Research and Technology Development Achievements During the COVID-19 Pandemic—An Overview

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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Probabilistic Forecasting Model for the COVID-19 Pandemic Based on the Composite Monte Carlo Model Integrated with Deep Learning and Fuzzy System

There are several techniques to support simulation of time series behavior. In this chapter, the approach will be based on the Composite Monte Carlo (CMC) simulation method. This method is able to model future outcomes of time series under analysis from the available data. The establishment of multiple correlations and causality between the data allows modeling the variables and probabilistic distributions and subsequently obtaining also probabilistic results for time series forecasting. To improve the predictor efficiency, computational intelligence techniques are proposed, including a fuzzy inference system and an Artificial Neural Network architecture. This type of model is suitable to be considered not only for the disease monitoring and compartmental classes, but also for managerial data such as clinical resources, medical and health team allocation, and bed management, which are data related to complex decision-making challenges. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

The Comparison of Different Linear and Nonlinear Models Using Preliminary Data to Efficiently Analyze the COVID-19 Outbreak

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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

The Application of Supervised and Unsupervised Computational Predictive Models to Simulate the COVID19 Pandemic

The application of different tools for predicting COVID19 cases spreading has been widely considered during the pandemic. Comparing different approaches is essential to analyze performance and the practical support they can provide for the current pandemic management. This work proposes using the susceptible-exposed-asymptomatic but infectious-symptomatic and infectious-recovered-deceased (SEAIRD) model for different learning models. The first analysis considers an unsupervised prediction, based directly on the epidemiologic compartmental model. After that, two supervised learning models are considered integrating computational intelligence techniques and control engineering: the fuzzy-PID and the wavelet-ANN-PID models. The purpose is to compare different predictor strategies to validate a viable predictive control system for the COVID19 relevant epidemiologic time series. For each model, after setting the initial conditions for each parameter, the prediction performance is calculated based on the presented data. The use of PID controllers is justified to avoid divergence in the system when the learning process is conducted. The wavelet neural network solution is considered here because of its rapid convergence rate. The proposed solutions are dynamic and can be adjusted and corrected in real time, according to the output error. The results are presented in each subsection of the chapter. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Analysis of the COVID19 Pandemic Behaviour Based on the Compartmental SEAIRD and Adaptive SVEAIRD Epidemiologic Models

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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Prediction for Decision Support During the COVID-19 Pandemic

The task known as prediction is widely applied in several different areas of knowledge, from popular applications such as weather forecasting, going through supply chain management, an increasing range of adoption in healthcare and, more specifically in epidemiology, the central topic of this book. The new challenges brought with the COVID-19 pandemic highlighted the possibilities and necessity of using prediction techniques to support decisions related to epidemiology in both managerial and clinical areas. In practice, the current outbreak created a strong need for the adoption of different computational models to support both medical teams and public health administrators. The methods vary from simple linear regressions to very complex algorithms based on Artificial Intelligence (AI) techniques. The present chapter contextualizes the use of prediction for decision support as a foundation of the following chapters which are focused on the application for the COVID-19 pandemic time series. With such a large number of methods for data-driven predictions, a clear distinction between explanation and prediction is firstly provided. From there, a methodological framework is presented, from the data source definition and selection of countries as references for the analysis, going through data handling for validation, until the definition of the evaluation criteria for the proposed models. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Predicting the Geographic Spread of the COVID-19 Pandemic: A Case Study from Brazil

The support provided by geographic data and the corresponding processing tools can play an essential role to support decision-making process, especially for public healthcare during the current pandemic outbreak of the COVID-19. Geographic data collection may be challenging when is necessary to obtain precise latitude and longitude, for example. The current chapter presents a new tool for the geographic location prediction of new cases of COVID-19, considering the confirmed cases in the city of Fortaleza, capital of the State of Ceara, Brazil. The methodology is based on a sequential approach of four clustering algorithms: Agglomerative Clustering, DBSCAN, Mean Shift, and K-Means followed by a two-dimensional predictor based on the Kalman filter. The results are presented following a case study approach with different examples of implementation and the corresponding analysis of the results. The proposed technique could generally predict the trend of the infection geographically in Fortaleza and effectively supported the decision-making process of public healthcare analysts and managers from the Secretariat of Health of the State of Ceara. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Nonlinear Prediction for the COVID-19 Data Based on Quadratic Kalman Filtering

Considering the application of prediction techniques to support the decision-making process during a dynamic environment such as the one faced during the COVID-19 pandemic, demands the evaluation of several different strategies to compare and define the most suitable solution for each necessity of prediction. Analyzing the epidemic time series, for example, the number of new confirmed cases of COVID-19 per day, classic compartmental models or linear regressions may not provide results with enough precision to support managerial or clinical decisions. The application of nonlinear models is an alternative to improve the performance of these models. The Kalman Filter (KF) is a state-space model that is used in several applications as a predictor. The filter algorithm requires low computational power and provides estimates of some unknown variables given the measurements observed over time. In this chapter, the KF predictor is considered in the analysis of five countries (China, United States, Brazil, Italy, and Singapore). Similarly to the ARIMA methodology, the results are evaluated based on three criteria: R2 Score, MAE (Mean Absolute Error), and MSE (Mean Square Error). It is important to notice that the definition of a predictor for epidemiological time series shall be carefully evaluated and more complex implementations do not always represent a better prediction on average. For the proposed KF predictor, there were specific time-series samples with no satisfactory result, achieving a negative R2 Score, for example, while, on the other, other samples achieved higher R2 Score and lower MAE and MSE, when compared to other linear predictors. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Forecasting COVID-19 Time Series Based on an Autoregressive Model

When considering time-series forecasting, the application of autoregressive models is a popular and simple technique that is usually considered. In this chapter, we present the basic theoretical aspects and assumptions of the ARIMA—Autoregressive Integrated Moving Average model. It is considered for the prediction of the COVID-19 epidemiological data series of five different countries (China, United States, Brazil, Italy, and Singapore), each of them with specific curves, which are results of the virus reproduction itself but also of policies and government decisions during the pandemic spread. The discussion about the results is performed with the focus on the three evaluation criteria of the model: R2 Score, MAE, and MSE. Higher R2 Score was obtained when the sample time series was smoothly increasing or decreasing. The error metrics were higher when the prediction was performed for oscillating data series. This may indicate that the use of ARIMA models may be suitable as a prediction tool for the COVID-19 when the country is not facing severe oscillations in the number of infections. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Epidemiology Compartmental Models—SIR, SEIR, and SEIR with Intervention

The process of decision-making when dealing with infectious diseases is firmly based on mathematical modeling nowadays. One usual approach is to consider the adoption of compartmental methods such as SIR and SEIR and a large number of corresponding variations for modeling and prediction epidemic time series. Nevertheless, the COVID-19 epidemic characteristics and curves are apparently challenging the results obtained by these models. This chapter presents the results of two traditional compartmental models, SIR (Susceptible—Infected–Recovered) and SEIR (Susceptible–Exposed–Infected–Recovered), and an adapted version of the SEIR, called SEIR with Intervention, which captures the impact of containment measures for the dynamics of the infection rate. The analysis is performed for five countries: China, United States, Brazil, Italy, and Singapore, each of them with specific characteristics of dealing with the pandemic. A sequence of results is presented, considering different parameters, in order to understand the feasibility of application for each model. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Artificial Intelligence Prediction for the COVID-19 Data Based on LSTM Neural Networks and H2O AutoML

The use of computational intelligence techniques is being considered for a vast number of applications not only because of its increasing popularity but also because the results achieve good performance and are promising to keep improving. In this chapter, we present the basic theoretical aspects and assumptions of the LSTM model and H20 AutoML framework. It is evaluated on the prediction of the COVID-19 epidemiological data series for five different countries (China, United States, Brazil, Italy, and Singapore), each of them with specific curves, which are results of policies and decisions during the pandemic spread. The discussion about the results is performed with the focus on three evaluation criteria: R2 Score, MAE, and MSE. Higher R2 Score was obtained when the sample time series was smoothly increasing or decreasing. The results obtained by the AutoML framework achieved a higher R2 Score and lower MAE and MSE when compared with LSTM and also with other techniques proposed in the book, such as ARIMA and Kalman predictor. The application of machine learning algorithm selector might be a promising candidate for a good predictor for epidemic time series. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.