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Analysis and prediction of infectious diseases based on spatial visualization and machine learning.

Yunyun Cheng1, Yanping Bai2, Jing Yang3

  • 1School of Information and Communication Engineering, North University of China, Taiyuan, 030051, China.

Scientific Reports
|November 20, 2024
PubMed
Summary

Early warning systems for infectious diseases are crucial. A novel multi-algorithm fusion model effectively predicted COVID-19 and showed promise for other diseases like AIDS and tuberculosis.

Keywords:
ArcGISCOVID-19Epidemic predictionFusion learning modelMachine learningSpatiotemporal analysis

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Area of Science:

  • Epidemiology and Public Health
  • Computational Biology and Bioinformatics
  • Data Science and Machine Learning

Background:

  • Infectious diseases represent a significant global health challenge, with new viruses emerging annually.
  • Early detection and prediction of infectious disease outbreaks are vital to mitigate socio-economic impacts.
  • The COVID-19 pandemic highlighted the need for robust predictive modeling tools.

Purpose of the Study:

  • To analyze the spatial patterns of COVID-19 in China.
  • To evaluate the predictive performance of various machine learning models for infectious disease data.
  • To develop and validate a superior ensemble learning model for infectious disease forecasting.

Main Methods:

  • Spatial analysis using ArcGIS, including global and local spatial autocorrelation, and center of gravity trajectory migration.
  • Comparative analysis of single predictive models: Autoregressive Integrated Moving Average (ARIMA), Extreme Learning Machine (ELM), Support Vector Regression (SVR), Wavelet Neural Network (Wavelet), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM).
  • Development of a stacking ensemble model using Radial Basis Function Network (RBF) as a meta-learner, optimized with Particle Swarm Optimization (PSO).

Main Results:

  • Identification of high-incidence COVID-19 areas in China.
  • Demonstration that the ensemble stacking model outperformed individual models in predicting COVID-19 cases in Guangdong Province.
  • Validation of the ensemble model's broad applicability by successfully predicting cases of AIDS and Pulmonary Tuberculosis (PTB).

Conclusions:

  • The developed multi-algorithm fusion model offers enhanced generalization and prediction accuracy for infectious diseases.
  • Spatial analysis aids in identifying high-risk regions for targeted public health interventions.
  • This approach provides a valuable framework for the early warning and management of emerging and existing infectious diseases.