Optimal policy learning for COVID-19 prevention using reinforcement learning

Abstract

COVID-19 has changed the lifestyle of many people due to its rapid human-to-human transmission. The spread started at the end of January 2020, and different countries used different approaches in terms of testing, sanitization, lock down and quarantine centres to control the spread of the virus. People are getting back to working and routine life activities with new normal standards of testing, sanitization, social distancing and lock down. People are regularly tested to identify those who are infected with COVID-19 and isolate them from general public. However, testing all people unnecessarily is an expensive operation in terms of resources usage. There must be an optimal policy to test only those who have higher chances of being COVID-19 positive. Similarly, sanitization is used for individuals and streets to disinfect people and places. However, sanitization is also an expensive operation in terms of resources, and it is not possible to disinfect each and every individual and street. Social separating or lock down or quarantine centres focuses are different methodologies that are utilised to control the human-to-human transmission of the infection and separate the individuals who are contaminated with COVID-19. However, lock down and quarantine centres are expensive operations in terms of resources as it disturbs the affairs of state and the growth of economy. At the same time, it negatively affects the quality of life of a society. It is also not possible to provide resources to all citizens by locking them inside homes or quarantine centres for infinite time. All these parameters are expensive in terms of resources and have an effect on controlling the spread of the virus, quality of life of human, resources and economy. In this article, a novel intelligent method based on reinforcement learning (RL) is built up that quantifies the unique levels of testing, disinfection and lock down alongside its impact on the spread of the infection, personal satisfaction or quality of life, resource use and economy. Different RL algorithms are actualized and agents are prepared with these algorithms to interact with the environment to gain proficiency with the best strategy. The examinations exhibit that deep learning–based algorithms, for example, DQN and DDPG are performing better than customary RL algorithms, for example, Q-Learning and SARSA.

Effective of Smart Mathematical Model by Machine Learning Classifier on Big Data in Healthcare Fast Response

“…The processing was calculated and divided into two parts: the first section is training, with 80%, with the data that contains a label, and the second section is testing, with 20%. The first big dataset reduced into 20123 records when applying reduce procedure to be the best fit dataset even though some attributes also reduced for parallel execution [ 30 , 31 ], after reducing some attributes in dataset highlighted such as gender, age, medical specialty, time in hospital, and weight, as shown in Figure 6 .…”

Prevention behavior of community for spreading COVID-19 in West Kalimantan Province, Indonesia

“…Study conducted in United Kingdom and United States found that 86.0% and 92.6%, respectively know the prevention way to avoid the [5]. The spreading of COVID-19 in the community is based on the existing knowledge of the virus and its effect to the quality of life and economy [6]. Ethiopian people showed how knowledge of COVID-19 is significantly influenced by age, educational status, and marital status [7].…”

Intelligent Data Analytics for Diagnosing Melanoma Skin Lesions via Deep Learning in IoT System

“…Despite several scoring systems and rule-based systems have been proposed to learn expert knowledge and improve the diagnostic performance of unskilled clinicians [40][41][42][43][44], the diagnosis process is time consuming, and the clinical diagnostic accuracy remains suboptimal for melanoma detection. erefore, automatic analysis of digitized images with high diagnostic accuracy to assist dermatologists in differentiating early melanoma from benign skin lesions is in high demand and very crucial for public health [45][46][47][48][49].…”