Erin D Zwick1, Caitlin S Pepperell2, Oguzhan Alagoz3. 1. Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI, USA. 2. Department of Medicine and Department of Medical Microbiology and Immunology, University of Wisconsin-Madison, Madison, WI, USA. 3. Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA, PhD.
Abstract
OBJECTIVE: A recent study reported a tuberculosis (TB) outbreak in which, among newly infected individuals, exposure to additional active infections was associated with a higher probability of developing active disease. Referred to as complex contagion, multiple reexposures to TB within a short period after initial infection is hypothesized to confer a greater likelihood of developing active infection in 1 y. The purpose of this article is to develop and validate an agent-based simulation model (ABM) to study the effect of complex contagion on population-level TB transmission dynamics. METHODS: We built an ABM of a TB epidemic using data from a series of outbreaks recorded in the 20th century in Saskatchewan, Canada. We fit 3 dynamical schemes: base, with no complex contagion; additive, in which each reexposure confers an independent risk of activated infection; and threshold, in which a small number of reexposures confers a low risk and a high number of reexposures confers a high risk of activation. RESULTS: We find that the base model fits the mortality and incidence output targets best, followed by the threshold and then the additive models. The threshold model fits the incidence better than the base model does but overestimates mortality. All 3 models produce qualitatively realistic epidemic curves. CONCLUSION: We find that complex contagion qualitatively changes the trajectory of a TB epidemic, although data from a high-incidence setting are reproduced better with the base model. Results from this model demonstrate the feasibility of using ABM to capture nuances in TB transmission.
OBJECTIVE: A recent study reported a tuberculosis (TB) outbreak in which, among newly infected individuals, exposure to additional active infections was associated with a higher probability of developing active disease. Referred to as complex contagion, multiple reexposures to TB within a short period after initial infection is hypothesized to confer a greater likelihood of developing active infection in 1 y. The purpose of this article is to develop and validate an agent-based simulation model (ABM) to study the effect of complex contagion on population-level TB transmission dynamics. METHODS: We built an ABM of a TB epidemic using data from a series of outbreaks recorded in the 20th century in Saskatchewan, Canada. We fit 3 dynamical schemes: base, with no complex contagion; additive, in which each reexposure confers an independent risk of activated infection; and threshold, in which a small number of reexposures confers a low risk and a high number of reexposures confers a high risk of activation. RESULTS: We find that the base model fits the mortality and incidence output targets best, followed by the threshold and then the additive models. The threshold model fits the incidence better than the base model does but overestimates mortality. All 3 models produce qualitatively realistic epidemic curves. CONCLUSION: We find that complex contagion qualitatively changes the trajectory of a TB epidemic, although data from a high-incidence setting are reproduced better with the base model. Results from this model demonstrate the feasibility of using ABM to capture nuances in TB transmission.
Authors: Christoph Lange; Dumitru Chesov; Jan Heyckendorf; Chi C Leung; Zarir Udwadia; Keertan Dheda Journal: Respirology Date: 2018-04-11 Impact factor: 6.424
Authors: Ashleigh R Tuite; Victor Gallant; Elaine Randell; Annie-Claude Bourgeois; Amy L Greer Journal: BMC Public Health Date: 2017-01-13 Impact factor: 3.295
Authors: Sarah F Ackley; Robyn S Lee; Lee Worden; Erin Zwick; Travis C Porco; Marcel A Behr; Caitlin S Pepperell Journal: R Soc Open Sci Date: 2019-03-20 Impact factor: 2.963