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A significant number of people infected by COVID19 do not get sick immediately but become carriers of the disease. These patients might have a certain incubation period. However, the classical compartmental model, SEIR, was not originally designed for COVID19. We used the simple, commonly used SEIR model to retrospectively analyse the initial pandemic data from Singapore. Here, the SEIR model was combined with the actual published Singapore pandemic data, and the key parameters were determined by maximizing the nonlinear goodness of fit R2 and minimizing the root mean square error. These parameters served for the fast and directional convergence of the parameters of an improved model. To cover the quarantine and asymptomatic variables, the existing SEIR model was extended to an infectious disease model with a greater number of population compartments, and with parameter values that were tuned adaptively by solving the nonlinear dynamics equations over the available pandemic data, as well as referring to previous experience with SARS. The contribution presented in this paper is a new model called the adaptive SEAIRD model; it considers the new characteristics of COVID19 and is therefore applicable to a population including asymptomatic carriers. The predictive value is enhanced by tuning of the optimal parameters, whose values better reflect the current pandemic.
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The COVID-19 pandemic spread generated an urgent need for computational systems to model its behavior and support governments and healthcare teams to make proper decisions. There are not many cases of global pandemics in history, and the most recent one has unique characteristics, which are tightly connected to the current society’s lifestyle and beliefs, creating an environment of uncertainty. Because of that, the development of mathematical/computational models to forecast the pandemic behavior since its beginning, i.e., with a restricted amount of data collected, is necessary. This chapter focuses on the analysis of different data mining techniques to allow the pandemic prediction with a small amount of data. A case study is presented considering the data from Wuhan, the Chinese city where the virus was first detected, and the place where the major outbreak occurred. The PNN + CF method (Polynomial Neural Network with Corrective Feedback) is presented as the technique with the best prediction performance. This is a promising method that might be considered in future eventual waves of the current pandemic or event to have a suitable model for future epidemic outbreaks around the world.
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At the beginning of 2020, the World Health Organization (WHO) started a coordinated global effort to counterattack the potential exponential spread of the SARS-Cov2 virus, responsible for the coronavirus disease, officially named COVID-19. This comprehensive initiative included a research roadmap published in March 2020, including nine dimensions, from epidemiological research to diagnostic tools and vaccine development. With an unprecedented case, the areas of study related to the pandemic received funds and strong attention from different research communities (universities, government, industry, etc.), resulting in an exponential increase in the number of publications and results achieved in such a small window of time. Outstanding research cooperation projects were implemented during the outbreak, and innovative technologies were developed and improved significantly. Clinical and laboratory processes were improved, while managerial personnel were supported by a countless number of models and computational tools for the decision-making process. This chapter aims to introduce an overview of this favorable scenario and highlight a necessary discussion about ethical issues in research related to the COVID-19 and the challenge of low-quality research, focusing only on the publication of techniques and approaches with limited scientific evidence or even practical application. A legacy of lessons learned from this unique period of human history should influence and guide the scientific and industrial communities for the future.
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Faculty of Business and Law
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- Alexandre Lobo (3)
- Ivan Arraut (1)
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