2018/09/05 by Matthew Engelhard, Hongteng Xu, Engelhard, Matthew +10 · 1 citation
Mathematics · Biochemistry, Genetics and Molecular Biology · Environmental Science · #Point processes and geometric inequalities #Diffusion and Search Dynamics #Ecosystem dynamics and resilience
paper · pdf · doi:10.48550/arxiv.1809.01740
Health risks from cigarette smoking -- the leading cause of preventable death\nin the United States -- can be substantially reduced by quitting. Although most\nsmokers are motivated to quit, the majority of quit attempts fail. A number of\nstudies have explored the role of self-reported symptoms, physiologic\nmeasurements, and environmental context on smoking risk, but less work has\nfocused on the temporal dynamics of smoking events, including daily patterns\nand related nicotine effects. In this work, we examine these dynamics and\nimprove risk prediction by modeling smoking as a self-triggering process, in\nwhich previous smoking events modify current risk. Specifically, we fit smoking\nevents self-reported by 42 smokers to a time-varying semi-parametric Hawkes\nprocess (TV-SPHP) developed for this purpose. Results show that the TV-SPHP\nachieves superior prediction performance compared to related and existing\nmodels, with the incorporation of time-varying predictors having greatest\nbenefit over longer prediction windows. Moreover, the impact function\nillustrates previously unknown temporal dynamics of smoking, with possible\nconnections to nicotine metabolism to be explored in future work through a\nrandomized study design. By more effectively predicting smoking events and\nexploring a self-triggering component of smoking risk, this work supports\ndevelopment of novel or improved cessation interventions that aim to reduce\ndeath from smoking.\n