Abstract
Understanding the impact of atmospheric conditions on SARS-CoV2 is critical to model COVID-19 dynamics and sheds a light on the future spread around the world. Furthermore, geographic distributions of expected clinical severity of COVID-19 may be closely linked to prior history of respiratory diseases and changes in humidity, temperature, and air quality. In this context, we postulate that by tracking topological features of atmospheric conditions over time, we can provide a quantifiable structural distribution of atmospheric changes that are likely to be related to COVID-19 dynamics. As such, we apply the machinery of persistence homology on time series of graphs to extract topological signatures and to follow geographical changes in relative humidity and temperature. We develop an integrative machine learning framework named Topological Lifespan LSTM (TLife-LSTM) and test its predictive capabilities on forecasting the dynamics of SARS-CoV2 cases. We validate our framework using the number of confirmed cases and hospitalization rates recorded in the states of Washington and California in the USA. Our results demonstrate the predictive potential of TLife-LSTM in forecasting the dynamics of COVID-19 and modeling its complex spatio-temporal spread dynamics.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Available at Source codes (repository).
- 2.
Available at https://www.ncei.noaa.gov.
- 3.
Available at https://github.com/CSSEGISandData/COVID-19.
- 4.
Available at https://midasnetwork.us/covid-19/.
References
Alazab, M., Awajan, A., Mesleh, A., Abraham, A., Jatana, V., Alhyari, S.: COVID-19 prediction and detection using deep learning. Int. J. Comput. Inf. Syst. Ind. Manage. Appl. 12, 168–181 (2020)
de Ángel Solá, D.E., Wang, L., Vázquez, M., Méndez Lázaro, P.A.: Weathering the pandemic: how the Caribbean Basin can use viral and environmental patterns to predict, prepare and respond to COVID-19. J. Med. Virol. 92(9), pp. 1460–1468 (2020)
Arora, P., Kumar, H., Panigrahi, B.: Prediction and analysis of COVID-19 positive cases using deep learning models: a descriptive case study of India. Chaos Solitons Fractals 139, 110017 (2020). https://doi.org/10.1016/j.chaos.2020.110017
Berumen, J., et al.: Trends of SARS-Cov-2 infection in 67 countries: role of climate zone, temperature, humidity and curve behavior of cumulative frequency on duplication time. medRxiv (2020)
Bouhamed, H.: COVID-19 cases and recovery previsions with Deep Learning nested sequence prediction models with Long Short-Term Memory (LSTM) architecture. Int. J. Sci. Res. Comput. Sci. Eng. 8, 10–15 (2020)
Carlsson, G.: Topology and data. BAMS 46(2), 255–308 (2009)
Carlsson, G.: Persistent homology and applied homotopy theory. In: Handbook of Homotopy Theory. CRC Press, Boca Raton (2019)
Chazal, F., Michel, B.: An introduction to topological data analysis: fundamental and practical aspects for data scientists. arxiv:1710.04019 (2017)
Chen, J., Gao, K., Wang, R., Nguyen, D.D., Wei, G.W.: Review of COVID-19 antibody therapies. Annu. Rev. Biophys. 50 (2020)
Chen, Y., Volic, I.: Topological data analysis model for the spread of the coronavirus. arXiv:2008.05989 (2020)
Costa, J.P., Škraba, P.: A topological data analysis approach to epidemiology. In: European Conference of Complexity Science (2014)
Dlotko, P., Rudkin, S.: Visualising the evolution of English COVID-19 cases with topological data analysis ball mapper. arXiv:2004.03282 (2020)
Dong, E., Du, H., Gardner, L.: An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect. Dis. 20(5), 533–534 (2020). https://doi.org/10.1016/S1473-3099(20)30120-1
Edelsbrunner, H., Harer, J.: Persistent homology - a survey. Contemp. Math. 453, 257–282 (2008)
Falk, M., et al.: Topological data analysis made easy with the topology toolkit, what is new? (2020)
Franch-Pardo, I., Napoletano, B.M., Rosete-Verges, F., Billa, L.: Spatial analysis and GIS in the study of COVID-19. A review. Sci. Total Environ. 739, 140033 (2020)
Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: a search space odyssey. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2222–2232 (2017). https://doi.org/10.1109/TNNLS.2016.2582924
Johnson, L., Schieberl, L.: Topological visualization of COVID-19 spread in California, Florida, and New York (2020)
Lo, D., Park, B.: Modeling the spread of the Zika virus using topological data analysis. PLoS One 13(2), e0192120 (2018)
Metcalf, C.J.E., et al.: Identifying climate drivers of infectious disease dynamics: recent advances and challenges ahead. Proc. R. Soc. B Biol. Sci. 284(1860), 20170901 (2017)
Otter, N., Porter, M.A., Tillmann, U., Grindrod, P., Harrington, H.A.: A roadmap for the computation of persistent homology. EPJ Data Sci. 6(1), 1–38 (2017). https://doi.org/10.1140/epjds/s13688-017-0109-5
Ramchandani, A., Fan, C., Mostafavi, A.: DeepCOVIDNet: an interpretable deep learning model for predictive surveillance of COVID-19 using heterogeneous features and their interactions. IEEE Access 8, 159915–159930 (2020). https://doi.org/10.1109/ACCESS.2020.3019989
Rouen, A., Adda, J., Roy, O., Rogers, E., Lévy, P.: COVID-19: relationship between atmospheric temperature and daily new cases growth rate. Epidemiol. Infect. 148 (2020)
Shahid, F., Zameer, A.: Predictions for COVID-19 with deep learning models of LSTM, GRU, and Bi-LSTM. Chaos, Solitons Fractals 140, 110212 (2020)
Soliman, M., Lyubchich, V., Gel, Y.: Ensemble forecasting of the Zika space-time spread with topological data analysis. Environmetrics 31(7), e2629 (2020). https://doi.org/10.1002/env.2629
Yu, Y., Si, X., Hu, C., Zhang, J.: A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput. 31(7), 1235–1270 (2019)
Zeroual, A., Harrou, F., Abdelkader, D., Sun, Y.: Deep learning methods for forecasting COVID-19 time-series data: a comparative study. Chaos Solitons Fractals 140, 110121 (2020). https://doi.org/10.1016/j.chaos.2020.110121
Acknowledgments
This work has been supported in part by grants NSF DMS 2027793 and NASA 20-RRNES20-0021. Huikyo Lee’s research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Segovia-Dominguez, I., Zhen, Z., Wagh, R., Lee, H., Gel, Y.R. (2021). TLife-LSTM: Forecasting Future COVID-19 Progression with Topological Signatures of Atmospheric Conditions. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12712. Springer, Cham. https://doi.org/10.1007/978-3-030-75762-5_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-75762-5_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-75761-8
Online ISBN: 978-3-030-75762-5
eBook Packages: Computer ScienceComputer Science (R0)