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Network-based uncertainty quantification for mathematical models in epidemiology.

Beatrix Rahnsch1, Leila Taghizadeh1

  • 1Technical University of Munich, Germany; TUM School of Computation, Information and Technology, Department of Mathematics.

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|November 18, 2023
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Summary

A network-based inference method, incorporating individual interactions via a contact matrix, accurately forecasts COVID-19 evolution in Germany. This approach surpasses logistic regression and neural networks for short- to mid-term pandemic predictions.

Keywords:
Basic reproductive numberCOVID-19 pandemicEpidemic modelsNetwork inferenceNeural networks

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Area of Science:

  • Epidemiology
  • Computational Biology
  • Network Science

Background:

  • The emergence of COVID-19 in Germany necessitated accurate forecasting for effective intervention.
  • Understanding virus spread dynamics is crucial for public health response.
  • Existing models often lack detailed individual interaction data.

Purpose of the Study:

  • To forecast the evolution of the COVID-19 pandemic in Germany.
  • To evaluate the efficacy of a network-based inference method compared to traditional models.
  • To estimate the basic reproduction number for improved prediction accuracy.

Main Methods:

  • Network-based inference utilizing a contact matrix for individual interactions.
  • Comparison with predictions lacking a contact matrix and logistic regression.
  • Neural network approach for estimating the basic reproduction number.
  • Application of the SIR (Susceptible-Infectious-Recovered) model.
  • LASSO (Least Absolute Shrinkage and Selection Operator) for parameter estimation.
  • Mean Absolute Percentage Error (MAPE) for accuracy assessment.

Main Results:

  • The network-inference based approach demonstrated superior performance over logistic regression, neural networks, and SIR model calibration without network data.
  • This method is particularly effective for short- to mid-term COVID-19 outbreak forecasting, even with limited initial data.
  • While reproduction number estimation improved with more data, it did not outperform the network-inference approach.

Conclusions:

  • Network-informed algorithms provide a more accurate and reliable method for COVID-19 pandemic forecasting in Germany.
  • The incorporation of contact matrices significantly enhances prediction accuracy.
  • The network-based approach offers a valuable tool for public health decision-making during emerging infectious disease outbreaks.