In this chapter, you’ll carry out a set of sequential methods (or time series methods) for forecasting to discern patterns in confirmed COVID-19 cases in the US. To begin, you’ll use the Gaussian Hidden Markov Model to inherit the series, model it, and consider the hidden states, including the means and covariance in those states. Subsequently, you’ll use the Monte Carlo simulation method to replicate confirmed US COVID-19 ...
© The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature 2022
T. C. NokeriArtificial Intelligence in Medical Sciences and Psychologyhttps://doi.org/10.1007/978-1-4842-8217-5_33. A Case for COVID-19: Considering the Hidden States and Simulation Results
Tshepo Chris Nokeri1
(1)
Pretoria, South Africa
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