Modelling and analysis of COVID-19 epidemic in India

A longitudinal study of depression, anxiety, and stress among Indians during COVID-19 pandemic

The COVID-19 has caused significant public health chaos. Whether infected or uninfected, people have reported significant mental health issues during the COVID-19 pandemic. The present study explored symptoms of depression, anxiety, and stress of uninfected people with the disease over three time periods. A total of 132 participants took part in all the three phases. Data at Time 1 and Time 2 were collected during the national lockdown with 1 month apart, while data at Time 3 were collected immediately once the lockdown was uplifted in India. Participants completed the Depression, Anxiety and Stress Scale-21 online. The results indicated that despite an increase in the average number of COVID-19 cases, depression, anxiety, and stress symptoms decreased over time with the lowest level once the lockdown was ended. These results suggest that the restrictions implemented during the national lockdown led to distress, and not an increase in COVID-19 cases per se.

Last Honors and Life Experiences of Bereaved Families in the Context of COVID-19 in Kashmir: A Qualitative Inquiry About Exclusion, Family Trauma, and Other Issues

This study examined the changing character of the last honours of those who died of COVID-19 in Kashmir and the life experiences of the families of the deceased. A semi-structured interview schedule was used to collect information from 21 participants. Using qualitative data analysis approaches, five key themes were identified vis-à-vis the impact of COVID-19 on burial rituals and customs; effects on bereaved families, shades of grief, bereavement care, community response, and coping with loss. Based on examining the pandemic-induced changes related to customs and rituals around death, the study found that the bereaved family members were in danger of marginalization, economic burdens, psychological traumas, and overall reduced quality of life. This study would be a credible addition to the existing literature on death practices as there is a shortage of research on funeral rituals during the post-pandemic period in Kashmir.

An Improved Epidemiological Model for the Underprivileged People in the Contemporary Pandemics

In this work, we introduce an improved form of the basic SEIRD model based on Python simulation for the troublesome people who are oblivious about the contemporary pandemics due to diverse social impediments, especially those economically underprivileged. In the extant epidemiological models, some unorthodox issues are yet to be considered, such as poverty, illiteracy, and carelessness towards health issues, significantly influencing the data modeling. Our focus is to overcome these issues by adding two more branches, for instance, uncovered and apathetic people, which significantly influence the practical purposes. For the data simulation, we have used the Python-based algorithm that trains the desired system based on a set of real-time data with the proposed model and provides predicted data with a certain level of accuracy. Comparative discussions, statistical error analysis, and correlation-regression analysis have been introduced to validate the proposed epidemiological model. To show the numerical evidence, the investigation comprised the figurative and tabular modes for both real-time and predicted data. Finally, we discussed some concluding remarks based on our findings.

Analytical investigation of compartmental models and measure for reactions of governments

The ongoing coronavirus disease 2019 (COVID-19) pandemic poses a major threat to the worldwide health care. In this context, epidemic modelling is an integral part of containment strategies. Compartmental models are typically used for this purpose. Analytical solutions of the two distinct but connected Susceptible-Infectious-Recovered-Deceased (SIRD) and Susceptible-Infectious-Quarantine-Recovered (SIQR) models are presented in this study. Furthermore, the behaviour at the start of a disease outbreak is derived. This analysis shows that a combination of transmission, recovery and isolation rates dominates the behaviour at the start of an epidemic. In addition, the loss occurring due to quarantine and lockdown measures is investigated, where it can be observed that quarantine procedures lead to a smaller loss in comparison with lockdown regulations. Within this framework, optimized strategies that lead to a constant epidemic peak or a minimized loss are presented.

Clusters of COVID-19 Indicators in India: Characterization, Correspondence and Change Analysis

We conduct a long-term epidemiology study of COVID-19 in India from Mar 2020 to May 2021 using a number of indicators such as active cases, daily new cases, and deaths, on a micro (district level, per capita) and macro level (state level). Our automated shape-based cluster discovery of the per capita daily new cases (case rate) during the first wave in India (between Mar 2020 and Jan 2021) revealed four distinct shape patterns: sharp-rise and decline, steady-rise and decline, plateau and multiple relatively high peaks. These clusters exhibit a strong geographical correlation. To determine the correspondence between clusters obtained by different indicators, we design a novel metric for determining edge-weights in their intersection graph. This is used for comparative analysis and to develop informative hierarchical cartographic visualizations. We then perform dynamic cluster analysis for different time windows to answer some pertinent questions. Is the second wave similar to or different from the first wave? How has the relative ranking (on micro- and macro-level indicators) of the states varied over the last one year? How much medical resources have been stressed during the peak? We demonstrate that using multiple indicators, we can assess the impact of the epidemic holistically in a particular geography. Our analysis techniques and insights obtained can help the local and state governments in monitoring and managing COVID-19 situation and fine-tuning the ongoing vaccination drive in India.

Extended Epidemiological Models for Weak Economic Region: Case Studies of the Spreading of COVID-19 in the South Asian Subcontinental Countries

The ascendancy of coronavirus has become widespread all around the world. For the prevention of viral transmission, the pattern of disease is explored. Epidemiological modeling is a vital component of the research. These models assist in studying various aspects of infectious diseases, such as death, recovery, and infection rates. Coronavirus trends across several countries may analyze sufficiently using SIR, SEIR, and SIQR models. Across this study, we propose two modified versions of the SEIRD method for evaluating the transmission of this infectious disease in the South Asian countries, more precisely, in the south Asian subcontinent. The SEIRD model is updated further by fusing some new factors, namely, isolation for the suspected people and recovery and death of the people who are not under the coverage of healthcare schemes or reluctant to receive treatment for various catastrophes. We will investigate the influences of those ingredients on public health-related issues. Finally, we will predict and display the infection scenario and relevant elements with the concluding remarks through the statistical analysis.