Mathematical Model with Social Distancing Parameter for Early Estimation of COVID-19 Spread

COVID-19 is well known to everyone in the world. It has spread around the world. No vaccine or antiviral treatment is available till now. COVID-19 patients are increasing day by day. All countries have adopted social distancing as a preventive measure to reduce spread. It becomes necessary to estimate the number of people going to be affected by COVID-19 in advance so that necessary arrangements can be done. Mathematical models are used to provide early disease estimation based on limited parameters. In the present manuscript, a novel mathematical model with a social distancing parameter has been proposed to provide early COVID-19 spread estimation. The model has been validated with real data set. It has been observed that the proposed model is more accurate in spread estimation. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Prolonged detection of sars cov2 rna in extracellular vesicles in nasal swab rt-pcr negative patients

Background: There is a prolonged RT-PCR positivity seen in COVID-19 infected patients up to 2-3 months.It is assumed that this virus is usually non-infective but there are hardly any study on the reactivation of this virus within the respiratory tract. We aim to investigate the presence of viral particles inside Extracellular vesicles (EV) and its role in underlying liver disease patients. Methods: SARS CoV2 nasal and throat swab RT-PCR positive n=64 {n=12(18.7%) chronic liver disease (CLD);n=52 (81.3%) non-liver disease} n=5 RT PCR negative subjects (HC) were studied. SARS CoV2 patients were also followed up for day(d) 7 and 14. Nasal swab [collected in viral transport media (VTM)] and plasma samples were investigated at each time point. Extracellular vesicles were isolated using differential ultracentrifugation. SARS CoV2 RNA was measured using qRT-PCR by Altona Real Star kit. Cellular origin of EV was confirmed using epithelial cells (Epcam+ CK19+ CDh1+), endothelial cells (CD31+CD45-), hepatocytes (ASGPR+) surface markers by Flow cytometry. Results: The COVID19 patients {Mean age 54±23 years;41 males} were having severity between moderate to severe. In patients with cirrhosis, the most common aetiology of liver disease was alcohol (MELD 22±8). In baseline RT-PCR positive patients, SARS-CoV2 RNA inside the EV was present in 53/64 (82%) patients with comparable viral load between VTM and EV (mean 1CT - 0.033±0.005 vs. 1CT- 0.029±0.014, p=ns). On follow-up at day 7, of the 24 patients negative for COVID19, 10 (41%) had persistence of virus in the EV (1CT - 0.028±0.004) and on day 14, 14 of 40 (35%) negative RT-PCR had EVs with SARS CoV2 RNA (1CT - 0.028±0.06). The mean viral load decreased at day7 and day14 in EV from baseline (p=0.008;0.002 respectively). The probability of detecting SARS-CoV2 in EVs in the VTM negative patients was significantly (p=0.001) greater { relative risk ratio 2.25 (95% of CI 1.08 to 4.67;p=0.02, odds ratio 28.1(95% of CI -1.27 to 619.9;p=0.03)}.SARS-CoV2 RNA otherwise undetectable in plasma, was found to be positive in EV in 12.5% of COVID19 positive patients. Interestingly, significantly prolonged and high viral load was found in EV at day 14 in CLD-COVID19 patients compared to COVID19 alone (p=0.002). The high cellular injury was seen in CLDCOVID19 infected patients with significant high levels of EV associated with epithelial cells and hepatocytes than COVID19 alone (p=0.004;0.001). Conclusion: Identification of SARS-CoV2 RNA in EV, in RT-PCR negative patients indicates persistence of infection for and likely recurrence of the infection. It is suggestive of another route of transmission as EV harbour SARS CoV2 RNA. EV associated RNA may determine the ongoing inflammation and clinical course of subjects with undetectable SARS-CoV2 virus and this may also have relevance in management of chronic liver disease patients.

Psychological assessment and lived experiences of recovered COVID-19 patients who presented for convalescent plasma donation.

BACKGROUND: Increasingly, it has been seen that patients recovering from COVID-19 may face a second battle of coping with its mental health ramifications. These psychological issues can even be experienced by patients who were asymptomatic or had mild to moderate symptoms, potentially impacting their quality of life. METHODOLOGY: This was a prospective observational study to analyse the psychological impact of COVID-19 in recovered patients who presented as prospective convalescent plasma (CP) donors. An interview for the psychological assessment of the prospective donors was carried out. Depression and anxiety in the participants were assessed by HAM-A, and HAM-D scores and Quality of Life were assessed using the WHOQOL-BREF scale. RESULTS: A total of 51 prospective donors were assessed, with a mean age of 34.37 (±9.08) years, with the majority being males (46). No clinically significant depression and anxiety were found on the basis of HAM-D and HAM-A scores. The worst affected quality of life parameter, based on the WHOQOL-BREF scale, was physical quality of life followed by environmental, psychological, and social relationships. Moreover, due to infection, social stigma was experienced by 49.02% of the donors, while 21.97% had anxiety related to convalescent plasma donation as a common livid experience. CONCLUSION: Poor quality of life and social stigma during the recovery phase is prevalent in COVID-19 recovered patients, for which formulation of holistic support strategies are the need of the hour.

SEIHCRD model for COVID-19 spread scenarios, disease predictions and estimates the basic reproduction number, case fatality rate, hospital, and ICU beds requirement

We have proposed a new mathematical method, the SEIHCRD model, which has an excellent potential to predict the incidence of COVID-19 diseases. Our proposed SEIHCRD model is an extension of the SEIR model. Three-compartments have added death, hospitalized, and critical, which improves the basic understanding of disease spread and results. We have studied COVID-19 cases of six countries, where the impact of this disease in the highest are Brazil, India, Italy, Spain, the United Kingdom, and the United States. After estimating model parameters based on available clinical data, the model will propagate and forecast dynamic evolution. The model calculates the Basic reproduction number over time using logistic regression and the Case fatality rate based on the selected countries' age-category scenario. The model calculates two types of Case fatality rate one is CFR daily, and the other is total CFR. The proposed model estimates the approximate time when the disease is at its peak and the approximate time when death cases rarely occur and calculate how much hospital beds and ICU beds will be needed in the peak days of infection. The SEIHCRD model outperforms the classic ARX model and the ARIMA model. RMSE, MAPE, and R squared matrices are used to evaluate results and are graphically represented using Taylor and Target diagrams. The result shows RMSE has improved by 56%-74%, and MAPE has a 53%-89% improvement in prediction accuracy. © 2020 Tech Science Press. All rights reserved.

Study of non-pharmacological interventions on covid-19 spread

COVID-19 disease has emerged as one of the life threatening threat to the society. A novel beta coronavirus causes it. It began as unidentified pneumonia of unknown etiology in Wuhan City, Hubei province in China emerged in December 2019. No vaccine has been produced till now. Mathematical models are used to study the impact of different measures used to decrease pandemic. Mathematical models have been designed to estimate the numbers of spreaders in different scenarios in the present manuscript. In the present manuscript, three different mathematical models have been proposed with different scenarios, such as screening, quarantine, and NPIs, to estimate the number of virus spreaders. The analysis shows that the numbers of COVID-19 patients will be more without screening the peoples coming from other countries. Since every people suffering from COVID-19 disease are spreaders. The screening and quarantine with NPIs have been implemented to study their impact on the spreaders. It has been found that NPI measures can reduce the number of spreaders. The NPI measures reduce the spread function's growth and provide decision makers more time to prepare with in dealing with the disease. © 2020 Tech Science Press. All rights reserved.