Early warning signals of critical transitions in infectious disease dynamics

Duration: 29 mins 56 secs

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Description:	Drake, J (University of Georgia) Wednesday 21 August 2013, 09:00-09:30


Created:	2013-08-23 10:27
Collection:	Infectious Disease Dynamics
Publisher:	Isaac Newton Institute
Copyright:	Drake, J
Language:	eng (English)


Abstract:	Anticipating infectious disease emergence and documenting progress in disease elimination are important applications for the theory of disease dynamics in changing environments. A key problem is the development of ideas relating the dynamical processes of transmission to observable phenomena. Here, we consider compartmental epidemiological SIS and SIR models that are slowly forced through a critical transition. We derived expressions for the behavior of several candidate indicators, including the autocorrelation coefficient, variance, coefficient of variation, and power spectra of SIS and SIR epidemics during the approach to emergence or elimination and validated these expressions using individual-based simulations. Our results show that moving-window estimates of the candidate indicators may provide useful model-independent signals for anticipating critical transitions in infectious disease systems. Although these leading indicators were highly predictive of elimination, we found the approach to emergence to be more difficult to detect. It is hoped that these results, which show the anticipation of critical transitions in infectious disease systems to be theoretically possible, may be used to guide the construction of online algorithms for processing surveillance data.

Abstract:

Anticipating infectious disease emergence and documenting progress in disease elimination are important applications for the theory of disease dynamics in changing environments. A key problem is the development of ideas relating the dynamical processes of transmission to observable phenomena. Here, we consider compartmental epidemiological SIS and SIR models that are slowly forced through a critical transition. We derived expressions for the behavior of several candidate indicators, including the autocorrelation coefficient, variance, coefficient of variation, and power spectra of SIS and SIR epidemics during the approach to emergence or elimination and validated these expressions using individual-based simulations. Our results show that moving-window estimates of the candidate indicators may provide useful model-independent signals for anticipating critical transitions in infectious disease systems. Although these leading indicators were highly predictive of elimination, we found the approach to emergence to be more difficult to detect. It is hoped that these results, which show the anticipation of critical transitions in infectious disease systems to be theoretically possible, may be used to guide the construction of online algorithms for processing surveillance data.

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