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SPADE4: Sparsity and Delay Embedding Based Forecasting of Epidemics

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Abstract

Predicting the evolution of diseases is challenging, especially when the data availability is scarce and incomplete. The most popular tools for modelling and predicting infectious disease epidemics are compartmental models. They stratify the population into compartments according to health status and model the dynamics of these compartments using dynamical systems. However, these predefined systems may not capture the true dynamics of the epidemic due to the complexity of the disease transmission and human interactions. In order to overcome this drawback, we propose Sparsity and Delay Embedding based Forecasting (SPADE4) for predicting epidemics. SPADE4 predicts the future trajectory of an observable variable without the knowledge of the other variables or the underlying system. We use random features model with sparse regression to handle the data scarcity issue and employ Takens’ delay embedding theorem to capture the nature of the underlying system from the observed variable. We show that our approach outperforms compartmental models when applied to both simulated and real data.

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Data Availability

The code is available at https://github.com/esha-saha/spade4 and data sources are COVID-19 in Canada: https://health-infobase.canada.ca/covid-19/epidemiological-summary-covid-19-cases.html Ebola in Guinea: https://www.kaggle.com/datasets/imdevskp/ebola-outbreak-20142016-complete-dataset Zika virus in Giradot: Rojas et al. (2016) Influenza A/H7N9 in China: https://datadryad.org/stash/dataset/doi:10.5061/dryad.2g43n

Notes

  1. https://health-infobase.canada.ca/covid-19/epidemiological-summary-covid-19-cases.html

  2. https://www.kaggle.com/datasets/imdevskp/ebola-outbreak-20142016-complete-dataset

  3. https://datadryad.org/stash/dataset/doi:10.5061/dryad.2g43n

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Acknowledgements

LSTH was supported by the Canada Research Chairs program, the NSERC Discovery Grant RGPIN-2018-05447, and the NSERC Discovery Launch Supplement DGECR-2018-00181. E. Saha and G. Tran were supported by the NSERC Discovery Grant and the NSERC Discovery Launch Supplement.

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Authors and Affiliations

  1. Department of Applied Mathematics, University of Waterloo, Waterloo, Canada

    Esha Saha & Giang Tran

  2. Department of Mathematics and Statistics, Dalhousie University, Halifax, NS, Canada

    Lam Si Tung Ho

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  1. Esha Saha
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  2. Lam Si Tung Ho
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  3. Giang Tran
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Correspondence to Lam Si Tung Ho.

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Appendix A Results on Simulated Data with \(2\%\) Noise

Appendix A Results on Simulated Data with \(2\%\) Noise

See Fig. 13.

Fig. 13
figure 13

Results on simulated data with \(2\%\) noise added to input data. First row: Noisy training datasets from 0 upto 81st and 125th days out of 180 days (top left). The next two figures are the predicted values of the infectious variable I(t) in the next seven days using SPADE4 (blue), SEIR model (magenta), and S\(\mu \)EIR model (green) correspond to those two training datasets versus ground truth (black). Second row: Prediction of I(t) for next seven days around the peak of the wave using SPADE4 (blue), SEIR model (magenta), and S\(\mu \)EIR model (green) versus ground truth (black) (Color figure online)

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Saha, E., Ho, L.S.T. & Tran, G. SPADE4: Sparsity and Delay Embedding Based Forecasting of Epidemics. Bull Math Biol 85, 71 (2023). https://doi.org/10.1007/s11538-023-01174-z

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