Single and Multilayer LSTM Models for Positive COVID-19 Cases
Prediction
Asmae Berhich
a
, Fatima-Zahra Belouadha
b
and Asmae El Kassiri
c
AMIPS Research Team, E3S Research Center, Ecole Mohammadia d’Ingénieurs,
Mohammed V University in Rabat, Morocco
Keywords: COVID-19, prediction, single layer LSTM, multilayer LSTM, deep learning, health.
Abstract: COVID-19 is a global pandemic that has been reported first in Wuhan, China in December 2019. According
to the World Health Organization (WHO), around 1 out of every 5 people who get COVID-19 get seriously
ill and develop difficulty breathing. The virus is spreading from one person to others causing fear and a big
struggle in the world. Building accurate learning models for forecasting positive new cases would help to
better manage the crisis situation thereby helping to fight COVID-19 and save lives. For this purpose, we use
LSTM (Long Short Time Memory) model in Morocco’s case and evaluate its performance according to six
architectures. The results demonstrate that the architecture with three cells outperforms the other models and
shows the best fitting.
1 INTRODUCTION
In the past decades, technologies have played an
important role in solving several problems of
epidemics and pandemics. For the same purpose,
Artificial Intelligence, and data science have emerged
with new methods and techniques that help humanity
to prevent the spread of pandemics, and mitigate the
related risk.
Nowadays, the whole countries in the world suffer
from the COVID-19 epidemic and there is no
medicine or vaccine that prevents or cures this disease
until now. For this reason, researchers are invited to
discover and find new solutions to help governments
dealing and managing this dilemma. Many papers and
work were suggested for different purposes using
especially Machine Learning (ML) and Deep
Learning (DL) algorithms and techniques. However,
new methods and approaches still remain needed to
prevent the spread of the global pandemic. In this
context, our research aims at finding a solution for
this challenging problem using one of the most
powerful and known algorithms of DL called Long
Short-Term Memory (LSTM).
a
https://orcid.org/0000-0002-4388-100X
b
https://orcid.org/0000-0002-2355-4204
c
https://orcid.org/0000-0002-4842-588X
LSTM is a Recurrent Neural Network (RNN)
proposed and developed in 1997 (Hochreiter et al.,
1997). It is widely used in solving complex and hard-
learned problems in many different fields, especially
for time series data. For instance, it is used in the
seismic field (one of the most complex fields) to warn
from the incoming earthquake in a specific region
(BERHICH et al., 2020; Siami-Namini et al., 2019;
Wang et al., 2017). Our objective in this work is to
evaluate the predictions’ accuracy of the infected
cases in Morocco by applying six different LSTM
model’s architectures and comparing their efficiency
using the most popular performance metrics: MSE
(Mean Squared Error), MAE (Mean Absolute Error),
RMSE (Root Mean Squared Error), and R squared.
The rest of this paper is organized in the following
way: Section II presents the related work. Section III
gives an overview of our comparative approach by
highlighting the important steps of building our
models such as the data collection, preprocessing, and
parameterization of the learning process. Section IV
discusses and evaluates the performance of our
applied models. Section V summarizes the
conclusions and perspectives of this work.
2 RELATED WORKS
Recently, several researchers are trying to develop
and find suitable solutions and strategies to stop the
outbreak of the coronavirus disease. Data scientists
suggested some work for predicting and forecasting
new positive Covid-19 cases using ML and DL
techniques. DL and ML indeed provide effective
tools that learn trends from collected data, among
them the recurrent neural network LSTM which was
used in a lot of work as well as in this case study.
Authors in (Chimmula et al., 2020) predict the
possible ending point of coronavirus in Canada. They
apply the LSTM algorithm on the available data until
March 13, 2020 and they give predictions for 2
successive days from the 2nd to 14th day. The
findings of this work expect that the possible stopping
time of Coronavirus in Canada could be around June
2020, and a small number of infections may be
reported until December 2020. Besides, the aim in
(Arora et al., 2020) was to predict the daily and the
weekly number of positive cases in 32 states and
union territories of India. Four deep learning
techniques: LSTM, deep LSTM, convolutional
LSTM, and bidirectional LSTM were used. The
bidirectional LSTM gives the best performance
evaluated using the MAE metric. Moreover, another
research in (Tomar et al., 2020) predicts the number
of COVID-19 cases, recovered cases, and deceased
cases during 30 days ahead in India using the LSTM
model and curve fitting. Authors in (Yang et al.,
2020) apply a modified Susceptible-Exposed-
Infectious-Removed (SEIR) model to derive the
epidemic curve and artificial intelligence to predict
COVID-19 epidemic trends while giving it peaks and
sizes in China. Author in (Bouhamed, 2020) develops
DL nested sequence prediction models with also
LSTM to predict the cumulative case number and
recoveries in 79 countries. The models use the dataset
until March 13, 2020, and they are evaluated using
the R squared metric. The results were encouraging
for the newly infected cases. Predictions of
cumulative number of deaths, daily number of new
cases worldwide, and cumulative number of cases in
Europe and middle east regions were given in
(Direkoglu et al., 2020). This research provides the
predictions of the next ten days. It is based on the
reported time series data of Covid-19 and the LSTM
model with the dropout layer. The obtained results
were evaluated by the RMSE and were considered
promising since they were able to predict the possible
scenarios regionally and globally. In the same
manner, authors in (Yan et al., 2020) predict the
confirmed cases using the LSTM algorithm. They
compared the deviation between LSTM results and
the results of the digital prediction models (like
Logistic and Hill equations) with the real data. They
found that the proposed model has a smaller
prediction deviation and better fitting effect.
A hybrid model is applied in (Zandavi et al., 2020)
to forecast the number of cases and deaths in the top
ten most affected countries in Australia. This model
combines the algorithm LSTM with dynamic
behavioural models. The proposed approach
considers the effect of multiple factors, and the
parameters are optimized using the genetic algorithm.
The results showed that the hybrid model outperforms
the LSTM model. From another angle, authors in
(Alakus et al., 2020) use laboratory data to predict
which patients are likely to receive coronavirus. Their
predictive model based on DL approaches identified
patients that have COVID-19 with good accuracy.
In addition, three approaches were applied in
(Kırbaş et al., 2020) to predict the confirmed cases in
Europe: Autoregressive Integrated Moving Average
(ARIMA), Nonlinear Autoregressive neural network
(NARNN) and Long-Short term Memory (LSTM).
The LSTM model was more efficient for forecasting
14 future days. It expects that the rate of positive
cases will decrease slightly in many countries. In
(Ayyoubzadeh et al., 2020) LSTM and Linear
Regression (LR) models are suggested to forecast the
number of positive COVID-19 cases in Iran. The
results showed that LR predicted the incidence with
an RMSE of 7.5 and LSTM with an RMSE of 27.18.
These works and predictions have been performed
for different purposes under the scope of COVID-19
outbreak forecasting and would help the governments
to face the COVID-19 pandemic and help the
authorities and decision-makers to manage and deal
with their strategies. The LSTM model used
according to different learning approaches was
seeming to be promising in most of them. However,
it would be interesting to explore more approaches
using this model in order to reach better accuracy.
Besides, no study with accurate predictions, has
considered the case of the outbreak of COVID-19 in
Morocco using LSTM. Only three research
contributions consider the Morocco’s case while
using LSTM-based models (Ayris et al., n.d.;
Bouhamed, 2020; Ksantini et al., 2020). In (Ayris et
al., n.d.), authors use DSPM (Deep Sequential
Prediction Model) which is a stacked LSTM to
predict cumulative number of confirmed cases in
different countries in the world, among them
Morocco. Note that the obtained average MAE Error
Rate was 388.43 which is not a good result if we
consider Morocco’s case. We note that the studied
period matches with the confinement period in
Morocco until May 5, 2020 and that on this date, there
were 5219 confirmed cases reported while the
predicted value is 7422. Authors in (Ksantini et al.,
2020) use LSTM to predict new weekly cases of
COVID-19 pandemic based on the confinement and
the protection tools factors for different countries,
among them Morocco. We outline that this paper was
received in March 6, 2020 while the first confirmed
case in Morocco was reported in March 2, 2020, only
7 confirmed cases were reported in March 13, 2020,
and the confinement strategy was applied in March
20, 2020. We think that exploring LSTM with more
data would be interesting to have more reliable and
credible results.
In (Bouhamed, 2020), author uses LSTM to
predict the cumulative confirmed cases number in 79
countries, among them Morocco, and also considers
a dataset that range from the beginning of COVID-19
until only March 25, 2020. As we have mentioned
above, we think that this period and related data are
not sufficient to perform predictions about the virus
spread in Morocco. In addition, it is worth noting that
this work only provides projections for the next day,
which would not be interesting for decision makers
since it does not give them enough time to be able to
react to a critical situation. In this context and given
all of these reasons, our work was conducted.
3 METHODOLOGY
COVID-19 is a global pandemic and every day
millions of infected cases are reported around the
world. Our work aims to accurately predict the new
positive COVID-19 cases. For this purpose, we
explore different architectures of the LSTM
algorithm which is suitable to be used for forecasting
such time series data, and we experiment and evaluate
them in Morocco’s case. This section presents at first
the basis architecture of this recurrent neural network
before explaining the important steps that we follow
to build our models and perform our comparative
study.
3.1 RNNs Architecture and LSTM
RNNs are a category of Artificial Neural Networks
(ANNs) characterized by their state of memory. They
are composed of hidden states which are distributed
over time, allowing them to store a lot of information
about the past. They are mostly used in forecasting
applications because of their capacity to handle
sequential data of variable length (Graves, 2013).
However, their major disadvantage is their lack of
reducing and handling the problems of vanishing
gradient and explosion gradient. They can only store
short term memory because they require activations
of only the hidden layer of the pre-previous time step
(Hochreiter et al., 1997a).
The main goal of RNNs is to consider the
influence of past information in producing the output
result. To this end, they use cells represented by gates
which influence the generated output according to the
historical observations. They are especially effective
for learning temporal information (Oksuz et al.,
2019). In RNNs, a hidden state ht can be calculated
for a given input xt sequence by the equation 1 where
Whh is the weight of the previous hidden state ht-1,
xt is the current input, Wxh is the weight of the
current input state, tanh is the activation function.
The output state yt is computed according to the
equation 2 where Why is the weight at the output
state.
h
t
=tanh (W
hh
h
t-1
+W
xh
x
t
) (1)
y
t
=W
hy
h
t
(2)
LSTM is considered as a sophisticated RNN and
gated memory unit, designed to avoid and resolve the
vanishing gradient problems that limit the efficiency
of simple RNNs (Hochreiter et al., 1997a). The
LSTM cells are supported by three components called
gates: the input gate, the forget gate and the output
gate. This makes it possible to address the limitations
of traditional time series forecasting techniques by
adapting the non-linearities of a given dataset and to
produce state-of-the-art results on the temporal data.
Each block of LSTM works at different time steps and
passes its output to the next block until the final
LSTM block generates the sequential output. Besides,
LSTM is hence a powerful algorithm for
implementing a sequential time series model. Its key
component is memory blocks which have been
released to tackle vanishing gradients by memorizing
network parameters for long durations. The memory
blocks in the LSTM architecture are similar to the
differential storage systems of a digital system. The
gates in LSTM help to process the information using
an activation function (sigmoid) which generates a
value between 0 and 1 as an output. The main reason
why the sigmoid activation function is used is to
transmit only positive values to the following gates to
get a clear output (Chimmula et al., 2020).
LSTM is flexible and estimates dependencies of
different time scales. The commonly used RNN
variations such as LSTM use gates and memory cells
for sequence’s prediction. In the beginning, LSTM
starts with a forget gate layer (ft) that uses a sigmoid
function combined with the previous hidden layer (ht-
1) and the current input (xt) as described in the
following equations (3, 4, 5, 6, 7 and 8) where it, čt,
ft, ot, ct, ht are the input gate, cell input activation,
forget gate, output gate, cell state, and the hidden state
respectively. Wi, Wc, Wf and Wo are their weight
matrices respectively. bi, bc, bf, and bo are the biases.
Xt is the input, ht-1 is the last hidden state, ht is the
internal state. σ is the sigmoid function.
i
t
= σ (W
i
. [h
t-1
, x
t
] + b
i
) (3)
č
t
=tanh (W
c
[h
t-1
, x
t
] + b
c
) (4)
f
t
= σ (W
f
. [h
t-1
, x
t
] + b
f
) (5)
o
t
= σ (W
o
[h
t-1
, x
t
] + b
o
) (6)
c
t
= f
t
* c
t-1
+ i
t
* č
t
(7)
h
t
=ot * tanh (c
t
) (8)
3.2 Our Approach for Predicting
COVID-19 Positive Cases by Single
and Multilayer LSTM
In this work, we apply six different LSTM
architectures to predict the new positive COVID-19
cases in Morocco for the 7 future incoming days. The
six-architecture called LSTM1, LSTM2, LSTM3,
LSTM4, LSTM5 and LSTM6, are respectively
single, two, three, four, five and finally six LSTM
layers. The aim of this work is to compare the
relevance of the mentioned architectures in the
context of COVID-19 spread prediction.
In the following subsections, we present the
dataset we use in this work, the preprocessing steps
of our models, the feature selection, the applied
parametrization, and finally the performance metrics
used to evaluate and compare the results.
3.2.1 Dataset
The COVID-19 dataset we use in this work is from
“Our World in Data” Website4. It shares and reports
data collected from the European Center for Disease
Prevention and Control (ECDC). The COVID-19 data
are updated daily on this website which provides data
collected from around the world.
4
https://ourworldindata.org/coronavirus-source-data
3.2.2 Preprocessing and Feature Selection
The preprocessing and feature selection are
fundamental stages in ML and DL approaches. The
preprocessing gives many ways and operations to
convert and transform the source data into a clean
dataset ready to be feed in the ML and DL models. It
affects the quality of the model and its results. The
feature selection provides the relevant features that
adequately affect the learning process, and may
reduce the number of variables to evolve the model
efficiency and to avoid costly computations.
Eventually, in this work we are following these steps
to prepare and select feature from the source data:
extracting the targeted data inputs, selecting
appropriate feature, filling null values, normalizing
data and adapting the timesteps to be considered for
prediction.
The source data report the worldwide COVID-19
pandemic data. Therefore, we selected just data
related to Morocco’s case we desire to study. The
time of our analyzed dataset starts from the beginning
of this pandemic in Morocco on March 02, 2020 to
June 15, 2020. This period matches with the
confinement period in Morocco. It was selected in
order to allow analysing the performance of the
proposed models in the same context since the
deconfinement data are not sufficient and could
influence their accuracy.
The source data give multiple features but not all
of them are registered for the instances of Morocco,
and not all of them are important for use in the
prediction. In our case, we have selected five
important features: the new cases, the total cases, the
new deaths, the total deaths and population. These
features are the most and highly correlated variables
to the targeted output (new COVID-19 positive
cases). Note that the correlations of total cases, new
cases, total deaths, new deaths and population are
respectively 96,63%, 100%, 96,17%, 86,75% and
67,67%.
To impute null values, we used two methods: the
first one consists of filling with the median value
whereas the second consists of applying the Key
Nearest Neighbor (KNN) algorithm. We have
experimented our data with both methods and we
have proceeded with the median since it gave better
results than the KNN algorithm.
The features in a given dataset are generally
presented in different scales. In our case, for example,
the population is presented by millions, total cases are
presented by thousands since they describe the
cumulative number of cases, and the new cases are
presented by hundreds. To make all these values on
the same scale and to add the uniformity to our
dataset, we apply the Min-Max scaler that transforms
all values between the range 0 and 1. This will delete
the noise from our data and facilitate the learning
process of our models.
Besides, COVID-19 data are time series, and
hence, the values of the actual data are required as
inputs to perform predictions for the following days.
Time series data cannot use future values as input
features, then the inputs of a time series model are the
past feature values. In this work, we adapt our model
to learn from the past timesteps in order to predict the
positive COVID-19 cases for the future 7 timesteps.
This choice was adopted due to the data size and also
in order to consider a minimum sufficient time to be
given for decision makers.
3.2.3 Parametrization
The architectures of our six LSTM models are
differentiated by the number of LSTM cells. Table I
illustrates the architecture and parametrization of
each model. All the models are using Adam
optimizer which is one of the most used stochastic
optimizers thanks to its ability to learn faster as it has
been demonstrated in (Kingma et al., 2015) using
empirical experiments. The other mentioned
parameters have been fixed after we have tuned and
tested multiple parameters until larger batch sizes
were giving better results. The adopted size is 64. In
addition, the activation function that was giving good
fitting is the Tanh function. We also note that time lag
and timestep were respectively fixed to 2 and 7 days.
3.2.4 Performance Metrics
DL and ML models’ results are measured according
to various metrics. There is exist several methods to
evaluate regression models. In our work, we are using
four performance metrics MAE, MSE, RMSE and R
squared (R2), as mentioned above.
MAE presents the average of the absolute
difference between the real and the predicted values.
MSE represents the average of the square of the
difference between the original and the predicted
values. It is sensitive to outliers and data containing a
lot of noise. RMSE is the root of the value of MSE
and it presents the standard deviation of errors. It is
useful when high errors are present. Finally, R
squared indicates the efficiency of the model fitting.
Table 1: architectures and parametrization of our models.
Mode
l
Parametrization
Layers
Activation
function
optimize
r
Batch
size
LSTM-1
LSTM cell of 75
units
Dense layer of 7
out
p
uts
Tanh
Ada
m
64
LSTM-2
LSTM cell of 75 units
LSTM cell of 70 units
Dense layer of 7
outputs
Tanh
Ada
m
64
LSTM-3
LSTM cell of 75 units
LSTM cell of 70 units
LSTM cell of 60 units
Dense layer of outputs
Tanh
Ada
m
64
LSTM-4
LSTM cell of 75 units
LSTM cell of 70 units
LSTM cell of 65 units
LSTM cell of 60 units
Dense la
y
er of out
p
uts
Tanh
Ada
m
64
LSTM-5
LSTM cell of 75 units
LSTM cell of 70 units
LSTM cell of 65 units
LSTM cell of 63 units
LSTM cell of 60 units
Dense la
y
er of out
p
uts
Tanh
Ada
m
64
LSTM-6
LSTM cell of 75 units
LSTM cell of 70 units
LSTM cell of 65 units
LSTM cell of 63 units
LSTM cell of 60 units
LSTM cell of 55 units
Dense layer of outputs
Tanh
Ada
m
64
4 RESULTS AND DISSCUSSION
In this section, we present and discuss the results of
our LSTM models which are based on six different
architectures. The fitting curves of each model are
shown in Fig. 1. Unlike LSTM-1 and LSTM-2, the
loss curves of the other models indeed converge to the
minimum error corresponding to the training loss. We
can see that they do not present any limitation of
overfitting or underfitting.
Regarding the prediction quality, the results
illustrated in Table II, show that the LSTM model
with three layers outperforms the other models.
LSTM-3 provided the lowest MAE, MSE and RMSE
values which are respectively equal to 19.95, 685.65
and 25.66. It also globally provided good predicted
total positive cases per week. As shown in Table III,
the total predicted cases provided by LSTM-3 are
fairly close to the real ones at least for two among
three weeks (week 1 and week 3). In other terms, it
generally provided low deviations from the total real
cases in the way that it was able to predict values
which were equal respectively to the predicted cases
minus 5% and plus 8% in the third and first weeks. It
is also worth noting, that these values as well as the
quality metrics (RMSE, MSE and MAE) confirm a
good prediction capacity and fairly high accuracy,
especially, in comparison with all related work
presented in this paper.
Table 2: test results for 21 days.
Model
Performance metrics
MAE MSE RMSE R2
LSTM-1 23.78 830.51 28.82 0.03
LSTM-2 22.7 809.58 28.45 0.05
LSTM-3 19.95 658.65 25.66 0.23
LSTM-4 22.63 791.78 28.14 0.07
LSTM-5 21.73 759.59 27.56 0.11
LSTM-6 25.1 981.71 31.33 -0.15
Figure1: Fitting curves of prediction LSTM models.
Accordingly, we calculate the total number of real
and predicted cases: of the whole test set 21 days), the
first week, the second week, and the third week
(Table 3).
Besides, Fig. 2 presents the daily real and
predicted new cases’ curves corresponding to 21
days. We can see that LSTM-3 projections still
remain very close to the real values, except for some
high peaks that LSTM-3 doesn’t catch and also for
the period ranging from June 1, 2020 to June 3, 2020.
The peaks and the bending could be explained by
the industrial and residential clusters which are
reported from time to time in the last month of
confinement in Morocco. However, we think that the
reported deviation in general could be due to the fact
that our model doesn’t take into account other
important feature such as test kits, clusters,
asymptomatic case that would influence the COVID-
19 spread. Hence, we think that the obtained results
are promising. However, we suggest trying other
features in future work in order to help the models to
learn faster and easier the epidemic trend.
Table 3: LSTM-3 deviation per week.
Week 1 Week 2 Week
3
Real new cases 374 352 560
LSTM-3
Predicted new
cases
409,43 480,15 528,98
Deviation 35,43 128,15 -31,02
Deviation
ercenta
e
9,47 36,41 -5,54
5 CONCLUSIONS
In this paper, we give a comparative study of six
LSTM models’ architectures in order to predict new
positive COVID-19 cases using data of Morocco
from March 2, 2020 to June 15, 2020. The study
shows that the LSTM with three cells gives better
results and avoids both the overfitting and the
underfitting. The results are very close to the real
values for two among three weeks, and fairly close to
the other week. Therefore, we think that the powerful
DL model LSTM which is suitable for time series
problems, could also be a suitable and promising
model to learn complex insights from COVID-19
data. Our findings and conclusions are demonstrated
and enhanced by various illustrations we provide in
this paper. Nevertheless, we plan to more explore this
potential model under other perspectives by including
other important features and investigating also the
Figure 2: Real and predictive cases curves for 21 days.
deconfinement period in order to improve the
prediction accuracy and adapt the model to various
crisis situations.
ACKNOWLEDGEMENTS
This paper is produced as part of the COVID-19
project supported by the responsible ministry
MENFPESRS and the CNRST under the grant
number COV/2020/87.
REFERENCES
Alakus, T. B., & Turkoglu, I. (2020). Comparison of deep
learning approaches to predict COVID-19 infection.
Chaos, Solitons and Fractals, 140, 110120. doi:
10.1016/j.chaos.2020.110120
Arora, P., Kumar, H., & Panigrahi, B. K. (2020). Prediction
and analysis of COVID-19 positive cases using deep
learning models: A descriptive case study of India.
Chaos, Solitons and Fractals, 139, 110017. doi:
10.1016/j.chaos.2020.110017
Ayris, D., Horbury, K., Williams, B., Blackney, M., Shi, C.,
See, H., Afaq, S., & Shah, A. (n.d.). Deep Learning
Models for Early Detection and Prediction of the spread
of Novel Coronavirus (COVID-19).
Ayyoubzadeh, S. M., Ayyoubzadeh, S. M., Zahedi, H.,
Ahmadi, M., & R Niakan Kalhori, S. (2020). Predicting
COVID-19 incidence using Google Trends and data
mining techniques: A pilot study in Iran. JMIR Public
Health and Surveillance, 6(2), e18828. doi:
10.2196/18828
BERHICH, A., BELOUADHA, F., & KABBAJ, M. I.
(2020). LSTM-based Models for Earthquake
Prediction. 6. doi:
https://doi.org/10.1145/3386723.3387865
Bouhamed, H. (2020). Covid-19 Cases and Recovery
Previsions with Deep Learning Nested Sequence
Prediction Models with Long Short-Term Memory
(LSTM) Architecture. International Journal of
Scientific Research in Research Paper. Computer
Science and Engineering, 8(2), 10–15. Retrieved from
https://github.com/henibouhamed/COVID-19-LSTM
Chimmula, V. K. R., & Zhang, L. (2020). Time series
forecasting of COVID-19 transmission in Canada using
LSTM networks. Chaos, Solitons and Fractals, 135,
109864. doi: 10.1016/j.chaos.2020.109864
Direkoglu, C., & Sah, M. (2020). Worldwide and Regional
Forecasting of Coronavirus (Covid-19) Spread using a
Deep Learning Model. MedRxiv,
2020.05.23.20111039. doi:
10.1101/2020.05.23.20111039
Graves, A. (2013). Generating Sequences With Recurrent
Neural Networks. Retrieved from
http://arxiv.org/abs/1308.0850
Hochreiter, S., & Schmidhuber, J. (1997a). Long Short-
Term Memory. Neural Computation, 9(8), 1735–1780.
doi: 10.1162/neco.1997.9.8.1735
Hochreiter, S., & Schmidhuber, J. (1997b). Long Short-
Term Memory. Neural Computation, 9(8), 1735–1780.
doi: 10.1162/neco.1997.9.8.1735
Kingma, D. P., & Ba, J. L. (2015). Adam: A method for
stochastic optimization. 3rd International Conference
on Learning Representations, ICLR 2015 - Conference
Track Proceedings.
Kırbaş, İ., Sözen, A., Tuncer, A. D., & Kazancıoğlu, F. Ş.
(2020). Comparative analysis and forecasting of
COVID-19 cases in various European countries with
ARIMA, NARNN and LSTM approaches. Chaos,
Solitons and Fractals, 138, 110015. doi:
10.1016/j.chaos.2020.110015
Ksantini, M., Kadri, N., Ellouze, A., & Turki, S. (2020).
Artificial Intelligence Prediction Algorithms for Future
Evolution of COVID-19 Cases. Ingénierie Des
Systèmes d Information, 25(3), 319–325. doi:
10.18280/isi.250305
Oksuz, I., Cruz, G., Clough, J., Bustin, A., Fuin, N., Botnar,
R. M., Prieto, C., King, A. P., & Schnabel, J. A. (2019).
Magnetic resonance fingerprinting using recurrent
neural networks. Proceedings - International
Symposium on Biomedical Imaging, 2019-April,
1537–1540. doi: 10.1109/ISBI.2019.8759502
Siami-Namini, S., Tavakoli, N., & Siami Namin, A. (2019).
A Comparison of ARIMA and LSTM in Forecasting
Time Series. Proceedings - 17th IEEE International
Conference on Machine Learning and Applications,
ICMLA 2018, 1394–1401. doi:
10.1109/ICMLA.2018.00227
Tomar, A., & Gupta, N. (2020). Prediction for the spread of
COVID-19 in India and effectiveness of preventive
measures. Science of the Total Environment, 728,
138762. doi: 10.1016/j.scitotenv.2020.138762
Wang, Q., Guo, Y., Yu, L., & Li, P. (2017). Earthquake
Prediction based on Spatio-Temporal Data Mining: An
LSTM Network Approach. IEEE Transactions on
Emerging Topics in Computing, 6750(c), 1–1. doi:
10.1109/tetc.2017.2699169
Yan, B., Tang, X., Liu, B., Wang, J., Zhou, Y., Zheng, G.,
Zou, Q., Lu, Y., & Tu, W. (2020). An Improved Method
for the Fitting and Prediction of the Number of COVID-
19 Confirmed Cases Based on LSTM. Materials &
Continua CMC, 2020. Retrieved from
http://arxiv.org/abs/2005.03446
Yang, Z., Zeng, Z., Wang, K., Wong, S. S., Liang, W.,
Zanin, M., Liu, P., Cao, X., Gao, Z., Mai, Z., Liang, J.,
Liu, X., Li, S., Li, Y., Ye, F., Guan, W., Yang, Y., Li,
F., Luo, S., He, J. (2020). Modified SEIR and AI
prediction of the epidemics trend of COVID-19 in
China under public health interventions. Journal of
Thoracic Disease, 12(3), 165–174. doi:
10.21037/jtd.2020.02.64
Zandavi, S. M., Rashidi, T. H., & Vafaee, F. (2020).
Forecasting the Spread of Covid-19 Under Control
Scenarios Using LSTM and Dynamic Behavioral
Models. Retrieved from
http://arxiv.org/abs/2005.12270