Estimating effective population size changes from preferentially sampled genetic sequences

Michael D. Karcher, Marc A. Suchard, Gytis Dudas, Vladimir N. Minin


March 28, 2019


Coalescent theory combined with statistical modeling allows us to estimate effective population size fluctuations from molecular sequences of individuals sampled from a population of interest. When sequences are sampled serially through time and the distribution of the sampling times depends on the effective population size, explicit statistical modeling of sampling times improves population size estimation. Previous work assumed that the genealogy relating sampled sequences is known and modeled sampling times as an inhomogeneous Poisson process with log-intensity equal to a linear function of the log-transformed effective population size. We improve this approach in two ways. First, we extend the method to allow for joint Bayesian estimation of the genealogy, effective population size trajectory, and other model parameters. Next, we improve the sampling time model by incorporating additional sources of information in the form of time-varying covariates. We validate our new modeling framework using a simulation study and apply our new methodology to analyses of population dynamics of seasonal influenza and to the recent Ebola virus outbreak in West Africa.

Effects of infection history on dengue virus infection and pathogenicity

Tim K. Tsang, Samson L. Ghebremariam, Lionel Gresh, Aubree Gordon, M. Elizabeth Halloran, Leah C. Katzelnick, Diana Patricia Rojas, Guillermina Kuan, Angel Balmaseda, Jonathan Sugimoto, Eva Harris,
Ira M. Longini Jr., Yang Yang

Nature Communications

March 18, 2019


The understanding of immunological interactions among the four dengue virus (DENV) serotypes and their epidemiological implications is often hampered by the lack of individual-level infection history. Using a statistical framework that infers full infection history, we analyze a prospective pediatric cohort in Nicaragua to characterize how infection history modulates the risks of DENV infection and subsequent clinical disease. After controlling for age, one prior infection is associated with 54% lower, while two or more are associated with 91% higher, risk of a new infection, compared to DENV-naive children. Children >8 years old have 55% and 120% higher risks of infection and subsequent disease, respectively, than their younger peers. Among children with ≥1 prior infection, intermediate antibody titers increase, whereas high titers lower, the risk of subsequent infection, compared with undetectable titers. Such complex dependency needs to be considered in the design of dengue vaccines and vaccination strategies.

Fitting stochastic epidemic models to gene genealogies using linear noise approximation

Mingwei Tang, Gytis Dudas, Trevor Bedford, Vladimir N. Minin


February 24, 2019


Phylodynamics is a set of population genetics tools that aim at reconstructing demographic history of a population based on molecular sequences of individuals sampled from the population of interest. One important task in phylodynamics is to estimate changes in (effective) population size. When applied to infectious disease sequences such estimation of population size trajectories can provide information about changes in the number of infections. To model changes in the number of infected individuals, current phylodynamic methods use non-parametric approaches, parametric approaches, and stochastic modeling in conjunction with likelihood-free Bayesian methods. The first class of methods yields results that are hard-to-interpret epidemiologically. The second class of methods provides estimates of important epidemiological parameters, such as infection and removal/recovery rates, but ignores variation in the dynamics of infectious disease spread. The third class of methods is the most advantageous statistically, but relies on computationally intensive particle filtering techniques that limits its applications. We propose a Bayesian model that combines phylodynamic inference and stochastic epidemic models, and achieves computational tractability by using a linear noise approximation (LNA) --- a technique that allows us to approximate probability densities of stochastic epidemic model trajectories. LNA opens the door for using modern Markov chain Monte Carlo tools to approximate the joint posterior distribution of the disease transmission parameters and of high dimensional vectors describing unobserved changes in the stochastic epidemic model compartment sizes (e.g., numbers of infectious and susceptible individuals). We apply our estimation technique to Ebola genealogies estimated using viral genetic data from the 2014 epidemic in Sierra Leone and Liberia.

Impact of rotavirus vaccine introduction in children less than 2 years of age presenting for medical care with diarrhea in rural Matlab, Bangladesh

Lauren M Schwartz, K Zaman, Md Yunus, Ahasan-ul H Basunia, Abu Syed Golam Faruque, Tahmeed Ahmed, Mustafizur Rahman, Jonathan D Sugimoto, M Elizabeth Halloran, Ali Rowhani-Rahbar, Kathleen M Neuzil, John C Victor

Clinical Infectious Diseases

February 12, 2019



Following the conclusion of a Rotarix vaccine (HRV) cluster-randomized controlled trial (CRT) in Matlab, Bangladesh, HRV was included in Matlab’s routine immunization program. We describe the population-level impact of programmatic rotavirus vaccination in Bangladesh in children <2 years of age


Interrupted time series were used to estimate the impact of HRVintroduction. Diarrheal surveillance collected between 2000 and 2014 within the two service delivery areas (icddr,b service area [ISA] and government service area [GSA]) of the Matlab Health and Demographic Surveillance System administered by icddr,b was used. Age-group specific incidence rates were calculated for both rotavirus-positive (RV+) and rotavirus-negative (RV-) diarrhea of any severity presenting to the hospital. Two models were used to assess impact within each service area: Model 1 used the pre-vaccine time period in all villages (HRV- and control-only) and Model 2 combined the pre-vaccine time period and the CRT time period using outcomes from control-only villages.


Both models demonstrated a downward trend in RV+ diarrheal incidence in the ISA villages during 3.5 years of routine HRV use, though only Model 2 was statistically significant. Significant impact of HRV on RV+ diarrhea incidence in GSA villages was not observed in either model. Differences in population-level impact between the two delivery areas may be due to varied rotavirus vaccine coverage and presentation rate to the hospital.


This study provides initial evidence of the population-level impact of rotavirus vaccines in children <2 years of age in Matlab, Bangladesh. Further studies of rotavirus vaccine impact after nationwide introduction in Bangladesh are needed.