Estimating the herd immunity threshold by accounting for the hidden asymptomatics using a COVID-19 specific model

Key Attributes and Clusters of the Korean Exercise Healthcare Industry Viewed through Big Data: Comparison before and after the COVID-19 Pandemic

This study aims to predict the characteristics of the exercise healthcare industry in the post-pandemic era by comparing the periods before and after the coronavirus disease 2019 outbreak through big data analysis. TEXTOM, the Korean big data collection and analysis solution, was used for data collection. The pre-pandemic period was defined as 1 January 2018-31 December 2019 and the pandemic period as 1 January 2020-31 December 2021. The keywords for data collection were "exercise + healthcare + industry". Text mining and social network analysis were conducted to determine the overall characteristics of the Korean exercise healthcare industry. We identified 30 terms that appeared most frequently on social media. Four common (smart management, future technology, fitness, and research) and six different clusters (sports education, exercise leader, rehabilitation, services, business, and COVID-19) were obtained for the pre-pandemic and pandemic periods. Smart management, future technology, fitness, and research are still important values across both periods. The results provide meaningful data and offer valuable insights to explore the changing trends in exercise healthcare.

The Transmission Dynamics of a Compartmental Epidemic Model for COVID-19 with the Asymptomatic Population via Closed-Form Solutions

Unlike previous viral diseases, COVID-19 has an "asymptomatic" group that has no symptoms but can still spread the disease to others at the same rate as symptomatic patients who are infected. In the literature, the mass action or standard incidence rates are considered for compartmental models with asymptomatic compartment for studying the transmission dynamics of COVID-19, but the quarantined adjusted incidence rate is not. To bridge this gap, we developed a Susceptible Asymptomatic Infectious Quarantined (SAIQ) model with a Quarantine-Adjusted (QA) incidence to investigate the emergence and containment of COVID-19. COVID-19 models are investigated using various methods, but only a few studies take into account closed-form solutions. The knowledge of closed-form solutions simplifies the construction of the various epidemic indicators that describe the epidemic phenomenon and makes the sensitivity analysis to variations in the data under consideration possible. The closed-form solutions of the systems of four nonlinear first-order ordinary differential equations (ODEs) are established. The Epidemic Peak (EP), Force of Infection (FOI) and Rate of Infection (ROI) are the important indicators for the control and prevention of disease. We examined these indicators using closed-form solutions and particular parameter values. Different disease control scenarios are thoroughly examined. The four scenarios to analyze COVID-19 propagation and containment are (i) lockdown, (ii) quarantine and other preventative measures, (iii) stabilizing the basic reproduction rate to a level where the pandemic can be contained and (iv) containing the epidemic through an appropriate combination of lockdown, quarantine and other preventative measures.

Mathematical modeling of SARS-nCoV-2 virus in Tamil Nadu, South India

The purpose of this paper is to build a mathematical model for the study of the roles of lock-down, social distancing, vaccination, detection efficiency, and health care capacity planning of the COVID-19 pandemic taking into account the demographic topology of the State of Tamil Nadu, India. Two mathematical models are proposed for the evolution of the first and second wave of COVID-19 pandemic. The model for the first wave considers lock-down orders, social distancing measures, and detection efficiency. The model for the second wave considers more sub-populations and incorporates two more elements, vaccination and health care capacity. Daily reported data on the evolution of the COVID-19 pandemic are used to determine the parameter values. The dynamics produced by the mathematical model closely follow the evolution of COVID-19 in the State of Tamil Nadu. Numerical simulation shows that the lock-down effect is limited. Social distancing implementation and detection of positive cases are relatively ineffective compared with other big cities. Shortage of health care resources is one of the factors responsible for rapidly spreading in the second wave in Tamil Nadu.

Epidemiological challenges in pandemic coronavirus disease (COVID-19): Role of artificial intelligence

World is now experiencing a major health calamity due to the coronavirus disease (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus clade 2. The foremost challenge facing the scientific community is to explore the growth and transmission capability of the virus. Use of artificial intelligence (AI), such as deep learning, in (i) rapid disease detection from x-ray or computed tomography (CT) or high-resolution CT (HRCT) images, (ii) accurate prediction of the epidemic patterns and their saturation throughout the globe, (iii) forecasting the disease and psychological impact on the population from social networking data, and (iv) prediction of drug-protein interactions for repurposing the drugs, has attracted much attention. In the present study, we describe the role of various AI-based technologies for rapid and efficient detection from CT images complementing quantitative real-time polymerase chain reaction and immunodiagnostic assays. AI-based technologies to anticipate the current pandemic pattern, prevent the spread of disease, and face mask detection are also discussed. We inspect how the virus transmits depending on different factors. We investigate the deep learning technique to assess the affinity of the most probable drugs to treat COVID-19. This article is categorized under:Application Areas > Health CareAlgorithmic Development > Biological Data MiningTechnologies > Machine Learning.

Estimates of Presumed Population Immunity to SARS-CoV-2 by State in the United States, August 2021

Background

Information is needed to monitor progress toward a level of population immunity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sufficient to disrupt viral transmission. We estimated the percentage of the US population with presumed immunity to SARS-CoV-2 due to vaccination, natural infection, or both as of August 26, 2021.

Methods

Publicly available data as of August 26, 2021, from the Centers for Disease Control and Prevention were used to calculate presumed population immunity by state. Seroprevalence data were used to estimate the percentage of the population previously infected with SARS-CoV-2, with adjustments for underreporting. Vaccination coverage data for both fully and partially vaccinated persons were used to calculate presumed immunity from vaccination. Finally, we estimated the percentage of the total population in each state with presumed immunity to SARS-CoV-2, with a sensitivity analysis to account for waning immunity, and compared these estimates with a range of population immunity thresholds.

Results

In our main analysis, which was the most optimistic scenario, presumed population immunity varied among states (43.1% to 70.6%), with 19 states with ≤60% of their population having been infected or vaccinated. Four states had presumed immunity greater than thresholds estimated to be sufficient to disrupt transmission of less infectious variants (67%), and none were greater than the threshold estimated for more infectious variants (≥78%).

Conclusions

The United States remains a distance below the threshold sufficient to disrupt viral transmission, with some states remarkably low. As more infectious variants emerge, it is critical that vaccination efforts intensify across all states and ages for which the vaccines are approved.

Knowledge, Attitudes and Practices towards SARS-CoV-2 vaccination among morbid obese individuals: a pilot study

BACKGROUND AND AIM

Vaccinations have dramatically impacted on the ongoing pandemic of COVID-19, the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As morbid obese (MO) individuals are at high risk for severe complications, their acceptance of SARS-CoV-2 vaccines is of certain public health interest.

METHODS

We investigated the knowledge, attitudes and eventual acceptance of SARS-CoV-2/COVID-19 vaccination among MO individuals either in waiting list, or recipients of bariatric surgery from a reference center (Parma University Hospital) shortly before the inception of the Italian mass vaccination campaign (March 2021). Data were collected through a web-based questionnaire. Association of individual factors with acceptance of SARS-CoV-2 vaccine was assessed by means of a logistic regression analysis with eventual calculation of adjusted Odds Ratios (aOR) and corresponding 95% Confidence Intervals (95%CI).

RESULTS

Adequate, general knowledge of SARS-CoV-2/COVID-19 was found in the majority of MO patients. High perception of SARS-CoV-2 risk was found in around 80% of participants (79.2% regarding its occurrence, 73.6% regarding its potential severity). Acceptance of SARS-CoV-2/COVID-19 vaccination was reported by 65.3% of participants, and was more likely endorsed by MO patients who were likely to accept some sort of payment/copayment (aOR 5.783; 1.426; 23.456), or who were more likely towards a vaccination mandate (aOR 7.920; 1.995; 31.444).

CONCLUSIONS

Around one third of the MO individuals among potential recipient of bariatric surgery exhibited some significant hesitancy towards SARS-CoV-2 vaccine, and a rational approach may fail to capture and address specific barriers/motivators in this subset of individuals, stressing the importance for alternative interventions. (www.actabiomedica.it).

Mandate or Not Mandate: Knowledge, Attitudes, and Practices of Italian Occupational Physicians towards SARS-CoV-2 Immunization at the Beginning of Vaccination Campaign

Vaccinations used to prevent coronavirus disease (COVID-19)-the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-are critical in order to contain the ongoing pandemic. However, SARS-CoV-2/COVID-19 vaccination rates have only slowly increased since the beginning of the vaccination campaign, even with at-risk workers (e.g., HCWs), presumptively because of vaccine hesitancy. Vaccination mandates are considered instrumental in order to rapidly improve immunization rates (but they minimize the impact of vaccination campaigns). In this study, we investigated the acceptance (i.e., knowledge, attitudes, and practices) from occupational physicians (OPs)) in regard to SARS-CoV-2/COVID-19 vaccination mandates. A total of 166 OPs participated in an internet-based survey by completing structured questionnaires. Adequate, general knowledge of SARS-CoV-2/COVID-19 was found in the majority of OPs. High perception of SARS-CoV-2 risk was found in around 80% of participants (79.5% regarding its occurrence, 81.9% regarding its potential severity). SARS-CoV-2/COVID-19 vaccination was endorsed by 90.4% of respondents, acceptance for SARS-CoV-2 vaccine was quite larger for mRNA formulates (89.8%) over adenoviral ones (59.8%). Endorsement of vaccination mandates was reported by 60.2% of respondents, and was more likely endorsed by OPs who exhibited higher concern for SARS-CoV-2 infection occurrence (odds ratio 3.462, 95% confidence intervals 1.060-11.310), who were likely to accept some sort of payment/copayment for SARS-CoV-2/COVID-19 vaccination (3.896; 1.607; 9.449), or who were more likely to believe HCWs not vaccinates against SARS-CoV-2 as unfit for work (4.562; 1.935; 10.753). In conclusion, OPs exhibited wide acceptance of SARS-CoV-2/COVID-19 vaccinations, and the majority endorsed vaccination mandates for HCWs, which may help improve vaccination rates in occupational settings.

High seroprevalence for SARS-CoV-2 infection in South America, but still not enough for herd immunity!

Herd immunity is considered to be a relevant aspect of COVID-19 epidemiology. In this regard, seroprevalence studies are essential for understanding how far countries and regions are from that potential point. This study analyzed seroprevalence data in nine studies from South America, which is a region that has been badly affected by COVID-19. Seroprevalence values ​​were high, with percentages up to 70.0% (95% CI 67.0-73.4%) in Iquitos, Peru. A meta-analysis of such data enabled a pooled seroprevalence to be obtained, estimated at 33.6% (95% CI 28.6-38.5%). Despite this, the COVID-19 pandemic in South America continues to significantly affect countries such as Brazil, Colombia, and Peru.

Dynamical characterization of antiviral effects in COVID-19

Mathematical models describing SARS-CoV-2 dynamics and the corresponding immune responses in patients with COVID-19 can be critical to evaluate possible clinical outcomes of antiviral treatments. In this work, based on the concept of virus spreadability in the host, antiviral effectiveness thresholds are determined to establish whether or not a treatment will be able to clear the infection. In addition, the virus dynamic in the host - including the time-to-peak and the final monotonically decreasing behavior - is characterized as a function of the time to treatment initiation. Simulation results, based on nine patient data, show the potential clinical benefits of a treatment classification according to patient critical parameters. This study is aimed at paving the way for the different antivirals being developed to tackle SARS-CoV-2.