scholarly journals Regression Analysis of COVID-19 Spread in India and its Different States

2020 ◽  
Author(s):  
Poonam Chauhan ◽  
Ashok Kumar ◽  
Pooja Jamdagni
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Tamil Nadu ◽  
Polynomial Regression ◽  
Case Fatality ◽  
Epidemiological Data ◽  
Fatality Rate ◽  
Indian States ◽  
Number Of Patients ◽  
Polynomial Regression Model

AbstractLinear and polynomial regression model has been used to investigate the COVID-19 outbreak in India and its different states using time series epidemiological data up to 26th May 2020. The data driven analysis shows that the case fatality rate (CFR) for India (3.14% with 95% confidence interval of 3.12% to 3.16%) is half of the global fatality rate, while higher than the CFR of the immediate neighbors i.e. Bangladesh, Pakistan and Sri Lanka. Among Indian states, CFR of West Bengal (8.70%, CI: 8.21–9.18%) and Gujrat (6.05%, CI: 4.90–7.19%) is estimated to be higher than national rate, whereas CFR of Bihar, Odisha and Tamil Nadu is less than 1%. The polynomial regression model for India and its different states is trained with data from 21st March 2020 to 19th May 2020 (60 days). The performance of the model is estimated using test data of 7 days from 20th May 2020 to 26th May 2020 by calculating RMSE and % error. The model is then used to predict number of patients in India and its different states up to 16th June 2020 (21 days). Based on the polynomial regression analysis, Maharashtra, Gujrat, Delhi and Tamil Nadu are continue to remain most affected states in India.

scholarly journals Growth of D614G Virus : Morgan – Morgan – Finney ( MMF ) Regression 

2021 ◽  
Author(s):  
JAYDIP DATTA
Keyword(s):  
Experimental Study ◽  
Regression Model ◽  
Infected Patient ◽  
Polynomial Regression ◽  
P Value ◽  
Fatality Rate ◽  
Converting Enzyme ◽  
Pearson Coefficient ◽  
Best Fit ◽  
Polynomial Regression Model

Abstract In our earlier work [ 1 , ] a correlation was established between Infection fatality rate ( IFR ) vs age of the patient by logistic or sigmoid regression model . The regression model shows a more probable IFR with a sigmoid rise after 46 yrs and becoming most significant after 70 yrs and above. In the present study the frequency of infected patient by ‘ G ‘ type of mutants of SARS-COV-2 with time from Jan, 2020 to June, 2020 for 6.5 months duration of experimental study. The best fit analysis is compared to Morgan – Morgan – Finney ( MMF ) model ( 3,4 ) statistics and Polynomial regression model. Both the above model correlates a significant result with Pearson coefficient ( r ), Probability ( p-value ). The RBD of D614G( 10 ) interaction with Angiotensin Converting Enzyme-II ( ACE2) of host cell can be also correlated to another new spike mutant like N501Y ( 11) through Pearson coefficient ( r ) with p-value of significance.

scholarly journals District level correlates of COVID-19 pandemic in India

2020 ◽  
Author(s):  
Vandana Tamrakar ◽  
Ankita Srivastava ◽  
Mukesh C. Parmar ◽  
Sudheer Kumar Shukla ◽  
Shewli Shabnam ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Population Density ◽  
Prevalence Rate ◽  
Urban Population ◽  
Tamil Nadu ◽  
Andhra Pradesh ◽  
District Level ◽  
Indian States ◽  
Number Of Patients ◽  
Health Related

AbstractBackgroundThe number of patients with coronavirus infection (COVID-19) has amplified in India. Understanding the district level correlates of the COVID-19 infection ratio (IR) is therefore essential for formulating policies and intervention.ObjectivesThe present study examines the association between socio-economic and demographic characteristics of India’s population and the COVID-19 infection ratio at district level…Data and MethodsUsing crowdsourced data on the COVID-19 prevalence rate, we analyzed state and district level variation in India from March 14 to July 31 2020. We identified hotspot and cold spot districts for COVID-19 cases and infection ratio. We have also carried out a regression analysis to highlight the district level demographic, socio-economic, infrastructure, and health-related correlates of the COVID-19 infection ratio.ResultsThe results showed that the IR is 42.38 per one hundred thousand population in India. The highest IR was observed in Andhra Pradesh (145.0), followed by Maharashtra (123.6), and was the lowest in Chhattisgarh (10.1). About 80 per cent of infected cases, and 90 per cent of deaths were observed in nine Indian states (Tamil Nadu, Andhra Pradesh, Telangana, Karnataka, Maharashtra, Delhi, Uttar Pradesh, West Bengal, and Gujarat). Moreover, we observed COVID-19 cold-spots in central, northern, western, and north-eastern regions of India. Out of 736 districts, six metropolitan cities (Mumbai, Chennai, Thane, Pune, Bengaluru, and Hyderabad) emerged as the major hotspots in India, containing around 30 per cent of confirmed total COVID-19 cases in the country. Simultaneously, parts of the Konkan coast in Maharashtra, parts of Delhi, the southern part of Tamil Nadu, the northern part of Jammu & Kashmir were identified as hotspots of COVID-19 infection. Moran’s-I value of 0.333showed a positive spatial clusteringlevel in the COVID-19 IR case over neighboring districts. Our regression analysis found that district-level population density (β: 0.05, CI:004-0.06), the percent of urban population (β:3.08, CI: 1.05-5.11), percent of Scheduled Caste Population (β: 3.92, CI: 0.12-7.72),and district-level testing ratio (β: 0.03, CI: 0.01-0.04) are positively associated with the prevalence of COVID-19.ConclusionCOVID-19 cases were heavily concentrated in 9 states of India. Several demographic, socio-economic, and health-related variables are correlated with COVID-19 prevalence rate. However, after adjusting the role of socio-economic and health-related factors, the COVID-19 infection rate was found to be more rampant in districts with a higher population density, a higher percentage of the urban population, and a higher percentage of deprived castes and with a higher level of testing ratio. The identified hotspots and correlates in this study give crucial information for policy discourse.

scholarly journals Growth of D614G Virus : Morgan – Morgan – Finney ( MMF ) Regression 

2020 ◽  
Author(s):  
JAYDIP DATTA
Keyword(s):  
Experimental Study ◽  
Regression Model ◽  
Infected Patient ◽  
Polynomial Regression ◽  
P Value ◽  
Fatality Rate ◽  
Pearson Coefficient ◽  
Best Fit ◽  
Polynomial Regression Model

Abstract In our earlier work [ 1 , ] a correlation was established between Infection fatality rate ( IFR ) vs age of the patient by logistic or sigmoid regression model . The regression model shows a more probable IFR with a sigmoid rise after 46 yrs and becoming most significant after 70 yrs and above . In the present study the frequency of infected patient by ‘ G ‘ type of mutants of SARS-COV-2 with time from Jan ,2020 to June ,2020 for 6.5 months duration of experimental study . The best fit analysis is compared to Morgan – Morgan – Finney ( MMF ) model ( 3,4 ) statistics and Polynomial regression model . Both the above model correlates a significant result with Pearson coefficient ( r ) , Probability ( p-value ) .

scholarly journals Managing older adults’ fear of coronavirus disease: A new role for social work practice

2020 ◽  
pp. 147332502097329
Author(s):  
Hamed Mortazavi
Keyword(s):  
Older Adults ◽  
Case Fatality Rate ◽  
Severe Disease ◽  
Case Fatality ◽  
Social Work Practice ◽  
World Health ◽  
National Committee ◽  
Fatality Rate ◽  
Number Of Patients ◽  
Incidence And Mortality

As the number of patients infected with the 2019 novel coronavirus disease (nCOVID-19) increases, the number of deaths has also been increasing. According to World Health Organization (WHO), as of 4 October 2020, 34,804,348 cases had tested positive for nCOVID-19 globally, which among them, 1,030,738 confirmed deaths had occurred, equivalent to a case-fatality rate of 2.96%. However, in comparison with global statistics, the incidence and mortality of the nCOVID-19 infection are higher in Iran. As reported by the National Committee on COVID-19 Epidemiology of Ministry of Health of Iran, the total number of patients with confirmed COVID-19 infection has reached 468,119, of which 26,746 have died, equivalent to a case-fatality rate of 5.71%. Currently, there is solid evidence that older adults are at a higher risk of severe disease following infection from COVID-19.

scholarly journals Polynomial Regression Model Analysis of Abdominal Wall Tension and Intra-abdominal Pressure in Critically ill Patients

2021 ◽  
Author(s):  
Hao Tang ◽  
Dongchu Zhao ◽  
Chuan Zhang ◽  
Xiaoying Huang ◽  
Dong Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Regression Model ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Polynomial Regression ◽  
Prognostic Indicators ◽  
Wall Tension ◽  
Abdominal Hypertension ◽  
Abdominal Compliance ◽  
Polynomial Regression Model

Abstract BackgroundAbdominal wall tension (AWT) plays an important role in the pathogenesis of abdominal compliance (AC). This study uses a polynomial regression model to analyze the correlation between intra-vesical pressure(IVP) and AWT in critically ill patients and provides new ideas for the diagnosis and treatment of critically ill patients with intra-abdominal hypertension（IAH）.MethodsA retrospective analysis was conducted in critically ill patients who met the inclusion criteria and were admitted to the Department of intensive care unit of Daping Hospital of Army Medical University from March 14, 2019, to May 23, 2020. According to the IVP on the first day of ICU admission and death within 28 days, the patients were divided into the IAH group (IVP ≥12 mmHg), the non-IAH group, the survival group and the nonsurvival group. The demographic and clinical data, prognostic indicators, AWT and IVP on days 1-7 after entering the ICU, IAH risk factors, and 28-day death risk factors were collected.ResultsA total of 100 patients were enrolled, with an average age of 45.59±11.4 years. There were 55 males (55%), 30 patients from departments of internal medicine (30%), 43 patients from surgery departments (43%), and 27 trauma patients (27%). In the IAH group, there were 50 patients (29 males, 58%), with an average age of 45.28±12.27 years; there were 50 patients (26 males, 52%) in the non-IAH group, with an average age of 45.90±10.58 years. The IVP on the 1st day and the average IVP within 7 days of the IAH group was 18.99（17.52，20.77）mmHg and 19.43（16.87，22.25）mmHg, respectively, which was higher than that of the non-IAH group [ 6.14（3.48，8.70）mmHg, 6.66（2.74，9.08）mmHg], p＜0.001. The AWT on the 1st day and the average AWT within 7 days of the IAH group was 2.89±0.32 N/mm and 2.82±0.46 N/mm, respectively, which was higher than that of the non-IAH group [(2.45±0.29)N/mm,(2.43±0.39)N/mm],p＜0.001.The polynomial regression models showed that the average AWT and IVP on the 1st day and within 7 days were AWTday1 = -2.450×10-3IVP2+9.695×10-2 IVP+2.046，r=0.667（p＜0.0001），and AWTmean = -2.293×10-3IVP2+9.273×10-2 IVP+2.081, respectively. The logistic regression analysis showed that AWTday1 of 2.73-2.97 N/mm increased the patient's 28-day mortality risk (OR: 6.834; 95%: 1.105-42.266, p=0.010).ConclusionsThere is a nonlinear correlation between AWT and IVP in critically ill patients, and a high AWT may indicate poor prognosis.

Sign in / Sign up

Export Citation Format

Share Document