Prediction model for the spread of the COVID-19 outbreak in the global environment

Abstract

COVID-19 has long become a worldwide pandemic. It was responsible for the death of over one million people and posed an economic recession. This paper studies the spread pattern of COVID-19, aiming to establish a prediction model for this event. We harness Data Mining and Machine Learning methodologies to train regression models to predict the number of confirmed cases in a spatial-temporal space. We introduce an innovative concept ‒ the Center of Infection Mass (CoIM) ‒ adapted from the field of physics. We empirically evaluated our model on western European countries, based on the CoIM index and other features, and showed that a relatively high accurate prediction of the spread can be obtained. Our contribution is twofold: first, we introduced a prediction methodology and proved empirically that a prediction can be made even to the range of over a month; second, we showed promise in adopting the CoIM index to prediction models, when models that adopt the CoIM yield significantly better results than those that discard it. By applying our model, and better controlling the inherent tradeoff between life-saving and economy we believe that decision-makers can take close to optimal measures. Thus, this methodology may contribute to public welfare.

Predicting COVID-19 county-level case number trend by combining demographic characteristics and social distancing policies

“…Differences in demographic characteristics such as age, sex, and race can affect the likelihood of exposure to COVID-19. 9 , 12–14 , 19 , 20 The county-level demographic characteristics data were collected from the U.S. Census Bureau, latest available as of July 1, 2019. 21 We used 304 variables, derived from 16 population characteristics and 19 distinct age groups, with the first age group being the total of all age groups (0–85+), and most of the other groups being in 4-year increments (eg, 0–4, 5–9, …, 85+), as demonstrated in Table 1 .…”

“…In practice, various global-, country-, and state-level COVID-19 case predictions and feature importance analyses have been executed. 3 , 9–11 …”

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

“…Understanding this architecture requires delving into the intricate mechanisms of convolution, pooling, and fully connected layers as well as the strategic design considerations that guide the construction of these powerful models. The continual advancements in CNN architectures, coupled with the broadening spectrum of applications, especially in healthcare and COVID-19 detection, further underline the importance of in-depth research on these models [ 29 , 91 , 102 , 103 ].…”

“…This form of statistical reasoning is based on Bayes’ theorem, which enables us to update probabilities based on new evidence. In the context of optimization, particularly hyperparameter tuning, Bayesian inference emerges as a robust method to intelligently navigate the solution space [ 11 , 36 , 91 , 111 ].…”

“…They were also able to evaluate the effectiveness of the policies. Based on the research outcomes, the Gaussian Process Regression model performed better than the other two models, with 95% precision [ 11 , 91 , 132 ].…”