Recovered COVID-19 Cases Research Articles

Persistent Residual Lung Abnormalities (PRLAs) as Post COVID Lung Sequel in Recovered Severe Pneumonia Case with Lung Function Abnormality at Two Year Follow Up

Coronavirus related (SARS-CoV-2) global pandemic has caused significant mortality and morbidity globally in the last three years. Although, pandemic has waned since last year, we are not in great peace of relief due to lingering effects of COVID-19 persisted even after two years of illness. Radiological phenotypes are radiological patterns or observable characteristics of COVID-19 pneumonia. Radiological outcomes are initially interpreted as post covid lung fibrosis in short term follow-up of recovered COVID-19 cases in post covid care settings and during long term evaluation these abnormalities are defined as post covid sequalae’s. In present case report, 38-year male, presented in outdoor unit for shortness of breath on exertion with history of severe COVID-19 Pneumonia resulted into acute respiratory distress syndrome with hospitalization in intensive care unit one year back and required high flow nasal cannula (HFNC) with Non-invasive ventilatory support (NIV) for two weeks. He was offered oxygen supplementation at home with antibiotics for three months and no requirement of oxygen and some respiratory symptoms even after one year. His HRCT thorax documented at one year of discharge from hospital revealed typical interstitial opacities labelled as Persistent Residual lung abnormalities (RLAs) which are predominantly reticular and linear opacities in peripheral parts of lungs without honeycombing within typical pleural based areas with parenchymal bands and minimally altered lung architecture and preserved lung volume.

To determine the role of transcutaneous retrobulbar amphotericin B in COVID-19-related rhino-orbital-cerebral mucormycosis.

The purpose of this study was to determine the role of transcutaneous retrobulbar amphotericin B (TRAMB) injection in COVID-19-related rhino-orbital-cerebral mucormycosis (ROCM). This prospective interventional case series included 7 eyes of 7 patients diagnosed with COVID-19 ROCM presenting from May to September 2021 at a tertiary care center in North India. Active or recovered cases of COVID-19 with clinical features suggestive of ROCM and early orbital involvement were included. Following functional endoscopic sinus surgery, patients were given TRAMB 3.5 mg/ml in single ocular quadrant depending upon the muscle involved on contrast-enhanced magnetic resonance imaging (MRI). Patients were followed up for a period of 12 weeks. All patients were diabetic and COVID-19 reverse transcription-polymerase chain reaction positive. The mean age of patients was 48.4 years ± 11 years, out of which 5 patients were male and 2 were female. The left eye was involved in 57.14% of cases whereas 42.85% of cases had right eye involvement. Presenting complaints were drooping of the upper lid (57.14%; n = 4), proptosis (28.57%; n = 2), facial pain and swelling (85.71%; n = 6), and nasal block (57.14%; n = 4). Stagewise distribution was 3a in four patients (57.14%), 3b in two patients (28.57%), and 4a in one patient. TRAMB injections (median - 3) were given, and improvement was seen in all patients in terms of ocular movements, ptosis, proptosis, and muscle enhancement on MRI scan. Three patients had an increase in intraocular pressure and inflammation which settled within a week with favorable anatomical and functional outcomes. Transcutaneous retrobulbar amphotericin B should be considered an adjunctive treatment modality for ROCM patients with limited orbital disease to preserve ocular function.

An Overview of Forecast Analysis with ARIMA Models during the COVID-19 Pandemic: Methodology and Case Study in Brazil

This comprehensive overview focuses on the issues presented by the pandemic due to COVID-19, understanding its spread and the wide-ranging effects of government-imposed restrictions. The overview examines the utility of autoregressive integrated moving average (ARIMA) models, which are often overlooked in pandemic forecasting due to perceived limitations in handling complex and dynamic scenarios. Our work applies ARIMA models to a case study using data from Recife, the capital of Pernambuco, Brazil, collected between March and September 2020. The research provides insights into the implications and adaptability of predictive methods in the context of a global pandemic. The findings highlight the ARIMA models’ strength in generating accurate short-term forecasts, crucial for an immediate response to slow down the disease’s rapid spread. Accurate and timely predictions serve as the basis for evidence-based public health strategies and interventions, greatly assisting in pandemic management. Our model selection involves an automated process optimizing parameters by using autocorrelation and partial autocorrelation plots, as well as various precise measures. The performance of the chosen ARIMA model is confirmed when comparing its forecasts with real data reported after the forecast period. The study successfully forecasts both confirmed and recovered COVID-19 cases across the preventive plan phases in Recife. However, limitations in the model’s performance are observed as forecasts extend into the future. By the end of the study period, the model’s error substantially increased, and it failed to detect the stabilization and deceleration of cases. The research highlights challenges associated with COVID-19 data in Brazil, such as under-reporting and data recording delays. Despite these limitations, the study emphasizes the potential of ARIMA models for short-term pandemic forecasting while emphasizing the need for further research to enhance long-term predictions.

THE IMPACT OF COVID-19 ON STOCK MARKET RETURNS

This study is done to analyze the effect of COVID-19 on stock returns in manufacturing companies listed on the Indonesian Stock Exchange (IDX) from March 2, 2020 to December 31, 2020. Sample was selected using purposive sampling method and the valid data was 167 companies. This study uses a fixed effect model and multiple linear regression analysis and analyzed utilizing EViews 12. The results in this study indicate that the daily growth in total confirmed COVID-19 cases, daily growth in total COVID-19 deaths and Large-Scale Social Restrictions (PSBB) has a negative significant effect on stock returns, while daily growth in total recovered COVID-19 cases has an insignificant effect on stock returns.

The outcome of pulmonary function tests and high-resolution computed tomography of chest in post-coronavirus disease 2019-confirmed cases after 3 months of recovery

Background Coronavirus disease (COVID-19) is a recently prevalent infectious disease that is caused by a virus from the coronavirus family and causes acute respiratory syndrome. It is a pandemic catastrophe that has affected more than 60 million people around the world and has caused about 1.5 million deaths, as reported by the WHO. This disease affects the respiratory system and leads to different forms of symptoms and signs. Pneumonia is a common cause for hospitalization, with most patients treated in hospital wards and others requiring ICU. Although the number of complete recoveries from COVID-19 has increased, there is still concern about complications associated with the disease that appear after recovery. The studies that have looked at past types and other forms of coronavirus epidemics, such as SARS have shown that some cases had respiratory complications from the infection after being full recovered, as 36 and 30% of the entire study population had clinical and high-resolution computed tomography (HRCT) changes at 3 and 6 months after recovery, respectively. Mostly, the abnormalities seen in pulmonary function test (PFT) results are sequelae of diffusion capacity defect. In recovered cases of Middle East respiratory syndrome, 36% of patients showed HRCT sequelae at follow-up of 6 weeks, because of fibrosis. Data on COVID-19 indicate that prolonged disease and persistent symptoms show post-PFT affection and follow-up radiographic changes after recovery from COVID-19 as interstitial pulmonary changes and a degree of pulmonary vasculopathy. In recovered cases of COVID-19, capacity of diffusion is the commonest defect in lung function, followed by the restrictive pattern defects on spirometry; both are related to the degree of severity of pneumonic COVID-19. PFTs (involving spirometry as well as diffusion capacity) are considered as routine follow-up examinations for some of the recovered cases, especially severe cases. Rehabilitation programs of the respiratory system are an option strategy that might be considered. This study aims to show changes in pulmonary function and HRCT of chest in post-COVID-19-infected patients to detect long-term effects on the lungs after 3 months as obstructive or restrictive, or both, lung diseases. Patients and methods The study was conducted on 100 confirmed PCR-positive COVID-19 cases that were admitted to Ain Shams University Isolation Hospitals, and the follow-up was performed in the outpatient clinic. PCR samples (Combined nasopharyngeal and oropharyngeal swab) were taken after 3 months from discharge of patients above the age of 18 years who become negative with clinical improvement. PFT [spirometry and diffusion for carbon monoxide (DLCO)] and chest HRCT were done. All patients’ clinical data were recorded, and CT chest imaging data of these patients were correlated with the clinical data. Results A total of 100 patients were included in this study, where males represented 58% and female represented 42%. The mean±SD age of cases in this study was 45.05 ± 11.80 years and ranged from 20 to 79 years. CT chest severity score (SS) of abnormality in COVID-19-infectedd patients based on HRCT chest findings before and after 3 months from treatment showed a highly significant correlation (P=0.000). The results of PFT in the studied group after 3 months of discharge showed restrictive pattern in 14.9%, obstructive pattern in 17.8%, and both obstructive and restrictive patterns in 5.9% of the total number of cases. There was a significant correlation between DLCO abnormality findings and age of studied group (P=0.032), a significant correlation between abnormality findings on PFT and HRCT chest SS after discharge of the studied group (P<0.001). There was a significant correlation between abnormality findings of DLCO and HRCT chest SS after 3 months of the studied group (P=0.000) and before treatment (P=0.001), whereas there was no significant correlation between other findings of PFT and HRCT chest SS after 3 months and before. There was a significant correlation between HRCT chest SS of the studied group and their age after (P=0.003) and before treatment (P=0.000). This cohort study provided the long-term dynamic sequelae and variations of lung function after hospital discharge. Conclusion DLCO-predicted value detected at 3 months is the most important factor associated with severe respiratory COVID-19, which is identified as the cause of impairment of any ordinary basic activity of patients in the stage of postrecovery from COVID-19. The radiological chest findings must be analyzed in conjunction with other functional parameters to provide accurate assessment of the disease. These findings highlight the significance of a comprehensive follow-up after severe and critical COVID-19 infection, with appropriate management of pulmonary consequences. Patients after discharge and on follow-up in outpatient clinics in the post-COVID-19 stage should be strictly followed up with PFTs. Further studies with longer follow-up periods might be needed.

Differential impact of quarantine policies for recovered COVID-19 cases in England: a case cohort study of surveillance data, June to December 2020

BackgroundFrom 12th March 2020, individuals in England were advised to quarantine in their home if a household member tested positive for SARS-CoV-2. A mandatory isolation period of 10 days was introduced on 28th September 2020 and applied to all individuals with COVID-19. We assessed the frequency, timing, and characteristics of recovered COVID-19 cases requiring subsequent quarantine episodes due to household re-exposure.MethodsIn this case cohort study, all laboratory-confirmed COVID-19 cases notified in England (29th June to 28th December 2020) were analysed to identify consecutive household case(s). Multivariable logistic regression was used to determine associations between case characteristics and need to quarantine following recent infection (within 28 days of diagnosis).ResultsAmong 1,651,550 cases resident in private dwellings and Houses of Multiple Occupancy (HMOs), 744,548 (45.1%) were the only case in their home and 56,179 (3.4%) were succeeded by further household cases diagnosed within 11–28 days of their diagnosis. Of 1,641,412 cases arising in private homes, the likelihood of further household cases was highest for Bangladeshi (aOR = 2.20, 95% CI = 2.10–2.31) and Pakistani (aOR = 2.15, 95% CI = 2.08–2.22) individuals compared to White British, as well as among young people (17-24y vs. 25-64y; aOR = 1.19, 95% CI = 1.16–1.22), men (vs. women; aOR = 1.06, 95% CI = 1.04–1.08), London residents (vs. Yorkshire and Humber; aOR = 1.57, 95% CI = 1.52–1.63) and areas of high deprivation (IMD 1 vs. 10; aOR = 1.13, 95% CI = 1.09–1.19).ConclusionPolicies requiring quarantine on re-exposure differentially impact some of the most disadvantaged populations. Quarantine exemption for recently recovered individuals could mitigate the socioeconomic impact of responses to COVID-19 or similar infectious disease outbreaks.

Paraclinical Characteristics of Fatal and Recovered COVID-19 Cases: a Retrospective Study

Paraclinical Characteristics of Fatal and Recovered COVID-19 Cases: a Retrospective Study

Assessment of Post-COVID-19 Quality of Life Using the Quality of Life Index.

BackgroundCOVID-19 has impacted many aspects of peoples’ quality of life (QOL).AimTo assess the QOL of post-COVID-19 cases and determine their associated sociodemographic attributes.MethodsA cross-sectional community-based study targeted recovered COVID-19 cases using an online questionnaire. An Arabic version of the Quality of Life Index (QLI) was evaluated for reliability and validity using Cronbach’s alpha and Spearman’s product moment tests, respectively. Descriptive analysis, Mann–Whitney U, Kruskal–Wallis H, and Spearman correlation tests were used to investigate the relationships between QOL and study independent variables, including sociodemographic characteristics, time since infection, intention for vaccination, perception of the influence of COVID-19 on general health, history of hospitalization, and history of chronic illness. Subgroups were compared using a Student’s t-test for normally distributed data and the Wilcoxon rank-sum test or the Kruskal–Wallis test for non-normally distributed data.ResultsIn a sample of 449 respondents, the total QLI score was 22.64 ± 4.36; for subscales, it ranged from 20.86 ± 5.04 to 24.99 ± 4.74. Males had significantly higher health and functioning subscale (HFSUB) scores compared to females (p = 0.033). Employed cases had significantly higher social and economic subscale (SOCSUB) scores than unemployed cases (p = 0.000). Married people reported significantly higher family subscale (FAMSUB) scores (p = 0.000) than unmarried people. People who believe that their general health was affected by COVID-19 had significantly lower HFSUB and psychological and spiritual subscale (PSPSUB) scores (p = 0.000 and p = 0.021, respectively). Individuals with a history of hospital admissions scored lower on PSPSUB (p = 0.000). Age was significantly associated with both SOCSUB (p = 0.006) and FAMSUB (p = 0.040).ConclusionSociodemographic attributes of people with a history of COVID-19 revealed significant differences in some QOL domains, as measured by the Arabic version of the generic QLI. History of hospitalization and beliefs about COVID-19’s impact on general health negatively influenced individual’s HFSUB and PSPSUB.

COVID-19 forecast for 13 Caribbean countries using ARIMA modeling for confirmed, death, and recovered cases

Background: The rapid spread of the Covid-19 virus in the Caribbean region has led to increased surveillance with an increasing trend of confirmed cases of COVID-19 in 13 Caribbean countries. Our study aims to analyze the impact of Covid-19 (SARS nCoV-2) in 13 Caribbean countries in terms of the number of confirmed cases, deaths, and recovered cases. Methods: The study uses the ARIMA model based on the time series pattern according to data retrieved from John Hopkins University. The data were analyzed using Stata 14 SE software for the period January 22, 2020, and August 16, 2021, and forecasted till December 31, 2021. All chosen models were compared with other models in terms of various factors like AIC/BIC, log-likelihood, p-value significance, coefficient < 1, and 5% significance. The ACF and PACF graphs were plotted to reduce bias and select the best-fitting model. Results: The results show the predicted trend in terms of confirmed, death, and recovered cases of COVID-19 for 13 Caribbean countries. The projected ARIMA model forecast for the period December 16, 2021, to December 31, 2021, shows 2470272 (95% CI 2438965 - 2501579) confirmed cases, 27220 (95% CI 26886 - 27555) deaths, and 818105 (95% CI 818085 - 818125) recovered cases related to Covid-19. The final ARIMA model chosen for confirmed COVID-19 cases, several deaths, and recovered cases are ARIMA (9,2,4), ARIMA (1,2,1), and ARIMA (1,2,1), respectively. Conclusions As per the results of the forecasted COVID-19 models, there is a steady rise in confirmed, death, and recovered cases during the period June 1, 2020, until November 30, 2020, and April 1, 2021, until June 15, 2021. It shows an increasing trend for confirmed and recovered COVID-19 cases and a slowing of the number of deaths.

Evaluation of the prediction of CoVID-19 recovered and unrecovered cases using symptoms and patient's meta data based on support vector machine, neural network, CHAID and QUEST Models.

This paper aims to develop four prediction models for recovered and unrecovered cases using descriptive data of patients and symptoms of CoVID-19 patients. The developed prediction models aim to extract the important variables in predicting recovered cases by using the binary values for recovered cases. The data were collected from different countries all over the world. The input of the prediction model contains 28 symptoms and four variables of the patient's information. Symptoms of COVID-19 include a high fever, low fever, sore throat, cough, and so on, where patient metadata includes Province, county, sex, and age. The dataset contains 1254 patients with 664 recovered cases. To develop prediction models, four models are used including neural network, support vector machine, CHAID, and QUEST models. To develop prediction models, the dataset is divided into train and test datasets with splitting ratios equal to 70%, and 30%, respectively. The results showed that the neural network model is the most effective model in developing COVID-19 prediction with the highest performance metrics using train and test datasets. The results found that recovered cases are associated with the place of the patients mainly, province of the patient. Besides the results showed that high fever is not strongly associated with recovered cases, where cough and low fever are strongly associated with recovered cases. In addition, the country, sex, and age of the patients have higher importance than other patient's symptoms in COVID-19 development. The results revealed that the prediction models of the recovered COVID-19 cases can be effectively predicted using patient characteristics and symptoms, besides the neural network model is the most effective model to create a COVID -19 prediction model. Finally, the research provides empirical evidence that recovered cases of COVID-19 are closely related to patients' provinces.

What determines urban resilience against COVID-19: City size or governance capacity?

This study analyzed the effects of urban governance and city size on COVID-19 prevention and control measures. Based on real-time data in 276 prefecture-level Chinese cities, we used the ordinary least squares plus robust standard error strategy. It was found that: (1) despite the non-significant effect of city size, urban governance capacity was an important factor affecting the prevention and control of the COVID-19 pandemic; urban governance capacity was particularly significant in the late control of the pandemic, but not significant in the early prevention; for every unit increase of urban governance capacity, the number of recovered COVID-19 cases per capita increased by 2.4%. Moreover, (2) the influence mechanism of anti-pandemic measures in cities could be divided into the workforce, financial, and material effects, and their contribution rates were 26.15%, 32.55%, and 37.20%, respectively; namely, the effective/timely assistance from Chinese central government regarding the workforce, financial, and material resources in key pandemic areas and nationwide played a major role in pandemic control. Additionally, (3) cities with a high level of smart city construction were more capable of enhancing the pandemic prevention and control effect, indicating that smart city construction is conducive to enhanced coping with public crises.

Promoting Compliance to COVID-19 Vaccination in Military Units.

ABSTRACTBackgroundOn December 27th, 2020, the Israeli Defense Forces initiated a mass COVID-19 vaccination campaign aiming to vaccinate its personnel. This population upheld specific characteristics in terms of age and sex, lack of significant comorbidities, and a general scarcity of risk factors for sustaining a severe COVID-19 illness. We present the measures taken to increase vaccination compliance, and the vaccination rate that followed these actions. Our secondary goal was to compare between vaccination rates in frontline battalions and highly essential military units (group A) and rear administration and support military units (group B).MethodsThis was a retrospective review that included 70 military units that were composed of 18,719 individuals of both sexes, mostly free of significant comorbidities. We divided the challenges of maximizing vaccination rates into two main categories: vaccine compliance (including communication and information) and logistical challenges. We compared the vaccination rates in groups A and B using a multivariable linear regression model. A P-value of .05 was considered significant.ResultsThe mean age in 70 military units was 22.77 ± 1.35 (range 18-50) years, 71.13% males. A total of 726 (3.88%) individuals have been found positive for SARS-CoV-2 between March 1st, 2020 and February 18th, 2021. On February 18th, 2021, 54 days after the vaccination campaign was launched, 15,871 (84.79%) of the study population have been vaccinated by the first dose of Pfizer COVID-19 vaccine, expressing an 88.21% compliance rate (excluding recovered COVID-19 cases who were not prioritized to be vaccinated at this stage). Vaccination compliance in military units from group A was found to be higher when compared to group B (P < .001), leading to a 90.02% of group A population being either previously SARS-CoV-2 positive or COVID-19 vaccinated.ConclusionsA designated army campaign led by a multidisciplinary team could rapidly achieve a high COVID-19 vaccination rate. The information presented can serve organizations worldwide with similar characteristics that plan a mass COVID-19 vaccination campaign.

Comparative study of machine learning methods for COVID-19 transmission forecasting

Within the recent pandemic, scientists and clinicians are engaged in seeking new technology to stop or slow down the COVID-19 pandemic. The benefit of machine learning, as an essential aspect of artificial intelligence, on past epidemics offers a new line to tackle the novel Coronavirus outbreak. Accurate short-term forecasting of COVID-19 spread plays an essential role in improving the management of the overcrowding problem in hospitals and enables appropriate optimization of the available resources (i.e., materials and staff).This paper presents a comparative study of machine learning methods for COVID-19 transmission forecasting. We investigated the performances of deep learning methods, including the hybrid convolutional neural networks-Long short-term memory (LSTM-CNN), the hybrid gated recurrent unit-convolutional neural networks (GAN-GRU), GAN, CNN, LSTM, and Restricted Boltzmann Machine (RBM), as well as baseline machine learning methods, namely logistic regression (LR) and support vector regression (SVR). The employment of hybrid models (i.e., LSTM-CNN and GAN-GRU) is expected to eventually improve the forecasting accuracy of COVID-19 future trends. The performance of the investigated deep learning and machine learning models was tested using confirmed and recovered COVID-19 cases time-series data from seven impacted countries: Brazil, France, India, Mexico, Russia, Saudi Arabia, and the US. The results reveal that hybrid deep learning models can efficiently forecast COVID-19 cases. Also, results confirmed the superior performance of deep learning models compared to the two considered baseline machine learning models. Furthermore, results showed that LSTM-CNN achieved improved performances with an averaged mean absolute percentage error of 3.718%, among others.

Factors of the Prevalence of COVID-19 for Females in Amhara Region, Northern Ethiopia

The COVID-19 pandemic in Ethiopia is a global epidemic of coronavirus disease 2019 caused by severe acute respiratory syndrome coronavirus 2. Amhara Region is a regional state in northern Ethiopia and the homeland of the Amhara people. The main objective of this study was to identify factors of the prevalence of COVID-19 for females in Amhara region. The data set was obtained from Amhara Public Health Institute 2020. A negative Binomial regression model was conducted to find out the determinants of the prevalence of COVID-19 for females. Out of 5,627 confirmed cases, 96 patients have died and 1,483 confirmed cases were females from 138 daily reports. Number of recovered COVID-19 cases ( = -0.4566332, 95%CI: (-0.7364772, -0.1767892), P-value &lt; 0.05), severe cases ( = 1.038589, 95%CI: (0.7531619, 1.324017), P-value &lt; 0.05), total deaths ( = 0.5164175, 95% CI: (0.1438362, 0.8889987), P-value &lt;0.05) and the average age of patients per day ( = 1.511936, 95% C.I: (0.9220257, 2.101846), P-value &lt; 0.05 ) were statistically significant factors for the confirmed case of COVID-19 for females in Amhara region. Above 30 average age of patients per day, below 19 number of recovered cases, above 12 number of severe cases, and above two number of deaths are optimally high-risk factors of confirmed cases of COVID-19 for females.

Trend Analysis and Forecasting the Spread of COVID-19 Pandemic in Ethiopia Using Box-Jenkins Modeling Procedure.

IntroductionCOVID-19, which causes severe acute respiratory syndrome, is spreading rapidly across the world, and the severity of this pandemic is rising in Ethiopia. The main objective of the study was to analyze the trend and forecast the spread of COVID-19 and to develop an appropriate statistical forecast model.MethodologyData on the daily spread between 13 March, 2020 and 31 August 2020 were collected for the development of the autoregressive integrated moving average (ARIMA) model. Stationarity testing, parameter testing and model diagnosis were performed. In addition, candidate models were obtained using autocorrelation function (ACF) and partial autocorrelation functions (PACF). Finally, the fitting, selection and prediction accuracy of the ARIMA models was evaluated using the RMSE and MAPE model selection criteria.ResultsA total of 51,910 confirmed COVID-19 cases were reported from 13 March to 31 August 2020. The total recovered and death rates as of 31 August 2020 were 37.2% and 1.57%, respectively, with a high level of increase after the mid of August, 2020. In this study, ARIMA (0, 1, 5) and ARIMA (2, 1, 3) were finally confirmed as the optimal model for confirmed and recovered COVID-19 cases, respectively, based on lowest RMSE, MAPE and BIC values. The ARIMA model was also used to identify the COVID-19 trend and showed an increasing pattern on a daily basis in the number of confirmed and recovered cases. In addition, the 60-day forecast showed a steep upward trend in confirmed cases and recovered cases of COVID-19 in Ethiopia.ConclusionForecasts show that confirmed and recovered COVID-19 cases in Ethiopia will increase on a daily basis for the next 60 days. The findings can be used as a decision-making tool to implement health interventions and reduce the spread of COVID-19 infection.

Tracking COVID-19 burden in India: A review using SMAART RAPID tracker.

India has seen a rapid rise in COVID-19 cases. Examine spatiotemporal variation of COVID-19 burden Tracker across Indian states and union territories using SMAART RAPID Tracker. We used SMAART RAPID Tracker to visually display COVID-19 spread in space and time across various states and UTs of India. Data gathered from publicly available government information sources. Data analysis on COVID-19 conducted from March 1 2020 to October 1 2020. Variables recorded include COVID-19 cases and fatality, 7-day average change, recovery rate, labs and tests. Spatial and temporal trends of COVID-19 spread across Indian states and UTs is presented. The total number of COVID-19 cases were 63, 12,584 and total fatality was 86,821 (October 1 2020). More than 85,000 new cases of COVID-19 were reported. There were 1,867 total COVID-19 labs throughout India. More than half of them were Government labs. The total number of COVID-19 tests was 76,717,728 and total recovered COVID-19 cases was 5,273,201. Results show an overall decline in the 7-day average change of new COVID-19 cases and new COVID-19 fatality. States such as Maharashtra, Chandigarh, Puducherry, Goa, Karnataka and Andhra Pradesh continue to have high COVID-19 infectivity rate. Findings highlight need for both national guidelines combined with state specific recommendations to help manage the spread of COVD-19. The heterogeneity represented in India in terms of its geography and various population groups highlight the need of state specific approach to monitor and combat the ongoing pandemic. This would further facilitate the tailored approach for each state to mitigate and contain the spread of the disease.

COVID-19 Pandemic and Financial Contagion

The original contribution of this paper is to empirically document the contagion of the Covid-19 on financial markets. We merge databases from Johns Hopkins Coronavirus Center, Oxford-Man Institute Realized Library, NYU Volatility Lab, and St-Louis Federal Reserve Board. We deploy three types of models throughout our experiments: (i) the Susceptible-Infective-Removed (SIR) that predicts the infections’ peak on 2020-03-27; (ii) volatility (GARCH), correlation (DCC), and risk-management (Value-at-Risk (VaR)) models that relate how bears painted Wall Street red; and, (iii) data-science trees algorithms with forward prunning, mosaic plots, and Pythagorean forests that crunch the data on confirmed, deaths, and recovered Covid-19 cases and then tie them to high-frequency data for 31 stock markets.

Epidemiological study design of asymptomatic infection of the 2019 novel coronavirus

COVID-19 is caused by the 2019 novel coronavirus (2019-nCoV). COVID-19 clinical cases are considered as the principal source of infection, however, asymptomatic cases may also play a role in the transmission. Significant gap exists in terms of the proportion or prevalence and transmissibility of asymptomatic cases. This study design plans to use data from areas with different epidemiological profiles to investigate the COVID-19 epidemic in China. In each selected region, both general community residents and key populations at high risk of COVID-19 infection, including recovered COVID-19 cases, close contacts of confirmed COVID-19 cases, medical professionals, investigators at CDCs, and visitors to fever clinics, will be recruited and examined for viral RNA of 2019-nCoV and serum antibodies. Prevalence and characterization of asymptomatic cases will be determined, stratified by varied demographics and exposure risk. During the follow-up, the change in the serum antibodies will be studied prospectively in the symptomatic and asymptomatic cases to address the scientific and public health concerns of infectivity and transmissibility of 2019-nCoV.

Time series modelling to forecast the confirmed and recovered cases of COVID-19

Coronaviruses are enveloped RNA viruses from the Coronaviridae family affecting neurological, gastrointestinal, hepatic and respiratory systems. In late 2019 a new member of this family belonging to the Betacoronavirus genera (referred to as COVID-19) originated and spread quickly across the world calling for strict containment plans and policies. In most countries in the world, the outbreak of the disease has been serious and the number of confirmed COVID-19 cases has increased daily, while, fortunately the recovered COVID-19 cases have also increased. Clearly, forecasting the “confirmed” and “recovered” COVID-19 cases helps planning to control the disease and plan for utilization of health care resources. Time series models based on statistical methodology are useful to model time-indexed data and for forecasting. Autoregressive time series models based on two-piece scale mixture normal distributions, called TP–SMN–AR models, is a flexible family of models involving many classical symmetric/asymmetric and light/heavy tailed autoregressive models. In this paper, we use this family of models to analyze the real world time series data of confirmed and recovered COVID-19 cases.

Modeling and forecasting of confirmed and recovered cases of COVID-19 in India

Context: The novel coronavirus was reported in the past few weeks of 2019 in the Wuhan city, China, and the spread and outbreaks of disease require an epidemiological analysis of the disease in the shortest time and increased awareness of effective interventions Aims: This article proposes an objective approach to predicting the continuation of the COVID-19 cases in India using a simple, but powerful time-series method. Settings and Design: Cumulative confirmed and cumulative recovered cases of COVID-19 in India are taken to forecast the prevalence of incoming 3 weeks. Subjects and Methods: The model is built to predict the number of confirmed cases and recovered cases based on the data available from March 14, 2020, to April 26, 2020. Statistical Analysis Used: The autoregressive-integrated moving average model was applied to predict the number of confirmed cases and recovered cases of COVID-19 during the next 3 weeks. Results: Our forecasts suggest a continuing increase in the confirmed COVID-19 cases with sizable associated uncertainty assuming that the data used are reliable and that the future will continue to follow the past pattern of the disease. Conclusions: The timeline of a live-forecasting exercise with potential implications for planning and decision making is described. The following core competencies are addressed in this article: Medical knowledge, Practice-based learning and improvement, Systems-based practice.