ABSTRACT
The spread of the COVID-19 pandemic across the world has presented a unique problem to researchers and policymakers alike. In addition to uncertainty around the nature of the virus itself, the impact of rapidly changing policy decisions on the spread of the virus has been difficult to predict. Using an epidemiological Susceptible-Infected-Recovered-Dead (SIRD) model as a basis, this paper presents a methodology for modeling many uncertain factors impacting disease spread, ultimately to understand how a policy decision may impact the community long term. The COVID-19 Decision Support (CoviDeS) tool, utilizes an agent-based time simulation model that uses Bayesian networks to determine state changes of each individual. The model has a level of interpretability more extensive than many existing models, allowing for insights to be drawn regarding the relationships between various inputs and the transmission of the disease. Test cases are presented for different scenarios that demonstrate relative changes in transmission resulting from different policy decisions. Further, we will demonstrate the model's ability to support decisions for a smaller sub-community that is contained in a larger population center (e.g. a university within a city). Results of simulations for the city of Los Angeles are presented to demonstrate the use of the model for parametric analysis that could give insight to other real-world scenarios of interest. Though improvements can be made in the model's accuracy relative to real case data, the methods presented offer value for future use either as a predictive tool or as a decision-making tool for COVID-19 or future pandemic scenarios. © 2022 Probabilistic Safety Assessment and Management, PSAM 2022. All rights reserved.
ABSTRACT
Epidemics of infectious diseases can pose a serious threat to public health and the global economy. Despite scientific advances, containment and mitigation of infectious diseases remain a challenging task. In this paper, we investigate the potential of reinforcement learning as a decision making tool for epidemic control by constructing a deep Reinforcement Learning simulator, called EpidRLearn, composed of a contact-based, age-structured extension of the SEIR compartmental model, referred to as C-SEIR. We evaluate EpidRLearn by comparing the learned policies to two deterministic policy baselines. We further assess our reward function by integrating an alternative reward into our deep RL model. The experimental evaluation indicates that deep reinforcement learning has the potential of learning useful policies under complex epidemiological models and large state spaces for the mitigation of infectious diseases, with a focus on COVID-19. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Advanced healthcare technologies, including artificial intelligence (AI), the Internet of Things (IoT), big data, and deep learning, are required to counter and even prepare for new illnesses. As a result, we are examining IA's capacity to control and manage COVID-19 (Coronavirus) and other emerging pandemics. Using COVID-19 or Coronavirus and Artificial Intelligence or AI keywords, the material may be quickly found in the PubMed database. COVID-19 AI's existing understanding was analyzed to see how it may be used to increase COVID-19 AI's overall usefulness. Seven COVID-19 pandemic-related AI applications have been documented. The technology has the potential to locate the infection, track it through the system, and make forecasts about when the virus will infiltrate the whole system again. Decision-making tools are desperately needed to help combat this outbreak and allow healthcare institutions to gather enough information in real time to halt its spread. The primary objective of AI is to mimic human thinking using an expert methodology. COVID-19 vaccination production may also play a critical part in making sense of and advocating a similar project. This kind of technology is helpful in screening because of its emphasis on discoveries. © 2021 IEEE.
ABSTRACT
Understanding risks, putting in place preventative methods to seamlessly continue daily activities are essential tools to fight a pandemic. All social, commercial and leisure activities have an impact on the environmental media. Therefore, to accurately predict the fate and behavior of viruses in the environment, it is necessary to understand and analyze available detection methods, possible transmission pathways and preventative techniques. The aim of this review is to critically analyze and summarize the research done regarding SARS-COV-2 virus detection, focusing on sampling and laboratory detection methods in environmental media. Special attention will be given to wastewater and sewage sludge. This review has summarized the survival of the virus on surfaces to estimate the risk carried by different environmental media (water, wastewater, air and soil) in order to explain which communities are under higher risk. The critical analysis concludes that the detection of SARS-CoV-2 with current technologies and sampling strategies would reveal the presence of the virus. This information could be used to design systematic sampling points throughout the sewage systems when available, taking into account peak flows and more importantly economic factors on when to sample. Such approaches will provide clues for potential future viral outbreak, saving financial resources by reducing testing necessities for viral detection, hence contributing for more appropriate confinement policies by governments and could be further used to define more precisely post-pandemic or additional waves measures if/ when needed.