학술논문
Forecasting COVID-19 Transmission Patterns with Hidden Markov Model
Document Type
Conference
Author
Source
2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), 2024 IEEE International Conference on. 2:1-7 Mar, 2024
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Language
Abstract
Global health and healthcare system have faced major hurdles as a result of coronavirus. Pandemic still had impacts on community in every part of the world even with the efforts made to curb the disease transmission. We have sought to address some of these issues by utilising data sourced from JHU’s CSSE [1]. This article concentrated on U.S. COVID-19 statistics concerning the number of infections and deaths in major towns. Only the relevant infection rates, death rates, and time columns were left in the pre-processing dataset. The above finding proves that the pandemic is evolving and began as a low rate of infections and deaths which increase with every passing moment. Secondly, we look at how death rates correlates with the highest infection rate. In an attempt to improve the forecast of COVID-19 spread for health care, manufacturers, economies and academic institutions this research is developed.