Distributed lag inspired machine learning for predicting vaccine-induced changes in COVID-19 hospitalization and intensive care unit admission.

Distributed lags play important roles in explaining the short-run dynamic and long-run cumulative effects of features on a response variable. Unlike the usual lag length selection, important lags with significant weights are selected in a distributed lag model (DLM). Inspired by the importance of distributed lags, this research focuses on the construction of distributed lag inspired machine learning (DLIML) for predicting vaccine-induced changes in COVID-19 hospitalization and intensive care unit (ICU) admission rates.

Analysis and Prediction of COVID-19 Pandemic in Bangladesh by Using ANFIS and LSTM Network.

The dangerously contagious virus named "COVID-19" has struck the world strong and has locked down billions of people in their homes to stop the further spread. All the researchers and scientists in various fields are continually developing a vaccine and prevention methods to aid the world from this challenging situation. However, a reliable prediction of the epidemic may help control this contiguous disease until the cure is available.

4P Model for Dynamic Prediction of COVID-19: a Statistical and Machine Learning Approach.

Around the world, scientists are racing hard to understand how the COVID-19 epidemic is spreading and growing, thus trying to find ways to prevent it before medications are available. Many different models have been proposed so far correlating different factors. Some of them are too localized to indicate a general trend of the pandemic while some others have established transient correlations only.