ABV-CoViD: An Ensemble Forecasting Model to Predict Availability of Beds and Ventilators for COVID-19 Like Pandemics

Vivek Kumar Prasad; Pronaya Bhattacharya; Madhuri Bhavsar; Ashwin Verma; Sudeep Tanwar; Gulshan Sharma; Pitshou N Bokoro; Ravi Sharma

doi:10.1109/ACCESS.2022.3190497

ABV-CoViD: An Ensemble Forecasting Model to Predict Availability of Beds and Ventilators for COVID-19 Like Pandemics

IEEE Access. 2022 Jul 13:10:74131-74151. doi: 10.1109/ACCESS.2022.3190497. eCollection 2022.

Authors

Vivek Kumar Prasad¹, Pronaya Bhattacharya¹, Madhuri Bhavsar¹, Ashwin Verma¹, Sudeep Tanwar¹, Gulshan Sharma², Pitshou N Bokoro², Ravi Sharma³

Affiliations

¹ Department of Computer Science and EngineeringInstitute of Technology, Nirma University Ahmedabad Gujarat 382481 India.
² Department of Electrical Engineering TechnologyUniversity of Johannesburg Johannesburg Gauteng 2006 South Africa.
³ Centre for Inter-Disciplinary Research and InnovationUniversity of Petroleum and Energy Studies Dehradun 248001 India.

Abstract

Recently, healthcare stakeholders have orchestrated steps to strengthen and curb the COVID-19 wave. There has been a surge in vaccinations to curb the virus wave, but it is crucial to strengthen our healthcare resources to fight COVID-19 and like pandemics. Recent researchers have suggested effective forecasting models for COVID-19 transmission rate, spread, and the number of positive cases, but the focus on healthcare resources to meet the current spread is not discussed. Motivated from the gap, in this paper, we propose a scheme, ABV-CoViD (Availibility of Beds and Ventilators for COVID-19 patients), that forms an ensemble forecasting model to predict the availability of beds and ventilators (ABV) for the COVID-19 patients. The scheme considers a region-wise demarcation for the allotment of beds and ventilators (BV), termed resources, based on region-wise ABV and COVID-19 positive patients (inside the hospitals occupying the BV resource). We consider an integration of artificial neural network (ANN) and auto-regressive integrated neural network (ARIMA) model to address both the linear and non-linear dependencies. We also consider the effective wave spread of COVID-19 on external patients (not occupying the BV resources) through a [Formula: see text]- ARNN model, which gives us long-term complex dependencies of BV resources in the future time window. We have considered the COVID-19 healthcare dataset on 3 USA regions (Illinois, Michigan, and Indiana) for testing our ensemble forecasting scheme from January 2021 to May 2022. We evaluated our scheme in terms of statistical performance metrics and validated that ensemble methods have higher accuracy. In simulation, for linear modelling, we considered the [Formula: see text] model, and [Formula: see text] model for ANN modelling. We considered the [Formula: see text](12,6) forecasting. On a population of 2,93,90,897, the obtained mean absolute error (MAE) on average for 3 regions is 170.5514. The average root means square error (RMSE) of [Formula: see text]-ARNN is 333.18, with an accuracy of 98.876%, which shows the scheme's efficacy in ABV measurement over conventional and manual resource allocation schemes.

Keywords: ARIMA; Artificial neural networks; COVID-19; IoT; healthcare services; prediction models.