Algorithm 1 ARIMA Training Methods

Require: ILI, Respiratory, and AQI Dataset integration Ensure: Sum up the number of disease cases per week and order them    1:$tseriesdt=pd.Series\left(tseriesdt\right)$    2:$size=int\left(len\right(tseriesdt)\ast 0.8)$    3:$train,val=tseriesdt[0:size],tseriesdt[size:len(tseriesdt\left)\right]\leftarrow$Training and Validation data partition    4:$p-value<0.05\leftarrow$Execute Dickey-Fuller test to create p-value    5:$auto\_arima(train,$ start_p=0, start_q=0, max_p=10, max_q=10, start_P=0, start_Q=0, max_P=10, max_Q=10, m=52, seasonal=True, trace=True, d=1, D=1, error_action=’warn’, suppress_warnings=True, random_state = 20, n_fits=30$)\leftarrow$ Running the ARIMA model    6:$AIC\leftarrow$Get Akaike’s Information Criterion (AIC)    7:$RMSE\leftarrow$Calculate the RMSE