=Paper=
{{Paper
|id=Vol-2962/paper23
|storemode=property
|title=Two Machine-learning Approaches for Short-term COVID-19 Hospitalization Forecasting in Slovakia
|pdfUrl=https://ceur-ws.org/Vol-2962/paper23.pdf
|volume=Vol-2962
|authors=Veronika Kurilová,Martin Huba,Jozef Goga,Miloš Oravec,Jarmila Pavlovičová,Nora Majtánová
|dblpUrl=https://dblp.org/rec/conf/itat/KurilovaHGOPM21
}}
==Two Machine-learning Approaches for Short-term COVID-19 Hospitalization Forecasting in Slovakia==
https://ceur-ws.org/Vol-2962/paper23.pdf
Two machine-learning approaches for short-term COVID-19 hospitalization
forecasting in Slovakia
Veronika Kurilová
Faculty of Electrical Engineering and Information Technology, Slovak University of Technology,
Ilkovičova 3, 812 19 Bratislava, Slovakia
Department of ophthalmology of Slovak Medical University and University hospital in Bratislava,
Antolská 11, 85107 Bratislava, Slovakia
Martin Huba
Swiss Re, Management AG
Mlynské Nivy 12, 811 09 Slovakia
Jozef Goga, Miloš Oravec, Jarmila Pavlovičová
Faculty of Electrical Engineering and Information Technology, Slovak University of Technology,
Ilkovičova 3, 812 19 Bratislava, Slovakia
Nora Majtánová
Department of ophthalmology of Slovak Medical University and University hospital in Bratislava,
Antolská 11, 85107 Bratislava, Slovakia
Faculty of Medicine, Slovak Medical University,
Limbová 12, 833 03 Bratislava, Slovakia
Abstract. COVID-19 is a life-threatening novel respiratory onset, from 100% four days before onset to 67% one day
virus-borne disease, which was discovered in December 2019 before onset. On the day of onset, the probability of getting
in Wuhan and subsequently spread globally. Monitoring and a false negative result is less than 40% [5]. From zero to
predicting COVID-19 epidemic data is crucial to control
pandemic outbreaks. Machine learning-based methods, four days after symptom onset, PCR tests from
including deep learning, are promising approaches to predict nasopharyngeal swabs are positive [6] in most infected
COVID-19 data such as new cases, infected patients, and individuals, with a peak in the first week after onset [7].
deaths. Our study focused on short-term COVID-19 The median time between symptom onset and
hospitalizations forecasting using two machine learning hospitalization is 5 days [6], and the median number of
approaches— ensemble time-series method and multilayer days from symptom onset to death was 14 and less in
perceptron (MLP) feedforward network method. Both methods
make predictions based on hospitalization, polymerase chain patients aged over 70 [8]. Despite these observations,
reaction (PCR), and antigen (Ag) test data, which were which demonstrate the importance of time in COVID-19
collected between October 2020 and June 2021 in Slovakia for infections, predicting the number of hospitalized patients
our study. The ensemble time-series method was more from positive tests and average hospitalization period is not
sensitive in the beginning of experimental period but failed straightforward and depends on personal and regional
when the number of hospitalizations began to drop. The MLP factors. A nationwide cohort study reported that 20% of all
method was ineffective in the beginning because of lack of
training data but improved when more robust data was PCR-positive cases result in hospitalizations, and the
available; this method is promising for monitoring the third proportion increases with age and multimorbidity [9]. In
wave of pandemic in Slovakia. another study [10], stronger hospitalization risk is
associated with men aged ≥ 75 years with comorbidities,
1 Introduction particularly cardiovascular disease, diabetes chronic kidney
disease, hyperlipidemia and obesity than in other groups.
The first patients with the novel coronavirus SARS- Predicting COVID-19 epidemic data and monitoring
CoV-2, were hospitalized in Wuhan, China in December epidemiological changes of the virus spread are crucial for
2019 [1]. In January 2020 more cases were reported controlling pandemic outbreaks [1]. Machine learning
throughout China and abroad [2]. The most sensitive methods, including deep learning, show promise in
diagnostic method currently available for COVID-19 predicting COVID-19 epidemic data such as new cases,
testing is the polymerase chain reaction (PCR) test [3]. infected patients, and mortality. A multilayer perceptron
However, for effective screening, frequent repetition and (MLP) artificial neural network was used in [11] to create
fast reporting is more important than sensitivity [4], which a worldwide model for predicting the maximum number of
makes rapid antigen (Ag) tests or loop-mediated isothermal infected patients in a location from available data in time.
amplification tests (LAMP) tests advantageous in COVID- The MLP was shown to have slightly better performance
19 diagnostics. There are several studies on the relationship for analyzing contributing factors for COVID-19 spread
between symptom onset, positive PCR testing, and and deaths than the radial basis function in [12]. The
hospitalization. The fifth day post infection is a typically authors of [13] analyzed continuous variable quantum
when symptom onset occurs, and most infected people test neural networks and quantum backpropagating MLP
false negative before this day. The decrease in probability models for predicting COVID-19 cases in India and the
of false negativity is noted four days before symptoms USA. Both methods showed better performance than
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Creative Commons License Attribution 4.0 International (CC BY 4.0)
classical artificial neural networks. MLP and adaptive time during the second wave of pandemic in Slovakia. The
network-based fuzzy inference systems showed promising second was tested retrospectively.
results in outbreak predictions [14]. Hybrid machine
2.1 Data acquisition
learning prediction models of adaptive network-based
fuzzy inference system and MLP-imperialist competitive We used the dataset provided by The Institute for
algorithms were used in [15] to predict cases and mortality Healthcare Analysis [33], publicly available on Github. The
rate. Machine learning models, such as the linear dataset includes COVID-19 statistics in Slovakia, i.e., the
regression, linear regressor polynomial, support vector daily number of positive and total PCR and antigen (Ag)
regressor, random forest regressor, decision tree, and auto- tests; number of hospitalized patients including daily
regressive moving average were used to predict outbreaks hospital admissions and discharges; vaccination statistics,
[16] [17] [18] with the highest accuracy being shown by the etc. Available hospitalizations data are divided by districts
auto regressive moving-average [16] and random-forest and regions because of reporting from every hospital every
approaches [18]. A random forest model was also proposed day.
to predict mortality in the first 20–84 hours following 2.2 Preparing dataset for machine learning time-series
hospitalization [19]. ensemble method
Deep learning forecasting methods for new or, new and
recovered cases using recurrent neural networks were The model training time-series data were the daily
proposed in [20] and [21]. The authors tested the long numbers of PCR and Ag positive tests; daily percentage of
short-term memory (LSTM), bidirectional LSTM, gated positive PCR; and Ag tests from total PCR and Ag tests.
recurrent units, variational autoencoder, and convolutional All training data were filtered by a seven-day simple
LSTM; the variational autoencoder showed the best moving average (SMA) filter. We assumed that testing
performance among all of these [20]. The convolutional PCR or Ag positive leads to a hospitalization time of four
LSTM outperformed other models in predicting new cases and seven days on average, respectively. These time shifts
for a one-month period [21]. were inspired by [29], where the average time for positive
However, the development and subsequent practical PCR and Ag test hospitalization was taken as three and
application of models based on deep learning requires high seven days, respectively. We performed these time shifts
computing power [22], which aggravates the financial with time-series training data; the schematic of a sample
disparities between different universities [23]. At least a data preparation is shown on Fig. 1. The distributions of the
partial solution towards the democratization of research in variables in the dataset and correlations between all these
the field of deep learning is to use commercial cloud variables with hospitalizations are provided in the
computing platforms, which allow the direct purchase of Appendix. The last PCR and Ag test data from the dataset
necessary computing power [24]. This approach has proven were used as inputs for predicting hospitalizations from
to be effective and is beginning to be applied in fields such time-series data. The ensemble method was trained on the
as information retrieval [25], flexible maintenance [26], entire time-series dataset with 1/14-day shifts until the first
and time-series prediction [27], [28]. day of prediction without splitting into time periods. This
Prediction of COVID-19 hospitalizations in Slovakia means that a separate training dataset was used for every 14
based on linear regression was firstly conducted by [29]. days, ending exactly before prediction, allowing the
The problem of preventing the spread of the disease is continuation of this time series using the data from the
complex, and multidisciplinary approaches, including previous days for forecast computations. The method
artificial intelligence methods, are required. predicts for the next 14 days using the rest of time shifted
Our paper focuses on short-term COVID-19 data (Fig. 1). Because of the time shifting, we can use the
hospitalizations forecasting in Slovakia with a machine last four days from PCR testing and last seven days from
learning ensemble model implemented on the MS Azure the Ag testing as future values and consider these as
[30] cloud computing platform and an MLP feedforward prediction inputs. In this way, we created input data,
network implemented locally using in MATLAB [31]. The allocating the last sample representing 14 days, as a test set.
automated machine learning (AutoML) [32] approach We used 5-fold rolling origin cross validation (ROCV) with
enables acceleration of the development and deployment of a fixed starting point on the remaining training data.
machine learning models without extensive programming Shifting the data by 1/5 thus created cross-validation folds,
knowledge, making it suitable and user-friendly for which ensured that there was no data leakage.
epidemiologists and data analytics professionals. 2.3 Preparing dataset for MLP method
The goal of hospitalization predictions is to aid the
preparedness of hospitals and health-care professionals to The MLP trains with the input and output data. It needs
admit all patients required hospitalizations and provide to have a robust dataset to be well trained. Prediction was
them proper healthcare without rescheduling planned performed for 14 days using input data from 14 days before
elective care; it also aids the redistribution of hospitalized first prediction day. Training input data were moved by one
patients among different regions and districts as needed. day to the right until the end of the dataset. As in ensemble
method, the inputs were created from daily PCR and Ag
2 Methods test data. In addition, hospitalizations in last 14 days were
We propose two different machine learning methods: the used. To enlarge the dataset, data from all regions of
first based on ensemble learning and the second based on Slovakia (8 regions) and their summaries were used for
the MLP method. The first was developed and tested in real
2.4 Time-series ensemble method
The time-series machine learning ensemble method was
trained in the MS Azure cloud machine learning module,
which is a cloud-based machine learning service with a
user interface. This allows users without programming
knowledge to train; all that needs to be done is to upload
the training dataset, choose the built-in method and desired
features, and click to start. Azure trains huge number of
models by default and compares them. After considering
several models (such as decision tree, random forest,
AutoArima, ProphetModel, ElasticNet, GradientBoosting,
and LassoLars) we chose the voting ensemble model
because of smallest normalized root mean squared error
(RMSE) on prediction gained by this model compared to
other available machine learning models. Best performance
of this model among all available models was confirmed in
most of our experiments. The architecture of Azure Voting
ensemble model is shown in Fig. 2. The model consists of
six soft voting base regressors: three gradient boosting
regressors, one random forest regressor and two decision
tree regressors, each with different parameters. The voting
ensemble model considers the predictions of every
regressor, which are weighted and averaged, the final
prediction being the weighted average from all regressors
in the ensemble model. The hyperparameters used are listed
in Table 1.
After training the model, the online endpoint must be
created. An online endpoint is an HTTPS endpoint which is
called by the user to obtain output of trained model. It
contains deployments to receive data and send responses in
real time. Access to the endpoint is deployed through a
Python script in combination with the prediction input data.
Fig. 1. Preparing the training dataset for machine learning time-series
ensemble method using time shifts. The example dataset (left) is divided 2.5 MLP method
into the training part (upper right) and prediction input part (bottom right)
using time shifts. Missing days in the prediction input (labeled as For the 14-day prediction, we used a standard MLP with
“AUTO”) are automatically filled by MS Azure. The entire process results two hidden layers. We used 15 neurons with the hyperbolic
in 14 days of hospitalization forecasting (labeled as “14-DAYS
PREDICTION”). The example is shown without normalization for better
tangent activation function in each hidden layers. The
visualization. Following data are mentioned in the figure: number and output layer with 14 neurons represents a multi-step
percent of positive polymerase chase reaction tests (PCR and PCR%), prediction of hospitalization with a linear activation
number and percent of positive antigen tests (Ag and Ag%), number of function at the output.
hospitalized patients (Hosp).
The architecture of our MLP network is shown in Fig. 3.
Training and validation data are randomly divided using
training. This dataset was nine times larger than one with early-stopping with six validation checks. The Levenberg–
only summary data. Marquardt training algorithm was used, and performance
For example, in one of our models, trained from measured using the mean squared error.
11/10/2020 till 02/02/2020 (i.e., 85 days) we obtained 58
2.6 Evaluating the results
inputs and their outputs (14 × 58 inputs together with 14 ×
58 outputs moved by one day) once from every region and The results of both methods were evaluated with RMSE
once from the whole of Slovakia, giving 58 × 9 = 522 input and mean absolute percentage error (MAPE) using the
data and their outputs for training. We applied a seven-day standard formulas
moving average filter to the input data for preprocessing
and normalization to simplify the function fit. All data were , (1)
normalized to a scale of 0 to 1, with 1 corresponding to 1.5
times the maximum value in positive or hospitalized inputs
and 100% as the maximum if inputs are in percent. The
, (2)
dataset was divided randomly and 80% assigned as training
data and 20% as validation data. The last 14 days before
prediction period were used as the test data to get the final where n is number of fitted points, At is the actual value,
14 days prediction. The dataset was prepared in MATLAB and Ft is forecast value.
2020b.
positive antigen tests. Correlations of these variables in this
Table 1. Hyperparameters used in Azure Soft Voting Ensemble model.
model are shown in Appendix section.
Model Hyperparameters
Loss: least squares regression Learning_rate: 0.01; N_estimators:
In contrast, the MLP prediction method was quite
Gradient boosting
600; Subsample: 0.95 inaccurate during the first months of this year (RMSE in
Criterion: “friedman_mse”; Min_samples_split: 0.007532;
regressor 1
Min_samples_leaf: 0.006152; Max_depth: 5; Max_features: 0.9; range of 114.81–328.64, MAPE in range 2.76–12.94). The
Validation_fraction: 0.1; Tolerance: 0.0001
prediction accuracy improved with time; more training
Bootstrap: True; N_estimators: 200; Subsample: 0.95; samples led to better results. Table 3 gives the average
Criterion: “mse”; Min_samples_split: 0.001281;
Random forest regressor
Min_samples_leaf: 0.001953; Max_depth: None; RSME and MAPE values for all regions and for the whole
Max_features: 0.4
of Slovakia. Values for all of Slovakia were computed as a
Loss: least squares regression; Learning_rate: 0.1; summary of all regions. The best regional and summary
N_estimators: 600; Subsample: 0.45; Criterion: “mse”;
Gradient boosting regressor 2
Min_samples_split: 0.052854; Min_samples_leaf: 0.023458; results were obtained in May.
Max_depth: 3; Max_features: 0.1; Validation_fraction: 0.1;
Tolerance: 0.0001
Comparisons of the performances of both the proposed
methods in three random time periods are shown in Table 2
Criterion: “mse”; Min_samples_split: 0.003709;
Min_samples_leaf: 0.007595; Max_depth: None; and Fig. 4.
Decision tree regressor 1 Max_features: 0.9
Splitter: “best”
Predictions of MLP method in all regions and the whole
of Slovakia in random time periods in May improved
Loss: “hubel”; Alpha: 0.9; Learning_rate: 0.01; N_estimators:
Gradient boosting regressor 3
400; Subsample: 0.35; Criterion: “mse”; Min_samples_split: compared to those predicted earlier (Fig. 6, Table 3-
0.008992; Min_samples_leaf: 0.013218; Max_depth: 6;
Max_features: 0.9; Validation_fraction: 0.1; Tolerance: 0.0001
marked bold).
Criterion: “friedman_mse”; Min_samples_split: 0.007532; Table 2. Results of time series ensemble and MLP predictions for the whole of Slovakia. The MLP
Decision tree regressor 2
Min_samples_leaf: 0.009524; Max_depth: None; predictions were made directly for the whole Slovakia (and not as a summary of all regions
Max_features: None; Splitter: “best” predictions). Time periods are from the year 2021.
Weights (w1–w6): 0.400,0.0667,0.0667,0.0667,0.2667,0.1333
Time Metrics
period Ensemble Ensemble MLP MLP
RMSE MAPE RMSE MAPE
3 Results 3.2.–16.2. 61.62 1.43 328.64 9.31
17.2.–2.3. 91.49 2.19 - -
The results of the time-series machine learning ensemble
5.3.–18.3. 82.71 1.93 114.81 2.76
method are shown in Table 2. The ensemble method 3.–16.4. 440.19 16.21 311.44 12.94
performed well in first three predictions in February when
hospitalizations had risen and in March when it predicted
Table 3. Results of MLP method: average RMSE and MAPE in all regions and entire Slovakia
the peak of the second wave in Slovakia. When comparing (obtained as summary of all regional predictions). Metrics were computed from a seven-day
moving average of hospitalizations. Time periods are from the year 2021.
with real hospitalizations cases, the RMSE and MAPE
Time Metrics
values were in the range of 61.62–91.49 and 1.43–2.19, period Average RMSE Average MAPE
respectively. The method failed to predict the drop in RMSE
regions
whole
country
MAPE
regions
whole
country
hospitalizations from April (RMSE: 440.19 and MAPE: 3.2.–16.2. 72.22 308.98 15.57 8.88
16.21) and later (Fig. 4). The importance of the variables in 5.3.–18.3. 41.67 140.71 8.77 3.31
the ensemble model is listed in Fig. 5. Highest importance 3.4.–16.4. 34.69 350.91 14.68 14.35
1.5.–14.5. 17.81 66.95 17.77 5.94
showed percent of positive antigen test, following with
Fig. 2. Architecture of our implemented voting ensemble time-series model in MS Azure. The time-series ensemble model consists of six base regressors
whose predictions are weighted with weights (w1-w6) and enter the voting system. In the figure are mentioned following data: number and percent of
positive polymerase chase reaction tests (PCR and PCR %), number and percent of positive antigen tests (Ag and Ag %).
predictions with decreasing of hospitalizations were made.
In addition, predicting hospitalizations with cloud-based
user-friendly built-in services could make this solution
accessible to non-programmers and easier to implement.
Using the MLP method, the initial predictions were
inaccurate. Its performance improved with time with
accurate results being obtained from the time period in
May. This success was also observed in regional
predictions— with an average RMSE of 17.81. The RMSE
for all regional summaries was 66.95, which we consider as
best result for the MLP method for whole Slovakia. We
assume that The improvement of the MLP results with time
are due to the increase in training dataset size.
We propose that regional predictions with RMSE lower
than 20 and for all of Slovakia with RMSE lower than 100
can be valuable in practice.
We took 14 days as our forecasting periods; however,
shorter prediction periods are expected to give better
results. As prediction period increases, the discrepancy
between predicted and real numbers rises. In addition,
predicting in shorter time periods in the MLP method leads
to more robust dataset, which may lead to even better
results. This can be a promising direction for further
Fig. 3. Architecture of MLP feedforward network. Input layer consists of investigation.
five time-series inputs, following two hidden layers each containing 15
neurons and the 14-day time-series prediction as the output. Following Using only positive tests and previous hospitalizations as
data are mentioned in the figure: number and percent of positive inputs may not be sufficient in the future. This experiment
polymerase chase reaction tests (PCR and PCR%), number and percent of was done during the second wave of pandemic in Slovakia,
positive antigen tests (Ag and Ag%), number of hospitalized patients when the vaccination status was not an important factor,
(Hosp).
and therefore, we did not notice any sudden change in the
age distribution of positive tests. As vaccination begins,
4 Discussion new input variables would be necessary, such as the
We proposed two machine learning approaches for short- percentage of vaccinated individuals in the population or in
term hospitalization forecasting in Slovakia. The first the elderly and the daily mean age of tested positive. This
approach is time-series ensemble method and the second an would be especially relevant during the third wave, when
MLP neural network. due to vaccination, the age distribution among positive
The ensemble method performed well at the beginning of tested and hospitalized may differ. We hope that with new
the experimental period, with the best RMSE being 61.62, input variables and more robust data, these methods can
but failed when hospitalizations decreased. This could be adapt to such changes.
due to lack of training data— the method was trained with Forecasting COVID-19 hospitalizations is critical for
data from the whole of Slovakia only from November monitoring pandemic outbreaks and provide healthcare
2020. In that time hospitalizations had risen, and the data without compromising on elective care. Redistribution of
from the period when cases were decreasing could not be patients among district and regions can be considered based
learned. Surprisingly, the peak of the second wave, which on such predictions if there is a shortage of hospital beds.
followed the decrease in hospitalizations was predicted Our machine learning forecasting approaches are promising
successfully with this approach. After that, no successful
Fig. 4. Comparison of time-series ensemble and MLP method hospitalization predictions in three random time periods.
when sufficient training data is available. Augmenting the
training dataset using data from all regions, (as in our MLP
method) increases the accuracy of predictions, which gives
hope for forecasting hospitalizations in the coming third
wave of COVID-19.
Author contributions
V.K. developed the experimental premise, design, and
procedures. V.K., and M.H. conducted the research, trained
Fig. 5. Importance of variables in proposed ensemble model from March
the ensemble method and networks, and analyzed the data. 2021, obtained from model analysis in MS Azure. Following data are
V.K. and J.G. processed the figures and analyzed the data. mentioned in the figure: number and percent of positive polymerase chase
V. K. and J.G. prepared the manuscript. All authors reaction tests (PCR and PCR %), number and percent of positive antigen
interpreted the results, contributed to manuscript revision, tests (Ag and Ag %). Feature importance is computed in MS Azure using
permutation feature importance inspired by [34].
and approved the submitted version.
Fig. 6. Results of MLP method in random time periods in May in all Slovakian regions and the whole of Slovakia. Hospitalization data were filtered by a
seven-day moving average filter.
Appendix
Fig. 7 Basic data overview: distribution of number and percent of positive polymerase chain reaction tests (PCR, PCR perc.), number and percent of
positive antigen tests (Ag, Ag perc.) and hospitalizations in our dataset. Graphs were created in MS Azure.
Fig. 8, 9, 10, 11 Basic data overview: correlation of number and percent of positive polymerase chain reaction tests (PCR, PCR%.), number and percent
of positive antigen tests (Ag, Ag%) with hospitalizations.
Fig. 12, 13 Importance of Ag perc. and Ag in Ensemble model. Graphs were created in MS Azure:
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