Comparing Short-Term Univariate and Multivariate Time-Series Forecasting Models in Infectious Disease Outbreak

Abstract

Predicting infectious disease outbreak impacts on population, healthcare resources and economics and has received a special academic focus during coronavirus (COVID-19) pandemic. Focus on human disease outbreak prediction techniques in current literature, Marques et al. (Predictive models for decision support in the COVID-19 crisis. Springer, Switzerland, 2021) state that there are four main methods to address forecasting problem: compartmental models, classic statistical models, space-state models and machine learning models. We adopt their framework to compare our research with previous works. Besides being divided by methods, forecasting problems can also be divided by the number of variables that are considered to make predictions. Considering this number of variables, forecasting problems can be classified as univariate, causal and multivariate models. Multivariate approaches have been applied in less than 10% of research found. This research is the first attempt to evaluate, over real time-series data of 3 different countries with univariate and multivariate methods to provide a short-term prediction. In literature we found no research with that scope and aim. A comparison of univariate and multivariate methods has been conducted and we concluded that besides the strong potential of multivariate methods, in our research univariate models presented best results in almost all regions’ predictions.

A Machine Learning Framework Based on Extreme Gradient Boosting to Predict the Occurrence and Development of Infectious Diseases in Laying Hen Farms, Taking H9N2 as an Example

“…Meanwhile, the developed models just predicted the current outbreak status and were not forward-looking. Many studies have been using a time-series forecasting model to explain, evaluate and estimate the development trends in outbreak human diseases (e.g., , such as the further values of outbreak disease cases, deaths, or transmission rates [18,19]. Besides, XGBoost was proved to have more outstanding prediction accuracy and generalization ability than RF, support vector machines (SVM), k-nearest neighbor (KNN), and back propagation neural network (BPNN) in predicting the risk and outbreaks of diabetes [20], dermatomyositis [21], COVID-19 [14,15], wheat stripe rust [22], and other diseases of human and plants, due to the superiority of its architecture, which also provided a new idea and algorithm for risk/outbreak identification, early intervention, and practical application in animal diseases.…”