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Tredennick AT, O’Dea EB, Ferrari MJ, Park AW, Rohani P, Drake JM. Anticipating infectious disease re-emergence and elimination: a test of early warning signals using empirically based models. J R Soc Interface 2022; 19:20220123. [PMID: 35919978 PMCID: PMC9346357 DOI: 10.1098/rsif.2022.0123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 07/12/2022] [Indexed: 11/12/2022] Open
Abstract
Timely forecasts of the emergence, re-emergence and elimination of human infectious diseases allow for proactive, rather than reactive, decisions that save lives. Recent theory suggests that a generic feature of dynamical systems approaching a tipping point-early warning signals (EWS) due to critical slowing down (CSD)-can anticipate disease emergence and elimination. Empirical studies documenting CSD in observed disease dynamics are scarce, but such demonstration of concept is essential to the further development of model-independent outbreak detection systems. Here, we use fitted, mechanistic models of measles transmission in four cities in Niger to detect CSD through statistical EWS. We find that several EWS accurately anticipate measles re-emergence and elimination, suggesting that CSD should be detectable before disease transmission systems cross key tipping points. These findings support the idea that statistical signals based on CSD, coupled with decision-support algorithms and expert judgement, could provide the basis for early warning systems of disease outbreaks.
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Affiliation(s)
- Andrew T. Tredennick
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
- Western EcoSystems Technology, Inc., 1610 East Reynolds Street, Laramie, WY 82070, USA
| | - Eamon B. O’Dea
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
| | - Matthew J. Ferrari
- The Center for Infectious Disease Dynamics and Department of Biology, The Pennsylvania State University, University Park, PA 16802, USA
| | - Andrew W. Park
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
- Department of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
| | - Pejman Rohani
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
- Department of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
| | - John M. Drake
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
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Dablander F, Heesterbeek H, Borsboom D, Drake JM. Overlapping timescales obscure early warning signals of the second COVID-19 wave. Proc Biol Sci 2022; 289:20211809. [PMID: 35135355 PMCID: PMC8825995 DOI: 10.1098/rspb.2021.1809] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 01/13/2022] [Indexed: 11/12/2022] Open
Abstract
Early warning indicators based on critical slowing down have been suggested as a model-independent and low-cost tool to anticipate the (re)emergence of infectious diseases. We studied whether such indicators could reliably have anticipated the second COVID-19 wave in European countries. Contrary to theoretical predictions, we found that characteristic early warning indicators generally decreased rather than increased prior to the second wave. A model explains this unexpected finding as a result of transient dynamics and the multiple timescales of relaxation during a non-stationary epidemic. Particularly, if an epidemic that seems initially contained after a first wave does not fully settle to its new quasi-equilibrium prior to changing circumstances or conditions that force a second wave, then indicators will show a decreasing rather than an increasing trend as a result of the persistent transient trajectory of the first wave. Our simulations show that this lack of timescale separation was to be expected during the second European epidemic wave of COVID-19. Overall, our results emphasize that the theory of critical slowing down applies only when the external forcing of the system across a critical point is slow relative to the internal system dynamics.
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Affiliation(s)
- Fabian Dablander
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - Hans Heesterbeek
- Department of Population Health Sciences, Utrecht University, Utrecht, The Netherlands
| | - Denny Borsboom
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
| | - John M. Drake
- Odum School of Ecology, University of Georgia, Athens, GA, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA
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Southall E, Brett TS, Tildesley MJ, Dyson L. Early warning signals of infectious disease transitions: a review. J R Soc Interface 2021; 18:20210555. [PMID: 34583561 PMCID: PMC8479360 DOI: 10.1098/rsif.2021.0555] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 09/06/2021] [Indexed: 01/07/2023] Open
Abstract
Early warning signals (EWSs) are a group of statistical time-series signals which could be used to anticipate a critical transition before it is reached. EWSs are model-independent methods that have grown in popularity to support evidence of disease emergence and disease elimination. Theoretical work has demonstrated their capability of detecting disease transitions in simple epidemic models, where elimination is reached through vaccination, to more complex vector transmission, age-structured and metapopulation models. However, the exact time evolution of EWSs depends on the transition; here we review the literature to provide guidance on what trends to expect and when. Recent advances include methods which detect when an EWS becomes significant; the earlier an upcoming disease transition is detected, the more valuable an EWS will be in practice. We suggest that future work should firstly validate detection methods with synthetic and historical datasets, before addressing their performance with real-time data which is accruing. A major challenge to overcome for the use of EWSs with disease transitions is to maintain the accuracy of EWSs in data-poor settings. We demonstrate how EWSs behave on reported cases for pertussis in the USA, to highlight some limitations when detecting disease transitions with real-world data.
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Affiliation(s)
- Emma Southall
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK
- Mathematics for Real World Systems Centre for Doctoral Training, Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK
| | - Tobias S. Brett
- Odum School of Ecology, University of Georgia, Athens, GA, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA, USA
| | - Michael J. Tildesley
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK
| | - Louise Dyson
- The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry CV4 7AL, UK
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O'Regan SM, O'Dea EB, Rohani P, Drake JM. Transient indicators of tipping points in infectious diseases. J R Soc Interface 2020; 17:20200094. [PMID: 32933375 DOI: 10.1098/rsif.2020.0094] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
The majority of known early warning indicators of critical transitions rely on asymptotic resilience and critical slowing down. In continuous systems, critical slowing down is mathematically described by a decrease in magnitude of the dominant eigenvalue of the Jacobian matrix on the approach to a critical transition. Here, we show that measures of transient dynamics, specifically, reactivity and the maximum of the amplification envelope, also change systematically as a bifurcation is approached in an important class of models for epidemics of infectious diseases. Furthermore, we introduce indicators designed to detect trends in these measures and find that they reliably classify time series of case notifications simulated from stochastic models according to levels of vaccine uptake. Greater attention should be focused on the potential for systems to exhibit transient amplification of perturbations as a critical threshold is approached, and should be considered when searching for generic leading indicators of tipping points. Awareness of this phenomenon will enrich understanding of the dynamics of complex systems on the verge of a critical transition.
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Affiliation(s)
- Suzanne M O'Regan
- Department of Mathematics and Statistics, Marteena Hall, 1601 E. Market St., North Carolina A&T State University, Greensboro, NC 27411 USA
| | - Eamon B O'Dea
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA.,Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
| | - Pejman Rohani
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA.,Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA.,Department of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
| | - John M Drake
- Odum School of Ecology, University of Georgia, Athens, GA 30602, USA.,Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
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Tyson RC, Hamilton SD, Lo AS, Baumgaertner BO, Krone SM. The Timing and Nature of Behavioural Responses Affect the Course of an Epidemic. Bull Math Biol 2020; 82:14. [PMID: 31932981 PMCID: PMC7223272 DOI: 10.1007/s11538-019-00684-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Accepted: 12/02/2019] [Indexed: 11/24/2022]
Abstract
During an epidemic, the interplay of disease and opinion dynamics can lead to outcomes that are different from those predicted based on disease dynamics alone. Opinions and the behaviours they elicit are complex, so modelling them requires a measure of abstraction and simplification. Here, we develop a differential equation model that couples SIR-type disease dynamics with opinion dynamics. We assume a spectrum of opinions that change based on current levels of infection as well as interactions that to some extent amplify the opinions of like-minded individuals. Susceptibility to infection is based on the level of prophylaxis (disease avoidance) that an opinion engenders. In this setting, we observe how the severity of an epidemic is influenced by the distribution of opinions at disease introduction, the relative rates of opinion and disease dynamics, and the amount of opinion amplification. Some insight is gained by considering how the effective reproduction number is influenced by the combination of opinion and disease dynamics.
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Affiliation(s)
- Rebecca C Tyson
- Mathematics, Irving K. Barber School of Arts and Sciences Unit 5 BLDG SCI, University of British Columbia Okanagan, 1177 Research Road, Kelowna, BC, V1V 1V7, Canada.
| | - Stephanie D Hamilton
- Mathematics, Irving K. Barber School of Arts and Sciences Unit 5, University of British Columbia Okanagan, 1177 Research Road, Kelowna, BC, V1V 1V7, Canada
| | - Aboubakr S Lo
- ENSTA ParisTech, 828 Boulevard des Maréchaux, 91120, Palaiseau, France
| | - Bert O Baumgaertner
- Department of Politics and Philosophy, University of Idaho, 875 Perimeter Drive, MS 3165, Moscow, ID, 83844-3165, USA
| | - Stephen M Krone
- Department of Mathematics, University of Idaho, 875 Perimeter Drive, MS 1103, Moscow, ID, 83844-1103, USA
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Drake JM, Brett TS, Chen S, Epureanu BI, Ferrari MJ, Marty É, Miller PB, O’Dea EB, O’Regan SM, Park AW, Rohani P. The statistics of epidemic transitions. PLoS Comput Biol 2019; 15:e1006917. [PMID: 31067217 PMCID: PMC6505855 DOI: 10.1371/journal.pcbi.1006917] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Emerging and re-emerging pathogens exhibit very complex dynamics, are hard to model and difficult to predict. Their dynamics might appear intractable. However, new statistical approaches-rooted in dynamical systems and the theory of stochastic processes-have yielded insight into the dynamics of emerging and re-emerging pathogens. We argue that these approaches may lead to new methods for predicting epidemics. This perspective views pathogen emergence and re-emergence as a "critical transition," and uses the concept of noisy dynamic bifurcation to understand the relationship between the system observables and the distance to this transition. Because the system dynamics exhibit characteristic fluctuations in response to perturbations for a system in the vicinity of a critical point, we propose this information may be harnessed to develop early warning signals. Specifically, the motion of perturbations slows as the system approaches the transition.
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Affiliation(s)
- John M. Drake
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Tobias S. Brett
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Shiyang Chen
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Bogdan I. Epureanu
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
- Automotive Research Center, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Matthew J. Ferrari
- Center for Infectious Disease Dynamics, Pennsylvania State University, State College, Pennsylvania, United States of America
| | - Éric Marty
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Paige B. Miller
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Eamon B. O’Dea
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Suzanne M. O’Regan
- Department of Mathematics, North Carolina A&T State University, Greensboro, North Carolina, United States of America
| | - Andrew W. Park
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
| | - Pejman Rohani
- Odum School of Ecology, University of Georgia, Athens, Georgia, United States of America
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America
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Chen S, O'Dea EB, Drake JM, Epureanu BI. Eigenvalues of the covariance matrix as early warning signals for critical transitions in ecological systems. Sci Rep 2019; 9:2572. [PMID: 30796264 PMCID: PMC6385210 DOI: 10.1038/s41598-019-38961-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Accepted: 12/27/2018] [Indexed: 11/23/2022] Open
Abstract
Many ecological systems are subject critical transitions, which are abrupt changes to contrasting states triggered by small changes in some key component of the system. Temporal early warning signals such as the variance of a time series, and spatial early warning signals such as the spatial correlation in a snapshot of the system's state, have been proposed to forecast critical transitions. However, temporal early warning signals do not take the spatial pattern into account, and past spatial indicators only examine one snapshot at a time. In this study, we propose the use of eigenvalues of the covariance matrix of multiple time series as early warning signals. We first show theoretically why these indicators may increase as the system moves closer to the critical transition. Then, we apply the method to simulated data from several spatial ecological models to demonstrate the method's applicability. This method has the advantage that it takes into account only the fluctuations of the system about its equilibrium, thus eliminating the effects of any change in equilibrium values. The eigenvector associated with the largest eigenvalue of the covariance matrix is helpful for identifying the regions that are most vulnerable to the critical transition.
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Affiliation(s)
- Shiyang Chen
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Eamon B O'Dea
- Odum School of Ecology, University of Georgia, Athens, Georgia, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, USA
| | - John M Drake
- Odum School of Ecology, University of Georgia, Athens, Georgia, USA
- Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, USA
| | - Bogdan I Epureanu
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, USA.
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