Machine learning (ML)-based prediction is considered an important technique for improving decision making during the planning process. Modern ML models are used for prediction, prioritization, and decision making. Multiple ML algorithms are used to improve decision-making at different aspects after forecasting. This study focuses on the future prediction of the effectiveness of the COVID-19 vaccine effectiveness which has been presented as a light in the dark. People bear several reservations, including concerns about the efficacy of the COVID-19 vaccine. Under these presumptions, the COVID-19 vaccine would either lower the risk of developing the malady after injection, or the vaccine would impose side effects, affecting their existing health condition. In this regard, people have publicly expressed their concerns regarding the vaccine. This study intends to estimate what perception the masses will establish about the role of the COVID-19 vaccine in the future. Specifically, this study exhibits people’s predilection toward the COVID-19 vaccine and its results based on the reviews. Five models, e.g., random forest (RF), a support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), and an artificial neural network (ANN), were used for forecasting the overall predilection toward the COVID-19 vaccine. A voting classifier was used at the end of this study to determine the accuracy of all the classifiers. The results prove that the SVM produces the best forecasting results and that artificial neural networks (ANNs) produce the worst prediction toward the individual aptitude to be vaccinated by the COVID-19 vaccine. When using the voting classifier, the proposed system provided an overall accuracy of 89.9% for the random dataset and 45.7% for the date-wise dataset. Thus, the results show that the studied prediction technique is a promising and encouraging procedure for studying the future trends of the COVID-19 vaccine.

Future Prediction of COVID-19 Vaccine Trends Using a Voting Classifier

Objectives: Waves of epidemics associated with Omicron variant of Coronavirus Disease 2019 (COVID-19) in major cities in China this year have been controlled. It is of great importance to study the transmission characteristics of these cases to support further interventions. Methods: We simulate the transmission trajectory and analyze the intervention influences of waves associated with Omicron variant in major cities in China using the Suspected-Exposed-Infectious-Removed (SEIR) model. In addition, we propose a model using a function between the maximum daily infections and the duration of the epidemic, calibrated with data from Chinese cities. Results: An infection period of 5 days and basic reproduction number R0 between 2 and 8.72 are most appropriate for most cases in China. Control measures show a significant impact on reducing R0, and the earlier control measures are implemented, the shorter the epidemic will last. Our proposed model performs well in predicting the duration of the epidemic with an average error of 2.49 days. Conclusion: Our results show great potential in epidemic model simulation and predicting the end date of the Omicron epidemic effectively and efficiently.

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Transmission Characteristics and Predictive Model for Recent Epidemic Waves of COVID-19 Associated With OMICRON Variant in Major Cities in China

During the recent COVID-19 pandemic, quarantine and testing policies have been of vital importance since the causative agent has been a novel virus and no vaccine was developed at the time. In this work, a new epidemiological deterministic model is proposed, analyzed, and discussed. Such a model includes quarantine periods of people with symptoms that have been tested positive, and it will trigger a trace of their close contact, who will be also tested and put in quarantine if the result is positive. Moreover, how the model parameters affect its stability is analyzed with the basic reproduction number R0. Since the COVID-19 outbreak in Spain (approximately 13/03/2020) until 25/04/2021, different restrictions have been applied. For discussion of a real case study, data have been gathered and used from the Spanish Autonomous Community of Cantabria to estimate the parameters and to see how the restrictions have affected their values. In the parameter estimation process, it has been assumed that the constructed model follows the structure of an ARX model. Finally, by considering that the gathered data are subject to certain errors, the paper discusses how to adequate the model usefulness for its use in fitting and processing data through an estimation mechanism involving the provided daily total and positive performed tests.

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Analysis and Parametrical Estimation with Real COVID-19 Data of a New Extended SEIR Epidemic Model with Quarantined Individuals

The current novel Coronavirus Disease 2019 (COVID-19) is a multistage epidemic consisting of multiple rounds of alternating outbreak and containment periods that cannot be modeled with a conventional single-stage Suspected-Exposed-Infectious-Removed (SEIR) model. Seasonality and control measures could be the two most important driving factors of the multistage epidemic. Our goal is to formulate and incorporate the influences of seasonality and control measures into an epidemic model and interpret how these two factors interact to shape the multistage epidemic curves. New confirmed cases will be collected daily from seven Northern Hemisphere countries and five Southern Hemisphere countries from March 2020 to March 2021 to fit and validate the modified model. Results show that COVID-19 is a seasonal epidemic and that epidemic curves can be clearly distinguished in the two hemispheres. Different levels of control measures between different countries during different seasonal periods have different influences on epidemic transmission. Seasonality alone cannot cause the baseline reproduction number R0 to fall below one and control measures must be taken. A superposition of a high level of seasonality and a low level of control measures can lead to a dramatically rapid increase in reported cases.

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How Seasonality and Control Measures Jointly Determine the Multistage Waves of the COVID-19 Epidemic: A Modelling Study and Implications

The severity of the COVID-19 pandemic requires a better understanding of the spread SARS-COV2. As of December 2019, several mathematical models have been developed to explain how SARS-COV2 spreads within populations, and proposed models have evolved as more is learned about the dynamics of the outbreak. In this study, we propose a new mathematical model that includes demographic characteristics of the population. Social isolation and vaccination are also taken into account in the model. Besides transmission arising from intercourse with undiagnosed infected persons, we also consider transmission by contact with the exposed group. In this study, after the model is established, the basic reproduction number is calculated and local stability analysis of disease-free equilibrium is given. Finally, we give numerical simulations for the proposed model.

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A Novel Mathematical Model of the Dynamics of COVID-19

The authors are grateful to the institute Carlos III for grant COV20/01213, to the Spanish Government for Grants RTI2018-094336-B-I00 and RTI2018-094902-BC22 (MCIU/AEI/FEDER, UE) and to the Basque Government for Grant IT1207-19.

Jon Gabirondo Aranguren

Papers

A Study on COVID-19 Incidence in Europe through Two SEIR Epidemic Models Which Consider Mixed Contagions from Asymptomatic and Symptomatic Individuals