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On the predictability of infectious disease outbreaks

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Scarpino, Samuel V., Petri, Giovanni·2019·Nature Communications
DOI 10.1038/s41467-019-08616-0
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Abstract

Infectious disease outbreaks recapitulate biology: they emerge from the multi-level interaction of hosts, pathogens, and environment. Therefore, outbreak forecasting requires an integrative approach to modeling. While specific components of outbreaks are predictable, it remains unclear whether fundamental limits to outbreak prediction exist. Here, adopting permutation entropy as a model independent measure of predictability, we study the predictability of a diverse collection of outbreaks and identify a fundamental entropy barrier for disease time series forecasting. However, this barrier is often beyond the time scale of single outbreaks, implying prediction is likely to succeed. We show that forecast horizons vary by disease and that both shifting model structures and social network heterogeneity are likely mechanisms for differences in predictability. Our results highlight the importance of embracing dynamic modeling approaches, suggest challenges for performing model selection across long time series, and may relate more broadly to the predictability of complex adaptive systems.

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Metadata

Authors
Scarpino, S. V., & Petri, G.
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2019-02-22
Volume
10
Issue
1
Pages
898-898
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-019-08616-0
OpenAlex
W2743602240
Cited by
224
References
5
FWCI
27.66
Type
journal article
Language
en
Open access
gold
Licence

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Scarpino, S. V., & Petri, G. (2019). On the predictability of infectious disease outbreaks. Nature Communications. https://doi.org/10.1038/s41467-019-08616-0

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