Background: COVID-19 is an emerging infectious disease which has been declared a Pandemic by the World Health Organization (WHO) on 11th March 2020. The Indian public health care system is already overstretched, and this pandemic is making things even worse. That is why forecasting cases for India is necessary to meet the future demands of the health infrastructure caused due to COVID-19. Objective: Our study forecasts the confirmed and active cases for COVID-19 until July mid, using time series Autoregressive Integrated Moving Average (ARIMA) model. Additionally, we estimated the number of isolation beds, Intensive Care Unit (ICU) beds and ventilators required for the growing number of COVID-19 patients. Methods: We used ARIMA model for forecasting confirmed and active cases till the 15th July. We used time-series data of COVID-19 cases in India from 14th March to 22nd May. We estimated the requirements for ICU beds as 10%, ventilators as 5% and isolation beds as 85% of the active cases forecasted using the ARIMA model. Results: Our forecasts indicate that India will have an estimated 7,47,772 confirmed cases (95% CI: 493943, 1001601) and 296,472 active cases (95% CI:196820, 396125) by 15th July. While Maharashtra will be the most affected state, having the highest number of active and confirmed cases, Punjab is expected to have an estimated 115 active cases by 15th July. India needs to prepare 2,52,001 isolation beds (95% CI: 167297, 336706), 29,647 ICU beds (95% CI: 19682, 39612), and 14,824 ventilator beds (95% CI: 9841, 19806). Conclusion: Our forecasts show an alarming situation for India, and Maharashtra in particular. The actual numbers can go higher than our estimated numbers as India has a limited testing facility and coverage.
WHO declared the outbreak of the novel Coronavirus, COVID-19, as a pandemic on 11th March. On 24th March, a three-week nation-wide lockdown has been announced, which is now extended till 31st May. Effective Reproduction Number (Rt) helps in understanding how effective preventive measures have been in controlling an outbreak. This study assesses the impact of nation-wide lockdown in slowing down the spread of the COVID-19 at the national and state level. An attempt has also been made to examine the important state-level factors responsible for the uneven distribution of Rt of COVID-19 across different states of India. Bayesian approach based on the probabilistic formulation of standard SIR disease transmission models have been employed assuming serial interval of 4 days and basic reproduction number (R0) of three. India’s Rt has declined from 1.81 (90% HDI: 1.64, 2.00) on 1st April to Rt =1.04 (90% HDI: 0.96, 1.13) on 9th May, after that it started increasing, and Rt =1.14 (90% HDI: 1.06, 1.21) was observed on 17th May. The value of Rt at the state level has shown significant variations. The testing rate had a significant impact in reducing the Rt at the state-level. The strategy of lockdown has contributed to containing the spread of the virus to some extent, but India still has a long way to go. Testing Rate is the most significant factor at state-level, as Testing and isolating patients sooner significantly reduces the disease spread.
Background Existing evidence indicates that the link between socioeconomic status and mental health is complex and overlapping. Although cognitive functioning declines with age and is directly linked to biological brain changes as people age, socioeconomic factors play an essential role in the level and change of cognitive functioning and onset of depression in older adults. This study attempts to assess the association between social deprivation, cognitive status, and depression among older persons in India. Data and Methods The LASI Wave 1, collected in India between 2017 and 2018, was used for this study. Social deprivation Index (SDI) was constructed. Education, wealth quintile, working status, living arrangements etc. were SDI indicators. Multivariate logistic regressions were used to establish the association between outcome and explanatory variables. Results The findings reveal that 31% of people with higher social deprivation have poor cognitive health compared to only 8% of people with lower social deprivation. Further, 60.5% of people with higher social deprivation have depressive symptoms compared to 25.8% of people with lower social deprivation. The prevalence of poor cognitive health (18.5%) and depressive symptoms (32.1%) are highest among older adults with no schooling, and further the good cognitive health (0.3%) in older adults with ten or more years of education. The exploratory analysis indicated that cognitive health and depressive symptoms were significantly associated with age, place of residence, marital status, caste/tribe, and religion. Conclusion The findings suggest that older adults (75 and above) with depressive and cognitive dysfunctioning were the largest in the case of highly socially deprived. In other age groups, highly socially deprived people are more vulnerable to poor cognitive health and depressive symptoms. The findings from the study inform the policymakers and planners to devise policies considering equitable healthcare needs to improve mental health among older adults, which is generally ignored in India.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.