COS 155-2 - Disentangling the role of herd immunity versus novel strain introduction in outbreaks of an infectious disease

Thursday, August 9, 2012: 1:50 PM
D138, Oregon Convention Center
Tiffany L. Bogich1, Sebastien Ballesteros1, Jon Zelner1, Hoang Quoc Cuong2, Cameron Simmons2, Tran Tinh Hien2, Eddie Holmes3, Jeremy Farrar2, Nguyen van Vinh Chau4, H. Rogier van Doorn2, Jane Cardosa5 and Bryan T. Grenfell6, (1)Ecology & Evolutionary Biology, Princeton University, Princeton, NJ, (2)Oxford University Clinical Research Unit, Hanoi, Vietnam, (3)Eberly College of Science, Penn State University, State College, PA, (4)Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam, (5)Sentinext Therapeutics, Penang, Malaysia, (6)Ecology and Evolutionary Biology; Woodrow Wilson School of Public & International Affairs, Princeton University, Princeton, NJ
Background/Question/Methods

Hand Foot and Mouth Disease has become an increasingly important emerging infection across South East Asia over the last decade.  Highly episodic dynamics can result from periodic introductions of novel strains or from complex nonlinear dynamics. Disentangling these potential causes for observed dynamics requires sophisticated, computationally intensive model fitting methods along with detailed time series data on both infections and population processes such as birth and death rates. We analyze this question of strain introduction versus inherent dynamics (e.g. herd immunity) as the driving mechanism for observed pathogen dynamics using 12 years of weekly infection data on Hand Foot and Mouth Disease in Japan driven by the pathogen Enterovirus 71, a relative of poliovirus.  We used a standard S-I-R (Susceptible-Infected-Recovered) model and maximum likelihood methods (iterated particle filtering) to test the hypothesis that herd immunity (inherent dynamics) rather than strain introduction drives outbreaks.  

Results/Conclusions

Despite the irregularity of the data, and considerable observation error they reflect, we can discern a strong signature of nonlinear epidemic dynamics with complex coexisting attractors. Even with this complexity, the data are consistent with the pathogen acting like an immunizing (S-I-R) infection.  This has implications for the potential success of future control measures (e.g. vaccination). We generalize our toolbox for disease ecologists, presenting a general platform to confront data with a range of epidemiological models to disentangle the dynamics of infectious disease, even in the case of severe, nonlinear dynamics.