracing. One ecological study of intermediate quality showed that increases in contact tracing were associated with a drop in COVID-19 mortality, and a pre-post study of acceptable quality showed that prompt contact tracing of contacts of COVID-19 case clusters / symptomatic individuals led to a reduction in the reproduction number R. Within the seven observational studies exploring the effectiveness of contact tracing in the context of the implementation of other non-pharmaceutical interventions, contact tracing was found to have an effect on COVID-19 epidemic control in two studies and not in the remaining five studies. However, a limitation in many of these studies is the lack of description of the extent of implementation of contact tracing interventions. Based on the results from the mathematical modelling studies, we identified the following highly effective policies: (1) manual contact tracing with high tracing coverage and either medium-term immunity, highly efficacious isolation/quarantine and/ or physical distancing (2) hybrid manual and digital contact tracing with high app adoption with highly effective isolation/ quarantine and social distancing, (3) secondary contact tracing, (4) eliminating contact tracing delays, (5) bidirectional contact tracing, (6) contact tracing with high coverage in reopening educational institutions. We also highlighted the role of social distancing to enhance the effectiveness of some of these interventions in the context of 2020 lockdown reopening. While limited, the evidence from observational studies shows a role for manual and digital contact tracing in controlling the COVID-19 epidemic. More empirical studies accounting for the extent of contact tracing implementation are required.

High Rate of SARS-CoV-2 Transmission Due to Choir Practice in France at the Beginning of the COVID-19 Pandemic

OBJECTIVE

To document the case of a high transmission rate of SARS-CoV-2 generating a cluster linked to an indoor choir rehearsal hold at the beginning of the COVID-19 pandemic in France.

METHOD

Case study. Data were obtained via a questionnaire.

RESULTS

Twenty-seven participants, including 25 singers, 1 conductor and 1 accompanist attended a choir practice on March 12, 2020. The practice was indoor and took place in a non ventilated space of 45 m². No choir member reported having been symptomatic for COVID-19 between March 2 and March 12.The mean age of the participants was 66.9 (range 35-86) years. The secondary attack rate was 70%: 19/27 participants were diagnosed with COVID-19 from 1 to 12 days after the rehearsal with a median of 5.1 days. Thirty-six percent of the cases needed a hospitalization (7/19), and 21% (4/19) were admitted to an ICU. The index cases were asymptomatic and possibly multiple.

CONCLUSION

In the absence of valid barrier measures to prevent COVID-19 transmission, indoor choir practice should be suspended during the SARSCoV-2 surging phases. Transmission of the virus among gatherings from asymptomatic cases is a crucial issue and a main challenge to COVID-19 control.

Prevalence of Asymptomatic COVID-19 Infection in Hemodialysis Patients and the Risk of Hypercoagulability: Should we Consider Routine Screening?

Introduction

Coronavirus disease 2019 (COVID-19) has become a pandemic in late 2019. Its clinical presentation varies from asymptomatic infection to severe respiratory failure. Infection control strategies to minimize the risk of transmission of COVID-19 in end-stage renal disease (ESRD) patients receiving in-center hemodialysis (HD) have been implemented. Development of humoral response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in adult patients with ESRD receiving HD has not been sufficiently reported.

Methods

A total of 179 asymptomatic HD patients undergoing regular HD were screened for COVID-19 infection. Infection with SARS-CoV-2 was confirmed through a real-time reverse transcription polymerase chain reaction assay of nasopharyngeal swab specimens. They were classified into positive and negative groups according to the results of PCR.

Results

Of the 179 asymptomatic patients, we found that 23 patients (12.8%) were positive for COVID-19. Their mean age was 45.61 ± 13.38 years. There was a significant difference between both groups regarding C-reactive protein, lymphocytes, and platelet counts (P < 0.001). Also, TAT (thrombin-antithrombin complex) and D-dimer levels were significantly increased among the positive group (11.47 ± 1.51 vs. 7.53 ± 1.64 mcq/L, P < 0.001; 1171.52 ± 267.6 vs. 542.76 ± 107.06 ng/mL, P < 0.001, respectively).

Conclusion

Asymptomatic SARS-CoV-2 infection is detected in HD patients. They carry the risk of hypercoagulability complications. We need more strict infection control measures and proactive diagnosis to limit the spread of the infection and lethal thromboembolic complications.

Tissue injury and leukocyte changes in post-acute sequelae of SARS-CoV-2: review of 2833 post-acute patient outcomes per immune dysregulation and microbial translocation in long COVID

A significant number of persons with coronavirus disease 2019 (COVID-19) experience persistent, recurrent, or new symptoms several months after the acute stage of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. This phenomenon, termed post-acute sequelae of SARS-CoV-2 (PASC) or long COVID, is associated with high viral titers during acute infection, a persistently hyperactivated immune system, tissue injury by NETosis-induced micro-thrombofibrosis (NETinjury), microbial translocation, complement deposition, fibrotic macrophages, the presence of autoantibodies, and lymphopenic immune environments. Here, we review the current literature on the immunological imbalances that occur during PASC. Specifically, we focus on data supporting common immunopathogenesis and tissue injury mechanisms shared across this highly heterogenous disorder, including NETosis, coagulopathy, and fibrosis. Mechanisms include changes in leukocyte subsets/functions, fibroblast activation, cytokine imbalances, lower cortisol, autoantibodies, co-pathogen reactivation, and residual immune activation driven by persistent viral antigens and/or microbial translocation. Taken together, we develop the premise that SARS-CoV-2 infection results in PASC as a consequence of acute and/or persistent single or multiple organ injury mediated by PASC determinants to include the degree of host responses (inflammation, NETinjury), residual viral antigen (persistent antigen), and exogenous factors (microbial translocation). Determinants of PASC may be amplified by comorbidities, age, and sex.

How time-scale differences in asymptomatic and symptomatic transmission shape SARS-CoV-2 outbreak dynamics

Asymptomatic and symptomatic SARS-CoV-2 infections can have different characteristic time scales of transmission. These time-scale differences can shape outbreak dynamics as well as bias population-level estimates of epidemic strength, speed, and controllability. For example, prior work focusing on the initial exponential growth phase of an outbreak found that larger time scales for asymptomatic vs. symptomatic transmission can lead to under-estimates of the basic reproduction number as inferred from epidemic case data. Building upon this work, we use a series of nonlinear epidemic models to explore how differences in asymptomatic and symptomatic transmission time scales can lead to changes in the realized proportion of asymptomatic transmission throughout an epidemic. First, we find that when asymptomatic transmission time scales are longer than symptomatic transmission time scales, then the effective proportion of asymptomatic transmission increases as total incidence decreases. Moreover, these time-scale-driven impacts on epidemic dynamics are enhanced when infection status is correlated between infector and infectee pairs (e.g., due to dose-dependent impacts on symptoms). Next we apply these findings to understand the impact of time-scale differences on populations with age-dependent assortative mixing and in which the probability of having a symptomatic infection increases with age. We show that if asymptomatic generation intervals are longer than corresponding symptomatic generation intervals, then correlations between age and symptoms lead to a decrease in the age of infection during periods of epidemic decline (whether due to susceptible depletion or intervention). Altogether, these results demonstrate the need to explore the role of time-scale differences in transmission dynamics alongside behavioral changes to explain outbreak features both at early stages (e.g., in estimating the basic reproduction number) and throughout an epidemic (e.g., in connecting shifts in the age of infection to periods of changing incidence).

Nosocomial Omicron Outbreak in a Medical Ward During the Coronavirus Disease 2019 Pandemic

Background

The Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is 2 to 3 times more contagious than the Delta variant and poses a new challenge in containing transmission in community and health care settings. Transmission in hospitals can cause nosocomial outbreak infections and affect patients and health care workers. This study describes a coronavirus disease 2019 (COVID-19) outbreak in a medical ward. The purpose of the investigation was to determine the transmission source of the outbreak, as well as the measures implemented to prevent and control it.

Methods

A cluster of SARS-CoV-2 infections that affected health care workers, inpatients, and caregivers was thoroughly researched in a medical ward. There were several strict outbreak measures implemented in our hospital, and these measures controlled the nosocomial COVID-19 outbreak in this study.

Results

Seven cases of SARS-CoV-2 infection were diagnosed within 2 days in the medical ward. The infection control team declared a nosocomial outbreak of the COVID-19 Omicron variant. Several strict outbreak measures were implemented as follows: 1. The medical ward was closed and cleaning and disinfection were performed. 2. All patients and caregivers with negative test results were transferred to a spare COVID-19 isolation ward. 3. Relatives' visits were not allowed, and no new patient was admitted during the outbreak period. 4. Health care workers were retrained on wearing personal protective equipment and enhanced techniques and practices of hand hygiene, social distancing, and self-monitoring of fever and respiratory symptoms.

Conclusion

This outbreak occurred in a non-COVID-19 ward during the COVID-19 Omicron variant phase of the pandemic. Our strict outbreak measures halted and contained the nosocomial COVID-19 outbreak in 10 days. Future studies are needed to establish a standard policy of COVID-19 outbreak measure implementation.

Polypharmacology of ambroxol in the treatment of COVID-19

The pandemic of coronavirus disease 2019 (COVID-19) by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still underway. Due to the growing development of severe symptoms, it is necessary to promote effective therapies. Ambroxol [2-amino-3,5-dibromo-N-(trans-4-hydroxycyclohexyl) benzylamine] has long been used as one of the over-the-counter mucolytic agents to treat various respiratory diseases. Therefore, we focused on the mechanism of action of ambroxol in COVID-19 treatment. In vitro and in silico screening revealed that ambroxol may impede cell entry of SARS-CoV-2 by binding to neuropilin-1. Ambroxol could also interact with multiple inflammatory factors and signaling pathways, especially nuclear factor kappa B (NF-κB), to interfere cytokines cascade activated by SARS-CoV-2 internalization. Furthermore, multipathways and proteins, such as the cell cycle and matrix metalloproteinases (MMPs), were identified as significant ambroxol-targeting pathways or molecules in PBMC and lung of severe COVID-19 patients by bioinformatics analysis. Collectively, these results suggested that ambroxol may serve as a promising therapeutic candidate for the treatment of severe SARS-CoV-2 infection.

Prevalence of Anti-SARS-CoV-2 Antibodies and Associated Factors Among Health Care Workers in Santiago De Cali, Colombia

Purpose

This study aims to determine the seroprevalence of coronavirus disease (COVID-19) among health care workers and describe the associated sociodemographic and labor features.

Patients and Methods

An observational study with an analytical component was conducted at a clinic in Cali, Colombia. The sample size was 708 health workers and they were selected by stratified random sampling. A Bayesian analysis was developed to determine the raw and adjusted prevalence. A Poisson regression model was used to estimate the prevalence ratios.

Results

Overall seroprevalence of COVID-19 among healthcare workers was 29%. Miscellaneous services workers, healthcare, and administrative workers, was 38%, 33%, and 32%, respectively. Factors related to seropositivity were having a contact with a COVID-19 patient for >120 minutes and being diagnosed with COVID-19 by laboratory tests.

Conclusion

The present study shows a adjusted seroprevalence of 29% in health workers, indicating a high level of disease transmission and an increased risk of infection in this population group.

Adaptive behaviors and vaccination on curbing COVID-19 transmission: Modeling simulations in eight countries

Current persistent outbreak of COVID-19 is triggering a series of collective responses to avoid infection. To further clarify the impact mechanism of adaptive protection behavior and vaccination, we developed a new transmission model via a delay differential system, which parameterized the roles of adaptive behaviors and vaccination, and allowed to simulate the dynamic infection process among people. By validating the model with surveillance data during March 2020 and October 2021 in America, India, South Africa, Philippines, Brazil, UK, Spain and Germany, we quantified the protection effect of adaptive behaviors by different forms of activity function. The modeling results indicated that (1) the adaptive activity function can be used as a good indicator for fitting the intervention outcome, which exhibited short-term awareness in these countries, and it could reduce the total human infections by 3.68, 26.16, 15.23, 4.23, 7.26, 1.65, 5.51 and 7.07 times, compared with the reporting; (2) for complete prevention, the average proportions of people with immunity should be larger than 90%, 92%, 86%, 71%, 92%, 84%, 82% and 76% with adaptive protection behaviors, or 91%, 97%, 94%, 77%, 92%, 88%, 85% and 90% without protection behaviors; and (3) the required proportion of humans being vaccinated is a sub-linear decreasing function of vaccine efficiency, with small heterogeneity in different countries. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Real-time estimation and forecasting of COVID-19 cases and hospitalizations in Wisconsin HERC regions for public health decision making processes

BACKGROUND

The spread of the COVID-19 (SARS-CoV-2) and the surging number of cases across the United States have resulted in full hospitals and exhausted health care workers. Limited availability and questionable reliability of the data make outbreak prediction and resource planning difficult. Any estimates or forecasts are subject to high uncertainty and low accuracy to measure such components. The aim of this study is to apply, automate, and assess a Bayesian time series model for the real-time estimation and forecasting of COVID-19 cases and number of hospitalizations in Wisconsin healthcare emergency readiness coalition (HERC) regions.

METHODS

This study makes use of the publicly available Wisconsin COVID-19 historical data by county. Cases and effective time-varying reproduction number [Formula: see text] by the HERC region over time are estimated using Bayesian latent variable models. Hospitalizations are estimated by the HERC region over time using a Bayesian regression model. Cases, effective Rt, and hospitalizations are forecasted over a 1-day, 3-day, and 7-day time horizon using the last 28 days of data, and the 20%, 50%, and 90% Bayesian credible intervals of the forecasts are calculated. The frequentist coverage probability is compared to the Bayesian credible level to evaluate performance.

RESULTS

For cases and effective [Formula: see text], all three time horizons outperform the three credible levels of the forecast. For hospitalizations, all three time horizons outperform the 20% and 50% credible intervals of the forecast. On the contrary, the 1-day and 3-day periods underperform the 90% credible intervals. Questions about uncertainty quantification should be re-calculated using the frequentist coverage probability of the Bayesian credible interval based on observed data for all three metrics.

CONCLUSIONS

We present an approach to automate the real-time estimation and forecasting of cases and hospitalizations and corresponding uncertainty using publicly available data. The models were able to infer short-term trends consistent with reported values at the HERC region level. Additionally, the models were able to accurately forecast and estimate the uncertainty of the measurements. This study can help identify the most affected regions and major outbreaks in the near future. The workflow can be adapted to other geographic regions, states, and even countries where decision-making processes are supported in real-time by the proposed modeling system.

Real-world utilization of SARS-CoV-2 serological testing in RNA positive patients across the United States

BACKGROUND

As diagnostic tests for COVID-19 were broadly deployed under Emergency Use Authorization, there emerged a need to understand the real-world utilization and performance of serological testing across the United States.

METHODS

Six health systems contributed electronic health records and/or claims data, jointly developed a master protocol, and used it to execute the analysis in parallel. We used descriptive statistics to examine demographic, clinical, and geographic characteristics of serology testing among patients with RNA positive for SARS-CoV-2.

RESULTS

Across datasets, we observed 930,669 individuals with positive RNA for SARS-CoV-2. Of these, 35,806 (4%) were serotested within 90 days; 15% of which occurred <14 days from the RNA positive test. The proportion of people with a history of cardiovascular disease, obesity, chronic lung, or kidney disease; or presenting with shortness of breath or pneumonia appeared higher among those serotested compared to those who were not. Even in a population of people with active infection, race/ethnicity data were largely missing (>30%) in some datasets-limiting our ability to examine differences in serological testing by race. In datasets where race/ethnicity information was available, we observed a greater distribution of White individuals among those serotested; however, the time between RNA and serology tests appeared shorter in Black compared to White individuals. Test manufacturer data was available in half of the datasets contributing to the analysis.

CONCLUSION

Our results inform the underlying context of serotesting during the first year of the COVID-19 pandemic and differences observed between claims and EHR data sources-a critical first step to understanding the real-world accuracy of serological tests. Incomplete reporting of race/ethnicity data and a limited ability to link test manufacturer data, lab results, and clinical data challenge the ability to assess the real-world performance of SARS-CoV-2 tests in different contexts and the overall U.S. response to current and future disease pandemics.

Observed versus estimated actual trend of COVID-19 case numbers in Cameroon: A data-driven modelling

Controlling the COVID-19 outbreak remains a challenge for Cameroon, as it is for many other countries worldwide. The number of confirmed cases reported by health authorities in Cameroon is based on observational data, which is not nationally representative. The actual extent of the outbreak from the time when the first case was reported in the country to now remains unclear. This study aimed to estimate and model the actual trend in the number of COVID -19 new infections in Cameroon from March 05, 2020 to May 31, 2021 based on an observed disaggregated dataset. We used a large disaggregated dataset, and multilevel regression and poststratification model was applied prospectively for COVID-19 cases trend estimation in Cameroon from March 05, 2020 to May 31, 2021. Subsequently, seasonal autoregressive integrated moving average (SARIMA) modeling was used for forecasting purposes. Based on the prospective MRP modeling findings, a total of about 7450935 (30%) of COVID-19 cases was estimated from March 05, 2020 to May 31, 2021 in Cameroon. Generally, the reported number of COVID-19 infection cases in Cameroon during this period underestimated the estimated actual number by about 94 times. The forecasting indicated a succession of two waves of the outbreak in the next two years following May 31, 2021. If no action is taken, there could be many waves of the outbreak in the future. To avoid such situations which could be a threat to global health, public health authorities should effectively monitor compliance with preventive measures in the population and implement strategies to increase vaccination coverage in the population.

The challenge of asymptomatic carriers of COVID-19: A rapid review of literature

OBJECTIVES

To describe the epidemiological and sociodemographic characteristics of asymptomatic carriers reported in the literature, and to review the strategies used for diagnosis and control.

METHODS

Systematic literature review approach. As inclusion criteria, all studies published between January 1 and June 26, 2020, conducted in humans, that reported people who remained asymptomatic of COVID-19. Descriptors were adapted to the interfaces of eight bibliographic databases were configured: PubMed, Ovid, SciELO, Ebsco, Scopus, LILACS, Epistemonikos and Embase.

RESULTS

About 45% of the articles reported adult population, thirteen reported mixed population (adult and pediatric). 3 525 asymptomatic people were reported, with an average of 37,1 years [0.5-82 years]. Although the effectiveness of the control and prevention measures was not reported, the identification, isolation and follow-up of contacts stands out as a potential effective mechanism to prevent the transmission.

CONCLUSIONS

The use of this information could be relevant to guide evidence-based public health policies and the protection of populations and the improvement of health care that contributes to stopping this pandemic.

Antibodies, B Cell Responses and Immune Responses to SARS-CoV-2 Infections

Coronaviruses (CoV) are enveloped, positive-sense, single-stranded RNA viruses responsible for causing seasonal, mild respiratory disease in humans [...].