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


October 15, 2019


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


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—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 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


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


August 27, 2019


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


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


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


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


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


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


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


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).

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


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.

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


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


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


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.

Measurability of the epidemic reproduction number in data-driven contact networks

Quan-Hui Liu, Marco Ajelli, Alberto Aleta, Stefano Merler, Yamir Moreno, Alessandro Vespignani

Proceedings of the National Academy of Sciences

November 21, 2018


The basic reproduction number is one of the conceptual cornerstones of mathematical epidemiology. Its classical definition as the number of secondary cases generated by a typical infected individual in a fully susceptible population finds a clear analytical expression in homogeneous and stratified mixing models. Along with the generation time (the interval between primary and secondary cases), the reproduction number allows for the characterization of the dynamics of an epidemic. A clear-cut theoretical picture, however, is hardly found in real data. Here, we infer from highly detailed sociodemographic data two multiplex contact networks representative of a subset of the Italian and Dutch populations. We then simulate an infection transmission process on these networks accounting for the natural history of influenza and calibrated on empirical epidemiological data. We explicitly measure the reproduction number and generation time, recording all individual-level transmission events. We find that the classical concept of the basic reproduction number is untenable in realistic populations, and it does not provide any conceptual understanding of the epidemic evolution. This departure from the classical theoretical picture is not due to behavioral changes and other exogenous epidemiological determinants. Rather, it can be simply explained by the (clustered) contact structure of the population. Finally, we provide evidence that methodologies aimed at estimating the instantaneous reproduction number can operationally be used to characterize the correct epidemic dynamics from incidence data.

Dengue seroprevalence in a cohort of schoolchildren and their siblings in Yucatan, Mexico (2015-2016)

Norma Pavía-Ruz, Gloria Abigail Barrera-Fuentes, Salha Villanueva-Jorge, Azael Che-Mendoza, Julio César Campuzano-Rincón, Pablo Manrique-Saide, Diana Patricia Rojas, Gonzalo M. Vazquez-Prokopec, M. Elizabeth Halloran, Ira M. Longini, Héctor Gómez-Dantés

PLoS Neglected Tropical Diseases

November 21, 2018


Dengue is a major public health problem in Latin America. Its transmission is highly heterogeneous, and its burden varies by geographic region, age group affected, serotype and other factors. While surveillance of dengue in the region has improved, several limitations remain, including under detection, misdiagnosis and the complexity of controlling a vector that has adapted to human dwellings in tropical and subtropical urban contexts. Prospective studies have become crucial to understand the transmission of dengue in urban environments and assess the impact of control strategies, such as the introduction of a dengue vaccine or additional vector control interventions. Our findings provide epidemiological data regarding the serological profile and risk factors for dengue infections in a cohort of children 0 to 15 years old in an endemic state in Mexico and confirmed the high exposure in these age groups. Likewise, enhanced and passive surveillance of cases gave us the opportunity to measure the behavior of dengue activity during chikungunya and Zika viruses’ arrival, which we believe will contribute to improve the design of surveillance and control strategies.