Potential Test-Negative Case-Control Study Bias in Outbreak Settings: Application to Ebola vaccination in Democratic Republic of Congo

Carl Andrew Pearson, W John Edmunds, Thomas J Hladish, Rosalind M Eggo

medRxiv

January 10, 2020

ABSTRACT

Background: Infectious disease outbreaks present unique challenges to study designs for vaccine evaluation. Test-negative case-control (TNCC) studies have been used to estimate vaccine efficacy previously, and have been proposed for Ebola virus disease (EVD) vaccines. However, there are key differences in how cases and controls are recruited during outbreaks that have implications for the reliability of vaccine efficacy estimates from these studies. Methods: We use a modelling approach to quantify TNCC bias for a prophylactic vaccine distributed across varying study and epidemiological scenarios. Our model accounts for vaccine distribution heterogeneity and for the two potential routes of recruitment: self-reporting and contact-tracing. We derive the TNCC estimator for this model and suggest ways to translate outbreak response data into the parameters of the model. Result: We found systematic biases in vaccine estimates from a TNCC study in our model of outbreak conditions. Biases are introduced due to differential recruitment from self-report and contact-tracing, and by clustering of participation in vaccination. We estimate the magnitude of these biases, and highlight options to manage them via restricted recruitment. For the motivating example of EVD, the absolute bias should be less 10%. Conclusion: A TNCC study may generate biased estimates of vaccine efficacy during outbreaks. Bias can be limited via recruitment that either minimizes heterogeneity in vaccination in the recruited population or excludes recruitment of contact-traced individuals. TNCC studies for outbreak infections should record the reason for testing to quantify potential bias in the vaccine efficacy estimate. Perfectly distinguishing the recruitment route may be difficult in practice, so it will be challenging to entirely remove this bias.

Survival dynamical systems: individual-level survival analysis from population-level epidemic models

Wasiur R. KhudaBukhsh, Boseung Choi, Eben Kenah, Grzegorz A. Rempała

Royal Society Interface Focus

December 13, 2019

ABSTRACT

In this paper, we show that solutions to ordinary differential equations describing the large-population limits of Markovian stochastic epidemic models can be interpreted as survival or cumulative hazard functions when analysing data on individuals sampled from the population. We refer to the individual-level survival and hazard functions derived from population-level equations as a survival dynamical system (SDS). To illustrate how population-level dynamics imply probability laws for individual-level infection and recovery times that can be used for statistical inference, we show numerical examples based on synthetic data. In these examples, we show that an SDS analysis compares favourably with a complete-data maximum-likelihood analysis. Finally, we use the SDS approach to analyse data from a 2009 influenza A(H1N1) outbreak at Washington State University.

Genomic epidemiology supports multiple introductions and cryptic transmission of Zika virus in Colombia

Allison Black, Louise H. Moncla, Katherine Laiton-Donato, Barney Potter, Lissethe Pardo, Angelica Rico, Catalina Tovar, Diana P. Rojas, Ira M. Longini, M. Elizabeth Halloran, Dioselina Peláez-Carvajal, Juan D. Ramírez, Marcela Mercado-Reyes, Trevor Bedford

BMC Infectious Diseases

November 12, 2019

ABSTRACT

Background
Colombia was the second most affected country during the American Zika virus (ZIKV) epidemic, with over 109,000 reported cases. Despite the scale of the outbreak, limited genomic sequence data were available from Colombia. We sought to sequence additional samples and use genomic epidemiology to describe ZIKV dynamics in Colombia.

Methods
We sequenced ZIKV genomes directly from clinical diagnostic specimens and infected Aedes aegypti samples selected to cover the temporal and geographic breadth of the Colombian outbreak. We performed phylogeographic analysis of these genomes, along with other publicly-available ZIKV genomes from the Americas, to estimate the frequency and timing of ZIKV introductions to Colombia.

Results
We attempted PCR amplification on 184 samples; 19 samples amplified sufficiently to perform sequencing. Of these, 8 samples yielded sequences with at least 50% coverage. Our phylogeographic reconstruction indicates two separate introductions of ZIKV to Colombia, one of which was previously unrecognized. We find that ZIKV was first introduced to Colombia in February 2015 (95%CI: Jan 2015 – Apr 2015), corresponding to 5 to 8 months of cryptic ZIKV transmission prior to confirmation in September 2015. Despite the presence of multiple introductions, we find that the majority of Colombian ZIKV diversity descends from a single introduction. We find evidence for movement of ZIKV from Colombia into bordering countries, including Peru, Ecuador, Panama, and Venezuela.

Conclusions
Similarly to genomic epidemiological studies of ZIKV dynamics in other countries, we find that ZIKV circulated cryptically in Colombia. More accurately dating when ZIKV was circulating refines our definition of the population at risk. Additionally, our finding that the majority of ZIKV transmission within Colombia was attributable to transmission between individuals, rather than repeated travel-related importations, indicates that improved detection and control might have succeeded in limiting the scale of the outbreak within Colombia.

The contribution of host cell-directed vs. parasite-directed immunity to the disease and dynamics of malaria infections

Nina Wale, Matthew J. Jones, Derek G. Sim, Andrew F. Read, Aaron A. King

PNAS

October 15, 2019

ABSTRACT

Hosts defend themselves against pathogens by mounting an immune response. Fully understanding the immune response as a driver of host disease and pathogen evolution requires a quantitative account of its impact on parasite population dynamics. Here, we use a data-driven modeling approach to quantify the birth and death processes underlying the dynamics of infections of the rodent malaria parasite, Plasmodium chabaudi, and the red blood cells (RBCs) it targets. We decompose the immune response into 3 components, each with a distinct effect on parasite and RBC vital rates, and quantify the relative contribution of each component to host disease and parasite density. Our analysis suggests that these components are deployed in a coordinated fashion to realize distinct resource-directed defense strategies that complement the killing of parasitized cells. Early in the infection, the host deploys a strategy reminiscent of siege and scorched-earth tactics, in which it both destroys RBCs and restricts their supply. Late in the infection, a “juvenilization” strategy, in which turnover of RBCs is accelerated, allows the host to recover from anemia while holding parasite proliferation at bay. By quantifying the impact of immunity on both parasite fitness and host disease, we reveal that phenomena often interpreted as immunopathology may in fact be beneficial to the host. Finally, we show that, across mice, the components of the host response are consistently related to each other, even when infections take qualitatively different trajectories. This suggests the existence of simple rules that govern the immune system’s deployment.

Is there really more flu in the south? Surveillance systems show differences in influenza activity across regions.

Kristin Baltrusaitis, Alessandro Vespignani, Roni Rosenfeld, Josh Gray, Dorrie Raymond, Mauricio Santillana

JMIR Public Health and Surveillance

September 14, 2019

ABSTRACT

Background: The Centers for Disease Control and Prevention (CDC) tracks influenza-like illness (ILI) using information on patient visits to health care providers through the Outpatient Influenza-like Illness Surveillance Network (ILINet). As participation in this system is voluntary, the composition, coverage, and consistency of health care reports vary from state to state, leading to different measures of ILI activity between regions. The degree to which these measures reflect actual differences in influenza activity or systematic differences in the methods used to collect and aggregate the data is unclear.

Objective: The objective of our study was to qualitatively and quantitatively compare national and region-specific ILI activity in the United States across 4 surveillance data sources—CDC ILINet, Flu Near You (FNY), athenahealth, and HealthTweets.org—to determine whether these data sources, commonly used as input in influenza modeling efforts, show geographical patterns that are similar to those observed in CDC ILINet’s data. We also compared the yearly percentage of FNY participants who sought health care for ILI symptoms across geographical areas.

Methods: We compared the national and regional 2018-2019 ILI activity baselines, calculated using noninfluenza weeks from previous years, for each surveillance data source. We also compared measures of ILI activity across geographical areas during 3 influenza seasons, 2015-2016, 2016-2017, and 2017-2018. Geographical differences in weekly ILI activity within each data source were also assessed using relative mean differences and time series heatmaps. National and regional age-adjusted health care–seeking percentages were calculated for each influenza season by dividing the number of FNY participants who sought medical care for ILI symptoms by the total number of ILI reports within an influenza season. Pearson correlations were used to assess the association between the health care–seeking percentages and baselines for each surveillance data source.

Results: We observed consistent differences in ILI activity across geographical areas for CDC ILINet and athenahealth data. ILI activity for FNY displayed little variation across geographical areas, whereas differences in ILI activity for HealthTweets.org were associated with the total number of tweets within a geographical area. The percentage of FNY participants who sought health care for ILI symptoms differed slightly across geographical areas, and these percentages were positively correlated with CDC ILINet and athenahealth baselines.

Conclusions: Our findings suggest that differences in ILI activity across geographical areas as reported by a given surveillance system may not accurately reflect true differences in the prevalence of ILI. Instead, these differences may reflect systematic collection and aggregation biases that are particular to each system and consistent across influenza seasons. These findings are potentially relevant in the real-time analysis of the influenza season and in the definition of unbiased forecast models.

19 dubious ways to compute the marginal likelihood of a phylogenetic tree topology

Mathieu Fourment, Andrew F Magee, Chris Whidden, Arman Bilge, Frederick A Matsen, IV, Vladimir N Minin

Systematic Biology

August 28, 2019

ABSTRACT

The marginal likelihood of a model is a key quantity for assessing the evidence provided by the data in support of a model. The marginal likelihood is the normalizing constant for the posterior density, obtained by integrating the product of the likelihood and the prior with respect to model parameters. Thus, the computational burden of computing the marginal likelihood scales with the dimension of the parameter space. In phylogenetics, where we work with tree topologies that are high-dimensional models, standard approaches to computing marginal likelihoods are very slow. Here we study methods to quickly compute the marginal likelihood of a single fixed tree topology. We benchmark the speed and accuracy of 19 different methods to compute the marginal likelihood of phylogenetic topologies on a suite of real datasets under the JC69 model. These methods include several new ones that we develop explicitly to solve this problem, as well as existing algorithms that we apply to phylogenetic models for the first time. Altogether, our results show that the accuracy of these methods varies widely, and that accuracy does not necessarily correlate with computational burden. Our newly developed methods are orders of magnitude faster than standard approaches, and in some cases, their accuracy rivals the best established estimators.

Mapping person-to-person variation in viral mutations that escape polyclonal serum targeting influenza hemagglutinin

Juhye M Lee, Rachel Eguia, Seth J Zost, Saket Choudhary, Patrick C Wilson, Trevor Bedford, Terry Stevens-Ayers, Michael Boeckh, Aeron C Hurt, Seema S Lakdawala, Scott E Hensley, Jesse D Bloom

eLife

August 27, 2019

ABSTRACT

A longstanding question is how influenza virus evolves to escape human immunity, which is polyclonal and can target many distinct epitopes. Here, we map how all amino-acid mutations to influenza’s major surface protein affect viral neutralization by polyclonal human sera. The serum of some individuals is so focused that it selects single mutations that reduce viral neutralization by over an order of magnitude. However, different viral mutations escape the sera of different individuals. This individual-to-individual variation in viral escape mutations is not present among ferrets that have been infected just once with a defined viral strain. Our results show how different single mutations help influenza virus escape the immunity of different members of the human population, a phenomenon that could shape viral evolution and disease susceptibility.

Serostatus testing and dengue vaccine cost–benefit thresholds

Carl A. B. Pearson , Kaja M. Abbas , Samuel Clifford , Stefan Flasche, Thomas J. Hladish

Royal Society Interface

August 21, 2019

ABSTRACT

The World Health Organization (WHO) currently recommends pre-screening for past infection prior to administration of the only licensed dengue vaccine, CYD-TDV. Using a threshold modelling analysis, we identify settings where this guidance prohibits positive net-benefits, and are thus unfavourable. Generally, however, our model shows test-then-vaccinate strategies can improve CYD-TDV economic viability: effective testing reduces unnecessary vaccination costs while increasing health benefits. With sufficiently low testing cost, those trends outweigh additional screening costs, expanding the range of settings with positive net-benefits. This work highlights two aspects for further analysis of test-then-vaccinate strategies. We found that starting routine testing at younger ages could increase benefits; if real tests are shown to sufficiently address safety concerns, the manufacturer, regulators and WHO should revisit guidance restricting use to 9-years-and-older recipients. We also found that repeat testing could improve return-on-investment (ROI), despite increasing intervention costs. Thus, more detailed analyses should address questions on repeat testing and testing periodicity, in addition to real test sensitivity and specificity. Our results follow from a mathematical model relating ROI to epidemiology, intervention strategy, and costs for testing, vaccination and dengue infections. We applied this model to a range of strategies, costs and epidemiological settings pertinent to CYD-TDV. However, general trends may not apply locally, so we provide our model and analyses as an R package available via CRAN, denvax. To apply to their setting, decision-makers need only local estimates of age-specific seroprevalence and costs for secondary infections.

Age-structure and transient dynamics in epidemiological systems

F. M. G. Magpantay, A. A. King, P. Rohani

Royal Society Interface

July 31, 2019

ABSTRACT

Mathematical models of childhood diseases date back to the early twentieth century. In several cases, models that make the simplifying assumption of homogeneous time-dependent transmission rates give good agreement with data in the absence of secular trends in population demography or transmission. The prime example is afforded by the dynamics of measles in industrialized countries in the pre-vaccine era. Accurate description of the transient dynamics following the introduction of routine vaccination has proved more challenging, however. This is true even in the case of measles which has a well-understood natural history and an effective vaccine that confers long-lasting protection against infection. Here, to shed light on the causes of this problem, we demonstrate that, while the dynamics of homogeneous and age-structured models can be qualitatively similar in the absence of vaccination, they diverge subsequent to vaccine roll-out. In particular, we show that immunization induces changes in transmission rates, which in turn reshapes the age distribution of infection prevalence, which effectively modulates the amplitude of seasonality in such systems. To examine this phenomenon empirically, we fit transmission models to measles notification data from London that span the introduction of the vaccine. We find that a simple age-structured model provides a much better fit to the data than does a homogeneous model, especially in the transition period from the pre-vaccine to the vaccine era. Thus, we propose that age structure and heterogeneities in contact rates are critical features needed to accurately capture transient dynamics in the presence of secular trends.

Estimating the cost of illness and burden of disease associated with the 2014–2015 chikungunya outbreak in the U.S. Virgin Islands

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

PLOS Neglected Tropical Diseases

July 19, 2019

ABSTRACT

Chikungunya virus (CHIKV), an alphavirus that causes fever and severe polyarthralgia, swept through the Americas in 2014 with almost 2 million suspected or confirmed cases reported by April 2016. In this study, we estimate the direct medical costs, cost of lost wages due to absenteeism, and years lived with disability (YLD) associated with the 2014–2015 CHIKV outbreak in the U.S. Virgin Islands (USVI). For this analysis, we used surveillance data from the USVI Department of Health, medical cost data from three public hospitals in USVI, and data from two studies of laboratory-positive cases up to 12 months post illness. On average, employed case-patients missed 9 days of work in the 12 months following their disease onset, which resulted in an estimated cost of $15.5 million. Estimated direct healthcare costs were $2.9 million for the first 2 months and $0.6 million for 3–12 months following the outbreak. The total estimated cost associated with the outbreak ranged from $14.8 to $33.4 million (approximately 1% of gross domestic product), depending on the proportion of the population infected with symptomatic disease, degree of underreporting, and proportion of cases who were employed. The estimated YLDs associated with long-term sequelae from the CHIKV outbreak in the USVI ranged from 599–1,322. These findings highlight the significant economic burden of the recent CHIKV outbreak in the USVI and will aid policy-makers in making informed decisions about prevention and control measures for inevitable, future CHIKV outbreaks.

Recombinant vector vaccine evolution

James J. Bull, Scott L. Nuismer, Rustom Antia

PLOS Computational Biology

July 19, 2019

ABSTRACT

Replicating recombinant vector vaccines consist of a fully competent viral vector backbone engineered to express an antigen from a foreign transgene. From the perspective of viral replication, the transgene is not only dispensable but may even be detrimental. Thus vaccine revertants that delete or inactivate the transgene may evolve to dominate the vaccine virus population both during the process of manufacture of the vaccine as well as during the course of host infection. A particular concern is that this vaccine evolution could reduce its antigenicity—the immunity elicited to the transgene. We use mathematical and computational models to study vaccine evolution and immunity. These models include evolution arising during the process of manufacture, the dynamics of vaccine and revertant growth, plus innate and adaptive immunity elicited during the course of infection. Although the selective basis of vaccine evolution is easy to comprehend, the immunological consequences are not. One complication is that the opportunity for vaccine evolution is limited by the short period of within-host growth before the viral population is cleared. Even less obvious, revertant growth may only weakly interfere with vaccine growth in the host and thus have a limited effect on immunity to vaccine. Overall, we find that within-host vaccine evolution can sometimes compromise vaccine immunity, but only when the extent of evolution during vaccine manufacture is severe, and this evolution can be easily avoided or mitigated.

Within-host infectious disease models accommodating cellular coinfection, with an application to influenza

Katia Koelle, Alex P Farrell, Christopher B Brooke, Ruian Ke

Virus Evolution

July 8, 2019

ABSTRACT

Within-host models are useful tools for understanding the processes regulating viral load dynamics. While existing models have considered a wide range of within-host processes, at their core these models have shown remarkable structural similarity. Specifically, the structure of these models generally consider target cells to be either uninfected or infected, with the possibility of accommodating further resolution (e.g. cells that are in an eclipse phase). Recent findings, however, indicate that cellular coinfection is the norm rather than the exception for many viral infectious diseases, and that cells with high multiplicity of infection are present over at least some duration of an infection. The reality of these cellular coinfection dynamics is not accommodated in current within-host models although it may be critical for understanding within-host dynamics. This is particularly the case if multiplicity of infection impacts infected cell phenotypes such as their death rate and their viral production rates. Here, we present a new class of within-host disease models that allow for cellular coinfection in a scalable manner by retaining the low-dimensionality that is a desirable feature of many current within-host models. The models we propose adopt the general structure of epidemiological ‘macroparasite’ models that allow hosts to be variably infected by parasites such as nematodes and host phenotypes to flexibly depend on parasite burden. Specifically, our within-host models consider target cells as ‘hosts’ and viral particles as ‘macroparasites’, and allow viral output and infected cell lifespans, among other phenotypes, to depend on a cell’s multiplicity of infection. We show with an application to influenza that these models can be statistically fit to viral load and other within-host data, and demonstrate using model selection approaches that they have the ability to outperform traditional within-host viral dynamic models. Important in vivo quantities such as the mean multiplicity of cellular infection and time-evolving reassortant frequencies can also be quantified in a straightforward manner once these macroparasite models have been parameterized. The within-host model structure we develop here provides a mathematical way forward to address questions related to the roles of cellular coinfection, collective viral interactions, and viral complementation in within-host viral dynamics and evolution.

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 H. Watson, Ana Maria Henao-Restrepo, Ira M. Longini

Science Translational Medicine

July 3, 2019

ABSTRACT

Public health emergencies, such as an Ebola disease outbreak, provide a complex and challenging environment for the evaluation of candidate vaccines. Here, we outline the need for flexible and responsive vaccine trial designs to be used in public health emergencies, and we summarize recommendations for their use in this setting.

A general framework for modelling the impact of co-infections on pathogen evolution

Mary Bushman and Rustom Antia

Royal Society Interface

June 26, 2019

ABSTRACT

Theoretical models suggest that mixed-strain infections, or co-infections, are an important driver of pathogen evolution. However, the within-host dynamics of co-infections vary enormously, which complicates efforts to develop a general understanding of how co-infections affect evolution. Here, we develop a general framework which condenses the within-host dynamics of co-infections into a few key outcomes, the most important of which is the overall R0 of the co-infection. Similar to how fitness is determined by two different alleles in a heterozygote, the R0 of a co-infection is a product of the R0 values of the co-infecting strains, shaped by the interaction of those strains at the within-host level. Extending the analogy, we propose that the overall R0 reflects the dominance of the co-infecting strains, and that the ability of a mutant strain to invade a population is a function of its dominance in co-infections. To illustrate the utility of these concepts, we use a within-host model to show how dominance arises from the within-host dynamics of a co-infection, and then use an epidemiological model to demonstrate that dominance is a robust predictor of the ability of a mutant strain to save a maladapted wild-type strain from extinction (evolutionary emergence).

Characterization of antibody and memory T-cell response in H7N9 survivors: a cross-sectional analysis

M.-J.Ma, X.-X.Wang, M.-N.Wu, X.-J.Wang, C.-J.Bao, H.-J.Zhang, Y.Yang, K.Xu, G.-L.Wang, M.Zhao, W.Cheng, W.-J.Chen, W.-H.Zhang, L.-Q.Fang, W.J.Liu, E.-F.Chen, W.-C.Cao

Clinical Microbiology and Infection

June 20, 2019

ABSTRACT

Objectives

Despite the importance of immunological memory for protective immunity against viral infection, whether H7N9-specific antibodies and memory T-cell responses remain detectable years after the original infection is unknown.

Methods

A cross-sectional study was conducted to investigate the immune memory responses of H7N9 patients who contracted the disease and survived during the 2013–2016 epidemics in China. Sustainability of antibodies and T-cell memory to H7N9 virus were examined. Healthy individuals receiving routine medical examinations in a physical examination centre were recruited as control.

Results

A total of 75 survivors were enrolled and classified into four groups based on the time elapsed from illness onset to specimen collection: 3 months (n = 14), 14 months (n = 14), 26 months (n = 28) and 36 months (n = 19). Approximately 36 months after infection, the geometric mean titres of virus-specific antibodies were significantly lower than titres in patients 3 months after infection, but 16 of 19 (84.2%) survivors in the 36-month interval had microneutralization (MN) titres ≥40. Despite the overall declining trend, the percentages of virus-specific cytokine-secreting memory CD4+ and CD8+ T cells remained higher in survivors at nearly all time-points in comparison with control individuals. Linear regression analysis showed that severe disease (mean titre ratio 2.77, 95% CI 1.17–6.49) was associated with higher haemagglutination inhibition (HI) titre and female sex for both HI (1.92, 1.02–3.57) and MN (3.33, 1.26–9.09) antibody, whereas female sex (mean percentage ratio 1.69, 95% CI 1.08–2.63), underlying medical conditions (1.94, 95% CI 1.09–3.46) and lack of antiviral therapy (2.08, 95% CI 1.04–4.17) were predictors for higher T-cell responses.

Conclusions

Survivors of H7N9 virus infection produced long-term antibodies and memory T-cell responses. Our findings warrant further serological investigation in general and high-risk populations and have important implications for vaccine design and development.

Evaluating the probability of silent circulation of polio in small populations using the silent circulation statistic

Celeste Vallejo, Carl A.B. Pearson, James Koopman, Thomas J. Hladish

Infectious Disease Modeling

June 14, 2019

ABSTRACT

As polio-endemic countries move towards elimination, infrequent first infections and incomplete surveillance make it difficult to determine when the virus has been eliminated from the population. Eichner and Dietz [American Journal of Epidemiology, 143, 8 (1996)] proposed a model to estimate the probability of silent polio circulation depending upon when the last paralytic case was detected. Using the same kind of stochastic model they did, we additionally model waning polio immunity in the context of isolated, small, and unvaccinated populations. We compare using the Eichner and Dietz assumption of an initial case at the start of the simulation to a more accurate determination that observes the first case. The former estimates a higher probability of silent circulation in small populations, but this effect diminishes with increasing model population. We also show that stopping the simulation after a specific time estimates a lower probability of silent circulation than when all replicates are run to extinction, though this has limited impact on small populations. Our extensions to the Eichner and Dietz work improve the basis for decisions concerning the probability of silent circulation. Further model realism will be needed for accurate silent circulation risk assessment.

Panel data analysis via mechanistic models

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

Journal of the American Statistical Association

June 7, 2019

ABSTRACT

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 due to the combination of model complexity and dataset size. Supplementary materials for this article are available online.

Successes and failures of the live-attenuated influenza vaccine, can we do better?

Laura Matrajt, M. Elizabeth Halloran, Rustom Antia

Clinical Infectious Diseases

May 6, 2019

ABSTRACT

Live-attenuated vaccines are usually highly effective against many acute viral infections. However, the effective- ness of the live attenuated influenza vaccine (LAIV) can vary widely, ranging from 0% effectiveness in some studies done in the United States to 50% in studies done in Europe. The reasons for these discrepancies remain largely unclear. In this paper we use mathematical models to explore how the efficacy of LAIV is affected by the degree of mismatch with the currently circulating influenza strain and interference with pre-existing immunity. The model incorporates two key antigenic distances - the distance between pre-existing immunity and the currently circulating strain as well as the LAIV strain. Our models show that a LAIV that is matched with the currently circulating strain is likely to have only modest efficacy. Our results suggest that the efficacy of the vaccine would be increased (optimized) if, rather than being matched to the circulating strain, it is antigenically slightly further from pre-existing immunity compared with the circulating strain. The models also suggest two regimes in which LAIV that is matched to circulating strains may provide effective protection. The first is in children before they have built immunity from circulating strains. The second is in response to novel strains (such as antigenic shifts) which are at substantial antigenic distance from previously circulating strains. Our models provide an explanation for the variation in vaccine effectiveness, both between children and adults as well as between studies of vaccine effectiveness observed during the 2014-15 influenza season in different countries.

A Spatio-Temporal Modeling Framework for Surveillance Data of Multiple Infectious Pathogens With Small Laboratory Validation Sets

Xueying Tang, Yang Yang, Hong-Jie Yu, Qiao-Hong Liao, Nikolay Bliznyuk

Journal of the American Statistical Association

April 30, 2019

ABSTRACT

Many surveillance systems of infectious diseases are syndrome-based, capturing patients by clinical manifestation. Only a fraction of patients, mostly severe cases, undergo laboratory validation to identify the underlying pathogen. Motivated by the need to understand transmission dynamics and associate risk factors of enteroviruses causing the hand, foot, and mouth disease (HFMD) in China, we developed a Bayesian spatio-temporal modeling framework for surveillance data of infectious diseases with small validation sets. A novel approach was proposed to sample unobserved pathogen-specific patient counts over space and time and was compared to an existing sampling approach. The practical utility of this framework in identifying key parameters was assessed in simulations for a range of realistic sizes of the validation set. Several designs of sampling patients for laboratory validation were compared with and without aggregation of sparse validation data. The methodology was applied to the 2009 HFMD epidemic in southern China to evaluate transmissibility and the effects of climatic conditions for the leading pathogens of the disease, enterovirus 71, and Coxsackie A16. Supplementary materials for this article are available online.

Estimating effective population size changes from preferentially sampled genetic sequences

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

arXiv

March 28, 2019

ABSTRACT

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.