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EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-317151


Data analysis and visualization are essential for exploring and communicating findings in medical research, especially in epidemiological surveillance. Data on COVID-19 diagnosed cases and mortality, from crowdsourced website COVID-19 India Tracker, Census 2011, and Google Mobility reports have been used to develop a real-time analytics and monitoring system for the COVID-19 outbreak in India. We have developed a dashboard application for data visualization and analysis of several indicators to follow the SARS-CoV-2 epidemic using data science techniques. A district-level tool for basic epidemiological surveillance, in an interactive and user-friendly manner which includes time trends, epidemic curves, key epidemiological parameters such as growth rate, doubling time, and effective reproduction number have been estimated. This demonstrates the application of data science methods and epidemiological techniques in public health decision-making while addressing the gap of timely and reliable decision aiding tools.

PLoS One ; 15(9): e0239026, 2020.
Article in English | MEDLINE | ID: covidwho-771769


The Government of India in-network with the state governments has implemented the epidemic curtailment strategies inclusive of case-isolation, quarantine and lockdown in response to ongoing novel coronavirus (COVID-19) outbreak. In this manuscript, we attempt to estimate the impact of these steps across ten selected Indian states using crowd-sourced data. The trajectory of the outbreak was parameterized by the reproduction number (R0), doubling time, and growth rate. These parameters were estimated at two time-periods after the enforcement of the lockdown on 24th March 2020, i.e. 15 days into lockdown and 30 days into lockdown. The authors used a crowd sourced database which is available in the public domain. After preparing the data for analysis, R0 was estimated using maximum likelihood (ML) method which is based on the expectation minimum algorithm where the distribution probability of secondary cases is maximized using the serial interval discretization. The doubling time and growth rate were estimated by the natural log transformation of the exponential growth equation. The overall analysis shows decreasing trends in time-varying reproduction numbers (R(t)) and growth rate (with a few exceptions) and increasing trends in doubling time. The curtailment strategies employed by the Indian government seem to be effective in reducing the transmission parameters of the COVID-19 epidemic. The estimated R(t) are still above the threshold of 1, and the resultant absolute case numbers show an increase with time. Future curtailment and mitigation strategies thus may take into account these findings while formulating further course of action.

Betacoronavirus , Communicable Disease Control/methods , Coronavirus Infections/prevention & control , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Basic Reproduction Number , Betacoronavirus/physiology , COVID-19 , COVID-19 Testing , Clinical Laboratory Techniques/statistics & numerical data , Coronavirus Infections/diagnosis , Coronavirus Infections/epidemiology , Coronavirus Infections/transmission , Crowdsourcing , Databases, Factual , Geography, Medical , Government Agencies , Health Policy , Humans , Incidence , India/epidemiology , Models, Biological , Pneumonia, Viral/epidemiology , Pneumonia, Viral/transmission , Quarantine , SARS-CoV-2
Int J Soc Psychiatry ; 67(5): 587-600, 2021 08.
Article in English | MEDLINE | ID: covidwho-740307


INTRODUCTION: Mental health concerns and treatment usually take a backseat when the limited resources are geared for pandemic containment. In this global humanitarian crisis of the COVID-19 pandemic, mental health issues have been reported from all over the world. OBJECTIVES: In this study, we attempt to review the prevailing mental health issues during the COVID-19 pandemic through global experiences, and reactive strategies established in mental health care with special reference to the Indian context. By performing a rapid synthesis of available evidence, we aim to propose a conceptual and recommendation framework for mental health issues during the COVID-19 pandemic. METHODS: A search of the PubMed electronic database and google scholar were undertaken using the search terms 'novel coronavirus', 'COVID-19', 'nCoV', SARS-CoV-2, 'mental health', 'psychiatry', 'psychology', 'anxiety', 'depression' and 'stress' in various permutations and combinations. Published journals, magazines and newspaper articles, official webpages and independent websites of various institutions and non-government organizations, verified social media portals were compiled. RESULTS: The major mental health issues reported were stress, anxiety, depression, insomnia, denial, anger and fear. Children and older people, frontline workers, people with existing mental health illnesses were among the vulnerable in this context. COVID-19 related suicides have also been increasingly common. Globally, measures have been taken to address mental health issues through the use of guidelines and intervention strategies. The role of social media has also been immense in this context. State-specific intervention strategies, telepsychiatry consultations, toll free number specific for psychological and behavioral issues have been issued by the Government of India. CONCLUSION: Keeping a positive approach, developing vulnerable-group-specific need-based interventions with proper risk communication strategies and keeping at par with the evolving epidemiology of COVID-19 would be instrumental in guiding the planning and prioritization of mental health care resources to serve the most vulnerable.

COVID-19/epidemiology , Mental Health/statistics & numerical data , Aged , Anger , Anxiety/epidemiology , Child , Cross-Sectional Studies , Depression/epidemiology , Female , Humans , India/epidemiology , Middle Aged , Sleep Initiation and Maintenance Disorders/epidemiology , Stress, Psychological/epidemiology , Suicide/statistics & numerical data , Vulnerable Populations/psychology , Vulnerable Populations/statistics & numerical data