Detecting critical slowing down in high-dimensional epidemiological systems

PLoS Computational Biology
Tobias S BrettPejman Rohani

Abstract

Despite medical advances, the emergence and re-emergence of infectious diseases continue to pose a public health threat. Low-dimensional epidemiological models predict that epidemic transitions are preceded by the phenomenon of critical slowing down (CSD). This has raised the possibility of anticipating disease (re-)emergence using CSD-based early-warning signals (EWS), which are statistical moments estimated from time series data. For EWS to be useful at detecting future (re-)emergence, CSD needs to be a generic (model-independent) feature of epidemiological dynamics irrespective of system complexity. Currently, it is unclear whether the predictions of CSD-derived from simple, low-dimensional systems-pertain to real systems, which are high-dimensional. To assess the generality of CSD, we carried out a simulation study of a hierarchy of models, with increasing structural complexity and dimensionality, for a measles-like infectious disease. Our five models included: i) a nonseasonal homogeneous Susceptible-Exposed-Infectious-Recovered (SEIR) model, ii) a homogeneous SEIR model with seasonality in transmission, iii) an age-structured SEIR model, iv) a multiplex network-based model (Mplex) and v) an agent-based simulator (FRED). A...Continue Reading

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Citations

Oct 6, 2020·Frontiers in Public Health·Taranjot KaurPartha Sharathi Dutta
Sep 12, 2020·International Journal of Environmental Research and Public Health·Yiannis ContoyiannisStelios M Potirakis
Jan 26, 2021·PLoS Computational Biology·Vinícius Rezende CarvalhoEduardo Mazoni Andrade Marçal Mendes
Jul 18, 2021·Physical Review. E·Gil Ariel, Yoram Louzoun
Aug 15, 2021·The Science of the Total Environment·M ConvertinoC Munoz-Zanzi
Sep 30, 2021·Journal of the Royal Society, Interface·Emma SouthallLouise Dyson

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Software Mentioned

- Sampler
Mplex
No
POLYMOD
pymc3

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