Epidemiological studies on coronavirus disease 2019 pandemic in India: Too little and too late?

Gandhi, P. Aravind; Kathirvel, Soundappan

doi:10.1016/j.mjafi.2020.05.003

Cited by 8 publications

(8 citation statements)

References 3 publications

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“…The reference period for the RSV data for the search terms was from January 30, 2020 to April 15, 2020. India reported its first case of COVID-19 on January 30, 2020 [2].…”

Section: Data On Google Search Termsmentioning

confidence: 99%

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Prediction of COVID-19 Outbreaks Using Google Trends in India: A Retrospective Analysis

Venkatesh

Gandhi

2020

Healthc Inform Res

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I. Introduction Coronavirus disease 2019 (COVID-19) is rapidly spreading across the globe and has become a significant public health threat to humankind infecting millions worldwide [1]. India is a low middle-income country in the SouthEast Asia region with a population of 1.3 billion. India reported its first case of COVID-19 on January 30, 2020 [2]. The case numbers were almost static for over a month and gradually started to increase during early March. As of July 7, 2020, India recorded 719,665 cases and 20,160 people succumbed to COVID-19 [1]. Considering the rising menace of CO-VID-19, it is essential to explore the methods and resources that might predict the case numbers expected and help in identifying the locations of outbreaks. This will help us understand what to expect and prepare for in terms of caseload

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“…The reference period for the RSV data for the search terms was from January 30, 2020 to April 15, 2020. India reported its first case of COVID-19 on January 30, 2020 [2].…”

Section: Data On Google Search Termsmentioning

confidence: 99%

“…India is a low middle-income country in the South-East Asia region with a population of 1.3 billion. India reported its first case of COVID-19 on January 30, 2020 [ 2 ]. The case numbers were almost static for over a month and gradually started to increase during early March.…”

Section: Introductionmentioning

confidence: 99%

Prediction of COVID-19 Outbreaks Using Google Trends in India: A Retrospective Analysis

Venkatesh

Gandhi

2020

Healthc Inform Res

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“… 5 , 6 The situation is quite different for India what the developed world experienced in controlling the situation. 7 The situation of India is more critical for its huge population, poor infrastructure, and complex socio-economic structure, 8 where self-isolation, social distancing, and quality treatment is the key controlling factors to neutralize the impact of disease. 9 , 10 , 11 , 12 Inter-state migration of labor in the lockdown period has brought challenges to controlled community spread.…”

Section: Introductionmentioning

confidence: 99%

COVID-19: District level vulnerability assessment in India

Sarkar¹,

Chouhan²

2021

Clinical Epidemiology and Global Health

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Objectives COVID-19 Pandemic has brought a threatening challenge to the world and as well as for Indian society and economy. In India, it has become a public health disaster and its' intensity increasing continuously. For the disaster risk reduction, and capacity building against the COVID-19 pandemic understanding of the relationship between socio-environmental conditions with the pandemic is very necessary. The objective of the present work is to construct a socio-environmental vulnerability index of the potential risk of community spread of COVID-19 using socio-economic and environmental variables. Methodology In this, cross-sectional study principal component analyses have been used to drive SoEVI. 4 uncorrelated sub-index has been extracted from 16 sub-indicators which reflects 59% of the variance. Aggregation of 4 Sub-Index has been done to obtain the final vulnerability Index. Results Results show that there is spatial variability in vulnerability based on environmental and socio-economic conditions. Districts of north and central India found more vulnerable then south India. Statistical significance has been tested using regression analysis, positive relation has been found between vulnerability index and confirmed and active cases. Conclusion The vulnerability index has highlighted environmentaly and socioeconomicallybackward districts. These areas will suffer more critical problems against COVID-19 pandemic for their socio-environmental problem.

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“…The rate of evolution of the epidemic curve in Rajasthan is among the top eight states of India [12]. The mean age of COVID-19 cases was 37.08 years in Rajasthan, which was lower than the mean age-based on 65 research articles [13][14][15]. The age distribution of the general population of Rajasthan was rightskewed.…”

Section: Discussionmentioning

confidence: 99%

Evidence-based decision making and covid-19: what a posteriori probability distributions speak

et al. 2020

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Background: In the absence of any pharmaceutical interventions, the management of the COVID-19 pandemic is based on public health measures. The present study fosters evidence-based decision making by estimating various “a posteriori probability distributions" from COVID-19 patients. Methods: In this retrospective observational study, 987 RT-PCR positive COVID-19 patients from SMS Medical College, Jaipur, India, were enrolled after approval of the institutional ethics committee. The data regarding age, gender, and outcome were collected. The univariate and bivariate distributions of COVID-19 cases with respect to age, gender, and outcome were estimated. The age distribution of COVID-19 cases was compared with the general population's age distribution using the goodness of fit c2 test. The independence of attributes in bivariate distributions was evaluated using the chi-square test for independence. Results: The age group ‘25-29’ has shown highest probability of COVID-19 cases (P [25-29] = 0.14, 95% CI: 0.12- 0.16). The men (P [Male] = 0.62, 95%CI: 0.59-0.65) were dominant sufferers. The most common outcome was recovery (P [Recovered] = 0.79, 95%CI: 0.76-0.81) followed by admitted cases (P [Active]= 0.13, 95%CI: 0.11-0.15) and death (P [Death] = 0.08, 95%CI: 0.06-0.10). The age distribution of COVID-19 cases differs significantly from the age distribution of the general population (c2 =399.04, P < 0.001). The bivariate distribution of COVID-19 across age and outcome was not independent (c2 =106.21, df = 32, P < 0.001). Conclusion: The knowledge of disease frequency patterns helps in the optimum allocation of limited resources and manpower. The study provides information to various epidemiological models for further analysis.

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Epidemiological studies on coronavirus disease 2019 pandemic in India: Too little and too late?

Cited by 8 publications

References 3 publications

Prediction of COVID-19 Outbreaks Using Google Trends in India: A Retrospective Analysis

Prediction of COVID-19 Outbreaks Using Google Trends in India: A Retrospective Analysis

COVID-19: District level vulnerability assessment in India

Evidence-based decision making and covid-19: what a posteriori probability distributions speak

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