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Spatiotemporal disease case prediction using contrastive predictive coding

Published: 01 November 2022 Publication History

Abstract

Time series prediction models have played a vital role in guiding effective policymaking and response during the COVID-19 pandemic by predicting future cases and deaths at the country, state, and county levels. However, for emerging diseases, there is not sufficient historic data to fit traditional supervised prediction models. In addition, such models do not consider human mobility between regions. To mitigate the need for supervised models and to include human mobility data in the prediction, we propose Spatial Probabilistic Contrastive Predictive Coding (SP-CPC) which leverages Contrastive Predictive Coding (CPC), an unsupervised time-series representation learning approach. We augment CPC to incorporate a covariate mobility matrix into the loss function, representing the relative number of individuals traveling between each county on a given day. The proposal distribution learned by the algorithm is then sampled by the Metropolis-Hastings algorithm to give a final prediction of the number of COVID-19 cases. We find that the model applied to COVID-19 data can make accurate short-term predictions, more accurate than ARIMA and simple time-series extrapolation methods, one day into the future. However, for longer-term prediction windows of seven or more days into the future, we find that our predictions are not as competitive and require future research.

References

[1]
V. Araujo. Vgaraujov/cpc-nlp-pytorch: Implementation of contrastive predictive coding for natural language https://github.com/vgaraujov/CPC-NLP-PyTorch.
[2]
F. Brauer. Compartmental models in epidemiology. In Mathematical epidemiology, pages 19--79. Springer, 2008.
[3]
CDC. Coronavirus Disease 2019 (COVID-19), Feb. 2020.
[4]
S. Deldari, D. V. Smith, H. Xue, and F. D. Salim. Time series change point detection with self-supervised contrastive predictive coding. In Proceedings of the Web Conference 2021, pages 3124--3135, 2021.
[5]
J. Elarde, J.-S. Kim, H. Kavak, A. Züfle, and T. Anderson. Change of human mobility during covid-19: A united states case study. PLOS ONE, 16(11), 2021.
[6]
M. Gutmann and A. Hyvärinen. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In Artificial Intelligence and Statistics, pages 297--304, Mar. 2010.
[7]
K. Healy. County and state fips codes https://github.com/kjhealy/fips-codes.
[8]
O. J. Hénaff, A. Srinivas, J. D. Fauw, A. Razavi, C. Doersch, S. M. A. Eslami, and A. van den Oord. Data-efficient image recognition with contrastive predictive coding. CoRR, abs/1905.09272, 2019.
[9]
R. J. Hyndman, A. V. Kostenko, et al. Minimum sample size requirements for seasonal forecasting models. foresight, 6(Spring):12--15, 2007.
[10]
Y. Kang, S. Gao, Y. Liang, M. Li, J. Rao, and J. Kruse. Multiscale dynamic human mobility flow dataset in the us during the covid-19 epidemic. Scientific data, 7(1):1--13, 2020.
[11]
F. Kato, Y. Cao, and M. Yoshikawa. Pct-tee: Trajectory-based private contact tracing system with trusted execution environment. ACM Transactions on Spatial Algorithms and Systems (TSAS), 8(2):1--35, 2021.
[12]
R. Ke, E. Romero-Severson, S. Sanche, and N. Hengartner. Estimating the reproductive number R0 of SARS-CoV-2 in the United States and eight European countries and implications for vaccination. Journal of Theoretical Biology, 517:110621, 2021.
[13]
W. O. Kermack and A. G. McKendrick. A contribution to the mathematical theory of epidemics. Proceedings of the royal society of london. Series A, Containing papers of a mathematical and physical character, 115(772):700--721, 1927.
[14]
C. C. Kerr, R. M. Stuart, D. Mistry, R. G. Abeysuriya, et al. Covasim: an agent-based model of covid-19 dynamics and interventions. PLOS Computational Biology, 17(7):e1009149, 2021.
[15]
B. Millidge, A. Seth, and C. L. Buckley. Predictive Coding: A Theoretical and Experimental Review, July 2022.
[16]
M. Mokbel, M. Sakr, L. Xiong, A. Züfle, et al. Dagstuhl reports, vol. 12, issue 1 issn 2192-5283. 2022.
[17]
C. J. Murray and P. Piot. The potential future of the covid-19 pandemic: will sars-cov-2 become a recurrent seasonal infection? Jama, 325(13):1249--1250, 2021.
[18]
B. Nikparvar, M. Rahman, F. Hatami, J.-C. Thill, et al. Spatio-temporal prediction of the covid-19 pandemic in us counties: modeling with a deep lstm neural network. Scientific reports, 11(1):1--12, 2021.
[19]
Nytimes. Nytimes/covid-19-data: An ongoing repository of data on coronavirus cases and deaths in the u.s.
[20]
A. v. d. Oord, Y. Li, and O. Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018.
[21]
J. Pesavento, A. Chen, R. Yu, J.-S. Kim, H. Kavak, T. Anderson, and A. Züfle. Data-driven mobility models for covid-19 simulation. In ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, pages 29--38, 2020.
[22]
S. Rambhatla, S. Zeighami, K. Shahabi, C. Shahabi, and Y. Liu. Toward accurate spatiotemporal covid-19 risk scores using high-resolution real-world mobility data. ACM TSAS, 8(2):1--30, 2022.
[23]
M. Rivière, A. Joulin, P.-E. Mazaré, and E. Dupoux. Unsupervised pretraining transfers well across languages, 2020.
[24]
C. P. Robert and G. Casella. The metropolis---hastings algorithm. In Monte Carlo statistical methods, pages 231--283. Springer, 1999.
[25]
R. K. Singh, M. Rani, A. S. Bhagavathula, et al. Prediction of the covid-19 pandemic for the top 15 affected countries: Advanced autoregressive integrated moving average (arima) model. JMIR Public Health Surveill, 6(2):e19115, May 2020.
[26]
J. Tolles and T. Luong. Modeling epidemics with compartmental models. Jama, 323(24):2515--2516, 2020.
[27]
F.-M. Tseng and G.-H. Tzeng. A fuzzy seasonal arima model for forecasting. Fuzzy Sets and Systems, 126(3):367--376, 2002.

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  • (2024)Leveraging Simulation Data to Understand Bias in Predictive Models of Infectious Disease SpreadACM Transactions on Spatial Algorithms and Systems10.1145/366063110:2(1-22)Online publication date: 1-Jul-2024
  • (2024)A systematic literature review of time series methods applied to epidemic predictionInformatics in Medicine Unlocked10.1016/j.imu.2024.10157150(101571)Online publication date: 2024
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cover image ACM Conferences
SpatialEpi '22: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Spatial Computing for Epidemiology
November 2022
50 pages
ISBN:9781450395434
DOI:10.1145/3557995
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 01 November 2022

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Author Tags

  1. COVID-19
  2. contrastive predictive coding
  3. metropolis-hastings
  4. mobility data
  5. spatiotemporal prediction

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View all
  • (2024)DPP: A Novel Disease Progression Prediction Method for Ginkgo Leaf Disease Based on Image SequencesInformation10.3390/info1507041115:7(411)Online publication date: 16-Jul-2024
  • (2024)Leveraging Simulation Data to Understand Bias in Predictive Models of Infectious Disease SpreadACM Transactions on Spatial Algorithms and Systems10.1145/366063110:2(1-22)Online publication date: 1-Jul-2024
  • (2024)A systematic literature review of time series methods applied to epidemic predictionInformatics in Medicine Unlocked10.1016/j.imu.2024.10157150(101571)Online publication date: 2024
  • (2023)SpatialEpi'2022 Workshop Report: The 3rd ACM SIGSPATIAL International Workshop on Spatial Computing for EpidemiologySIGSPATIAL Special10.1145/3632268.363227714:1(28-31)Online publication date: 7-Nov-2023

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