Modelling the dynamics of a public health care system: evidence from time-series data

AbstractBackgroundCOVID-19 is an emerging infectious disease which has been declared a Pandemic by the World Health Organization (WHO) on March 11 2020. This pandemic has spread over the world in more than 200 countries. India is also adversely affected by this pandemic, and there are no signs of slowing down of the virus in coming time. The absence of a vaccine for COVID-19 is making the situation worse for the already overstretched Indian public health care system. As economic burden makes it increasingly difficult for our country to continue imposing control measures, it is vital for states like West Bengal to make predictions using time series forecasting for the upcoming cases, test kits, health care and estimated the requirement of Quarantine centers, isolation beds, ICU beds and ventilators for COVID-19 patients.ObjectiveThis study is forecasting the confirmed and active cases for COVID-19 until August, using time series ARIMA model & Public Health in West Bengal, India.MethodsWe used ARIMA model, and Auto ARIMA model for forecasting confirmed and active cases till the end of August month using time series data of COVID-19 cases in West Bengal, India from March 1, 2020, to June 4, 2020.ResultsWe are expecting that West Bengal will have around 62279 ± 5000 Cases by the end of August based on our forecasts. Meanwhile Maharashtra, Punjab, Gujarat and Delhi (UT) will be the most affected states, having the highest number of active and confirmed cases at the end of August.Discussion and ConclusionThis forecasts show a very crucial situation for West Bengal in coming days and, the actual numbers can go higher than our estimates of confirmed case as Lockdown 5.0 & Unlock 1.0 will be implemented from 1st June 2020 in India, West Bengal will be observing a partial lift of the lockdown and in that case, there will be a surge in the number of daily confirmed and active cases. The requirement of Health care sector needs to be further improved isolation beds, ICUs and ventilators will also be needed to increase in that scenario. Inter State & Intra State Movement restrictions are lifted. Hence, Migrants returning to their homes due to loss of livelihood and income in the lockdown period may lead to a rise in the number of cases, which could not be accounted for in our projections. We suggest more of Public-Private Partnership (PPP) model in the health sector to accommodate COVID-19 patients adequately and reduce the burden of the already overstretched Indian public health care system, which will directly or indirectly affect the States in the time of crisis.

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COVID 19 Pandemic: A Real-time Forecasts & Prediction of Confirmed Cases, Active Cases using the ARIMA model &Public Health in West Bengal, India.

10.21203/rs.3.rs-33982/v1 ◽

2020 ◽

Author(s):

Dibash Sarkar

Keyword(s):

Public Health ◽

Health Care ◽

Time Series ◽

Health Care System ◽

West Bengal ◽

Arima Model ◽

Public Health Care ◽

Public Health Care System ◽

The World ◽

Care System

Abstract Background: COVID-19 is an emerging infectious disease which has been declared a Pandemic by the World Health Organization (WHO) on March 11 2020. This pandemic has spread over the world in more than 200 countries. India is also adversely affected by this pandemic, and there are no signs of slowing down of the virus in coming time. The absence of a vaccine for COVID-19 is making the situation worse for the already overstretched Indian public health care system. As economic burden makes it increasingly difficult for our country to continue imposing control measures, it is vital for states like West Bengal to make predictions using time series forecasting for the upcoming cases , test kits , health care and estimated the requirement of Quarantine centers, isolation beds, ICU beds and ventilators for COVID-19 patients .Objective: This study is forecasting the confirmed and active cases for COVID-19 until August, using time series ARIMA model & Public Health in West Bengal, India.Methods: We used ARIMA model, and Auto ARIMA model for forecasting confirmed and active cases till the end of August month using time series data of COVID-19 cases in West Bengal, India from March 1, 2020, to June 4, 2020.Discussion and Conclusion: This forecasts show a very crucial situation for West Bengal in coming days and, the actual numbers can go higher than our estimates of confirmed case as Lockdown 5.0 & Unlock 1.0 will be implemented from 1st June 2020 in India, West Bengal will be observing a partial lift of the lockdown and in that case, there will be a surge in the number of daily confirmed and active cases. The requirement of Health care sector needs to be further improved isolation beds, ICUs and ventilators will also be needed to increase in that scenario. Inter State & Intra State Movement restrictions are lifted. Hence, Migrants returning to their homes due to loss of livelihood and income in the lockdown period may lead to a rise in the number of cases, which could not be accounted for in our projections. We suggest more of Public-Private Partnership (PPP) model in the health sector to accommodate COVID-19 patients adequately and reduce the burden of the already overstretched Indian public health care system, which will directly or indirectly affect the States in the time of crisis.

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COVID 19 Pandemic: A Real-time Forecasts & Prediction of Confirmed Cases, Active Cases using the ARIMA model & Public Health in West Bengal, India.

10.35543/osf.io/crk95 ◽

2020 ◽

Author(s):

Dibash Sarkar

Keyword(s):

Public Health ◽

Health Care ◽

Time Series ◽

Health Care System ◽

West Bengal ◽

Arima Model ◽

Public Health Care ◽

Public Health Care System ◽

The World ◽

Care System

Background: COVID-19 is an emerging infectious disease which has been declared a Pandemic by the World Health Organization (WHO) on March 11 2020. This pandemic has spread over the world in more than 200 countries. India is also adversely affected by this pandemic, and there are no signs of slowing down of the virus in coming time. The absence of a vaccine for COVID-19 is making the situation worse for the already overstretched Indian public health care system. As economic burden makes it increasingly difficult for our country to continue imposing control measures, it is vital for states like West Bengal to make predictions using time series forecasting for the upcoming cases , test kits , health care andestimated the requirement of Quarantine centers, isolation beds, ICU beds and ventilators for COVID-19 patients Objective: This study is forecasting the confirmed and active cases for COVID-19 until August, using time series ARIMA model & Public Health in West Bengal, India.Methods: We used ARIMA model, and Auto ARIMA model for forecasting confirmed and active cases till the end of August month using time series data of COVID-19 cases in West Bengal, India from March 1, 2020, to June 4, 2020. Results: We are expecting that West Bengal will have around 62279 ± 5000 Cases by the end of Augustbased on our forecasts. Meanwhile Maharashtra, Punjab, Gujarat and Delhi (UT) will be the most affected states, having the highest number of active and confirmed cases at the end of August.Discussion and Conclusion: This forecasts show a very crucial situation for West Bengal in coming days and, the actual numbers can go higher than our estimates of confirmed case as Lockdown 5.0 & Unlock 1.0 will be implemented from 1st June 2020 in India, West Bengal will be observing a partial liftof the lockdown and in that case, there will be a surge in the number of daily confirmed and active cases. The requirement of Health care sector needs to be further improved isolation beds, ICUs and ventilators will also be needed to increase in that scenario. Inter State & Intra State Movement restrictions are lifted. Hence, Migrants returning to their homes due to loss of livelihood and income in the lockdown period may lead to a rise in the number of cases, which could not be accounted for in our projections. We suggest more of Public-Private Partnership (PPP) model in the health sector to accommodate COVID-19 patients adequately and reduce the burden of the already overstretched Indian public health care system,which will directly or indirectly affect the States in the time of crisis.

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An Economic Evaluation of Disallowing Body Checking in 11- to 12-Year-Old Ice Hockey Leagues

Sports Health A Multidisciplinary Approach ◽

10.1177/19417381211021551 ◽

2021 ◽

pp. 194173812110215

Author(s):

Gillian R. Currie ◽

Raymond Lee ◽

Amanda M. Black ◽

Luz Palacios-Derflingher ◽

Brent E. Hagel ◽

...

Keyword(s):

Public Health ◽

Health Care ◽

Health Care System ◽

Policy Change ◽

National Policy ◽

Ice Hockey ◽

Public Health Care ◽

Public Health Care System ◽

Body Checking ◽

Care System

Background: After a national policy change in 2013 disallowing body checking in Pee Wee ice hockey games, the rate of injury was reduced by 50% in Alberta. However, the effect on associated health care costs has not been examined previously. Hypothesis: A national policy removing body checking in Pee Wee (ages 11-12 years) ice hockey games will reduce injury rates, as well as costs. Study Design: Cost-effectiveness analysis alongside cohort study. Level of Evidence: Level 3. Methods: A cost-effectiveness analysis was conducted alongside a cohort study comparing rates of game injuries in Pee Wee hockey games in Alberta in a season when body checking was allowed (2011-2012) with a season when it was disallowed after a national policy change (2013-2014). The effectiveness measure was the rate of game injuries per 1000 player-hours. Costs were estimated based on associated health care use from both the publicly funded health care system and privately paid health care cost perspectives. Probabilistic sensitivity analysis was conducted using bootstrapping. Results: Disallowing body checking significantly reduced the rate of game injuries (−2.21; 95% CI [−3.12, −1.31] injuries per 1000 player-hours). We found no statistically significant difference in public health care system (−$83; 95% CI [−$386, $220]) or private health care costs (−$70; 95% CI [−$198, $57]) per 1000 player-hours. The probability that the policy of disallowing body checking was dominant (with both fewer injuries and lower costs) from the perspective of the public health care system and privately paid health care was 78% and 92%, respectively. Conclusion: Given the significant reduction in injuries, combined with lower public health care system and private costs in the large majority of iterations in the probabilistic sensitivity analysis, our findings support the policy change disallowing body checking in ice hockey in 11- and 12-year-old ice hockey leagues.

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