Since we have already analyzed all these datasets in the target countries section, we see that using the global dataset for all our modeling is the best option for a few reasons:

  • it contains data for all the countries we are covering
  • it has up to date data that includes the whole lifetime of the pandemic
  • the individual countries datasets are not as complete in some cases
  • a single dataset is arguably easier to work with compared to many
  • the data is already clean and
  • we have already confirmed the creadability of the data

Objectives

our main objective is to see which one of our chosen 4 countries have handled the virus in a way that can be generalized to everyone as simple guidelines, the targeted countries are

  • United States
  • Germany
  • Italy
  • South Korea

Data Exploration

import pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport tensorflow as tffrom datetime import datetimefrom sklearn.preprocessing import MinMaxScalerfrom keras.preprocessing.sequence import TimeseriesGeneratorfrom keras.models import Sequentialfrom keras.layers import Dense, LSTM, Dropout, Activation, GlobalMaxPooling1D, Bidirectionalfrom keras.optimizers import Adamfrom tensorflow.keras.callbacks import ModelCheckpointfrom statsmodels.tsa.arima_model import ARIMAfrom statsmodels.tsa.api import ExponentialSmoothing, SimpleExpSmoothing, Holt%matplotlib inline# supress annoying warningimport warningsfrom statsmodels.tools.sm_exceptions import ConvergenceWarningwarnings.simplefilter('ignore', ConvergenceWarning)
df_confirmed = pd.read_csv("../input/covid-19/time_series_covid19_confirmed_global.csv")df_deaths = pd.read_csv("../input/covid-19/time_series_covid19_deaths_global.csv")df_reco = pd.read_csv("../input/covid-19/time_series_covid19_recovered_global.csv")

after reading in our dataset lets take a look at it by showing the first few countries for confirmed case, deaths, and recoveries

df_confirmed.head()
Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 ... 10/22/20 10/23/20 10/24/20 10/25/20 10/26/20 10/27/20 10/28/20 10/29/20 10/30/20 10/31/20
0 NaN Afghanistan 33.93911 67.709953 0 0 0 0 0 0 ... 40626 40687 40768 40833 40937 41032 41145 41268 41334 41425
1 NaN Albania 41.15330 20.168300 0 0 0 0 0 0 ... 18250 18556 18858 19157 19445 19729 20040 20315 20634 20875
2 NaN Algeria 28.03390 1.659600 0 0 0 0 0 0 ... 55357 55630 55880 56143 56419 56706 57026 57332 57651 57942
3 NaN Andorra 42.50630 1.521800 0 0 0 0 0 0 ... 3811 4038 4038 4038 4325 4410 4517 4567 4665 4756
4 NaN Angola -11.20270 17.873900 0 0 0 0 0 0 ... 8582 8829 9026 9381 9644 9871 10074 10269 10558 10805

5 rows × 288 columns

df_deaths.head()
Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 ... 10/22/20 10/23/20 10/24/20 10/25/20 10/26/20 10/27/20 10/28/20 10/29/20 10/30/20 10/31/20
0 NaN Afghanistan 33.93911 67.709953 0 0 0 0 0 0 ... 1505 1507 1511 1514 1518 1523 1529 1532 1533 1536
1 NaN Albania 41.15330 20.168300 0 0 0 0 0 0 ... 465 469 473 477 480 487 493 499 502 509
2 NaN Algeria 28.03390 1.659600 0 0 0 0 0 0 ... 1888 1897 1907 1914 1922 1931 1941 1949 1956 1964
3 NaN Andorra 42.50630 1.521800 0 0 0 0 0 0 ... 63 69 69 69 72 72 72 73 75 75
4 NaN Angola -11.20270 17.873900 0 0 0 0 0 0 ... 260 265 267 268 270 271 275 275 279 284

5 rows × 288 columns

df_reco.head()
Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 ... 10/22/20 10/23/20 10/24/20 10/25/20 10/26/20 10/27/20 10/28/20 10/29/20 10/30/20 10/31/20
0 NaN Afghanistan 33.93911 67.709953 0 0 0 0 0 0 ... 33831 34010 34023 34129 34150 34217 34237 34239 34258 34321
1 NaN Albania 41.15330 20.168300 0 0 0 0 0 0 ... 10395 10466 10548 10654 10705 10808 10893 11007 11097 11189
2 NaN Algeria 28.03390 1.659600 0 0 0 0 0 0 ... 38618 38788 38932 39095 39273 39444 39635 39635 40014 40201
3 NaN Andorra 42.50630 1.521800 0 0 0 0 0 0 ... 2470 2729 2729 2729 2957 3029 3144 3260 3377 3475
4 NaN Angola -11.20270 17.873900 0 0 0 0 0 0 ... 3305 3384 3461 3508 3530 3647 3693 3736 4107 4523

5 rows × 288 columns

after taking a look at the data as a whole lets now get our target countries each in their own dataframes

us_confirmed = df_confirmed[df_confirmed["Country/Region"] == "US"]us_deaths = df_deaths[df_deaths["Country/Region"] == "US"]us_reco = df_reco[df_reco["Country/Region"] == "US"]germany_confirmed = df_confirmed[df_confirmed["Country/Region"] == "Germany"]germany_deaths = df_deaths[df_deaths["Country/Region"] == "Germany"]germany_reco = df_reco[df_reco["Country/Region"] == "Germany"]italy_confirmed = df_confirmed[df_confirmed["Country/Region"] == "Italy"]italy_deaths = df_deaths[df_deaths["Country/Region"] == "Italy"]italy_reco = df_reco[df_reco["Country/Region"] == "Italy"]sk_confirmed = df_confirmed[df_confirmed["Country/Region"] == "Korea, South"]sk_deaths = df_deaths[df_deaths["Country/Region"] == "Korea, South"]sk_reco = df_reco[df_reco["Country/Region"] == "Korea, South"]
us_reco
Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 ... 10/22/20 10/23/20 10/24/20 10/25/20 10/26/20 10/27/20 10/28/20 10/29/20 10/30/20 10/31/20
231 NaN US 40.0 -100.0 0 0 0 0 0 0 ... 3353056 3375427 3406656 3422878 3460455 3487666 3518140 3554336 3578452 3612478

1 rows × 288 columns

with the current data structure shown above we cant do much so lets first convert it to a form that can used to make graphs or train a model

## structuring timeseries datadef confirmed_timeseries(df):    df_series = pd.DataFrame(df[df.columns[4:]].sum(),columns=["confirmed"])    df_series.index = pd.to_datetime(df_series.index,format = '%m/%d/%y')    return df_seriesdef deaths_timeseries(df):    df_series = pd.DataFrame(df[df.columns[4:]].sum(),columns=["deaths"])    df_series.index = pd.to_datetime(df_series.index,format = '%m/%d/%y')    return df_seriesdef reco_timeseries(df):    # no index to timeseries conversion needed (all is joined later)    df_series = pd.DataFrame(df[df.columns[4:]].sum(),columns=["recovered"])    return df_series
us_con_series = confirmed_timeseries(us_confirmed)us_dea_series = deaths_timeseries(us_deaths)us_reco_series = reco_timeseries(us_reco)germany_con_series = confirmed_timeseries(germany_confirmed)germany_dea_series = deaths_timeseries(germany_deaths)germany_reco_series = reco_timeseries(germany_reco)italy_con_series = confirmed_timeseries(italy_confirmed)italy_dea_series = deaths_timeseries(italy_deaths)italy_reco_series = reco_timeseries(italy_reco)sk_con_series = confirmed_timeseries(sk_confirmed)sk_dea_series = deaths_timeseries(sk_deaths)sk_reco_series = reco_timeseries(sk_reco)
# join all data frames for each county (makes it easier to graph and compare)us_df = us_con_series.join(us_dea_series, how = "inner")us_df = us_df.join(us_reco_series, how = "inner")germany_df = germany_con_series.join(germany_dea_series, how = "inner")germany_df = germany_df.join(germany_reco_series, how = "inner")italy_df = italy_con_series.join(italy_dea_series, how = "inner")italy_df = italy_df.join(italy_reco_series, how = "inner")sk_df = sk_con_series.join(sk_dea_series, how = "inner")sk_df = sk_df.join(sk_reco_series, how = "inner")
us_df
confirmed deaths recovered
2020-01-22 1 0 0
2020-01-23 1 0 0
2020-01-24 2 0 0
2020-01-25 2 0 0
2020-01-26 5 0 0
... ... ... ...
2020-10-27 8778055 226696 3487666
2020-10-28 8856413 227685 3518140
2020-10-29 8944934 228656 3554336
2020-10-30 9044255 229686 3578452
2020-10-31 9125482 230548 3612478

284 rows × 3 columns

Visual and Descriptive Analysis

data visualization and descriptive analysis for each country

USA

us_df.plot(figsize=(14,7),title="United States confirmed, deaths and recoverd cases")
<matplotlib.axes._subplots.AxesSubplot at 0x7f541b66df50>

png

the number of confirmed cases started up slow until around April, it started to go up at a much faster rate and it kept that pace even during quarantine, in July the rate at which the cases are increasing got higher and the cases started increasing faster, this can be attributed to the recent protests and people’s ignorance to the CDC guidelines.

deaths are the only cases that have had a continuously increasing rate, all the way from April the number of deaths is increasing at an increasing rate, until august where the increase rate is slower despite the higher number of cases.

when it comes to the recoveries, the recovery starts at the same time as the confirmed cases with a very unstable increase rate, the highest increase rate is also from around July which is surprising considering the rate of confirmed cases also went up around that time.

us_cases_outcome = (us_df.tail(1)['deaths'] + us_df.tail(1)['recovered'])[0]us_outcome_perc = (us_cases_outcome / us_df.tail(1)['confirmed'] * 100)[0]us_death_perc = (us_df.tail(1)['deaths'] / us_cases_outcome * 100)[0]us_reco_perc = (us_df.tail(1)['recovered'] / us_cases_outcome * 100)[0]us_active = (us_df.tail(1)['confirmed'] - us_cases_outcome)[0]print(f"Number of cases which had an outcome: {us_cases_outcome}")print(f"percentage of cases that had an outcome: {round(us_outcome_perc, 2)}%")print(f"Deaths rate: {round(us_death_perc, 2)}%")print(f"Recovery rate: {round(us_reco_perc, 2)}%")print(f"Currently Active cases: {us_active}")
Number of cases which had an outcome: 3843026percentage of cases that had an outcome: 42.11%Deaths rate: 6.0%Recovery rate: 94.0%Currently Active cases: 5282456

the percentage of cases that had an outcome is just 38.06% of the total cases, which is very low, the other 61.4 of the cases which are not accounted for have probably not been released officially by the government, however, the recovery rate is high at 91.79% while the death rate is at 8.21%

number of currently active cases is still very high, and it’s going up if the current increase rates are to be quoted.

Modeling

for modeling and predicting the number of cases in the upcoming days the following types of models will be implemented:

  • Bidrectional Long Short Term Memory (BiLSTM)

    LSTMs’ are known and widely used in time sensitive data where a variable is increaing with time depending on the values from prior days.

  • Autoregressive Integrated Moving Average (ARIMA)

    models the next step in the sequence as a linear function of the observations and resiudal errors at prior time steps.

  • Holt’s Exponential Smoothing (HES)

    also referred to as holt’s linear trend model or double exponential smoothing, models the next time step as an exponentially weighted linear function of observations at prior time step taking into account trends (the only difference from SES)

each country will have a total number of 3 models and the results will be compared accordingly.

our data is in a daily format and we want to predict n days at a time so we will take out the last n days and use them to test and predict outcomes it 2 weeks time.

n_input = 10  # number of stepsn_features = 1 # number of y# prepare required input datadef prepare_data(df):    # drop rows with zeros    df = df[(df.T != 0).any()]    num_days = len(df) - n_input    train = df.iloc[:num_days]    test = df.iloc[num_days:]    # normalize the data according to largest value    scaler = MinMaxScaler()    scaler.fit(train) # find max value    scaled_train = scaler.transform(train) # divide every point by max value    scaled_test = scaler.transform(test)    # feed in batches [t1,t2,t3] --> t4    generator = TimeseriesGenerator(scaled_train,scaled_train,length = n_input,batch_size = 1)    validation_set = np.append(scaled_train[55],scaled_test) # random tbh    validation_set = validation_set.reshape(n_input + 1,1)    validation_gen = TimeseriesGenerator(validation_set,validation_set,length = n_input,batch_size = 1)    return scaler, train, test, scaled_train, scaled_test, generator, validation_gen

Building the models

# create, train and return LSTM modeldef train_lstm_model():    model = Sequential()    model.add(Bidirectional(LSTM(84, recurrent_dropout = 0, unroll = False, return_sequences = True, use_bias = True, input_shape = (n_input,n_features))))    model.add(LSTM(84, recurrent_dropout = 0.1, use_bias = True, return_sequences = True,))    model.add(GlobalMaxPooling1D())    model.add(Dense(84, activation = "relu"))    model.add(Dense(units = 1))    # compile model    model.compile(loss = 'mae', optimizer = Adam(1e-5))    # finally train the model using generators    model.fit_generator(generator,validation_data = validation_gen, epochs = 100, steps_per_epoch = round(len(train) / n_input), verbose = 0)    return model
# predict, rescale and append needed columns to final data framedef lstm_predict(model):    # holding predictions    test_prediction = []    # last n points from training set    first_eval_batch = scaled_train[-n_input:]    current_batch = first_eval_batch.reshape(1,n_input,n_features)    # predict first x days from testing data    for i in range(len(test) + n_input):        current_pred = model.predict(current_batch)[0]        test_prediction.append(current_pred)        current_batch = np.append(current_batch[:,1:,:],[[current_pred]],axis=1)    # inverse scaled data    true_prediction = scaler.inverse_transform(test_prediction)    MAPE, accuracy, sum_errs, interval, stdev, df_forecast = gen_metrics(true_prediction)    return MAPE, accuracy, sum_errs, interval, stdev, df_forecast# plotting model lossesdef plot_lstm_losses(model):    pd.DataFrame(model.history.history).plot(figsize = (14,7), title = "loss vs epochs curve")
'''incrementally trained ARIMA:    - train with original train data    - predict the next value    - appened the prediction value to the training data    - repeat training and appending for n times (days in this case)    this incremental technique significantly improves the accuracy    by always using all data up to previous day for predeicting next value    unlike predecting multiple values at the same time which is not incremeital.    PARAMETERS:    p: autoregressive(AR) order    d: order of differencing    q: moving average(MA) order'''def arima_predict(p: int, d: int, q: int):    values = [x for x in train.values]    predictions = []    for t in range(len(test) + n_input): # the number of testing days + the future days to predict        model = ARIMA(values, order = (p,d,q))        model_fit = model.fit()        fcast = model_fit.forecast()        predictions.append(fcast[0][0])        values.append(fcast[0])    MAPE, accuracy, sum_errs, interval, stdev, df_forecast = gen_metrics(predictions)    return MAPE, accuracy, sum_errs, interval, stdev, df_forecast
'''incremental Holt's (Method) Exponential Smoothing    - trained the same way as above arima'''def hes_predict():    values = [x for x in train.values]    predictions = []    for t in range(len(test) + n_input): # the number of testing days + the future days to predict        model = Holt(values)        model_fit = model.fit()        fcast = model_fit.predict()        predictions.append(fcast[0])        values.append(fcast[0])    MAPE, accuracy, sum_errs, interval, stdev, df_forecast = gen_metrics(predictions)    return MAPE, accuracy, sum_errs, interval, stdev, df_forecast
# generate a dataframe with given rangedef get_range_df(start: str, end: str, df):    target_df = df.loc[pd.to_datetime(start, format='%Y-%m-%d'):pd.to_datetime(end, format='%Y-%m-%d')]    return target_df
# fill na values in a range predicted data frame with actual values from the original dataframedef pad_range_df(df, original_df):    df['confirmed'] = df.confirmed.fillna(original_df['confirmed']) # fill confirmed Na    # fill daily na    daily_act = []    daily_df = pd.DataFrame(columns = ["daily"], index = df[n_input:].index)    for num in range(n_input - 1, (n_input * 2) - 1):        daily_act.append(df["confirmed"].iloc[num + 1] - df["confirmed"].iloc[num])    daily_df['daily'] = daily_act    df['daily'] = df.daily.fillna(daily_df['daily'])    return df
# generate metrics and final dfdef gen_metrics(pred):    # create time series    time_series_array = test.index    for k in range(0, n_input):        time_series_array = time_series_array.append(time_series_array[-1:] + pd.DateOffset(1))    # create time series data frame    df_forecast = pd.DataFrame(columns = ["confirmed","confirmed_predicted"],index = time_series_array)    # append confirmed and predicted confirmed    df_forecast.loc[:,"confirmed_predicted"] = pred    df_forecast.loc[:,"confirmed"] = test["confirmed"]    # create and append daily cases (for both actual and predicted)    daily_act = []    daily_pred = []    #actual    daily_act.append(abs(df_forecast["confirmed"].iloc[1] - train["confirmed"].iloc[-1]))    for num in range((n_input * 2) - 1):        daily_act.append(df_forecast["confirmed"].iloc[num + 1] - df_forecast["confirmed"].iloc[num])    # predicted    daily_pred.append(df_forecast["confirmed_predicted"].iloc[1] - train["confirmed"].iloc[-1])    for num in range((n_input * 2) - 1):        daily_pred.append(df_forecast["confirmed_predicted"].iloc[num + 1] - df_forecast["confirmed_predicted"].iloc[num])    df_forecast["daily"] = daily_act    df_forecast["daily_predicted"] = daily_pred    # calculate mean absolute percentage error    MAPE = np.mean(np.abs(np.array(df_forecast["confirmed"][:n_input]) - np.array(df_forecast["confirmed_predicted"][:n_input])) / np.array(df_forecast["confirmed"][:n_input]))    accuracy = round((1 - MAPE) * 100, 2)    # the error rate    sum_errs = np.sum((np.array(df_forecast["confirmed"][:n_input]) - np.array(df_forecast["confirmed_predicted"][:n_input])) ** 2)    # error standard deviation    stdev = np.sqrt(1 / (n_input - 2) * sum_errs)    # calculate prediction interval    interval = 1.96 * stdev    # append the min and max cases to final df    df_forecast["confirm_min"] = df_forecast["confirmed_predicted"] - interval    df_forecast["confirm_max"] = df_forecast["confirmed_predicted"] + interval    # round all df values to 0 decimal points    df_forecast = df_forecast.round()    return MAPE, accuracy, sum_errs, interval, stdev, df_forecast
# print metrics for given countydef print_metrics(mape, accuracy, errs, interval, std, model_type):    m_str = "LSTM" if model_type == 0 else "ARIMA" if model_type == 1 else "HES"    print(f"{m_str} MAPE: {round(mape * 100, 2)}%")    print(f"{m_str} accuracy: {accuracy}%")    print(f"{m_str} sum of errors: {round(errs)}")    print(f"{m_str} prediction interval: {round(interval)}")    print(f"{m_str} standard deviation: {std}")
# for plotting the range of predicetionsdef plot_results(df, country, algo):    fig, (ax1, ax2) = plt.subplots(2, figsize = (14,20))    ax1.set_title(f"{country} {algo} confirmed predictions")    ax1.plot(df.index,df["confirmed"], label = "confirmed")    ax1.plot(df.index,df["confirmed_predicted"], label = "confirmed_predicted")    ax1.fill_between(df.index,df["confirm_min"], df["confirm_max"], color = "indigo",alpha = 0.09,label = "Confidence Interval")    ax1.legend(loc = 2)    ax2.set_title(f"{country} {algo} confirmed daily predictions")    ax2.plot(df.index, df["daily"], label = "daily")    ax2.plot(df.index, df["daily_predicted"], label = "daily_predicted")    ax2.legend()    import matplotlib.dates as mdates    ax1.xaxis.set_major_formatter(mdates.DateFormatter('%b %-d'))    ax2.xaxis.set_major_formatter(mdates.DateFormatter('%b %-d'))    fig.show()

USA Predictions

# prepare the datascaler, train, test, scaled_train, scaled_test, generator, validation_gen = prepare_data(us_con_series)
# train lstm modelus_lstm_model = train_lstm_model()# plot lstm lossesplot_lstm_losses(us_lstm_model)

png

# Long short memory methodus_mape, us_accuracy, us_errs, us_interval, us_std, us_lstm_df = lstm_predict(us_lstm_model)print_metrics(us_mape, us_accuracy, us_errs, us_interval, us_std, 0)us_lstm_df
LSTM MAPE: 4.82%LSTM accuracy: 95.18%LSTM sum of errors: 2058053740601.0LSTM prediction interval: 994121.0LSTM standard deviation: 507204.8083122943
confirmed confirmed_predicted daily daily_predicted confirm_min confirm_max
2020-10-22 8409341.0 8205034.0 155448.0 -94666.0 7210912.0 9199155.0
2020-10-23 8493088.0 8242974.0 83747.0 37941.0 7248853.0 9237096.0
2020-10-24 8576818.0 8276551.0 83730.0 33577.0 7282430.0 9270672.0
2020-10-25 8637625.0 8306049.0 60807.0 29498.0 7311928.0 9300171.0
2020-10-26 8704423.0 8331693.0 66798.0 25644.0 7337572.0 9325815.0
2020-10-27 8778055.0 8354006.0 73632.0 22313.0 7359885.0 9348127.0
2020-10-28 8856413.0 8373629.0 78358.0 19623.0 7379508.0 9367750.0
2020-10-29 8944934.0 8392144.0 88521.0 18515.0 7398022.0 9386265.0
2020-10-30 9044255.0 8408804.0 99321.0 16660.0 7414683.0 9402925.0
2020-10-31 9125482.0 8423733.0 81227.0 14929.0 7429611.0 9417854.0
2020-11-01 NaN 8435789.0 NaN 12056.0 7441668.0 9429910.0
2020-11-02 NaN 8456020.0 NaN 20231.0 7461899.0 9450142.0
2020-11-03 NaN 8474728.0 NaN 18708.0 7480607.0 9468849.0
2020-11-04 NaN 8492286.0 NaN 17558.0 7498164.0 9486407.0
2020-11-05 NaN 8509005.0 NaN 16720.0 7514884.0 9503127.0
2020-11-06 NaN 8525149.0 NaN 16143.0 7531027.0 9519270.0
2020-11-07 NaN 8540892.0 NaN 15744.0 7546771.0 9535014.0
2020-11-08 NaN 8556397.0 NaN 15504.0 7562275.0 9550518.0
2020-11-09 NaN 8571668.0 NaN 15272.0 7577547.0 9565790.0
2020-11-10 NaN 8586776.0 NaN 15108.0 7592655.0 9580897.0 
plot_results(us_lstm_df, "USA", "LSTM")

png

# Auto Regressive Integrated Moving Averageus_mape, us_accuracy, us_errs, us_interval, us_std, us_arima_df = arima_predict(8, 1, 1)print_metrics(us_mape, us_accuracy, us_errs, us_interval, us_std, 1)us_arima_df
ARIMA MAPE: 0.72%ARIMA accuracy: 99.28%ARIMA sum of errors: 58884624871.0ARIMA prediction interval: 168156.0ARIMA standard deviation: 85793.81160044324
confirmed confirmed_predicted daily daily_predicted confirm_min confirm_max
2020-10-22 8409341.0 8406811.0 155448.0 138884.0 8238655.0 8574967.0
2020-10-23 8493088.0 8476524.0 83747.0 69713.0 8308368.0 8644680.0
2020-10-24 8576818.0 8536828.0 83730.0 60303.0 8368672.0 8704983.0
2020-10-25 8637625.0 8593830.0 60807.0 57002.0 8425674.0 8761986.0
2020-10-26 8704423.0 8654269.0 66798.0 60439.0 8486113.0 8822425.0
2020-10-27 8778055.0 8716789.0 73632.0 62520.0 8548633.0 8884945.0
2020-10-28 8856413.0 8782879.0 78358.0 66089.0 8614723.0 8951034.0
2020-10-29 8944934.0 8853301.0 88521.0 70422.0 8685145.0 9021457.0
2020-10-30 9044255.0 8921778.0 99321.0 68477.0 8753622.0 9089933.0
2020-10-31 9125482.0 8984015.0 81227.0 62237.0 8815859.0 9152170.0
2020-11-01 NaN 9043905.0 NaN 59891.0 8875750.0 9212061.0
2020-11-02 NaN 9104850.0 NaN 60944.0 8936694.0 9273006.0
2020-11-03 NaN 9167791.0 NaN 62941.0 8999635.0 9335947.0
2020-11-04 NaN 9234322.0 NaN 66531.0 9066166.0 9402478.0
2020-11-05 NaN 9303277.0 NaN 68955.0 9135121.0 9471433.0
2020-11-06 NaN 9369836.0 NaN 66560.0 9201681.0 9537992.0
2020-11-07 NaN 9431964.0 NaN 62128.0 9263808.0 9600120.0
2020-11-08 NaN 9491890.0 NaN 59926.0 9323734.0 9660046.0
2020-11-09 NaN 9551959.0 NaN 60069.0 9383803.0 9720114.0
2020-11-10 NaN 9613919.0 NaN 61961.0 9445763.0 9782075.0 
plot_results(us_arima_df, "USA", "incremental ARIMA")

png

# Holts Exponential Smoothingus_mape, us_accuracy, us_errs, us_interval, us_std, us_hes_df = hes_predict()print_metrics(us_mape, us_accuracy, us_errs, us_interval, us_std, 2)us_hes_df
HES MAPE: 0.83%HES accuracy: 99.17%HES sum of errors: 75839516016.0HES prediction interval: 190835.0HES standard deviation: 97364.98088117794
confirmed confirmed_predicted daily daily_predicted confirm_min confirm_max
2020-10-22 8409341.0 8400415.0 155448.0 125551.0 8209580.0 8591251.0
2020-10-23 8493088.0 8463191.0 83747.0 62776.0 8272356.0 8654027.0
2020-10-24 8576818.0 8525967.0 83730.0 62776.0 8335132.0 8716802.0
2020-10-25 8637625.0 8588743.0 60807.0 62776.0 8397907.0 8779578.0
2020-10-26 8704423.0 8651518.0 66798.0 62776.0 8460683.0 8842354.0
2020-10-27 8778055.0 8714294.0 73632.0 62776.0 8523459.0 8905129.0
2020-10-28 8856413.0 8777070.0 78358.0 62776.0 8586234.0 8967905.0
2020-10-29 8944934.0 8839845.0 88521.0 62776.0 8649010.0 9030681.0
2020-10-30 9044255.0 8902621.0 99321.0 62776.0 8711786.0 9093457.0
2020-10-31 9125482.0 8965397.0 81227.0 62776.0 8774562.0 9156232.0
2020-11-01 NaN 9028173.0 NaN 62776.0 8837337.0 9219008.0
2020-11-02 NaN 9090948.0 NaN 62776.0 8900113.0 9281784.0
2020-11-03 NaN 9153724.0 NaN 62776.0 8962889.0 9344559.0
2020-11-04 NaN 9216500.0 NaN 62776.0 9025664.0 9407335.0
2020-11-05 NaN 9279275.0 NaN 62776.0 9088440.0 9470111.0
2020-11-06 NaN 9342051.0 NaN 62776.0 9151216.0 9532887.0
2020-11-07 NaN 9404827.0 NaN 62776.0 9213992.0 9595662.0
2020-11-08 NaN 9467603.0 NaN 62776.0 9276767.0 9658438.0
2020-11-09 NaN 9530378.0 NaN 62776.0 9339543.0 9721214.0
2020-11-10 NaN 9593154.0 NaN 62776.0 9402319.0 9783989.0 
plot_results(us_hes_df, "USA", "incremental HES")

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Effectiveness of mandated lockdown

was the US lockdown effective in reducing the cases?
the US started their lockdwon in 2020-03-17 and it was ended by the erupting protests. tracking the lockdown might be tricky in the US at least because each state started their lockdown at their own pace and there was no federally mandated lockdown while some other states never went into lockdowns, taking that into account we will consider the end of the lockdown to be the end of may which was the start of the Gorge Floyed protests.

Time frame from 2020-03-17 until 2020-05-31

us_lockdown = get_range_df('2020-03-17', '2020-05-31', us_con_series)
fig, ax = plt.subplots(1, figsize = (14,7))ax.plot(us_lockdown.index, us_lockdown['confirmed'], label = 'confirmed')ax.plot(us_lockdown.index, us_lockdown.rolling(7).mean(), label = 'confirmed mean')ax.legend()
<matplotlib.legend.Legend at 0x7f53d0630f90>

png

the actual values are above the moving average of each 7 days meaning the lockdown did not work as inteded and the number of cases was still very high when compared to the average of each 7 days, to make sure our previous model predictions are accurate we will use this period of time as a base and train the model on it and do prediction for the days after that which we already have the data on. we will use the ARIMA model becuase the amount of data we have will not train a neural network ideally.

scaler, train, test, scaled_train, scaled_test, generator, validation_gen = prepare_data(us_lockdown)
# Auto Regressive Integrated Moving Averageus_mape, us_accuracy, us_errs, us_interval, us_std, us_arima_df = arima_predict(8, 1, 1)print_metrics(us_mape, us_accuracy, us_errs, us_interval, us_std, 1)us_arima_df = pad_range_df(us_arima_df, us_con_series)us_arima_df
ARIMA MAPE: 0.55%ARIMA accuracy: 99.45%ARIMA sum of errors: 1187672403.0ARIMA prediction interval: 23881.0ARIMA standard deviation: 12184.377305753513
confirmed confirmed_predicted daily daily_predicted confirm_min confirm_max
2020-05-22 1608604.0 1610272.0 44636.0 47633.0 1586391.0 1634154.0
2020-05-23 1629802.0 1632799.0 21198.0 22527.0 1608918.0 1656681.0
2020-05-24 1649916.0 1653311.0 20114.0 20511.0 1629429.0 1677192.0
2020-05-25 1668235.0 1674419.0 18319.0 21108.0 1650537.0 1698300.0
2020-05-26 1687761.0 1695924.0 19526.0 21505.0 1672042.0 1719805.0
2020-05-27 1706351.0 1719538.0 18590.0 23614.0 1695657.0 1743419.0
2020-05-28 1729299.0 1744487.0 22948.0 24949.0 1720606.0 1768369.0
2020-05-29 1753651.0 1768770.0 24352.0 24283.0 1744889.0 1792652.0
2020-05-30 1777495.0 1791120.0 23844.0 22350.0 1767239.0 1815002.0
2020-05-31 1796670.0 1812184.0 19175.0 21064.0 1788303.0 1836066.0
2020-06-01 1814034.0 1833132.0 17364.0 20948.0 1809251.0 1857014.0
2020-06-02 1835408.0 1854887.0 21374.0 21755.0 1831006.0 1878768.0
2020-06-03 1855386.0 1878310.0 19978.0 23423.0 1854428.0 1902191.0
2020-06-04 1877125.0 1902606.0 21739.0 24296.0 1878724.0 1926487.0
2020-06-05 1902294.0 1926287.0 25169.0 23682.0 1902406.0 1950169.0
2020-06-06 1924132.0 1948548.0 21838.0 22261.0 1924667.0 1972430.0
2020-06-07 1941920.0 1969746.0 17788.0 21198.0 1945864.0 1993627.0
2020-06-08 1959448.0 1990779.0 17528.0 21033.0 1966898.0 2014661.0
2020-06-09 1977820.0 2012666.0 18372.0 21887.0 1988784.0 2036547.0
2020-06-10 1998646.0 2035860.0 20826.0 23194.0 2011979.0 2059742.0 
plot_results(us_arima_df, "USA", "incremental ARIMA")

png

we can see that the predicted totals and predicted daily cases are fairly accurate thus our previous predictions can be taken with some degree of accuracy, and might be used for making decisions.

Conclusion

from all the graphs, functions and numbers above we can come to a simple conclusion that is, there is no single model that will perform best in all scenario even when the data is very similar (in trend not numbers), each model was best for a specific country and wasn’t so far behind in the others for example the HES model is the most accurate with the South Korean dataset but is almost the same as the ARIMA model in Italy.

whats the difference between ARIMA and HES?
ARIMA uses a non-linear function for coefficient calculations, that’s why the graph does curve sometimes (Italy) while HES is a pure linear method that uses a linear function and is always a straight line

Considering LSTM is usually the least accurate, is it worth the training time?
here may be, however, deep learning has its place among machine learning algorithms and can perform tasks these other functions could never, also the LSTM model always predicts a wider interval compared to the other 2, in a practical scenario where range is important the other 2 models will not be ideal because their results are limited by the original value and don’t spread as much, the LSTM model could provide better estimates.

ARIMA or HES?
HES, because it takes much less time to train and is as accurate or even more accurate sometimes.