  Background. Forecasting the time of forthcoming pandemic reduces the impact
of diseases by taking precautionary steps such as public health messaging and
raising the consciousness of doctors. With the continuous and rapid increase in
the cumulative incidence of COVID-19, statistical and outbreak prediction
models including various machine learning (ML) models are being used by the
research community to track and predict the trend of the epidemic, and also in
developing appropriate strategies to combat and manage its spread. Methods. In
this paper, we present a comparative analysis of various ML approaches
including Support Vector Machine, Random Forest, K-Nearest Neighbor and
Artificial Neural Network in predicting the COVID-19 outbreak in the
epidemiological domain. We first apply the autoregressive distributed lag
(ARDL) method to identify and model the short and long-run relationships of the
time-series COVID-19 datasets. That is, we determine the lags between a
response variable and its respective explanatory time series variables as
independent variables. Then, the resulting significant variables concerning
their lags are used in the regression model selected by the ARDL for predicting
and forecasting the trend of the epidemic. Results. Statistical measures i.e.,
Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute
Percentage Error (MAPE) are used for model accuracy. The values of MAPE for the
best selected models for confirmed, recovered and deaths cases are 0.407, 0.094
and 0.124 respectively, which falls under the category of highly accurate
forecasts. In addition, we computed fifteen days ahead forecast for the daily
deaths, recover, and confirm patients and the cases fluctuated across time in
all aspects. Besides, the results reveal the advantages of ML algorithms for
supporting decision making of evolving short term policies.
