Estimation of Effective Reproduction Numbers for COVID-19 using Real-Time Bayesian Method for India and its States

Tyagi, Rishabh; Dwivedi, Laxmi Kant; Sanzgiri, Ashutosh

doi:10.21203/rs.3.rs-45937/v1

Cited by 9 publications

(5 citation statements)

References 14 publications

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“…Another similar study was done by Tyagi et al in [18] where they concluded to have predicted the number of COVID-19 cases till end of month June and predicted the requirement of ICU beds, ventilators and isolation beds in India using ARIMA(1,3,1) model. The main objective of their work was to predict the medical requirement (ICU 10% of the Active cases, BEDS, Ventilators for 5% of the active cases and isolation bed).…”

Section: Related Workmentioning

confidence: 79%

Time Series Analysis of COVID-19 Data to Study the Effect of Lockdown and Unlock in India

Singh

Chowdhury

Panja

et al. 2020

Preprint

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The ongoing COVID-19 pandemic has caused worldwide socioeconomic unrest, forcing governments to introduce extreme measures to reduce its spread. Being able to accurately forecast the effect of unlocking in India, would allow governments to alter their policies accordingly and plan ahead. The study investigated prediction forecasts using the ARIMA model on the COVID-19 data on the lockdown period and the unlock period. In this work, we have considered not only the no of positive COVID cases but also considered the number of tests carried out. The time-series data sample was collected till June 2020 and the prediction and analysis are done for August 2020. The model developed and the forecasted results align very closely with the actual no of cases and we have drawn some important inferences through experimentation.

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Section: Related Workmentioning

confidence: 79%

Time Series Analysis of COVID-19 Data to Study the Effect of Lockdown and Unlock in India

Singh

Chowdhury

Panja

et al. 2020

Preprint

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“…Our forecasts show an alarming situation for India in the future. In coming months, the actual numbers can go higher than our forecasts of confirmed cases, active cases and healthcare infrastructure as migrant workers are returning home in large numbers currently in large numbers (Tyagi et al , 2020). Currently, India has lifted lockdown with restrictions applying only on the containment zones.…”

Section: Discussionmentioning

confidence: 96%

COVID 19: Real-time Forecasts of confirmed cases, active cases, and health infrastructure requirements for India and its states using the ARIMA model

Tyagi

Bramhankar

Pandey

et al. 2020

Preprint

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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.

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“…Several previous studies have estimated R e for various scenarios. The effective reproduction number for COVID-19 of India and its states has been determinated in [47] using Real-Time Bayesian Method. [48] determines the effective reproduction number for COVID-19 at the first 10 days of Latin American countries where the highest was in Ecuador(R e = 3.95) and the smallest in Peru (R e = 2.36) and make a comparison with one of Spain(R e = 2.9) and Italy(R e = 2.83).…”

Section: Basic Reproduction Number and Effective Reproduction Numbermentioning

confidence: 99%

Cost Effective Reproduction Number Based Strategies for Reducing Deaths from COVID-19

Thron¹,

Mbazumutima²,

Tamayo³

et al. 2021

Preprint

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In epidemiology, the effective reproduction number R e is used to characterize the growth rate of an epidemic outbreak. If R e > 1, the epidemic worsens, and if R e < 1, then it subsides and eventually dies out. In this paper, we investigate properties of R e for a modified SEIR model of COVID-19 in the city of Houston, TX USA, in which the population is divided into low-risk and high-risk subpopulations. The response of R e to two types of control measures (testing and distancing) applied to the two different subpopulations is characterized. A nonlinear cost model is used for control measures, to include the effects of diminishing returns. Lowest-cost control combinations for reducing instantaneous R e to a given value are computed. We propose three types of heuristic strategies for mitigating COVID-19 that are targeted at reducing R e , and we exhibit the tradeoffs between strategy implementation costs and number of deaths. We also consider two variants of each type of strategy: basic strategies, which consider only the effects of controls on R e , without regard to subpopulation; and high-risk prioritizing strategies, which maximize control of the high-risk subpopulation. Results showed that of the three heuristic strategy types, the most cost-effective involved setting a target value for R e and applying sufficient controls to attain that target value. This heuristic led to strategies that begin with strict distancing of the entire population, later followed by increased testing. Strategies that maximize control on high-risk individuals were less cost-effective than basic strategies that emphasize reduction of the rate of spreading of the disease. The model shows that delaying the start of control measures past a certain point greatly worsens strategy outcomes. We conclude that the effective reproduction can be a valuable real-time indicator in determining cost-effective control strategies.

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Estimation of Effective Reproduction Numbers for COVID-19 using Real-Time Bayesian Method for India and its States

Cited by 9 publications

References 14 publications

Time Series Analysis of COVID-19 Data to Study the Effect of Lockdown and Unlock in India

Time Series Analysis of COVID-19 Data to Study the Effect of Lockdown and Unlock in India

COVID 19: Real-time Forecasts of confirmed cases, active cases, and health infrastructure requirements for India and its states using the ARIMA model

Cost Effective Reproduction Number Based Strategies for Reducing Deaths from COVID-19

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