Pattern Formation of the COVID-19 Pandemic Based on Lunar Calendar by Employing LSTM Auto Encoder
Ganapathy Ramanathan, Ilakiya Arunachalam · ECS Transactions · 2022
The number of detected COVID-19 cases for the states of Massachusetts, Colorado, and Nevada are taken from the Center for Disease Control and Prevention dashboard from the period of March 2019 to December 2019. The above-mentioned raw dataset is used for the present work as a nonlinear time series signal. Furthermore, the raw dataset is pre-processed by classifying the same into eight categories depending on the lunar calendar, which in turn is based on the octet phases of the moon. The susceptible-exposed-infectious-removed model is developed from this pre-processed data by employing the Long Short Term Memory auto encoder. The ensuing pattern formation from the auto encoder is investigated.