Predictive Information Workflow of Forecasting Number of COVID-19 Confirmed Cases

There are questions about how to accurately prepare with the correct number of resources for distribution in order to properly manage the healthcare resources (e.g., healthcare workers, Masks, ART-19 TestKit) required to tighten the grip on the COVID-19 pandemic. Mathematical and computational forecasting models have well served the means to address these questions, as well as the resulting advisories to governments. A workflow is proposed in this research, aiming to develop a forecasting simulation that makes accurate predictions on COVID-19 confirmed cases in Singapore. According to the analysis of the prior works, six candidate forecasting models are evaluated and compared in the workflow: polynomial regression, linear regression, SVM, Prophet, Holt's linear, and LSTM models. The study's goal is to determine the most suitable forecasting model for COVID-19 cases in Singapore. Two algorithms are also proposed to better compute the performance of two models: the order algorithm to determine optimal degree order for the polynomial regression model, and the optimizing algorithm for the Holt's linear model to calculate the optimal smoothing parameters. Observed from the experiment results with the COVID-19 dataset, the Prophet method model achieves the best performance with the lowest Root Mean Square Error (RMSE) score of 1557.744836 and Mean Absolute Percentage Error (MAPE) score of 0.468827, compared to the other five models. The Prophet method model achieving average accuracy range of 90% when forecasting the number of confirmed COVID-19 cases in Singapore for the next 87 days ahead. is chosen and recommended to be used as a system model for forecast the COVID-19 confirm cases in Singapore. The developed workflow will greatly assist the authorities in taking timely actions and making decisions to contain the COVID-19 pandemic. © 2022 IEEE.

Node Research on the Involvement of China's Carbon Tax Policy in the Context of COVID-19

The outbreak of COVID-19 provides a rare opportunity for the implementation of the carbon tax. To determine which stage is the most appropriate for introducing the policy, a simulation model based on China's panel data is established to analyze the impact of the carbon tax on government revenue and residents' income from five scenarios. A new GM-SD modeling method is proposed to ensure the accuracy of the model. The results show that the impact of the carbon tax on the government and the public is significantly different at different stages, and even the implementation of the carbon tax in the early stage of COVID-19 will reduce the government's tax revenue. The score analysis of government tax revenue, residents' surplus disposable income, residents' emotional value, and government administrative power finds that the middle period of COVID-19 is the best time to implement the policy. In addition, a more detailed analysis of five aspects, including total population, energy consumption, and national income, shows that the best time to implement the carbon tax policy is when the damage degree of COVID-19 is moderate. The analysis results can provide a reference and basis for China to introduce the carbon tax in the event of similar events as COVID-19, and have reference significance for other countries that have not implemented a carbon tax. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

ALFABETO: Supporting COVID-19 hospital admissions with Bayesian Networks

The ongoing pandemics of coronavirus disease has accelerated the implementation of machine learning methods (ML) to support clinical decisions. Within this context, we present the ALFABETO project, whose aim is to aid clinicians during COVID-19 patients hospital admission through the application of ML approaches exploiting clinical and chest x-ray features. Yet, non linear ML classifiers are often perceived as not easily interpretable by users, thus hampering trust in ML predictions. Moreover, these ML models, such as Neural Networks or Random Forest, are not able to include pre-exisisting knowledge about a specific domain and are not designed to find causal relationships between variables. For these reasons, we wanted to investigate if Bayesian Networks were able to properly describe the hospital admission decision process. Bayesian Networks are probabilistic graphical models representing a set of variables and their conditional dependencies. The network structure was derived both from existing medical knowledge and from patients data collected during the first wave of the pandemic. While being explainable, we show that the Bayesian network has similar performance when compared to a less explainable ML model and that was able to generalize well across COVID-19 waves. © 2021 Copyright for this paper by its authors.

Gases Theory Representation Instrument (GeTRI): A Practical Tool to Analyze Sundanese Students’ Conception in the COVID-19 Pandemic

This research purposes to promote a Gases Theory Representation Instrument (GTRI) as a tool to identify the students’ conception on kinetic theory of gases. The method used in this research was FODEM (Formative Development Methods) model which has three comprehensive steps, which are need analysis, implementation, and formative evaluation. The participants involved in this research were 26 high school students in Sundanese tribe. The students’ responses were analyzed using Rasch model, which involved item reliability, person reliability, validity, difficulty level and students’ conception distributions. Students’ conception were classified into six categories which are Sound Understanding (SU), Partial Positive (PP), Partial Negative (PN), Misconception (MC), No Understanding (NU), and No Coding (NC). Based on the data analysis, it can be concluded that students’ conception are typically in the SU and PP categories. Besides, the Gases Theory Representation Instrument (GTRI) is reliable and valid to identify students’ conception on kinetic theory of gases. © 2021 Institute of Physics Publishing. All rights reserved.