Prevalence of Asymptomatic SARS-CoV-2 Infection : A Narrative Review

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.

Multiparametric analysis of the specific immune response against SARS-CoV-2

BACKGROUND

SARS-CoV-2, which causes COVID-19, has killed more than 7 million people worldwide. Understanding the development of postinfectious and postvaccination immune responses is necessary for effective treatment and the introduction of appropriate antipandemic measures.

OBJECTIVES

We analysed humoral and cell-mediated anti-SARS-CoV-2 immune responses to spike (S), nucleocapsid (N), membrane (M), and open reading frame (O) proteins in individuals collected up to 1.5 years after COVID-19 onset and evaluated immune memory.

METHODS

Peripheral blood mononuclear cells and serum were collected from patients after COVID-19. Sampling was performed in two rounds: 3-6 months after infection and after another year. Most of the patients were vaccinated between samplings. SARS-CoV-2-seronegative donors served as controls. ELISpot assays were used to detect SARS-CoV-2-specific T and B cells using peptide pools (S, NMO) or recombinant proteins (rS, rN), respectively. A CEF peptide pool consisting of selected viral epitopes was applied to assess the antiviral T-cell response. SARS-CoV-2-specific antibodies were detected via ELISA and a surrogate virus neutralisation assay.

RESULTS

We confirmed that SARS-CoV-2 infection induces the establishment of long-term memory IgG+ B cells and memory T cells. We also found that vaccination enhanced the levels of anti-S memory B and T cells. Multivariate comparison also revealed the benefit of repeated vaccination. Interestingly, the T-cell response to CEF was lower in patients than in controls.

CONCLUSION

This study supports the importance of repeated vaccination for enhancing immunity and suggests a possible long-term perturbation of the overall antiviral immune response caused by SARS-CoV-2 infection.

Benefits of Repeated SARS-CoV-2 Vaccination and Virus-induced Cross-neutralization Potential in Immunocompromised Transplant Patients and Healthy Individuals

Background

Current COVID-19 vaccines primarily target the Spike protein of defined virus variants, offering limited protection against emerging variants in immunocompetent individuals. Similarly, protective immunity following natural SARS-CoV-2 infection is variable and of short duration, raising concerns about immunocompromised individuals' vaccination strategies.

Methods

This prospective multicenter study examined 66 sera from 59 immunocompromised and 451 sera from 215 immunocompetent individuals from different pandemic periods. We establish and validate a live virus-based neutralization assay to determine the virus-inactivating potential against ancestral and current SARS-CoV-2 isolates.

Results

Our virus-based neutralization assay demonstrated superior performance over surrogate neutralization assays. We found strong but transient immunity after complete vaccination schemes, with single doses providing minimum neutralization, regardless of vaccine type. Combining vaccination-induced immunity with SARS-CoV-2 infection before or after vaccination yielded higher neutralizing titers than vaccination or infection alone, consistent across both study groups. Additional doses after a full vaccination course restored neutralization levels.

Conclusions

Potentially protective SARS-CoV-2 neutralization is reliably induced in immunocompromised individuals by prior attenuation of immunosuppression. First-generation vaccines protect against various SARS-CoV-2 variants in immunocompetent individuals, with effective cross-neutralization demonstrated up to the Delta variant but largely absent for later Omicron variants. Continuous vaccine updates are necessary to address emerging SARS-CoV-2 variants.

SARS-CoV-2-specific plasma cells are not durably established in the bone marrow long-lived compartment after mRNA vaccination

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mRNA vaccines are effective at protecting from severe disease, but the protective antibodies wane rapidly even though SARS-CoV-2-specific plasma cells can be found in the bone marrow (BM). Here, to explore this paradox, we enrolled 19 healthy adults at 2.5-33 months after receipt of a SARS-CoV-2 mRNA vaccine and measured influenza-, tetanus- or SARS-CoV-2-specific antibody-secreting cells (ASCs) in long-lived plasma cell (LLPC) and non-LLPC subsets within the BM. Only influenza- and tetanus-specific ASCs were readily detected in the LLPCs, whereas SARS-CoV-2 specificities were mostly absent. The ratios of non-LLPC:LLPC for influenza, tetanus and SARS-CoV-2 were 0.61, 0.44 and 29.07, respectively. In five patients with known PCR-proven history of recent infection and vaccination, SARS-CoV-2-specific ASCs were mostly absent from the LLPCs. We show similar results with measurement for secreted antibodies from BM ASC culture supernatant. While serum IgG titers specific for influenza and tetanus correlated with IgG LLPCs, serum IgG levels for SARS-CoV-2, which waned within 3-6 months after vaccination, were associated with IgG non-LLPCs. In all, our studies suggest that rapid waning of SARS-CoV-2-specific serum antibodies could be accounted for by the absence of BM LLPCs after these mRNA vaccines.

Adult patients with severe respiratory syncytial virus infections in the emergency department

Respiratory syncytial virus (RSV) is a seasonal virus known to cause significant morbidity in pediatric patients; however, morbidity in adult patients has not been well investigated. We aimed to characterize adult patients with RSV infection in the emergency department (ED) and their clinical course. During the winter term 2022/23, all adult ED patients were screened for RSV, severe acute respiratory syndrome coronavirus type 2, and influenza infection using point-of-care polymerase chain reaction tests. All symptomatic RSV+ patients were further characterized based on their clinical presentation and course. A group comparison between RSV+ inpatients and RSV+ outpatients was conducted. The potential risk factors for inpatient treatment were evaluated using univariate and multivariate analyses. Of the 135 symptomatic RSV+ patients, 51.9% (70/135) were inpatients. Their length of stay were 9.4 (±10.4) days. Inpatients had a significantly higher mean age, lower oxygen saturation, higher leukocyte count, and higher C-reactive protein levels than outpatients. Among the preconditions, pulmonary diseases, tumors, and immunosuppression were significantly more frequent in the inpatient group. Thirty percent (21/70) of the inpatients required ICU treatment, 11% (8/70) required mechanical ventilation, and 9% (6/70) died. Malaise (P = .021, odds ratio 8.390) and detection of pulmonary infiltrations (P < .001, odds ratio 12.563) were the only independent predictors of inpatient treatment in the multivariate analysis. Our data show that RSV is a medically relevant pathogen among adult ED patients, often requiring inpatient treatment. In particular, elderly patients with some medical preconditions seem to be more prone to a severe course of infection requiring inpatient treatment. Lower respiratory tract involvement, proven by pulmonary infiltrates, seems to be crucial for a more severe disease course.

Data-driven mathematical modeling approaches for COVID-19: A survey

In this review, we successively present the methods for phenomenological modeling of the evolution of reported and unreported cases of COVID-19, both in the exponential phase of growth and then in a complete epidemic wave. After the case of an isolated wave, we present the modeling of several successive waves separated by endemic stationary periods. Then, we treat the case of multi-compartmental models without or with age structure. Eventually, we review the literature, based on 260 articles selected in 11 sections, ranging from the medical survey of hospital cases to forecasting the dynamics of new cases in the general population. This review favors the phenomenological approach over the mechanistic approach in the choice of references and provides simulations of the evolution of the number of observed cases of COVID-19 for 10 states (California, China, France, India, Israel, Japan, New York, Peru, Spain and United Kingdom).

Post-Hoc Analysis of Potential Correlates of Protection of a Recombinant SARS-CoV-2 Spike Protein Extracellular Domain Vaccine Formulated with Advax-CpG55.2-Adjuvant

SpikoGen® vaccine is a subunit COVID-19 vaccine composed of an insect cell expressed recombinant spike protein extracellular domain formulated with Advax-CpG55.2™ adjuvant. A randomized double-blind, placebo-controlled Phase II clinical trial was conducted in 400 adult subjects who were randomized 3:1 to receive two intramuscular doses three weeks apart of either SpikoGen® vaccine 25 μg or saline placebo, as previously reported. This study reports a post hoc analysis of the trial data to explore potential immune correlates of SpikoGen® vaccine protection. A range of humoral markers collected pre- and post-vaccination, including spike- and RBD-binding IgG and IgA, surrogate (sVNT), and conventional (cVNT) virus neutralization tests were compared between participants who remained infection-free or got infected over three months of follow-up. From 2 weeks after the second vaccine dose, 21 participants were diagnosed with SARS-CoV-2 infection, 13 (4.2%) in the SpikoGen® group and 8 (9%) in the placebo group. Those in the vaccinated group who experienced breakthrough infections had significantly lower sVNT titers (GMT 5.75 μg/mL, 95% CI; 3.72-8.91) two weeks after the second dose (day 35) than those who did not get infected (GMT 21.06 μg/mL, 95% CI; 16.57-26.76). Conversely, those who did not develop SARS-CoV-2 infection during follow-up had significantly higher baseline sVNT, cVNT, spike-binding IgG and IgA, and RBD-binding IgG, consistent with a past SARS-CoV-2 infection. SpikoGen® further reduced the risk of re-infection (OR 0.29) in baseline seropositive (previously infected) as well as baseline seronegative participants. This indicates that while SpikoGen vaccine is protective in seronegative individuals, those with hybrid immunity have the most robust protection.

Occult Serologically Confirmed Cases of SARS-CoV-2 Coronavirus among the General Population in the Era of the Fourth Vaccination

Background: Asymptomatic SARS-CoV-2 infection can significantly increase the spread of the COVID-19 pandemic. We aimed to investigate the epidemiological and clinical predictors of occult serologically confirmed SARS-CoV-2 cases among the general population during the fourth vaccination era in Israel. Methods: We conducted a cross-sectional study among individuals aged ≥18 years who had not been tested for COVID-19 in the preceding 5 months. Occult serologically confirmed cases were based on the presence of anti-N IgG antibodies. Potential risk factors were examined. Multivariable regression analysis identified independent predictors of subclinical SARS-CoV-2 infection. Results: This study included 504 participants. The prevalence of occult serologically confirmed SARS-CoV-2 was 12.5%. Chronic disease was found to be an independent predictor for the absence of occult disease (aOR) 0.4 [95% (CI): 0.18-0.87], p-value = 0.02). No significant differences were observed in age, sex, marital status, number of children, vaccination status, or exposure to COVID-19 infection between participants with and without SARS-CoV-2 sub-infection. Conclusions: We found a lower prevalence of occult serologically confirmed SARS-CoV-2 cases, compared to previous reports, and a negative correlation between chronic disease and occult SARS-CoV-2. Continued research, surveillance, and intervention strategies are needed to optimize long-term health outcomes and provide valuable insights for public health policymakers and clinicians.

The public health and economic burden of long COVID in Australia, 2022-24: a modelling study

OBJECTIVE

To estimate the number of people in Australia with long COVID by age group, and the associated medium term productivity and economic losses.

STUDY DESIGN

Modelling study: a susceptible-exposed-infected-recovered (SEIR) model to estimate the number of people with long COVID over time following single infections, and a labour supply model to estimate productivity losses as a proportion of gross domestic product (GDP).

SETTING

Australia, 2022-2024.

MAIN OUTCOME MEASURES

Estimated number of people infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during 2022-2023 (based on serosurvey data) who have long COVID, 2022-2024, by age group; estimated GDP loss during 2022 caused by reduced labour supply attributable to long COVID.

RESULTS

Our model projected that the number of people with long COVID following a single infection in 2022 would peak in September 2022, when 310 341-1 374 805 people (1.2-5.4% of Australians) would have symptoms of long COVID, declining to 172 530-872 799 people (0.7-3.4%) in December 2024, including 7902-30 002 children aged 0-4 years (0.6-2.2%). The estimated mean labour loss attributable to long COVID in 2022 was projected to be 102.4 million (95% confidence interval [CI], 50.4-162.2 million) worked hours, equivalent to 0.48% (95% CI, 0.24-0.76%) of total worked hours in Australia during the 2020-21 financial year. The estimated mean GDP loss caused by the projected decline in labour supply and reduced use of other production factors was $9.6 billion (95% CI, $4.7-15.2 billion), or 0.5% of GDP. The estimated labour loss was greatest for people aged 30-39 years (27.5 million [95% CI, 16.0-41.0 million] hours; 26.9% of total labour loss) and people aged 40-49 years (24.5 million [95% CI, 12.1-38.7 million] hours; 23.9% of total labour loss).

CONCLUSION

Widespread SARS-CoV-2 infections in Australia mean that even a small proportion of infected people developing long COVID-related illness and disability could have important population health and economic effects. A paradigm shift is needed, from a sole focus on the immediate effects of coronavirus disease 2019 (COVID-19) to preventing and treating COVID-19 and treating long COVID, with implications for vaccine and antiviral policy and other mitigation of COVID-19.

Continuous Monitoring of Heart Rate Variability in Free-Living Conditions Using Wearable Sensors: Exploratory Observational Study

BACKGROUND

Wearable physiological monitoring devices are promising tools for remote monitoring and early detection of potential health changes of interest. The widespread adoption of such an approach across communities and over long periods of time will require an automated data platform for collecting, processing, and analyzing relevant health information.

OBJECTIVE

In this study, we explore prospective monitoring of individual health through an automated data collection, metrics extraction, and health anomaly analysis pipeline in free-living conditions over a continuous monitoring period of several months with a focus on viral respiratory infections, such as influenza or COVID-19.

METHODS

A total of 59 participants provided smartwatch data and health symptom and illness reports daily over an 8-month window. Physiological and activity data from photoplethysmography sensors, including high-resolution interbeat interval (IBI) and step counts, were uploaded directly from Garmin Fenix 6 smartwatches and processed automatically in the cloud using a stand-alone, open-source analytical engine. Health risk scores were computed based on a deviation in heart rate and heart rate variability metrics from each individual's activity-matched baseline values, and scores exceeding a predefined threshold were checked for corresponding symptoms or illness reports. Conversely, reports of viral respiratory illnesses in health survey responses were also checked for corresponding changes in health risk scores to qualitatively assess the risk score as an indicator of acute respiratory health anomalies.

RESULTS

The median average percentage of sensor data provided per day indicating smartwatch wear compliance was 70%, and survey responses indicating health reporting compliance was 46%. A total of 29 elevated health risk scores were detected, of which 12 (41%) had concurrent survey data and indicated a health symptom or illness. A total of 21 influenza or COVID-19 illnesses were reported by study participants; 9 (43%) of these reports had concurrent smartwatch data, of which 6 (67%) had an increase in health risk score.

CONCLUSIONS

We demonstrate a protocol for data collection, extraction of heart rate and heart rate variability metrics, and prospective analysis that is compatible with near real-time health assessment using wearable sensors for continuous monitoring. The modular platform for data collection and analysis allows for a choice of different wearable sensors and algorithms. Here, we demonstrate its implementation in the collection of high-fidelity IBI data from Garmin Fenix 6 smartwatches worn by individuals in free-living conditions, and the prospective, near real-time analysis of the data, culminating in the calculation of health risk scores. To our knowledge, this study demonstrates for the first time the feasibility of measuring high-resolution heart IBI and step count using smartwatches in near real time for respiratory illness detection over a long-term monitoring period in free-living conditions.

Development of a SARS-CoV-2 viral dynamic model for patients with COVID-19 based on the amount of viral RNA and viral titer

The target-cell limited model, which is one of the mathematical modeling approaches providing a quantitative understanding of viral dynamics, has been applied to describe viral RNA profiles of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in previous studies. However, these models have been developed mainly using patient data from the early phase of the pandemic. Furthermore, no reports focused on the profiles of the viral titer. In this study, the dynamics of both viral RNA and viral titer were characterized using data reflecting the current clinical situation in which the Omicron variant has become epidemic and vaccines for SARS-CoV-2 have become available. Consecutive data for 5212 viral RNA levels and 5216 viral titers were obtained from 720 patients with coronavirus disease 2019 (COVID-19) in a phase II/III study for ensitrelvir. Our model assumed that productively infected cells would produce only infectious viruses, which could be transformed into non-infectious viruses, and has been used to describe the dynamics of both viral RNA levels and viral titer. The time from infection to symptom onset (tinf) of unvaccinated patients was estimated to be 3.0 days, which was shorter than that of the vaccinated patients. The immune-related parameter as a power function for the vaccinated patients was 1.1 times stronger than that for the unvaccinated patients. Our model allows the prediction of the viral dynamics in patients with COVID-19 from the time of infection to symptom onset. Vaccination status was identified as a factor influencing tinf and the immune function.

A proactive/reactive mass screening approach with uncertain symptomatic cases

We study the problem of mass screening of heterogeneous populations under limited testing budget. Mass screening is an essential tool that arises in various settings, e.g., the COVID-19 pandemic. The objective of mass screening is to classify the entire population as positive or negative for a disease as efficiently and accurately as possible. Under limited budget, testing facilities need to allocate a portion of the budget to target sub-populations (i.e., proactive screening) while reserving the remaining budget to screen for symptomatic cases (i.e., reactive screening). This paper addresses this decision problem by taking advantage of accessible population-level risk information to identify the optimal set of sub-populations for proactive/reactive screening. The framework also incorporates two widely used testing schemes: Individual and Dorfman group testing. By leveraging the special structure of the resulting bilinear optimization problem, we identify key structural properties, which in turn enable us to develop efficient solution schemes. Furthermore, we extend the model to accommodate customized testing schemes across different sub-populations and introduce a highly efficient heuristic solution algorithm for the generalized model. We conduct a comprehensive case study on COVID-19 in the US, utilizing geographically-based data. Numerical results demonstrate a significant improvement of up to 52% in total misclassifications compared to conventional screening strategies. In addition, our case study offers valuable managerial insights regarding the allocation of proactive/reactive measures and budget across diverse geographic regions.

Immune responses in COVID-19 patients: Insights into cytokine storms and adaptive immunity kinetics

SARS-CoV-2 infection can trigger cytokine storm in some patients, which characterized by an excessive production of cytokines and chemical mediators. This hyperactive immune response may cause significant tissue damage and multiple organ failure (MOF). The severity of COVID-19 correlates with the intensity of cytokine storm, involving elements such as IFN, NF-κB, IL-6, HMGB1, etc. It is imperative to rapidly engage adaptive immunity to effectively control the disease progression. CD4+ T cells facilitate an immune response by improving B cells in the production of neutralizing antibodies and activating CD8+ T cells, which are instrumental in eradicating virus-infected cells. Meanwhile, antibodies from B cells can neutralize virus, obstructing further infection of host cells. In individuals who have recovered from the disease, virus-specific antibodies and memory T cells were observed, which could confer a level of protection, reducing the likelihood of re-infection or attenuating severity. This paper discussed the roles of macrophages, IFN, IL-6 and HMGB1 in cytokine release syndrome (CRS), the intricacies of adaptive immunity, and the persistence of immune memory, all of which are critical for the prevention and therapeutic strategies against COVID-19.

Combining genomic data and infection estimates to characterize the complex dynamics of SARS-CoV-2 Omicron variants in the US

Omicron surged as a variant of concern in late 2021. Several distinct Omicron variants appeared and overtook each other. We combined variant frequencies and infection estimates from a nowcasting model for each US state to estimate variant-specific infections, attack rates, and effective reproduction numbers (Rt). BA.1 rapidly emerged, and we estimate that it infected 47.7% of the US population before it was replaced by BA.2. We estimate that BA.5 infected 35.7% of the US population, persisting in circulation for nearly 6 months. Other variants-BA.2, BA.4, and XBB-together infected 30.7% of the US population. We found a positive correlation between the state-level BA.1 attack rate and social vulnerability and a negative correlation between the BA.1 and BA.2 attack rates. Our findings illustrate the complex interplay between viral evolution, population susceptibility, and social factors during the Omicron emergence in the US.

Insights into SARS-CoV-2 Surveillance among Prison Populations in Mato Grosso do Sul, Brazil, in 2022

This study examines the epidemiological and genomic characteristics, along with the transmission dynamics, of SARS-CoV-2 within prison units I and II in Campo Grande, Mato Grosso do Sul, Brazil. Conducted between May and October 2022, it reveals how the virus spreads in the confined settings of prisons, emphasizing the roles of overcrowded cells, frequent transfers, and limited healthcare access. The research involved 1927 participants (83.93% of the total prison population) and utilized nasopharyngeal swabs and RT-qPCR testing for detection. Contact tracing monitored exposure within cells. Out of 2108 samples, 66 positive cases were identified (3.13%), mostly asymptomatic (77.27%), with the majority aged 21-29 and varying vaccination statuses. Next-generation sequencing generated 28 whole genome sequences, identifying the Omicron variant (subtypes BA.2 and BA.5) with 99% average coverage. Additionally, the study seeks to determine the relationship between immunization levels and the incidence of SARS-CoV-2 cases within this enclosed population. The findings underscore the necessity of comprehensive control strategies in prisons, including rigorous screening, isolation protocols, vaccination, epidemiological monitoring, and genomic surveillance to mitigate disease transmission and protect both the incarcerated population and the broader community.

Knowledge and Practice of Precautions and Prevention of COVID-19 Among Adolescents in Umuahia, Nigeria: A Cross-Sectional Study

Background The coronavirus disease 2019 (COVID-19) pandemic has posed significant challenges worldwide, particularly in developing countries with limited healthcare resources. Among the various demographics, adolescents represent a crucial group in understanding and curbing the spread of the virus. Aim This research aims to assess the level of knowledge and practice of COVID-19 prevention measures among adolescents in a developing country. Methodology This study's descriptive cross-sectional study design was appropriate for capturing a snapshot of adolescents' understanding of COVID-19 in Umuahia, Abia State, Nigeria, a developing country. The study's participants were secondary school students in Umuahia's urban and rural secondary schools, aged 10-19. Fisher's formula was used to calculate the sample size. A multistage systematic sampling method was used to select 424 participants. Data were gathered using a self-administered questionnaire. The Statistical Package for the Social Sciences (SPSS) software version 25 (IBM SPSS Statistics, Armonk, NY) was used to handle and evaluate the data that had been obtained. Results All the respondents were aware of COVID-19, primarily informed through social media and television (TV). It is concerning that less than half correctly identify the disease as caused by a virus (46.9%), with some falsely attributing it to bacteria (31.1%) or fungi (15.6%). While the majority of respondents knew about the existence of COVID-19 vaccines, only 63.4% were aware that there was no definitive cure for the disease. The most worrying finding was the limited awareness and practice of recommended precautions to prevent the spread of COVID-19. Handwashing with soap and water, social distancing, and using hand sanitizers were the most frequently recognized precautions. However, even these were poorly practiced, with less than 30% of the respondents following them regularly. It was also noted that misconceptions about precautions exist, including unusual practices such as regularly drinking hot water or eating bitter kola/pepperfruit. Also, the most prominent reason for refusal among the respondents for COVID-19 vaccines was parental refusal, accounting for 57.5% of negative responses. Conclusion The study's findings underscore the urgent need for tailored, accessible, and effective health education strategies to improve adolescents' understanding and adoption of COVID-19 preventive measures in the region.

Prediction equations for detecting COVID-19 infection using basic laboratory parameters

Objectives

Coronavirus disease 2019 (COVID-19) emerged as a global pandemic during 2019 to 2022. The gold standard method of detecting this disease is reverse transcription-polymerase chain reaction (RT-PCR). However, RT-PCR has a number of shortcomings. Hence, the objective is to propose a cheap and effective method of detecting COVID-19 infection by using machine learning (ML) techniques, which encompasses five basic parameters as an alternative to the costly RT-PCR.

Materials and Methods

Two machine learning-based predictive models, namely, Artificial Neural Network (ANN) and Multivariate Adaptive Regression Splines (MARS), are designed for predicting COVID-19 infection as a cheaper and simpler alternative to RT-PCR utilizing five basic parameters [i.e., age, total leucocyte count, red blood cell count, platelet count, C-reactive protein (CRP)]. Each of these parameters was studied, and correlation is drawn with COVID-19 diagnosis and progression. These laboratory parameters were evaluated in 171 patients who presented with symptoms suspicious of COVID-19 in a hospital at Kharagpur, India, from April to August 2022. Out of a total of 171 patients, 88 and 83 were found to be COVID-19-negative and COVID-19-positive, respectively.

Results

The accuracies of the predicted class are found to be 97.06% and 91.18% for ANN and MARS, respectively. CRP is found to be the most significant input parameter. Finally, two predictive mathematical equations for each ML model are provided, which can be quite useful to detect the COVID-19 infection easily.

Conclusion

It is expected that the present study will be useful to the medical practitioners for predicting the COVID-19 infection in patients based on only five very basic parameters.

Mathematical Assessment of the Role of Human Behavior Changes on SARS-CoV-2 Transmission Dynamics in the United States

The COVID-19 pandemic has not only presented a major global public health and socio-economic crisis, but has also significantly impacted human behavior towards adherence (or lack thereof) to public health intervention and mitigation measures implemented in communities worldwide. This study is based on the use of mathematical modeling approaches to assess the extent to which SARS-CoV-2 transmission dynamics is impacted by population-level changes of human behavior due to factors such as (a) the severity of transmission (such as disease-induced mortality and level of symptomatic transmission), (b) fatigue due to the implementation of mitigation interventions measures (e.g., lockdowns) over a long (extended) period of time, (c) social peer-pressure, among others. A novel behavior-epidemiology model, which takes the form of a deterministic system of nonlinear differential equations, is developed and fitted using observed cumulative SARS-CoV-2 mortality data during the first wave in the United States. The model fits the observed data, as well as makes a more accurate prediction of the observed daily SARS-CoV-2 mortality during the first wave (March 2020-June 2020), in comparison to the equivalent model which does not explicitly account for changes in human behavior. This study suggests that, as more newly-infected individuals become asymptomatically-infectious, the overall level of positive behavior change can be expected to significantly decrease (while new cases may rise, particularly if asymptomatic individuals have higher contact rate, in comparison to symptomatic individuals).

Agent-based modeling to estimate the impact of lockdown scenarios and events on a pandemic exemplified on SARS-CoV-2

In actual pandemic situations like COVID-19, it is important to understand the influence of single mitigation measures as well as combinations to create most dynamic impact for lockdown scenarios. Therefore we created an agent-based model (ABM) to simulate the spread of SARS-CoV-2 in an abstract city model with several types of places and agents. In comparison to infection numbers in Germany our ABM could be shown to behave similarly during the first wave. In our model, we implemented the possibility to test the effectiveness of mitigation measures and lockdown scenarios on the course of the pandemic. In this context, we focused on parameters of local events as possible mitigation measures and ran simulations, including varying size, duration, frequency and the proportion of events. The majority of changes to single event parameters, with the exception of frequency, showed only a small influence on the overall course of the pandemic. By applying different lockdown scenarios in our simulations, we could observe drastic changes in the number of infections per day. Depending on the lockdown strategy, we even observed a delayed peak in infection numbers of the second wave. As an advantage of the developed ABM, it is possible to analyze the individual risk of single agents during the pandemic. In contrast to standard or adjusted ODEs, we observed a 21% (with masks) / 48% (without masks) increased risk for single reappearing participants on local events, with a linearly increasing risk based on the length of the events.

Long COVID in Brain Health Research: A Call to Action

The COVID-19 pandemic has brought attention to the long-term consequences of the virus, particularly the persistent symptoms that characterize long COVID. This syndrome, which can last for months after the initial infection, includes a range of neurological and neuropsychiatric manifestations that have significant implications for brain health and dementia research. This review explores the current understanding of long COVID's cognitive, neurological, and psychiatric symptoms and their potential impact on brain stimulation and neuroimaging studies. It argues that researchers must adapt their study designs and screening processes to account for the confounding effects of long COVID and ensure the accuracy and reliability of their findings. To advance the understanding of this condition and its long-term effects on brain health, the review proposes a series of strategies, including the development of standardized screening tools, the investigation of underlying mechanisms, and the identification of risk factors and protective factors. It also emphasizes the importance of collaborative research efforts and international data sharing platforms in accelerating the pace of discovery and developing targeted interventions for individuals with long COVID. As the prevalence of this condition continues to grow, it is imperative that the neuroscience community comes together to address this challenge and support those affected by long COVID.

Prevalence, under-reporting, and epidemiological surveillance of COVID-19 in the Araguaína City of Brazil

Asymptomatic and underreported individuals remain a source of coronafig disease 2019 (COVID-19) transmission to others. Data on the prevalence and epidemiological factors influencing transmission are fundamental for establishing control measures, especially in vulnerable regions such as the Amazon. This study aimed to determine the point prevalence and active infection of COVID-19 among the population in Araguaína, a Brazilian city located in the Amazon region, analyzed the socioeconomic and behavioral variables of a statistically representative sample of this population using an epidemiological survey, and identify the viral genomic diversity in the region. During the sixth epidemiological week of 2021 (February 8 to 12), samples of 497 inhabitants of the municipality asymptomatic for respiratory syndromes underwent reverse transcription-quantitative polymerase chain reaction and serological tests (immunoglobulin M and immunoglobulin G). A questionnaire collated data on socioeconomic factors, prevention measures, and health status history. The active infection rate was 6.2%, and the prevalence was 13.5% of the study population. Active infection cases were under-reported; each reported positive case represented 14-28 under-reported cases. Lineages P.2, P.1, and B.1.1 were detected. Working from home was a protective factor against the infection, and clinical signs of fever, dry cough, and loss of taste or smell were associated with testing positive (p <0.05). A descriptive analysis of the indicators revealed that the entire population was susceptible to the disease. Intensified vaccination strategies are required regardless of socioeconomic factors, health conditions, and preventive measures. Implementation of objective, comprehensive, and efficient management tools to minimize the spread of COVID-19 in this municipality can serve as a model for other regions of Brazil.

Estimating geographic variation of infection fatality ratios during epidemics

Objectives

We aim to estimate geographic variability in total numbers of infections and infection fatality ratios (IFR; the number of deaths caused by an infection per 1,000 infected people) when the availability and quality of data on disease burden are limited during an epidemic.

Methods

We develop a noncentral hypergeometric framework that accounts for differential probabilities of positive tests and reflects the fact that symptomatic people are more likely to seek testing. We demonstrate the robustness, accuracy, and precision of this framework, and apply it to the United States (U.S.) COVID-19 pandemic to estimate county-level SARS-CoV-2 IFRs.

Results

The estimators for the numbers of infections and IFRs showed high accuracy and precision; for instance, when applied to simulated validation data sets, across counties, Pearson correlation coefficients between estimator means and true values were 0.996 and 0.928, respectively, and they showed strong robustness to model misspecification. Applying the county-level estimators to the real, unsimulated COVID-19 data spanning April 1, 2020 to September 30, 2020 from across the U.S., we found that IFRs varied from 0 to 44.69, with a standard deviation of 3.55 and a median of 2.14.

Conclusions

The proposed estimation framework can be used to identify geographic variation in IFRs across settings.

COVID-19 pandemic - Cocktail of variants, a study from Northern India

Context

The aim of the study was to identify and monitor the circulating strains of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the samples received at our center and update the existing national and international genomic surveillance data.

Introduction

SARS-CoV-2 is no exception to the basic nature of the viruses ability to change and evolve. Since its first report in December 2019 from Wuhan, China, multiple variants of the virus have emerged and been reported. Five variants of concern have been recognized and reported by the Centers for Disease Control and Prevention, which are associated with variable degrees of transmissibility and mortality.

Materials and Methods

Nasopharyngeal and oropharyngeal swabs received in viral transport medium at the Viral Research Diagnostic Laboratory were processed for reverse transcription-polymerase chain reaction for SARS-CoV-2. Whole genome sequencing (WGS) was performed for selective positive samples using Oxford Nanopore sequencing technology, using MinKNOW software for data acquisition.

Statistical Analysis

The clades were assigned using Nextclade v2.4.1 software. The statistical analysis was calculated using OpenEpi version 3, an open-source calculator, and two by two.

Results

Variants reported over the study period included Alpha, Kappa, Delta, and Omicron. Delta dominated in the year 2021, while Omicron was the dominant variant in 2022. In both the dominant variants, asymptomatics contributed to around 30-40% of positives. Intensive care unit admissions and mortality were higher in the Delta variant, while vaccination history and travel history were higher in the patients with Omicron variant.

Conclusion

The trend tracking of these variants has been important in view of public health, enabling early interventions to control the spread of the disease and foresight in preparation for the situation.

Genetic features of SARS-CoV-2 Alpha, Delta, and Omicron variants and their association with the clinical severity of COVID-19 in Vietnam

Objectives

We investigated the genetic variations in the Alpha, Delta, and Omicron variants of SARS-CoV-2 and their association with clinical status and treatment outcomes in patients with COVID-19.

Methods

MiSeq was used to sequence the Alpha, Delta, and Omicron genomes, and MEGA 6.6 was used to define the nucleotide variations. We determined the association between clinical severity and treatment outcomes for the SARS-CoV-2 variants.

Results

The BA.1.1 and BA.2 lineages of the Omicron variant had 57-59 mutations, which is 2-2.7-fold higher than that of the B.1.1.7 (Alpha), B.1.617.2, and AY.57 (Delta) lineages. We found distinct mutations in SARS-CoV-2: five in Alpha (C26305T, G26558T, G7042T, C14120T, and C27509T); seven in Delta (C26408T, C1403T, C5184T, C9891T, T11418C, C11514T, and C22227T); and three in Omicron (C26408T, C8991T, and C25810T). Patients with the Delta variant had a severe rate of 23.8%, a critical rate of 53.7%, and a mortality rate of 38.9%, which were significantly higher than those with the Omicron and Alpha variants.

Conclusions

The Alpha, Delta, and Omicron variants in this study had genetic diversity and differed from the strains reported in other countries, with the Delta variant producing significantly more clinical severity and mortality than the Alpha and Omicron variants.

COVID-19 transmission between the community and meat processing plants in Ireland: A retrospective modelling study

Outbreaks of COVID-19 in meat processing plants (MPPs) were recorded globally throughout the pandemic. There was speculation these outbreaks resulted in dissemination of COVID-19 throughout the surrounding county leading to high incidence rates. We aimed to investigate the dynamics of spread between MPPs and their surrounding counties. In this retrospective longitudinal study, data were collected on the number and size of outbreaks in 33 MPPs and county infections in Ireland between March 2020 and May 2021. These data were used to investigate the relationship between outbreaks in MPPs and county infection rates through statistical analysis, and the development of a novel SEIR model. We found an association between the number of MPPs present in a county and county incidence rates, however, incidence rates in the counties did not increase as a consequence of an outbreak in an MPP. The model results indicate that county incidence rates in the weeks prior to an MPP outbreak could reliably predict the size of that outbreak in a plant, r(49) = 0·62, p < 0·0001, RMSD = 5·6. In Ireland, outbreaks in MPPs were strongly correlated with high levels of infection in the surrounding county, rather than being a driver of infection in the county. The modified SEIR model described here can provide an explanation of the generative process required to cause outbreaks of the size and scale that occur in MPPs.

COVID-19 in the Arab countries: Three-year study

Background

Twenty-two Arab countries share a common language, history, and culture. Nevertheless, governmental policies, healthcare systems, and resources differ from one Arab country to another. We have been following Coronavirus (COVID-19) from the beginning in each Arab country. In the present study, we aimed to assess the prevalence of COVID-19 in the Arab world and to compare these findings with other significantly affected countries.

Methods

Websites of the World Health Organization, World COVID-vaccinations tracker, Worldometer, and Ministries of Health were used to extract COVID-19 data in all Arab countries between the period January 2020 to December 2022.

Results

All Arab countries had 14,218,042 total confirmed COVID-19 cases, 13,384,924 total recovered cases and 173,544 total related deaths. The trend demonstrated that the third quarter of 2021 recorded the highest death toll and the first quarter of 2022 recorded the highest number of confirmed and recovered cases. Compared to the top 15 affected countries, the Arab world ranked last as it had the lowest overall incidence per million population (PMP) of 31,609. The data on total deaths PMP showed that India had the lowest number of deaths with only 377 cases followed by the Arab world with 386 cases.

Conclusions

Although the number of confirmed, death, and recovered cases of COVID-19 have greatly reduced in the last quarter of 2022 in most Arab countries, many Arab countries still need to re-campaign about COVID-19 vaccines and raise awareness programs about boosters. COVID-19 has had a relatively smaller impact on Arab countries than on other countries that have been significantly affected.

A scoping review of global SARS-CoV-2 wastewater-based epidemiology in light of COVID-19 pandemic

Recently, wastewater-based epidemiology (WBE) research has experienced a strong impetus during the Coronavirus disease 2019 (COVID-19) pandemic. However, a few technical issues related to surveillance strategies, such as standardized procedures ranging from sampling to testing protocols, need to be resolved in preparation for future infectious disease outbreaks. This review highlights the study characteristics, potential use of WBE and overview of methods, as well as methods utilized to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) including its variant in wastewater. A literature search was performed electronically in PubMed and Scopus according to PRISMA guidelines for relevant peer-reviewed articles published between January 2020 and March 2022. The search identified 588 articles, out of which 221 fulfilled the necessary criteria and are discussed in this review. Most global WBE studies were conducted in North America (n = 75, 34 %), followed by Europe (n = 68, 30.8 %), and Asia (n = 43, 19.5 %). The review also showed that most of the application of WBE observed were to correlate SARS-CoV-2 ribonucleic acid (RNA) trends in sewage with epidemiological data (n = 90, 40.7 %). The techniques that were often used globally for sample collection, concentration, preferred matrix recovery control and various sample types were also discussed. Overall, this review provided a framework for researchers specializing in WBE to apply strategic approaches to their research questions in achieving better functional insights. In addition, areas that needed more in-depth analysis, data collection, and ideas for new initiatives were identified.

Outcomes of a home telemonitoring program for SARS-CoV-2 viral infection at a large academic medical center

INTRODUCTION

Telemedicine serves as a viable option during the COVID-19 pandemic to provide in-home care, maintain home isolation precautions, reduce unnecessary healthcare exposures, and de-burden hospitals.

METHODS

We created a novel telemedicine program to closely monitor patients infected with SARS-CoV-2 (COVID-19) at home. Adult patients with COVID-19 were enrolled in the program at the time of documented infection. Patients were followed by a team of providers via telephone or video visits at frequent intervals until resolution of their acute illness. Additionally, patients were stratified into high-risk and low-risk categories based on demographics and underlying comorbidities. The primary outcome was hospitalization after enrollment in the home monitoring program, including 30 days after discharge from the program.

RESULTS

Over a 3.5-month period, 1128 patients met criteria for enrollment in the home monitoring program. 30.7% were risk stratified as high risk for poor outcomes based on their comorbidities and age. Of the 1128 patients, 6.2% required hospitalization and 1.2% required ICU admission during the outcome period. Hospitalization was more frequent in patients identified as high risk (14.2% vs 2.7%, P < 0.001).

DISCUSSION

Enrollment in a home monitoring program appears to be an effective and sustainable modality for the ambulatory management of COVID-19.

A robust web-based tool to predict viral shedding in patients with Omicron SARS-CoV-2 variants

Background

Data on viral kinetics and variants affecting the duration of viral shedding were limited. Our objective was to determine viral shedding in distinct severe acute respiratory syndrome coronavirus 2 variants, including Omicron BA.4/5 and BF.7, and to identify the relevant influencing factors.

Methods

We carried out a longitudinal cohort study at Beijing Xiaotangshan Fangcang shelter hospital from May to June 2022 (Omicron BA.4/5) and from November to December 2022 (Omicron BF.7). Nucleocapsid protein (N) and open reading frame (ORF) genes were considered as the target genes of the reverse transcription PCR. The daily results of cycle threshold (CT), including lowest ORF1ab-CT values for days 1-3 post-hospitalisation and lowest N-CT values for days 1-3 post-hospitalisation (CT3minN) and demographic and clinical characteristics were collected.

Results

1433 patients with coronavirus disease 2019 (COVID-19) were recruited from the Fangcang shelter hospital, in which 278 patients were diagnosed with Omicron BA.4/5 and 1155 patients with Omicron BF.7. Patients with BF.7 infection showed a longer duration of viral shedding. The duration of viral shedding was associated with the variants age, alcohol use, the severity of COVID-19 and CT3minN. Moreover, the nomogram had excellent accuracy in predicting viral shedding.

Conclusions

Our results indicated that patients with Omicron BF.7 had a longer period of contagiousness than those with BA.4/5. The duration of viral shedding was affected by a variety of factors and the nomogram may become an applicable clinical instrument to predict viral shedding. Furthermore, we developed a new COVID-19 viral shedding predicting model that can accurately predict the duration of viral shedding for COVID-19, and created a user-friendly website to apply this prediction model (https://puh3.shinyapps.io/CVSP_Model/).

Development of rapid nucleic acid testing techniques for common respiratory infectious diseases in the Chinese population

Background: Most respiratory viruses can cause serious lower respiratory diseases at any age. Therefore, timely and accurate identification of respiratory viruses has become even more important. This study focused on the development of rapid nucleic acid testing techniques for common respiratory infectious diseases in the Chinese population. Methods: Multiplex fluorescent quantitative polymerase chain reaction (PCR) assays were developed and validated for the detection of respiratory pathogens including the novel coronavirus (SARS-CoV-2), influenza A virus (FluA), parainfluenza virus (PIV), and respiratory syncytial virus (RSV). Results: The assays demonstrated high specificity and sensitivity, allowing for the simultaneous detection of multiple pathogens in a single reaction. These techniques offer a rapid and reliable method for screening, diagnosis, and monitoring of respiratory pathogens. Conclusion: The implementation of these techniques might contribute to effective control and prevention measures, leading to improved patient care and public health outcomes in China. Further research and validation are needed to optimize and expand the application of these techniques to a wider range of respiratory pathogens and to enhance their utility in clinical and public health settings.

SARS-CoV-2 genomic surveillance and reliability of PCR single point mutation assay (SNPsig® SARS-CoV-2 EscapePLEX CE) for the rapid detection of variants of concern in Cameroon

Background

Surveillance of SARS-CoV-2 variants of concern (VOCs) and lineages is crucial for decision-making. Our objective was to study the SARS-CoV-2 clade dynamics across epidemiological waves and evaluate the reliability of SNPsig® SARS-CoV-2 EscapePLEX CE in detecting VOCs in Cameroon.

Material and methods

A laboratory-based study was conducted on SARS-CoV-2 positive nasopharyngeal specimens cycle threshold (Ct)≤30 at the Chantal BIYA International Reference Centre in Yaoundé-Cameroon, between April-2020 to August-2022. Samples were analyzed in parallel with Sanger sequencing and (SNPsig® SARS-CoV-2 EscapePLEX CE), and performance characteristics were evaluated by Cohen's coefficient and McNemar test.

Results

Of the 130 sequences generated, SARS-CoV-2 clades during wave-1 (April-November 2020) showed 97 % (30/31) wild-type lineages and 3 % (1/31) Gamma-variant; wave-2 (December-2020 to May-2021), 25 % (4/16) Alpha-variant, 25 % (4/16) Beta-variant, 44 % (7/16) wild-type and 6 % (1/16) mu; wave-3 (June-October 2021), 94 % (27/29) Delta-variant, 3 % (1/29) Alpha-variant, 3 % (1/29) wild-type; wave-4 (November-2021 to August-2022), 98 % (53/54) Omicron-variant and 2 % (1/54) Delta-variant. Omicron sub-variants were BA.1 (47 %), BA.5 (34 %), BA.2 (13 %) and BA.4 (6 %). Globally, the two genotyping methods accurately identified the SARS-CoV-2 VOCs (P = 0.17, McNemar test; Ka = 0.67).

Conclusion

Genomic surveillance reveals a rapid dynamic in SARS-CoV-2 strains between epidemiological waves in Cameroon. For wide-spread variant surveillance in resource-limited settings, SNPsig® SARS-CoV-2 EscapePLEX CEkit represents a suitable tool, pending upgrading for distinguishing Omicron sub-lineages.

Theoretical Investigation of the Green-Synthesized Carbon-Based Nanomaterial Potential as Inhibitors of ACE2 for Blocking SARS-CoV-2 Binding

Since the emergence of SARS-CoV-2 in 2020, the world has faced a global pandemic, emphasizing the urgent need for effective treatments to combat COVID-19. This study explores the use of green-synthesized carbon-based nanomaterials as potential inhibitors of ACE2, a critical receptor for SARS-CoV-2 entry into host cells. Specifically, the study examines four carbon-based nanomaterials, namely, CD1, CD2, CD3, and CD4 in amino, graphitic, pyridinic, and pyrrolic forms, respectively, synthesized from curcumin, to investigate their binding affinity with ACE2. Molecular docking studies revealed that CD3 (pyridinic form) exhibited the highest binding affinity with ACE2, surpassing that of the control compound, curcumin. Notably, CD3 formed hydrophobic interactions and hydrogen bonds with key ACE2 residues, suggesting its potential to block the binding of SARS-CoV-2 to human cells. Moreover, molecular dynamics simulations demonstrated the stability of these ligand-ACE2 complexes, further supporting the promise of CD3 as an inhibitor. Quantum chemical analyses, including frontier molecular orbitals, natural bond orbital analysis, and the quantum theory of atoms in molecules, unveiled valuable insights into the reactivity and interaction strengths of these ligands. CD3 exhibited desirable chemical properties, signifying its suitability for therapeutic development. The study's findings suggest that green-synthesized carbon-based nanomaterials, particularly CD3, have the potential to serve as effective inhibitors of ACE2, offering a promising avenue for the development of treatments against COVID-19. Further experimental validation is warranted to advance these findings and establish new therapies for the ongoing global pandemic.

Proteome profiling of home-sampled dried blood spots reveals proteins of SARS-CoV-2 infections

BACKGROUND

Self-sampling of dried blood spots (DBS) offers new routes to gather valuable health-related information from the general population. Yet, the utility of using deep proteome profiling from home-sampled DBS to obtain clinically relevant insights about SARS-CoV-2 infections remains largely unexplored.

METHODS

Our study involved 228 individuals from the general Swedish population who used a volumetric DBS sampling device and completed questionnaires at home during spring 2020 and summer 2021. Using multi-analyte COVID-19 serology, we stratified the donors by their response phenotypes, divided them into three study sets, and analyzed 276 proteins by proximity extension assays (PEA). After normalizing the data to account for variances in layman-collected samples, we investigated the association of DBS proteomes with serology and self-reported information.

RESULTS

Our three studies display highly consistent variance of protein levels and share associations of proteins with sex (e.g., MMP3) and age (e.g., GDF-15). Studying seropositive (IgG+) and seronegative (IgG-) donors from the first pandemic wave reveals a network of proteins reflecting immunity, inflammation, coagulation, and stress response. A comparison of the early-infection phase (IgM+IgG-) with the post-infection phase (IgM-IgG+) indicates several proteins from the respiratory system. In DBS from the later pandemic wave, we find that levels of a virus receptor on B-cells differ between seropositive (IgG+) and seronegative (IgG-) donors.

CONCLUSIONS

Proteome analysis of volumetric self-sampled DBS facilitates precise analysis of clinically relevant proteins, including those secreted into the circulation or found on blood cells, augmenting previous COVID-19 reports with clinical blood collections. Our population surveys support the usefulness of DBS, underscoring the role of timing the sample collection to complement clinical and precision health monitoring initiatives.

Incidence of neurological manifestations and complications in patients with COVID-19 infection: a propensity score matching analysis

BACKGROUND

The novel coronavirus, SARS-CoV-2, can lead to potential neurological complications in COVID-19 patients. This study aims to assess the prevalence and associated risk factors of these complications among Thai individuals.

METHODS

We conducted a retrospective study on hospitalized adults with COVID-19 at Thammasat University Hospital from April 2020 to September 2021, totaling 990 patients. We used propensity score matching and logistic regression to determine associated risk factors.

RESULTS

Of the patients, 21.7% (215 individuals) manifested neurological complications, categorized as follows: Skeletal muscle injury: 16.3% Acute cerebrovascular disease: 0.8% Delirium: 2.4% Impaired consciousness: 2.6% Seizures: 0.5% Post-matching revealed risk factors significantly associated with neurological complications: overweight status, hypertension, diabetes mellitus, dyslipidemia, and severe infection. However, in a multivariable analysis, only severe infection remained significant (adjusted OR 3.71; p = 0.010). The presence of neurological complications was linked with an elevated mortality risk (OR 1.77, p = 0.005).

CONCLUSIONS

In the Thai COVID-19 patient cohort, roughly 22% experienced neurological complications, with severe infection standing out as a major risk factor. This underscores the importance of closely monitoring neurological symptoms in COVID-19 patients.

Inverse association with COVID-19 vaccination status of the incidence of pneumonia after SARS-CoV-2 infection: A nationwide retrospective cohort study

BACKGROUND

Although one of the characteristics of COVID-19 is accompanied by acute pneumonia immediately after infection, large-scale cohort studies focused on this issue are lacking. In addition, there is interest in how COVID-19 vaccinations reduce the incidence of acute pneumonia for people infected with different strains of SARS-CoV-2. Thus, we assess the short-term incidence of pneumonia after COVID-19 with the vaccination and SARS-CoV-2 variants.

METHODS

As data for 2136,751 COVID-19 patients between January 01, 2020 and February 28, 2022 was collected, they were observed for one month from the day of infection. Patients in retrospective cohort study were classified according to doses of the received vaccine and the epidemic phase when SARS-CoV-2 variants prevailed. Multivariable logistic regression analysis calculated adjusted odds ratios (aOR) and 95% confidence intervals (CIs) for the pneumonia risk.

RESULTS

In B.1.1.7-B.1.351, B.1.617.2, and B.1.617.2 variants, the aORs (95% CIs; p-value) for incidence of pneumonia were 0.93 (0.89-0.98; <0.001), 0.74 (0.70-0.78; <0.001), and 0.04 (0.038-0.043; <0.001), respectively, compared to the original strain. More than 80% of patients have received the second and more doses of the vaccine (average age=44.67 years). The aORs (95% CIs; p-value) for pneumonia were 0.61 (0.58-0.64; <0.001), 0.39 (0.38-0.40; <0.001), and 0.18 (0.166-0.184; <0.001) in patients who received the first (N = 68,216), second (N = 898,838), and ≥ third doses (N = 836,173), respectively, compared to unvaccinated patients. According to the received vaccine (second dose of mRNA or viral vector), those who received BNT162b2 and mRNA-1273 (N = 787,980) had lower risk of pneumonia, compared to that in those who received h ChAdOx1 nCov-19 and AD26. COV2-S (N = 89,024).

CONCLUSIONS

Our findings suggest that the second and ≥ third doses (61% and 82% of risk aversion effect increased, respectively) of the COVID-19 vaccine can prevent the COVID-19-related pneumonia, regardless of the variants.

Early predictors of intensive care unit admission among COVID-19 patients in Qatar

Background

COVID-19 is associated with significant morbidity and mortality. This study aimed to explore the early predictors of intensive care unit (ICU) admission among patients with COVID-19.

Methods

This was a case-control study of adult patients with confirmed COVID-19. Cases were defined as patients admitted to ICU during the period February 29-May 29, 2020. For each case enrolled, one control was matched by age and gender.

Results

A total of 1,560 patients with confirmed COVID-19 were included. Each group included 780 patients with a predominant male gender (89.7%) and a median age of 49 years (interquartile range = 18). Predictors independently associated with ICU admission were cardiovascular disease (adjusted odds ratio (aOR) = 1.64, 95% confidence interval (CI): 1.16-2.32, p = 0.005), diabetes (aOR = 1.52, 95% CI: 1.08-2.13, p = 0.016), obesity (aOR = 1.46, 95% CI: 1.03-2.08, p = 0.034), lymphopenia (aOR = 2.69, 95% CI: 1.80-4.02, p < 0.001), high AST (aOR = 2.59, 95% CI: 1.53-4.36, p < 0.001), high ferritin (aOR = 1.96, 95% CI: 1.40-2.74, p < 0.001), high CRP (aOR = 4.09, 95% CI: 2.81-5.96, p < 0.001), and dyspnea (aOR = 2.50, 95% CI: 1.77-3.54, p < 0.001).

Conclusion

Having cardiovascular disease, diabetes, obesity, lymphopenia, dyspnea, and increased AST, ferritin, and CRP were independent predictors for ICU admission in patients with COVID-19.

Incidence of new-onset hypertension before, during, and after the COVID-19 pandemic: a 7-year longitudinal cohort study in a large population

BACKGROUND

While the augmented incidence of diabetes after COVID-19 has been widely confirmed, controversial results are available on the risk of developing hypertension during the COVID-19 pandemic.

METHODS

We designed a longitudinal cohort study to analyze a closed cohort followed up over a 7-year period, i.e., 3 years before and 3 years during the COVID-19 pandemic, and during 2023, when the pandemic was declared to be over. We analyzed medical records of more than 200,000 adults obtained from a cooperative of primary physicians from January 1, 2017, to December 31, 2023. The main outcome was the new diagnosis of hypertension.

RESULTS

We evaluated 202,163 individuals in the pre-pandemic years and 190,743 in the pandemic years, totaling 206,857 when including 2023 data. The incidence rate of new hypertension was 2.11 (95% C.I. 2.08-2.15) per 100 person-years in the years 2017-2019, increasing to 5.20 (95% C.I. 5.14-5.26) in the period 2020-2022 (RR = 2.46), and to 6.76 (95% C.I. 6.64-6.88) in 2023. The marked difference in trends between the first and the two successive observation periods was substantiated by the fitted regression lines of two Poisson models conducted on the monthly log-incidence of hypertension.

CONCLUSIONS

We detected a significant increase in new-onset hypertension during the COVID-19 pandemic, which at the end of the observation period affected ~ 20% of the studied cohort, a percentage higher than the diagnosis of COVID-19 infection within the same time frame. This observation suggests that increased attention to hypertension screening should not be limited to individuals who are aware of having contracted the infection but should be extended to the entire population.

Patient Characteristics and Public Health Office Factors Associated With Long Reporting Delay of COVID-19 Cases in Sapporo City, Japan

BACKGROUND

For therapeutic efficacy, molnupiravir and nirmatrelvir-ritonavir must be started to treat patients within 5 days of disease onset to treat patients with novel coronavirus disease 2019 (COVID-19). However, some patients spend more than 5 days from disease onset before reporting to the Public Health Office. This study aimed to clarify the characteristics of patients with reporting delay.

METHODS

This study included data from 12,399 patients with COVID-19 who reported to the Public Health Office from March 3rd, 2021 to June 30th, 2021. Patients were stratified into "linked" (n = 7,814) and "unlinked" (n = 4,585) cases depending on whether they were linked to other patients. A long reporting delay was defined as the difference between the onset and reporting dates of 5 days or more. Univariate and multivariate analyses were performed using log-binomial regression to identify factors related to long reporting delay, and prevalence ratios with corresponding 95% confidence intervals were calculated.

RESULTS

The proportion of long reporting delay was 24.4% (1,904/7,814) and 29.3% (1,344/4,585) in linked and unlinked cases, respectively. Risks of long reporting delay among linked cases were living alone and onset on the day with a higher 7-day daily average confirmed cases or onset on weekends; whereas, risks for unlinked cases were age over 65 years, without occupation, and living alone.

CONCLUSION

Our results suggest the necessity to establish a Public Health Office system that is less susceptible to the rapid increase in the number of patients, promotes educational activities for people with fewer social connections, and improves access to health care.

Humoral and cellular immune responses against SARS-CoV-2 post-vaccination in immunocompetent and immunocompromised cancer populations

Cancer patients are at risk for severe coronavirus disease 2019 (COVID-19) outcomes due to impaired immune responses. However, the immunogenicity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination is inadequately characterized in this population. We hypothesized that cancer vs non-cancer individuals would mount less robust humoral and/or cellular vaccine-induced immune SARS-CoV-2 responses. Receptor binding domain (RBD) and SARS-CoV-2 spike protein antibody levels and T-cell responses were assessed in immunocompetent individuals with no underlying disorders (n = 479) and immunocompromised individuals (n = 115). All 594 individuals were vaccinated and of varying COVID-19 statuses (i.e., not known to have been infected, previously infected, or "Long-COVID"). Among immunocompromised individuals, 59% (n = 68) had an underlying hematologic malignancy; of those, 46% (n = 31) of individuals received cancer treatment <30 days prior to study blood collection. Ninety-eight percentage (n = 469) of immunocompetent and 81% (n = 93) of immunocompromised individuals had elevated RBD antibody titers (>1,000 U/mL), and of these, 60% (n = 281) and 44% (n = 41), respectively, also had elevated T-cell responses. Composite T-cell responses were higher in individuals previously infected with SARS-CoV-2 or those diagnosed with Long-COVID compared to uninfected individuals. T-cell responses varied between immunocompetent vs carcinoma (n = 12) cohorts (P < 0.01) but not in immunocompetent vs hematologic malignancy cohorts. Most SARS-CoV-2 vaccinated individuals mounted robust cellular and/or humoral responses, though higher immunogenicity was observed among the immunocompetent compared to cancer populations. The study suggests B-cell targeted therapies suppress antibody responses, but not T-cell responses, to SARS-CoV-2 vaccination. Thus, vaccination continues to be an effective way to induce humoral and cellular immune responses as a likely key preventive measure against infection and/or subsequent more severe adverse outcomes.

IMPORTANCE

The study was prompted by a desire to better assess the immune status of patients among our cancer host cohort, one of the largest in the New York metropolitan region. Hackensack Meridian Health is the largest healthcare system in New Jersey and cared for more than 75,000 coronavirus disease 2019 patients in its hospitals. The John Theurer Cancer Center sees more than 35,000 new cancer patients a year and performs more than 500 hematopoietic stem cell transplants. As a result, the work was undertaken to assess the effectiveness of vaccination in inducing humoral and cellular responses within this demographic.

Clinical Signs and Treatment of New-Onset Bone Marrow Failure Associated SARS-CoV-2 Infection in Children: A Single Institution Prospective Cohort Study

Background

Viral infections can cause direct and indirect damage to hematopoietic stem cells. The objectives of this study were to identify the frequency and severity of aplastic anemia in children infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as well as recognize the response to treatment.

Methodology

13 children with newly diagnosed severe aplastic anemia were enrolled in this prospective clinical trial. Blood samples were obtained from all patients to detect SARS-CoV-2 antibodies, and nasopharyngeal swabs were collected for reverse-transcription Polymerase Chain Reaction to detect SARS-CoV-2 viruses. According to the laboratory results, patients were classified as having SARS-CoV-2 positive antibodies and SARS-CoV-2 negative antibodies. Both groups received combined cyclosporine (CsA) + Eltrombopag (E-PAG). The hematological response, either complete response (CR) or partial response (PR), no response (NR), and overall response (OR) rates of combined E-PAG + CsA treatment after 6 months were evaluated.

Results

Four children were recognized to have aplastic anemia and SARS-CoV-2 positive antibodies. Two patients fulfilled the hematological criteria for CR and no longer required transfusion of packed red blood cells (PRBCs) or platelets, and one had PR and was still PRBC transfusion-dependent but no longer required platelet transfusion. The remaining patient showed NR, and he had died before reaching the top of the HSCT waiting list. Moreover, six patients in the SARS-CoV-2 negative antibodies group had CR, while three patients had PR. The difference in ANC, Hg, and platelet counts between both groups was not significant.

Conclusion

The SARS-CoV-2 virus is added to several viral infections known to be implicated in the pathogenesis of aplastic anemia. Studies are needed to establish a definitive association and determine whether the response of bone marrow failure to standard therapy differs from that of idiopathic cases.

Lung abnormalities on computed tomography of Vietnamese patients with COVID-19 and the association with medical variables

Objectives

Patients with COVID-19 may experience a lung injury without presenting clinical symptoms. Early detection of lung injury in patients with COVID-19 is required to enhance prediction and prevent severe progression.

Methods

Lung lesions in patients with COVID-19 were defined using the Fleischner Society terminology. Chest computed tomography lesions and their correlation with demographic characteristics and medical variables were identified.

Results

Patients with mild and moderate COVID-19 had up to 45% lung injuries, whereas critical patients had 55%. However, patients with mild and moderate COVID-19 typically had low-level lung injuries. Ground-glass (68.1%), consolidation (48.8%), opacity (36.3%), and nodular (6.9%) lung lesions were the most prevalent in patients with COVID-19. Patients with COVID-19 infected with the Delta variant had worse lung injury than those infected with the Alpha and Omicron. People vaccinated with ≥2 doses showed a lower risk of lung injury than those vaccinated with <1 dose. Patients <18 years old were less likely to have a lung injury than patients >18 years old. The treatment outcomes were unaffected by the severity of the lung injury.

Conclusion

Patients with mild COVID-19 had a similar risk of lung injury as patients with severe COVID-19. Thus, using chest computed tomography to detect lung injury can enhance the treatment outcomes and reduce the patient's risk of pulmonary complications.

Outbreaks of COVID-19 in indoor places of worship: a systematic review

AIMS

This review aimed to describe what has been published on COVID-19 outbreaks originating from indoor places of worship.

METHODS

A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist by searching PubMed, Scopus and Embase from 1 January 2020 to 29 March 2021. Citation chasing was also performed. Studies with information about COVID-19 outbreaks originating in indoor places of worship of any religion were included.

RESULTS

A total of 9729 records were identified and 36 were selected. The articles reported 119 descriptions of outbreaks linked to churches, mosques, synagogues, and temples, referring to approximately 52-74 unique outbreaks. The outbreaks were mostly located in three major areas: East and Southeast Asia (46%), the USA (27%), Europe (22%). All the outbreaks began in 2020. Mainly, there were no restrictive measures, or such measures were not followed at the time of the outbreak. Choir practices presented the highest attack rate (up to 0.867).

CONCLUSIONS

The lack of preventive measures and the role of singing practices were highlighted. Reports were often lacking contact tracing and sometimes did not report the date of outbreak extinction. Moreover, reports came from few geographical areas. Thus, the impact of transmission in places of worship may be largely underestimated.

Optimal environmental testing frequency for outbreak surveillance

Public health surveillance for pathogens presents an optimization problem: we require enough sampling to identify intervention-triggering shifts in pathogen epidemiology, such as new introductions or sudden increases in prevalence, but not so much that costs due to surveillance itself outweigh those from pathogen-associated illness. To determine this optimal sampling frequency, we developed a general mathematical model for the introduction of a new pathogen that, once introduced, increases in prevalence exponentially. Given the relative cost of infection vs. sampling, we derived equations for the expected combined cost per unit time of disease burden and surveillance for a specified sampling frequency, and thus the sampling frequency for which the expected total cost per unit time is lowest.

Wording the trajectory of the three-year COVID-19 epidemic in a general population - Belgium

The trajectory of COVID-19 epidemic waves in the general population of Belgium was analysed by defining quantitative criteria for epidemic waves from March 2020 to early 2023. Peaks and starting/ending times characterised nine waves numerated I to IX based on the daily reported incidence number (symbol INCID) and three "endemic" interval periods between the first four waves. The SIR compartmental model was applied to the first epidemic wave by fitting the daily prevalence pool (symbol I) calculated as the sum of the daily incidence rate and estimated number of subjects still infectious from the previous days. The basic reproductive number R0 was calculated based on the exponential growth rate during the early phase and on medical literature knowledge of the time of generation of SARS-CoV-2 infection. The first COVID-19 wave was well fitted by an open SIR model. According to this approach, dampened recurrent epidemic waves evolving through an endemic state would have been expected. This was not the case with the subsequent epidemic waves being characterised by new variants of concern (VOC). Evidence-based observations: 1) each epidemic wave affected less than a fifth of the general population; 2) the Vth epidemic wave (VOC Omicron) presented the greatest amplitude. The lack of recurrence of the same VOC during successive epidemic waves strongly suggests that a VOC has a limited persistence, disappearing from the population well before the expected proportion of the theoretical susceptible cohort being maximally infected. Fitting the theoretical SIR model, a limited persistence of VOCs in a population could explain that new VOCs replace old ones, even if the new VOC has a lower transmission rate than the preceding one. In conclusion, acquisition of potential defective mutations in VOC during an epidemic wave is a potential factor explaining the absence of resurgence of a same VOC during successive waves. Such an hypothesis is open to discussion and to rebuttal. A modified SIR model with epidemic waves of variable amplitude related not only to R0 and public health measures but also to acquisition of defective fitting in virus within a population should be tested.

Estimating the Epidemic Size of Superspreading Coronavirus Outbreaks in Real Time: Quantitative Study

BACKGROUND

Novel coronaviruses have emerged and caused major epidemics and pandemics in the past 2 decades, including SARS-CoV-1, MERS-CoV, and SARS-CoV-2, which led to the current COVID-19 pandemic. These coronaviruses are marked by their potential to produce disproportionally large transmission clusters from superspreading events (SSEs). As prompt action is crucial to contain and mitigate SSEs, real-time epidemic size estimation could characterize the transmission heterogeneity and inform timely implementation of control measures.

OBJECTIVE

This study aimed to estimate the epidemic size of SSEs to inform effective surveillance and rapid mitigation responses.

METHODS

We developed a statistical framework based on back-calculation to estimate the epidemic size of ongoing coronavirus SSEs. We first validated the framework in simulated scenarios with the epidemiological characteristics of SARS, MERS, and COVID-19 SSEs. As case studies, we retrospectively applied the framework to the Amoy Gardens SARS outbreak in Hong Kong in 2003, a series of nosocomial MERS outbreaks in South Korea in 2015, and 2 COVID-19 outbreaks originating from restaurants in Hong Kong in 2020.

RESULTS

The accuracy and precision of the estimation of epidemic size of SSEs improved with longer observation time; larger SSE size; and more accurate prior information about the epidemiological characteristics, such as the distribution of the incubation period and the distribution of the onset-to-confirmation delay. By retrospectively applying the framework, we found that the 95% credible interval of the estimates contained the true epidemic size after 37% of cases were reported in the Amoy Garden SARS SSE in Hong Kong, 41% to 62% of cases were observed in the 3 nosocomial MERS SSEs in South Korea, and 76% to 86% of cases were confirmed in the 2 COVID-19 SSEs in Hong Kong.

CONCLUSIONS

Our framework can be readily integrated into coronavirus surveillance systems to enhance situation awareness of ongoing SSEs.

Real-Time Epidemiology and Acute Care Need Monitoring and Forecasting for COVID-19 via Bayesian Sequential Monte Carlo-Leveraged Transmission Models

COVID-19 transmission models have conferred great value in informing public health understanding, planning, and response. However, the pandemic also demonstrated the infeasibility of basing public health decision-making on transmission models with pre-set assumptions. No matter how favourably evidenced when built, a model with fixed assumptions is challenged by numerous factors that are difficult to predict. Ongoing planning associated with rolling back and re-instituting measures, initiating surge planning, and issuing public health advisories can benefit from approaches that allow state estimates for transmission models to be continuously updated in light of unfolding time series. A model being continuously regrounded by empirical data in this way can provide a consistent, integrated depiction of the evolving underlying epidemiology and acute care demand, offer the ability to project forward such a depiction in a fashion suitable for triggering the deployment of acute care surge capacity or public health measures, and support quantitative evaluation of tradeoffs associated with prospective interventions in light of the latest estimates of the underlying epidemiology. We describe here the design, implementation, and multi-year daily use for public health and clinical support decision-making of a particle-filtered COVID-19 compartmental model, which served Canadian federal and provincial governments via regular reporting starting in June 2020. The use of the Bayesian sequential Monte Carlo algorithm of particle filtering allows the model to be regrounded daily and adapt to new trends within daily incoming data-including test volumes and positivity rates, endogenous and travel-related cases, hospital census and admissions flows, daily counts of dose-specific vaccinations administered, measured concentration of SARS-CoV-2 in wastewater, and mortality. Important model outputs include estimates (via sampling) of the count of undiagnosed infectives, the count of individuals at different stages of the natural history of frankly and pauci-symptomatic infection, the current force of infection, effective reproductive number, and current and cumulative infection prevalence. Following a brief description of the model design, we describe how the machine learning algorithm of particle filtering is used to continually reground estimates of the dynamic model state, support a probabilistic model projection of epidemiology and health system capacity utilization and service demand, and probabilistically evaluate tradeoffs between potential intervention scenarios. We further note aspects of model use in practice as an effective reporting tool in a manner that is parameterized by jurisdiction, including the support of a scripting pipeline that permits a fully automated reporting pipeline other than security-restricted new data retrieval, including automated model deployment, data validity checks, and automatic post-scenario scripting and reporting. As demonstrated by this multi-year deployment of the Bayesian machine learning algorithm of particle filtering to provide industrial-strength reporting to inform public health decision-making across Canada, such methods offer strong support for evidence-based public health decision-making informed by ever-current articulated transmission models whose probabilistic state and parameter estimates are continually regrounded by diverse data streams.

Realistic Estimation of COVID-19 Infection by Seroprevalence Surveillance of SARS-CoV-2 Antibodies: An Experience From Korea Metropolitan Area From January to May 2022

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread globally, leading to the coronavirus disease 2019 (COVID-19) pandemic. Because a significant proportion of the COVID-19 confirmed cases were concentrated in the capital metropolitan area of South Korea, and a large proportion of the population in the area had been adequately vaccinated against COVID-19, we conducted a seroprevalence surveillance study focusing on the residents of the capital metropolitan area in South Korea.

METHODS

We used a quota-sampling method to obtain blood samples from 1,000 individuals per round, equally stratified across seven age categories and sexes and regions, from five medical institutions located within the capital metropolitan area of South Korea. During five consecutive months (rounds) between January 2022 and May 2022, a total of 5,000 samples were analyzed for anti-spike (S) and anti-nucleocapsid (N) antibodies.

RESULTS

High anti-S seropositivity was observed in all age groups, which corresponded to the vaccine coverage during the study period. Both the cumulative incidence based on polymerase chain reaction (PCR) and the estimated seroprevalence based on anti-N seropositivity increased in the fourth and fifth rounds, which corresponded to April 2022 and May 2022. Seroprevalence coincided with the cumulative incidence during the first three rounds, but exceeded from the fourth survey onwards when infection with omicron variants was increased rapidly in Korea.

CONCLUSION

Seroprevalence confirmed the number of infection cases outside of PCR testing-based surveillance. Seroepidemiological surveillance can help us understand vaccine responses and detect hidden infections, thereby providing appropriate public health guidance for achieving population-level immunity.

Inferring Incidence of Unreported SARS-CoV-2 Infections Using Seroprevalence of Open Reading Frame 8 Antigen, Hong Kong

We tested seroprevalence of open reading frame 8 antigens to infer the number of unrecognized SARS-CoV-2 Omicron infections in Hong Kong during 2022. We estimate 33.6% of the population was infected, 72.1% asymptomatically. Surveillance and control activities during large-scale outbreaks should account for potentially substantial undercounts.

SARS-CoV-2 infection and seropositivity among household contacts of laboratory confirmed cases of COVID-19 in residents of Delhi, India

The transmission of respiratory pathogens, including SARS-CoV-2, is often facilitated through household contact. To better understand the transmission rate of COVID-19 among households and factors that affect viral clearance and seroconversion, a case-ascertained community-based prospective study was conducted between December 2020 and June 2021 on the urban population of the national capital region of India. The study collected nasopharyngeal swabs for SARS-CoV-2 RT-PCR on the 1st, 7th, 14th, and 28th day, and blood samples for antibody detection on the 1st, 14th, and 28th day from household contacts (HCs) of laboratory-confirmed COVID-19 cases. The study monitored the demographic data, symptoms, and outcomes of 417 participants, including 99 index cases and 318 contacts, for a period of 28 days. The results of the study showed that SARS-CoV-2 was easily spread within households, with a secondary infection rate of 44.3 %. In fact, almost 70 % of the contacts got infected within 1-2 days of identification of the index case, while 34 % remained asymptomatic. Sero-conversion was found in 35.6 % of the participants while 22.9 % did not produce antibodies after 28 days of infection. The study also revealed that females, spouses, older members, and primary care providers were at higher risk of getting infected in a home setting. However, approximately one-third of individuals in the younger age group managed to avoid infection. The study demonstrated that most infected individuals became RT-PCR negative within two weeks, although viral clearance was delayed in older patients and those with lower cycle threshold values in RT-PCR.