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 District level correlates of COVID-19 pandemic in India during March-October 2020

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257533
Author(s):  
Vandana Tamrakar ◽  
Ankita Srivastava ◽  
Nandita Saikia ◽  
Mukesh C. Parmar ◽  
Sudheer Kumar Shukla ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Population Density ◽  
Urban Population ◽  
Tamil Nadu ◽  
Spatial Clustering ◽  
Uttar Pradesh ◽  
District Level ◽  
Cold Spot ◽  
Working Age ◽  
Health Related

Background COVID-19 is affecting the entire population of India. Understanding district level correlates of the COVID-19’s infection ratio (IR) is essential for formulating policies and interventions. Objective The present study aims to investigate the district level variation in COVID-19 during March-October 2020. The present study also examines the association between India’s socioeconomic and demographic characteristics and the COVID-19 infection ratio at the district level. Data and methods We used publicly available crowdsourced district-level data on COVID-19 from March 14, 2020, to October 31, 2020. We identified hotspot and cold spot districts for COVID-19 cases and infection ratio. We have also carried out two sets of regression analysis to highlight the district level demographic, socioeconomic, household infrastructure facilities, and health-related correlates of the COVID-19 infection ratio. Results The results showed on October 31, 2020, the IR in India was 42.85 per hundred thousand population, with the highest in Kerala (259.63) and the lowest in Bihar (6.58). About 80 percent infected cases and 61 percent deaths were observed in nine states (Delhi, Gujarat, West Bengal, Uttar Pradesh, Andhra Pradesh, Maharashtra, Karnataka, Tamil Nadu, and Telangana). Moran’s- I showed a positive yet poor spatial clustering in the COVID-19 IR over neighboring districts. Our regression analysis demonstrated that percent of 15–59 aged population, district population density, percent of the urban population, district-level testing ratio, and percent of stunted children were significantly and positively associated with the COVID-19 infection ratio. We also found that, with an increasing percentage of literacy, there is a lower infection ratio in Indian districts. Conclusion The COVID-19 infection ratio was found to be more rampant in districts with a higher working-age population, higher population density, a higher urban population, a higher testing ratio, and a higher level of stunted children. The study findings provide crucial information for policy discourse, emphasizing the vulnerability of the highly urbanized and densely populated areas.

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 Epidemiology and transmission dynamics of COVID-19 in two Indian states

2020 ◽  
Author(s):  
Ramanan Laxminarayan ◽  
Brian Wahl ◽  
Shankar Reddy Dudala ◽  
K Gopal ◽  
Chandra Mohan ◽  
...  
Keyword(s):  
Tamil Nadu ◽  
Andhra Pradesh ◽  
Case Fatality ◽  
Control Measures ◽  
Contact Tracing ◽  
Primary Data ◽  
Indian States ◽  
Middle Income ◽  
Time To Death ◽  
Middle Income Countries

Although most COVID-19 cases have occurred in low-resource countries, there is scarce information on the epidemiology of the disease in such settings. Comprehensive SARS-CoV-2 testing and contact-tracing data from the Indian states of Tamil Nadu and Andhra Pradesh reveal stark contrasts from epidemics affecting high-income countries, with 92.1% of cases and 59.7% of deaths occurring among individuals <65 years old. The per-contact risk of infection is 9.0% (95% confidence interval: 7.5-10.5%) in the household and 2.6% (1.6-3.9%) in the community. Superspreading plays a prominent role in transmission, with 5.4% of cases accounting for 80% of infected contacts. The case-fatality ratio is 1.3% (1.0-1.6%), and median time-to-death is 5 days from testing. Primary data are urgently needed from low- and middle-income countries to guide locally-appropriate control measures.

scholarly journals Effectiveness of Government Policies in Controlling COVID-19 in India

2020 ◽  
Author(s):  
ASHUTOSH PANDEY ◽  
Nitin Saxena
Keyword(s):  
Population Density ◽  
Rural Areas ◽  
Rural Population ◽  
Urban Areas ◽  
Urban Population ◽  
Demographic Factors ◽  
Government Policies ◽  
Cumulative Number ◽  
Indian States ◽  
The People

<p>The purpose of this study is to find the demographic factors which are responsible for the spread of COVID-19 and to suggest a measure to identify the effectiveness of government policies in controlling COVID-19. The study hypothesises that the cumulative number of confirmed COVID-19 patients depends on the urban population, rural population, number of persons aged more than fifty, the population density and poverty rate in the state. A log-linear model is used to test the stated hypothesis, with the cumulative number of confirmed COVID-19 patients up to period as a dependent variable and demographic factors as an independent variable. The regression result shows that out of the selected variables, only the urban population significantly impacts the total number of patients tested positive for COVID-19. Our study finds that the urban population significantly impacts the spread of COVID-19. On the other had the demographic factors like rural population, density, and age structure do not impact the spread of COVID-19 significantly. Thus the people residing in the urban areas face a more significant threat of COVID-19 as compared to the people in rural areas. The study identifies the Indian states which need greater effectiveness in the implementation of pandemic control policies. Our study finds that the urban population significantly impacts the spread of COVID-19. On the other had the demographic factors like rural population, density, and age structure do not impact the spread of COVID-19 significantly. Thus the people residing in the urban areas face a more significant threat of COVID-19 as compared to the people in rural areas. The study identifies the Indian states which need greater effectiveness in the implementation of pandemic control policies.</p>

Residual Analysis of Pesticides in Surface Water of Nagpur, India

2019 ◽  
pp. 280-300
Author(s):  
Binota Thokchom ◽  
Neeta Thacker
Keyword(s):  
Surface Water ◽  
Population Density ◽  
Comparative Study ◽  
Pesticide Residues ◽  
Tamil Nadu ◽  
Andhra Pradesh ◽  
High Population Density ◽  
Farming Practices ◽  
Intensive Farming ◽  
Present Study Area

Seventy-five percent of India's economy depends on agriculture with statewide pesticide consumption of 0.5 kg/h. The highest pesticide consuming states are Tamil Nadu and Andhra Pradesh in between 0.8 to 2 kg/ha. Maharashtra is the topmost consumer of pesticides with over 23.5% share. Nagpur city (the present study area) of Maharashtra has high population density with intensive farming practices. Organochlorine and organophorous pesticide residues were measured in surface water collected from major lakes and rivers located in and around this city. A comparative study with previous records has also been discussed. Monitoring experiments conducted during pre-monsoon, monsoon, and post-monsoon seasons allowed the different samples to show their susceptibility for the above-mentioned pesticide residues.

Current Status of Ground Water Arsenic Contamination in India and Recent Advancements in Removal Techniques from Drinking Water

2016 ◽  
Vol 2 (1-2) ◽  
Author(s):  
Seema Mishra ◽  
Sanjay Dwivedi ◽  
Amit Kumar ◽  
Jürgen Mattusch ◽  
R.D. Tripathi
Keyword(s):  
Drinking Water ◽  
Ground Water ◽  
Tamil Nadu ◽  
Andhra Pradesh ◽  
South India ◽  
North India ◽  
Current Status ◽  
Indian States ◽  
Jammu And Kashmir ◽  
The Government

India is consisting of 29 states and 7 union territories, including a national capital, Delhi. Elevated concentrations (>10 g l ) of arsenic (As) in ground water (GW)  -1 of many states of India have become a major concern in recent years. Up to now about 0.2 million GW samples have been analyzed for As contamination from all over India by various researchers and Government agencies. About 90% of these cover only the Eastern part of India while several states and UTs are still unexplored. However, from the available data, GW of eighteen Indian states and three union territories has been found to be As contaminated to different extents through natural or anthropogenic origin. Among these, As >300 μg l has been reported from at least one locality from fourteen states. The -1 maximum level of As (7350 μg l ) in GW has been reported from a highly industrialized -1 area, Patancheru in Medak district of Andhra Pradesh. However, the gravity of problem is more in West Bengal followed by Bihar and Uttar Pradesh. Five out of eight North-Eastern states are also affected by As contamination. Manipur is ranked first and Assam as second followed by Arunachal Pradesh, Tripura and Nagaland. The GW in these regions is naturally As enriched, and therefore wide spatial distribution of As has been found in these areas. In North India, Punjab and Haryana and in South India, Andhra Pradesh and Karnataka are suffering with GW As contamination. Low level of As (up to 17 μg l ) has also -1 been reported in Tamil Nadu from South India. Many of the states like Jammu and Kashmir, Uttarakhand, Odisha, Gujrat, Kerala, Telengana, Goa etc. are still unexplored for GW As contamination. Thus, according to current reports out of 640 districts in India, 141 are As affected (As >10 g l-1), among them 120 are above 50 g l-1. Considering its severity, the issue of As contamination in drinking water has been taken up by the Government of India and mitigation efforts are being initiated. In order to provide safe drinking water, different agencies/ organizations have developed eco-friendly, cost effective devices/ filtration techniques having higher As removal capacity. Here we elucidated the current status of GWAs contamination in different states of India and the new developments of mitigation options.

scholarly journals Epidemiology and transmission dynamics of COVID-19 in two Indian states

Science ◽  
2020 ◽  
Vol 370 (6517) ◽  
pp. 691-697 ◽  
Author(s):  
Ramanan Laxminarayan ◽  
Brian Wahl ◽  
Shankar Reddy Dudala ◽  
K. Gopal ◽  
Chandra Mohan B ◽  
...  
Keyword(s):  
Tamil Nadu ◽  
Andhra Pradesh ◽  
Case Fatality ◽  
Control Measures ◽  
Primary Data ◽  
Demographic Differences ◽  
Indian States ◽  
Low Resource ◽  
High Incidence ◽  
Transmission Pathways

Although most cases of coronavirus disease 2019 (COVID-19) have occurred in low-resource countries, little is known about the epidemiology of the disease in such contexts. Data from the Indian states of Tamil Nadu and Andhra Pradesh provide a detailed view into severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission pathways and mortality in a high-incidence setting. Reported cases and deaths have been concentrated in younger cohorts than would be expected from observations in higher-income countries, even after accounting for demographic differences across settings. Among 575,071 individuals exposed to 84,965 confirmed cases, infection probabilities ranged from 4.7 to 10.7% for low-risk and high-risk contact types, respectively. Same-age contacts were associated with the greatest infection risk. Case fatality ratios spanned 0.05% at ages of 5 to 17 years to 16.6% at ages of 85 years or more. Primary data from low-resource countries are urgently needed to guide control measures.

scholarly journals Who is at the highest risk from COVID-19 in India? Analysis of health, healthcare access, and socioeconomic indicators at the district level

2020 ◽  
Author(s):  
Arindam Nandi ◽  
Ruchita Balasubramanian ◽  
Ramanan Laxminarayan
Keyword(s):  
High Risk ◽  
Health Risk ◽  
Tamil Nadu ◽  
Local Level ◽  
Healthcare Access ◽  
Principal Component ◽  
District Level ◽  
Public Healthcare ◽  
Socioeconomic Indicators ◽  
Indian States

AbstractIntroductionDespite measures such as travel restrictions and lockdowns, the novel coronavirus (SARS-COV-2) is projected to spread across India. Considering that a vaccine for COVID-19 is will not be available soon, it is important to identify populations with high risk from COVID-19 and take measures to prevent outbreaks and build healthcare infrastructure at the local level.MethodsWe used data from two large nationally representative household surveys, administrative sources, and published studies to estimate the risk of COVID-19 at the district level in India. We employed principal component analysis to create an index of the health risk of COVID-19 from demographic and comorbidity indicators such as the proportions of elderly population and rates of diabetes, hypertension, and respiratory illnesses. Another principal component index examined the socioeconomic and healthcare access risk from COVID-19, based on the standard of living, proportion of caste groups, and per capita access to public healthcare in each district.ResultsDistricts in northern, southern and western Indian states such as Punjab, Tamil Nadu, Kerala, and Maharashtra were at the highest health risk from COVID-19. Many of these districts have been designated as COVID-19 hotspots by the Indian government because of emergent outbreaks. Districts in eastern and central states such as Uttar Pradesh, Bihar, and Madhya Pradesh have higher socioeconomic and healthcare access risk as compared with other areas.ConclusionDistricts at high risk of COVID-19 should prioritize policy measures for preventing outbreaks, and improving critical care infrastructure and socioeconomic safety nets.

scholarly journals Nexus between population density and COVID-19 pandemic in the south Indian states: A geo-statistical approach

2020 ◽  
Author(s):  
Mohammad Arif ◽  
Soumita Sengupta
Keyword(s):  
Population Density ◽  
Tamil Nadu ◽  
Spatial Relationship ◽  
The Novel ◽  
Global Public Health ◽  
Indian States ◽  
Health Crisis ◽  
South Indian ◽  
Novel Coronavirus

The unprecedented growth of the novel coronavirus (SARS-CoV-2) as a severe acute respiratory syndrome escalated to the 2019 coronavirus disease (COVID-19) pandemic. It has created an unanticipated global public health crisis. The virus is spreading rapidly in India which poses serious threat to 135 crore population. Population density poses some unforeseen challenges to control the COVID-19 contagion. In times of crisis, data is crucial to understand the spatial relationship between density and the infection. The article study the district wise transmissions of the novel coronavirus in five south Indian states until 6th June 2020 and its relationship with the respective population density. The five states are purposefully selected for better healthcare infrastructure vis-à-vis other states in India. We observed that corona virus spread depends on the spatial distribution of population density in three states especially in Tamil Nadu, Karnataka and Telangana. The results indicate that the long-term impacts of the COVID-19 crisis are likely to differ with demographic density. Policy initiatives aimed at reducing the health consequences of the COVID-19 pandemic should understand how vulnerabilities cluster together across districts.

scholarly journals Epidemiological study on cardiac emergencies in Indian states having GVK Emergency Management and Research Institute services

2016 ◽  
Vol 04 (02) ◽  
pp. 121-126
Author(s):  
G. Ramana Rao ◽  
H. Rajanarsing Rao ◽  
G. Reddy ◽  
M. Prasad
Keyword(s):  
Emergency Management ◽  
Tamil Nadu ◽  
Andhra Pradesh ◽  
Poverty Line ◽  
State Governments ◽  
Indian States ◽  
The Mean ◽  
Access To Medical Care ◽  
Cardiac Emergencies ◽  
Research Institute

Abstract Background: Emergency medical service (EMS) is critical for the healthcare system as it saves lives by providing care immediately. Rapid access to medical care after a major cardiovascular event decreases morbidity and mortality. GVK Emergency Management and Research Institute (GVK EMRI) is a pioneer in emergency management services operated as a public private partnership (PPP) with various state governments. GVK EMRI coordinates medical, fire, and police-related emergencies through a single toll-free number, 108, across 15 states and 2 union territories of India. Material and Methods: This is a retrospective study of reported cases of cardiac emergencies in 2015 across 11 states with GVK EMRI services: Andhra Pradesh, Telangana, Assam, Goa, Gujarat, Karnataka, Madhya Pradesh, Meghalaya, Rajasthan, Tamil Nadu and Uttarakhand. Descriptive statistics using frequencies, proportions and means were calculated. Results and Discussion: This study aimed to describe the epidemiology of cardiac emergencies presenting to GVK EMRI across 11 states in India in 2015. There were increased cases of cardiac emergencies reported by higher age group individual across all states. The mean age was reported between 43 years to 62 years across the states. In this study, men called EMS for cardiac emergencies more often than women, except in the state of Gujarat. A higher number of cardiac emergency cases were reported by individuals living below the poverty line in Andhra Pradesh, Telangana, Assam, and Goa. Often (82.8%) people called 108 greater than six hours of symptom onset. Variation in call volume per day was minimal between the days of the week. At 48 hours, there were 2,675 reported deaths (1.1%). Conclusions: The current study stresses the scale and seriousness of the emerging challenge of cardiac emergencies, with particular emphasis on socioeconomic deprived groups in the operated states of GVK EMRI.

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