  COVID-19 is a pandemic disease that began to rapidly spread in the US with
the first case detected on January 19, 2020, in Washington State. March 9,
2020, and then increased rapidly with total cases of 25,739 as of April 20,
2020. The Covid-19 pandemic is so unnerving that it is difficult to understand
how any person is affected by the virus. Although most people with coronavirus
81%, according to the U.S. Centers for Disease Control and Prevention (CDC),
will have little to mild symptoms, others may rely on a ventilator to breathe
or not at all. SEIR models have broad applicability in predicting the outcome
of the population with a variety of diseases. However, many researchers use
these models without validating the necessary hypotheses. Far too many
researchers often "overfit" the data by using too many predictor variables and
small sample sizes to create models. Models thus developed are unlikely to
stand validity check on a separate group of population and regions. The
researcher remains unaware that overfitting has occurred, without attempting
such validation. In the paper, we present a combination algorithm that combines
similar days features selection based on the region using Xgboost, K Means, and
long short-term memory (LSTM) neural networks to construct a prediction model
(i.e., K-Means-LSTM) for short-term COVID-19 cases forecasting in Louisana
state USA. The weighted k-means algorithm based on extreme gradient boosting is
used to evaluate the similarity between the forecasts and past days. The
results show that the method with K-Means-LSTM has a higher accuracy with an
RMSE of 601.20 whereas the SEIR model with an RMSE of 3615.83.
