Quantifying the risk of local Zika virus transmission in the continental US during the 2015-2016 ZIKV epidemic

Kaiyuan Sun, Qian Zhang, Ana Pastore y Piontti, Matteo Chinazzi, Dina Mistry, Natalie E. Dean, Diana P. Rojas, Stefano Merler, Piero Poletti, Luca Rossi, M. Elizabeth Halloran, Ira M. Longini Jr., Alessandro Vespignani


April 11, 2018


Background: Local mosquito-borne Zika virus (ZIKV) transmission has been reported in two counties of the continental United State (US), prompting the issuance of travel, prevention, and testing guidance across the continental US. Large uncertainty, however, surrounds the quantification of the actual risk of ZIKV introduction and autochthonous transmission across different areas of the US. Method: We present a framework for the projection of ZIKV autochthonous transmission in the continental US during the 2015-2016 epidemic, using a data-driven stochastic and spatial epidemic model accounting for seasonal, environmental and detailed population data. The model generates an ensemble of travel-related case counts and simulate their potential to trigger local transmission at individual level. Results: We estimate the risk of ZIKV introduction and local transmission at the county level and at the 0.025 degree by 0.025 degree cell level across the continental US. We provide a risk measure based on the probability of observing local transmission in a specific location during a ZIKV epidemic modeled after the one observed during the years 2015-2016. The high spatial and temporal resolutions of the model allow us to generate statistical estimates of the number of ZIKV introductions leading to local transmission in each location. We find that the risk is spatially heterogeneously distributed and concentrated in a few specific areas that account for less than 1% of the continental US population. Locations in Texas and Florida that have actually experienced local ZIKV transmission are among the places at highest risk according to our results. We also provide an analysis of the key determinants for local transmission, and identify the key introduction routes and their contributions to ZIKV spread in the continental US. Conclusions: This framework provides quantitative risk estimates, fully captures the stochasticity of ZIKV introduction events, and is not biased by the under-ascertainment of cases due to asymptomatic infections. It provides general information on key risk determinants and data with potential uses in defining public health recommendations and guidance about ZIKV risk in the US.

How single mutations affect viral escape from broad and narrow antibodies to H1 influenza hemagglutinin

Michael B. Doud, Juhye M. Lee,  Jesse D. Bloom

Nature Communications

April 11, 2018


Influenza virus can escape most antibodies with single mutations. However, rare antibodies broadly neutralize many viral strains. It is unclear how easily influenza virus might escape such antibodies if there was strong pressure to do so. Here, we map all single amino-acid mutations that increase resistance to broad antibodies to H1 hemagglutinin. Our approach not only identifies antigenic mutations but also quantifies their effect sizes. All antibodies select mutations, but the effect sizes vary widely. The virus can escape a broad antibody to hemagglutinin’s receptor-binding site the same way it escapes narrow strain-specific antibodies: via single mutations with huge effects. In contrast, broad antibodies to hemagglutinin’s stalk only select mutations with small effects. Therefore, among the antibodies we examine, breadth is an imperfect indicator of the potential for viral escape via single mutations. Antibodies targeting the H1 hemagglutinin stalk are quantifiably harder to escape than the other antibodies tested here.

Deep mutational scanning of hemagglutinin helps predict evolutionary fates of human H3N2 influenza variants

Juhye M Lee,  John Huddleston,  Michael B Doud,  Kathryn A Hooper,  Nicholas C Wu,  Trevor Bedford,  Jesse D Bloom


April 10, 2018


Human influenza virus rapidly accumulates mutations in its major surface protein hemagglutinin (HA). The evolutionary success of influenza virus lineages depends on how these mutations affect HA's functionality and antigenicity. Here we experimentally measure the effects on viral growth in cell culture of all single amino-acid mutations to the HA from a recent human H3N2 influenza virus strain. We show that mutations that are measured to be more favorable for viral growth are enriched in evolutionarily successful H3N2 viral lineages relative to mutations that are measured to be less favorable for viral growth. Therefore, despite the well-known caveats about cell-culture measurements of viral fitness, such measurements can still be informative for understanding evolution in nature. We also compare our measurements for H3 HA to similar data previously generated for a distantly related H1 HA, and find substantial differences in which amino acids are preferred at many sites. For instance, the H3 HA has less disparity in mutational tolerance between the head and stalk domains than the H1 HA. Overall, our work suggests that experimental measurements of mutational effects can be leveraged to help understand the evolutionary fates of viral lineages in nature---but only when the measurements are made on a viral strain similar to the ones being studied in nature.

Influenza A(H7N9) Virus Antibody Responses in Survivors 1 Year after Infection, China, 2017

Mai-Juan Ma, Cheng Liu, Meng-Na Wu, Teng Zhao, Guo-Lin Wang, Yang Yang, Hong-Jing Gu, Peng-Wei Cui, Yuan-Yuan Pang, Ya-Yun Tan, Hui Hang, Bao Lin, Jiang-Chun Qin, Li-Qun Fang, Wu-Chun Cao , Li-Ling Cheng

Emerging Infectious Diseases

April 2, 2018


Avian influenza A(H7N9) virus has caused 5 epidemic waves in China since its emergence in 2013. We investigated the dynamic changes of antibody response to this virus over 1 year postinfection in 25 patients in Suzhou City, Jiangsu Province, China, who had laboratory-confirmed infections during the fifth epidemic wave, October 1, 2016–February 14, 2017. Most survivors had relatively robust antibody responses that decreased but remained detectable at 1 year. Antibody response was variable; several survivors had low or undetectable antibody titers. Hemagglutination inhibition titer was >1:40 for <40% of the survivors. Measured in vitro in infected mice, hemagglutination inhibition titer predicted serum protective ability. Our findings provide a helpful serologic guideline for identifying subclinical infections and for developing effective vaccines and therapeutics to counter H7N9 virus infections.

The impact of past vaccination coverage and immunity on pertussis resurgence

Matthieu Domenech de Cellès, Felicia M. G. Magpantay, Aaron A. King, Pejman Rohani

Science Translational Medicine

March 28, 2018


The resurgence of pertussis over the past decades has resulted in incidence levels not witnessed in the United States since the 1950s. The underlying causes have been the subject of much speculation, with particular attention paid to the shortcomings of the latest generation of vaccines. We formulated transmission models comprising competing hypotheses regarding vaccine failure and challenged them to explain 16 years of highly resolved incidence data from Massachusetts, United States. Our results suggest that the resurgence of pertussis is a predictable consequence of incomplete historical coverage with an imperfect vaccine that confers slowly waning immunity. We found evidence that the vaccine itself is effective at reducing overall transmission, yet that routine vaccination alone would be insufficient for elimination of the disease. Our results indicated that the core transmission group is schoolchildren. Therefore, efforts aimed at curtailing transmission in the population at large, and especially in vulnerable infants, are more likely to succeed if targeted at schoolchildren, rather than adults.

High dimensional random walks can appear low dimensional: application to influenza H3N2 evolution

James Moore, Hasan Ahmed, Rustom Antia

Journal of Theoretical Biology

March 21, 2018


One important feature of the mammalian immune system is the highly specific binding of antigens to antibodies. Antibodies generated in response to one infection may also provide some level of cross immunity to other infections. One model to describe this cross immunity is the notion of antigenic space, which assigns each antibody and each virus a point in Rn. Past studies using hemagglutination data have suggested the dimensionality of antigenic space, n, is low. We propose that influenza evolution may be modeled as a Gaussian random walk. We then show that hemagluttination data would be consistent with a walk in very high dimensions. The discrepancy between our result and prior studies is due to the fact that random walks can appear low dimensional according to a variety of analyses including principal component analysis (PCA) and multidimensional scaling (MDS). A high dimensionality of antigenic space is of importance to modelers, as it suggests a smaller role for pre-existing immunity within the host population.

Spatio-temporal coherence of dengue, chikungunya and Zika outbreaks in Merida, Mexico

Donal Bisanzio, Felipe Dzul-Manzanilla, Hector Gomez-Dantés, Norma Pavia-Ruz, Thomas J. Hladish, Audrey Lenhart, Jorge Palacio-Vargas, Jesus F. González Roldan, Fabian Correa-Morales, Gustavo Sánchez-Tejeda, Pablo Kuri Morales, Pablo Manrique-Saide, Ira M. Longini, M. Elizabeth Halloran, Gonzalo M. Vazquez-Prokopec 

PLOS Neglected Tropical Diseases

March 15, 2018


Response to Zika virus (ZIKV) invasion in Brazil lagged a year from its estimated February 2014 introduction, and was triggered by the occurrence of severe congenital malformations. Dengue (DENV) and chikungunya (CHIKV) invasions tend to show similar response lags. We analyzed geo-coded symptomatic case reports from the city of Merida, Mexico, with the goal of assessing the utility of historical DENV data to infer CHIKV and ZIKV introduction and propagation. About 42% of the 40,028 DENV cases reported during 2008–2015 clustered in 27% of the city, and these clustering areas were where the first CHIKV and ZIKV cases were reported in 2015 and 2016, respectively. Furthermore, the three viruses had significant agreement in their spatio-temporal distribution (Kendall W>0.63; p<0.01). Longitudinal DENV data generated patterns indicative of the resulting introduction and transmission patterns of CHIKV and ZIKV, leading to important insights for the surveillance and targeted control to emerging Aedes-borne viruses.

Design of vaccine trials during outbreaks with and without a delayed vaccination comparator

Natalie E. Dean, M. Elizabeth Halloran, Ira M. Longini

Annals of Applied Statistics

March 9, 2018


Conducting vaccine efficacy trials during outbreaks of emerging pathogens poses particular challenges. The “Ebola ça suffit” trial in Guinea used a novel ring vaccination cluster randomized design to target populations at highest risk of infection. Another key feature of the trial was the use of a delayed vaccination arm as a comparator, in which clusters were randomized to immediate vaccination or vaccination 21 days later. This approach, chosen to improve ethical acceptability of the trial, complicates the statistical analysis as participants in the comparison arm are eventually protected by vaccine. Furthermore, for infectious diseases, we observe time of illness onset and not time of infection, and we may not know the time required for the vaccinee to develop a protective immune response. As a result, including events observed shortly after vaccination may bias the per protocol estimate of vaccine efficacy. We provide a framework for approximating the bias and power of any given analysis period as functions of the background infection hazard rate, disease incubation period, and vaccine immune response. We use this framework to provide recommendations for designing standard vaccine efficacy trials and trials with a delayed vaccination comparator. Briefly, narrower analysis periods within the correct window can minimize or eliminate bias but may suffer from reduced power. Designs should be reasonably robust to misspecification of the incubation period and time to develop a vaccine immune response.

Modeling and Inference for Infectious Disease Dynamics: A Likelihood-Based Approach

Carles Bretó

Statistical Science

February, 2018


Likelihood-based statistical inference has been considered in most scientific fields involving stochastic modeling. This includes infectious disease dynamics, where scientific understanding can help capture biological processes in so-called mechanistic models and their likelihood functions. However, when the likelihood of such mechanistic models lacks a closed-form expression, computational burdens are substantial. In this context, algorithmic advances have facilitated likelihood maximization, promoting the study of novel data-motivated mechanistic models over the last decade. Reviewing these models is the focus of this paper. In particular, we highlight statistical aspects of these models like overdispersion, which is key in the interface between nonlinear infectious disease modeling and data analysis. We also point out potential directions for further model exploration.

Seroprevalence of Dengue Antibodies in Three Urban Settings in Yucatan, Mexico

Norma Pavía-Ruz, Diana Patricia Rojas, Salja Villanueva, Pilar Granja, Angel BalamMay, Ira M. Longini, M. Elizabeth Halloran, Pablo Manrique, Hector Gómez-Dantés

American Journal of Tropical Medicine and Hygiene

February 19, 2018


Dengue transmission in Mexico has become a major public health problem. Few epidemiological studies have examined the seroprevalence of dengue in Mexico, and recent estimates are needed to better understand dengue transmission dynamics. We conducted a dengue seroprevalence survey among 1,668 individuals including all age groups in three urban settings in Yucatan, Mexico. Children (< 19 years old) were selected randomly from schools. The adults (≥ 19 years old) were selected from healthcare facilities. Participants were asked to provide a venous blood sample and to answer a brief questionnaire with demographic information. Previous exposure to dengue was determined using indirect immunoglobulin G enzyme-linked immunosorbent assay. The overall seroprevalence was 73.6%. The age-specific seroprevalence increased with age, going from 51.4% (95% confidence interval [CI] = 45.0–57.9%) in children ≤ 8 years to 72% (95% CI = 66.3–77.2%) in the 9- to 14-years old. The highest seroprevalence was 83.4% (95% CI = 77–82.2%) in adults greater than 50 years. The seroprevalence in Merida was 68.6% (95% CI = 65–72%), in Progreso 68.7% (95% CI = 64.2–72.8%), and in Ticul 85.3% (95% CI = 81.9–88.3%). Ticul had the highest seroprevalence in all age groups. Logistic regression analysis showed that age and city of residence were associated with greater risk of prior dengue exposure. The results highlight the level of past exposure to dengue virus including young children. Similar studies should be conducted elsewhere in Mexico and other endemic countries to better understand the transmission dynamics of dengue.

Considerations for the design of vaccine efficacy trials during public health emergencies

Natalie E. Dean, Pierre-Stéphane Gsell, Ron Brookmeyer, Victor De Gruttola, Christl A. Donnelly, M. Elizabeth Halloran, Momodou Jasseh, Martha Nason, Ximena Riveros, Conall Watson, Ana Maria Henao-Restrepo, Ira M. Longini, Jr. 


February 13, 2018


Public Health Emergencies (PHEs) provide a complex and challenging environment for vaccine evaluation. Under the R&D Blueprint Plan of Action, the World Health Organization (WHO) has convened a group of experts to agree on standard procedures to rapidly evaluate experimental vaccines during PHEs while maintaining the highest scientific and ethical standards. The Blueprint priority diseases, selected for their likelihood to cause PHEs and the lack of adequate medical countermeasures, were used to frame our methodological discussions. Here, we outline major vaccine study designs to be used in PHEs and summarize high-level recommendations for their use in this setting. We recognize that the epidemiology and transmission dynamics of the Blueprint priority diseases may be highly uncertain and that the unique characteristics of the vaccines and outbreak settings may affect our study design. To address these challenges, our group underscores the need for novel, flexible, and responsive trial designs. We conclude that assignment to study groups using randomization is a key principle underlying rigorous study design and should be utilized except in exceptional circumstances. Advance planning for vaccine trial designs is critical for rapid and effective response to a PHE and to advance knowledge to address and mitigate future PHEs.

An Assessment of Household and Individual-Level Mosquito Prevention Methods during the Chikungunya Virus Outbreak in the United States Virgin Islands, 2014–2015

Leora R. Feldstein, Ali Rowhani-Rahbar, J. Erin Staples, M. Elizabeth Halloran, Esther M. Ellis

American Journal of Tropical Medicine and Hygiene

February 5, 2018


Recent large-scale chikungunya virus (CHIKV) and Zika virus epidemics in the Americas pose a growing public health threat. Given that mosquito bite prevention and vector control are the main prevention methods available to reduce transmission of these viruses, we assessed adherence to these methods in the United States Virgin Islands (USVI). We interviewed 334 USVI residents between December 2014 and February 2015 to measure differences in mosquito prevention practices by gender, income, presence of CHIKV symptoms, and age. Only 27% (91/334) of participants reported having an air conditioner, and of the 91 with air-conditioners, 18 (20%) reported never using it. Annual household income > $50,000 was associated with owning and using an air conditioner (41%; 95% confidence interval [CI]: 28–53% compared with annual household income ≤ $50,000: 17%; 95% CI: 12–22%). The majority of participants reported the presence of vegetation in their yard or near their home (79%; 265) and a cistern on their property (78%; 259). Only 52 (16%) participants reported wearing mosquito repellent more than once per week. Although the majority (80%; 268) of participants reported having screens on all of their windows and doors, most (82%; 273) of those interviewed still reported seeing mosquitoes in their homes. Given the uniformly low adherence to household- and individual-level mosquito bite prevention measures in the USVI, these findings emphasize the need for improved public health messaging and investment in therapeutic and vaccine research to mitigate vector-borne disease outbreaks.

Core pertussis transmission groups in England and Wales: A tale of two eras

Ana I. Bento, Maria A. Riolo, Yoon H. Choi, Aaron A. King, Pejman Rohani


February 1, 2018


The recent resurgence of pertussis in England and Wales has been marked by infant deaths and rising cases in teens and adults. To understand which age cohorts are most responsible for these trends, we employed three separate statistical methods to analyze high-resolution pertussis reports from 1982 to 2012. The fine-grained nature of the time-series allowed us to describe the changes in age-specific incidence and contrast the transmission dynamics in the 1980s and during the resurgence era. Our results identified infants and school children younger than 10 years of age as a core group, prior to 2002: pertussis incidence in these populations was predictive of incidence in other age groups. After 2002, no core groups were identifiable. This conclusion is independent of methodology used. Because it is unlikely that the underlying contact patterns substantially changed over the study period, changes in predictability likely result from the introduction of more stringent diagnostics tests that may have inadvertently played a role in masking the relative contributions of core transmission groups.

Resilience management during large-scale epidemic outbreaks

Emanuele Massaro, Alexander Ganin, Nicola Perra, Igor Linkov, Alessandro Vespignani

Scientific Reports

January 30, 2018


Assessing and managing the impact of large-scale epidemics considering only the individual risk and severity of the disease is exceedingly difficult and could be extremely expensive. Economic consequences, infrastructure and service disruption, as well as the recovery speed, are just a few of the many dimensions along which to quantify the effect of an epidemic on society’s fabric. Here, we extend the concept of resilience to characterize epidemics in structured populations, by defining the system-wide critical functionality that combines an individual’s risk of getting the disease (disease attack rate) and the disruption to the system’s functionality (human mobility deterioration). By studying both conceptual and data-driven models, we show that the integrated consideration of individual risks and societal disruptions under resilience assessment framework provides an insightful picture of how an epidemic might impact society. In particular, containment interventions intended for a straightforward reduction of the risk may have net negative impact on the system by slowing down the recovery of basic societal functions. The presented study operationalizes the resilience framework, providing a more nuanced and comprehensive approach for optimizing containment schemes and mitigation policies in the case of epidemic outbreaks.

Panel data analysis via mechanistic models

Carles Bretó, Edward L. Ionides, Aaron A. King


January 18, 2018


Panel data, also known as longitudinal data, consist of a collection of time series. Each time series, which could itself be multivariate, comprises a sequence of measurements taken on a distinct unit. Mechanistic modeling involves writing down scientifically motivated equations describing the collection of dynamic systems giving rise to the observations on each unit. A defining characteristic of panel systems is that the dynamic interaction between units should be negligible. Panel models therefore consist of a collection of independent stochastic processes, generally linked through shared parameters while also having unit-specific parameters. To give the scientist flexibility in model specification, we are motivated to develop a framework for inference on panel data permitting the consideration of arbitrary nonlinear, partially observed panel models. We build on iterated filtering techniques that provide likelihood-based inference on nonlinear partially observed Markov process models for time series data. Our methodology depends on the latent Markov process only through simulation; this plug-and-play property ensures applicability to a large class of models. We demonstrate our methodology on a toy example and two epidemiological case studies. We address inferential and computational issues arising for large panel datasets.

Simulations for Designing and Interpreting Intervention Trials in Infectious Diseases

M. Elizabeth Halloran, Kari Auranen, Sarah Baird, Nicole E. Basta, Steve Bellan, Ron Brookmeyer, Ben Cooper, Victor DeGruttola, James Hughes, Justin Lessler, Eric T. Lofgren, Ira M. Longini, Jukka-Pekka Onnela, Berk Ozler, George Seage, Thomas A. Smith, Alessandro Vespignani, Emilia Vynnycky, Marc Lipsitch

BMC Medicine

December 29, 2017


Here we urge the adoption of a new paradigm for the design and interpretation of intervention trials in infectious diseases, particularly in emerging infectious disease, that more accurately reflects the dynamics of the transmission process. Interventions in infectious diseases can have indirect effects on those not receiving the intervention as well as direct effects on those receiving the intervention. Combinations of interventions can have complex interactions at the population level. These often cannot be adequately addressed with standard study designs and analytic methods. Simulations can help to accurately represent transmission dynamics in an increasingly complex world which is critical for proper trial design and interpretation. Some ethical aspects of a trial can also be quantified using simulations. After a trial has been conducted, simulations can be used to explore possible explanations for the observed effects. A great deal is to be gained through a multidisciplinary approach that builds collaborations among experts in infectious disease dynamics, epidemiology, statistical science, economics, simulation methods and the conduct of clinical trials.

Models and analyses to understand threats to polio eradication

James S. Koopman

BMC Medicine

December 22, 2017


To achieve complete polio eradication, the live oral poliovirus vaccine (OPV) currently used must be phased out after the end of wild poliovirus transmission. However, poorly understood threats may arise when OPV use is stopped. To counter these threats, better models than those currently available are needed. Two articles recently published in BMC Medicine address these issues. Mercer et al. (BMC Med 15:180, 2017) developed a statistical model analysis of polio case data and characteristics of cases occurring in several districts in Pakistan to inform resource allocation decisions. Nevertheless, despite having the potential to accelerate the elimination of polio cases, their analyses are unlikely to advance our understanding OPV cessation threats. McCarthy et al. (BMC Med15:175, 2017) explored one such threat, namely the emergence and transmission of serotype 2 circulating vaccine derived poliovirus (cVDPV2) after OPV2 cessation, and found that the risk of persistent spread of cVDPV2 to new areas increases rapidly 1–5 years after OPV2 cessation. Thus, recently developed models and analysis methods have the potential to guide the required steps to surpass these threats. ‘Big data’ scientists could help with this; however, datasets covering all eradication efforts should be made readily available.

Online Bayesian Phylogenetic Inference: Theoretical Foundations via Sequential Monte Carlo

Vu Dinh, Aaron E Darling, Frederick A Matsen IV

Systematic Biology

December 13, 2017


Phylogenetics, the inference of evolutionary trees from molecular sequence data such as DNA, is an enterprise that yields valuable evolutionary understanding of many biological systems. Bayesian phylogenetic algorithms, which approximate a posterior distribution on trees, have become a popular if computationally expensive means of doing phylogenetics. Modern data collection technologies are quickly adding new sequences to already substantial databases. With all current techniques for Bayesian phylogenetics, computation must start anew each time a sequence becomes available, making it costly to maintain an up-to-date estimate of a phylogenetic posterior. These considerations highlight the need for an online Bayesian phylogenetic method which can update an existing posterior with new sequences. Here, we provide theoretical results on the consistency and stability of methods for online Bayesian phylogenetic inference based on Sequential Monte Carlo (SMC) and Markov chain Monte Carlo. We first show a consistency result, demonstrating that the method samples from the correct distribution in the limit of a large number of particles. Next, we derive the first reported set of bounds on how phylogenetic likelihood surfaces change when new sequences are added. These bounds enable us to characterize the theoretical performance of sampling algorithms by bounding the effective sample size (ESS) with a given number of particles from below. We show that the ESS is guaranteed to grow linearly as the number of particles in an SMC sampler grows. Surprisingly, this result holds even though the dimensions of the phylogenetic model grow with each new added sequence.

Comparative epidemiology of poliovirus transmission

Navideh Noori, John M. Drake, Pejman Rohani

Scientific Reports

December 12, 2017


Understanding the determinants of polio transmission and its large-scale epidemiology remains a public health priority. Despite a 99% reduction in annual wild poliovirus (WPV) cases since 1988, tackling the last 1% has proven difficult. We identified key covariates of geographical variation in polio transmission patterns by relating country-specific annual disease incidence to demographic, socio-economic and environmental factors. We assessed the relative contributions of these variables to the performance of computer-generated models for predicting polio transmission. We also examined the effect of spatial coupling on the polio extinction frequency in islands relative to larger land masses. Access to sanitation, population density, forest cover and routine vaccination coverage were the strongest predictors of polio incidence, however their relative effect sizes were inconsistent geographically. The effect of climate variables on polio incidence was negligible, indicating that a climate effect is not identifiable at the annual scale, suggesting a role for climate in shaping the transmission seasonality rather than intensity. We found polio fadeout frequency to depend on both population size and demography, which should therefore be considered in policies aimed at extinction. Our comparative epidemiological approach highlights the heterogeneity among polio transmission determinants. Recognition of this variation is important for the maintenance of population immunity in a post-polio era.

Effective Online Bayesian Phylogenetics via Sequential Monte Carlo with Guided Proposals

Mathieu Fourment, Brian C Claywell, Vu Dinh, Connor McCoy, Frederick A Matsen IV, Aaron E Darling

Systematic Biology

November 27, 2017


Modern infectious disease outbreak surveillance produces continuous streams of sequence data which require phylogenetic analysis as data arrives. Current software packages for Bayesian phylogenetic inference are unable to quickly incorporate new sequences as they become available, making them less useful for dynamically unfolding evolutionary stories. This limitation can be addressed by applying a class of Bayesian statistical inference algorithms called sequential Monte Carlo (SMC) to conduct online inference, wherein new data can be continuously incorporated to update the estimate of the posterior probability distribution. In this article, we describe and evaluate several different online phylogenetic sequential Monte Carlo (OPSMC) algorithms. We show that proposing new phylogenies with a density similar to the Bayesian prior suffers from poor performance, and we develop “guided” proposals that better match the proposal density to the posterior. Furthermore, we show that the simplest guided proposals can exhibit pathological behavior in some situations, leading to poor results, and that the situation can be resolved by heating the proposal density. The results demonstrate that relative to the widely used MCMC-based algorithm implemented in MrBayes, the total time required to compute a series of phylogenetic posteriors as sequences arrive can be significantly reduced by the use of OPSMC, without incurring a significant loss in accuracy.