The overall goal of this project is to develop and validate novel methods to perform joint inference from combined epidemiologic and genetic data. This inference methodology seeks to provide estimates of fundamental transmission parameters, such as RO, as well as provide estimates of unobserved transmission trees and unobserved counts of susceptible, infected and recovered individuals in the population through time. We focus on two common scenarios. In the first, we target densely sampled, but localized, epidemiologic and genetic data, in which the person, place and time are known, and in which pathogen genetic samples are obtained. These sorts of datasets are commonly generated during transmission studies in households, schools, and similar settings, but also in analyses of novel outbreaks such as SARS or H7N9. Our inference framework seeks to estimate host-to-host transmission networks from combined epidemiologic and genetic data. In the second scenario, we target sparsely sampled, but broader in scope, epidemiologic and genetic data, in which we observe a time series of case reports and sparsely sampled pathogen genetic sequences. In this inference framework, we seek to model population-level transmission processes from a relatively small samples of cases. This framework utilizes coalescent theory to extrapolate from sampled genetic sequences to population-level dynamics. In implementation, we plan to utilize sophisticated inference methodology that combines Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) approaches in what's termed particle MCMC (PMCMC). We plan to utilize these novel inference methods to investigate transmission heterogeneity and local transmission structure in influenza, phenomena that have been difficult to fully analyze without a combined epidemiologic and genetic inference framework in place.
As sequencing becomes increasingly ubiquitous, the ability to combine epidemiologic and genetic data will become increasingly relevant, and appropriate statistical tools will be become increasingly necessary. The methods developed in this project will have direct public health relevance in that they will allow better estimates of critical transmission parameters, such as RO, better reveal risk factors for transmission and provide knowledge of transmission heterogeneity and local transmission structure.
| Liu, Quan-Hui; Ajelli, Marco; Aleta, Alberto et al. (2018) Measurability of the epidemic reproduction number in data-driven contact networks. Proc Natl Acad Sci U S A 115:12680-12685 |
| Gallagher, Molly E; Brooke, Christopher B; Ke, Ruian et al. (2018) Causes and Consequences of Spatial Within-Host Viral Spread. Viruses 10: |
| Dean, Natalie E; Halloran, M Elizabeth; Longini, Ira M (2018) DESIGN OF VACCINE TRIALS DURING OUTBREAKS WITH AND WITHOUT A DELAYED VACCINATION COMPARATOR. Ann Appl Stat 12:330-347 |
| Bento, Ana I; Riolo, Maria A; Choi, Yoon H et al. (2018) Core pertussis transmission groups in England and Wales: A tale of two eras. Vaccine 36:1160-1166 |
| Moore, James; Ahmed, Hasan; Jia, Jonathan et al. (2018) What Controls the Acute Viral Infection Following Yellow Fever Vaccination? Bull Math Biol 80:46-63 |
| Ajelli, Marco; Zhang, Qian; Sun, Kaiyuan et al. (2018) The RAPIDD Ebola forecasting challenge: Model description and synthetic data generation. Epidemics 22:3-12 |
| Domenech de Cellès, Matthieu; Magpantay, Felicia M G; King, Aaron A et al. (2018) The impact of past vaccination coverage and immunity on pertussis resurgence. Sci Transl Med 10: |
| Dinh, Vu; Tung Ho, Lam Si; Suchard, Marc A et al. (2018) Consistency and convergence rate of phylogenetic inference via regularization. Ann Stat 46:1481-1512 |
| Yang, Yang; Meng, Ya; Halloran, M Elizabeth et al. (2018) Reply to Aguiar and Stollenwerk. Clin Infect Dis 66:642 |
| Bretó, Carles (2018) Modeling and inference for infectious disease dynamics: a likelihood-based approach. Stat Sci 33:57-69 |
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