Using Epidemic Modeling, Machine Learning and Control Feedback Strategy for Policy Management of COVID-19

Abstract

Coronavirus disease (COVID-19) is one of the world's most challenging pandemics, affecting people around the world to a great extent. Previous studies investigating the COVID-19 pandemic forecast have either lacked generalization and scalability or lacked surveillance data. City administrators have also often relied heavily on open-loop, belief-based decision-making, preventing them from identifying and enforcing timely policies. In this paper, we conduct mathematical and numerical analyses based on closed-loop decisions for COVID-19. Combining epidemiological theories with machine learning models gives this study a more accurate prediction of COVID-19's growth, and suggests policies to regulate it. The Susceptible, Infectious, and Recovered (SIR) model was analyzed using a machine learning model to estimate the optimal constant parameters, which are the recovery and infection rates of the coupled nonlinear differential equations that govern the epidemic model. To modulate the optimized parameters that regulate pandemic suppression and mitigation, a systematically designed feedback-based strategy was implemented. We also used pulse width modulation to modify on-off signals in order to regulate policy enforcement according to established metrics, such as infection recovery ratios. It was possible to determine what type of policy should be implemented in the country, as well as how long it should be implemented. Using datasets from John Hopkins University for six countries, India, Iran, Italy, Germany, Japan, and the United States, we show that our 30-day prediction errors are almost less than 3%. Our model proposes a threshold mechanism for policy control that divides the policy implementation into seven states, for example, if Infection Recovery Ratio (IRR) >80, we suggest a complete lockdown, vs if 10<IRR<20, we suggest encouraging people to stay at home and organizations to work at 50% capacity. All countries which implemented a policy control strategy at an early stage were accurately predicted by our model. Furthermore, it was determined that the implementation of closed-loop strategies during a pandemic at different times effectively controlled the pandemic.

Controlling a Pandemic: An Account of Successfully Applying Control Theory to the Covid-19 Pandemic in Denmark [Applications of Control]

“…Some of the considerations to that end can be found in [7]. A recent discussion on how to use feedback control strategies for policy management can be found in [8].…”

Epidemic Modeling using Hybrid of Time-varying SIRD, Particle Swarm Optimization, and Deep Learning

“…Many research studies based on compartmental/epidemic models have been conducted to evaluate the COVID-19 outbreak [14], [20]. Researchers have also enhanced epidemiological models by introducing new compartments and applying various machine learning techniques for better prediction accuracy [3], [5], [22]. Shinde et al [31] summarized various forecasting techniques for COVID-19 that include stochastic theory, mathematical models, data science, and machine learning techniques.…”

Machine Learning Techniques for Disease Prediction

“…[13] The authors conducted a mathematical and numerical analyses based on closed-loop decisions for COVID-19. The Susceptible, Infectious, and Recovered (SIR) model was analyzed using a machine learning model to estimate the optimal constant parameters, which are the recovery and infection rates of the coupled nonlinear differential equations that govern the epidemic model [14] The authors studied and investigated the transmission pathways of SARS-CoV-2 in the environment and provides current updates on the surveillance of viral outbreaks using WBE, viral air sampling, and AI. They framework based on an ensemble of ML and DL algorithms to provide a beneficial supportive tool for decision makers [15] 3 Methodology Machine learning algorithms used for disease prediction are often considered as "black boxes" due to their lack of interpretability.…”