Abstract
Since the outbreak of coronavirus disease-2019 (COVID-19), the whole world has taken interest in the mechanisms of its spread and development. Mathematical models have been valuable instruments for the study of the spread and control of infectious diseases. For that purpose, we propose a two-way approach in modeling COVID-19 spread: a susceptible, exposed, infected, recovered, deceased (SEIRD) model based on differential equations and a long short-term memory (LSTM) deep learning model. The SEIRD model is a compartmental epidemiological model with included components: susceptible, exposed, infected, recovered, deceased. In the case of the SEIRD model, official statistical data available online for countries of Belgium, Netherlands, and Luxembourg (Benelux) in the period of March 15 2020 to March 15 2021 were used. Based on them, we have calculated key parameters and forward them to the epidemiological model, which will predict the number of infected, deceased, and recovered people. Results show that the SEIRD model is able to accurately predict several peaks for all the three countries of interest, with very small root mean square error (RMSE), except for the mild cases (maximum RMSE was 240.79 ± 90.556), which can be explained by the fact that no official data were available for mild cases, but this number was derived from other statistics. On the other hand, LSTM represents a special kind of recurrent neural network structure that can comparatively learn long-term temporal dependencies. Results show that LSTM is capable of predicting several peaks based on the position of previous peaks with low values of RMSE. Higher values of RMSE are observed in the number of infected cases in Belgium (RMSE was 535.93) and Netherlands (RMSE was 434.28), and are expected because of thousands of people getting infected per day in those countries. In future studies, we will extend the models to include mobility information, variants of concern, as well as a medical intervention, etc. A prognostic model could help us predict epidemic peaks. In that way, we could react in a timely manner by introducing new or tightening existing measures before the health system is overloaded.
Highlights
Wuhan was registered on December 19, 2019 as the epicenter of the emergence of a new virus from the group of coronaviruses that showed the characteristics of inter-human transmission, causing a respiratory disease presenting with fever, dry cough, and, often, severe pneumonia with acute respiratory distress syndrome (ARDS) [1]
We propose two main approaches, a standard compartmental epidemiological SEIRD model and a deep learning model based on long short-term memory (LSTM) networks, to describe the spread of COVID-19
We present the results of the applied SEIRD model and LSTM encoder-decoder model, and compare them with the real situation of the COVID-19 outbreak and spread in Benelux countries
Summary
Wuhan was registered on December 19, 2019 as the epicenter of the emergence of a new virus from the group of coronaviruses that showed the characteristics of inter-human transmission, causing a respiratory disease presenting with fever, dry cough, and, often, severe pneumonia with acute respiratory distress syndrome (ARDS) [1]. Understanding the dynamics of transmission of infectious diseases in populations, regions, and countries will contribute to better approaches to reducing the spread of these diseases [3,4,5,6]. The number of infected people grows exponentially, and many countries have decided to impose a complete lockdown of affected cities in order to reduce the number of contacts and stop the spread of the virus [7]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
