Alun L. Lloyd

The Influence of HLA Class I Alleles and Heterozygosity on the Outcome of Human T Cell Lymphotropic Virus Type I Infection

The inflammatory disease human T cell lymphotropic virus type I (HTLV-I)-associated myelopathy (HAM/TSP) occurs in only 1–2% of HTLV-I-infected individuals and is associated with a high provirus load of HTLV-I. We hypothesize that a person’s risk of developing HAM/TSP depends upon the efficiency of their immune response to the virus, which differs between individuals because of polymorphism in genes that influence this response. Previously we showed that the possession of HLA-A*02 was associated with a lower risk of HAM/TSP, and with a lower provirus load in healthy carriers of HTLV-I. However, HLA-A*02 did not account for all the observed difference in the risk of HAM/TSP. Here we present evidence, in the same study population in Japan, that HLA-Cw*08 was also associated with disease protection (probability value, two-tailed test = 0.002) and with a lower proviral load in healthy carriers. Possession of the A*02 and/or Cw*08 genes prevented 36% of potential HAM/TSP cases. In contrast, HLA-B*5401 was associated with a higher susceptibility to HAM/TSP (probability value, two-tailed test = 0.0003) and with a higher provirus load in HAM/TSP patients. At a given provirus load, B*5401 appeared to increase the risk of disease. The fraction of HAM/TSP cases attributable to B*5401 was 17%. Furthermore, individuals who were heterozygous at all three HLA class I loci have a lower HTLV-I provirus load than those who were homozygous at one or more loci. These results are consistent with the proposal that a strong class I-restricted CTL response to HTLV-I reduces the proviral load and hence the risk of disease.

A systematic review of mathematical models of mosquito-borne pathogen transmission: 1970-2010

Mathematical models of mosquito-borne pathogen transmission originated in the early twentieth century to provide insights into how to most effectively combat malaria. The foundations of the Ross–Macdonald theory were established by 1970. Since then, there has been a growing interest in reducing the public health burden of mosquito-borne pathogens and an expanding use of models to guide their control. To assess how theory has changed to confront evolving public health challenges, we compiled a bibliography of 325 publications from 1970 through 2010 that included at least one mathematical model of mosquito-borne pathogen transmission and then used a 79-part questionnaire to classify each of 388 associated models according to its biological assumptions. As a composite measure to interpret the multidimensional results of our survey, we assigned a numerical value to each model that measured its similarity to 15 core assumptions of the Ross–Macdonald model. Although the analysis illustrated a growing acknowledgement of geographical, ecological and epidemiological complexities in modelling transmission, most models during the past 40 years closely resemble the Ross–Macdonald model. Modern theory would benefit from an expansion around the concepts of heterogeneous mosquito biting, poorly mixed mosquito-host encounters, spatial heterogeneity and temporal variation in the transmission process.

Destabilization of epidemic models with the inclusion of realistic distributions of infectious periods.

Most mathematical models used to understand the dynamical patterns seen in the incidence of childhood viral diseases, such as measles, employ a simple, but epidemiologically unrealistic, description of the infection and recovery process. The inclusion of more realistic descriptions of the recovery process is shown to cause a significant destabilization of the model. When there is seasonal variation in disease transmission this destabilization leads to the appearance of complex dynamical patterns with much lower levels of seasonality than previously predicted. More generally, this study illustrates how detailed dynamical properties of a model may depend in an important way on the assumptions made in the formulation of the model.

Skeeter Buster: a stochastic, spatially explicit modeling tool for studying Aedes aegypti population replacement and population suppression strategies.

Background Dengue is the most important mosquito-borne viral disease affecting humans. The only prevention measure currently available is the control of its vectors, primarily Aedes aegypti. Recent advances in genetic engineering have opened the possibility for a new range of control strategies based on genetically modified mosquitoes. Assessing the potential efficacy of genetic (and conventional) strategies requires the availability of modeling tools that accurately describe the dynamics and genetics of Ae. aegypti populations. Methodology/Principal findings We describe in this paper a new modeling tool of Ae. aegypti population dynamics and genetics named Skeeter Buster. This model operates at the scale of individual water-filled containers for immature stages and individual properties (houses) for adults. The biology of cohorts of mosquitoes is modeled based on the algorithms used in the non-spatial Container Inhabiting Mosquitoes Simulation Model (CIMSiM). Additional features incorporated into Skeeter Buster include stochasticity, spatial structure and detailed population genetics. We observe that the stochastic modeling of individual containers in Skeeter Buster is associated with a strongly reduced temporal variation in stage-specific population densities. We show that heterogeneity in container composition of individual properties has a major impact on spatial heterogeneity in population density between properties. We detail how adult dispersal reduces this spatial heterogeneity. Finally, we present the predicted genetic structure of the population by calculating FST values and isolation by distance patterns, and examine the effects of adult dispersal and container movement between properties. Conclusions/Significance We demonstrate that the incorporated stochasticity and level of spatial detail have major impacts on the simulated population dynamics, which could potentially impact predictions in terms of control measures. The capacity to describe population genetics confers the ability to model the outcome of genetic control methods. Skeeter Buster is therefore an important tool to model Ae. aegypti populations and the outcome of vector control measures.

The dependence of viral parameter estimates on the assumed viral life cycle: limitations of studies of viral load data

Estimation of viral parameters, such as the basic reproductive number (R0) and infected cell life span, is central to the quantitative study of the within–host dynamics of viral diseases such as human immunodeficiency virus, hepatitis B or hepatitis C. As these parameters can rarely be determined directly, they are usually estimated indirectly by fitting mathematical models to viral load data. This paper investigates how parameter estimates obtained by such procedures depend on the assumptions made concerning the viral life cycle. It finds that estimates of the basic reproductive number obtained using viral load data collected during the initial stages of infection can depend quite sensitively on these assumptions. The use of models which neglect the intracellular delay before virion production can lead to severe underestimates of R0 and, hence, to overly optimistic predictions of how efficacious treatment must be in order to prevent or eradicate the disease. These results are also of importance for attempts at estimating R0 from similar epidemiological data as there is a correspondence between within–host and between–host models. Estimates of the life span of infected cells obtained from viral load data collected during drug treatment studies also depend on the assumptions made in modelling the virus life cycle. The use of more realistic descriptions of the life cycle is seen to increase estimates of infected cell life span, in addition to providing a new explanation for the shoulder phase seen during drug treatment. This study highlights the limitations of what can be learnt by fitting mathematical models to infectious disease data without detailed independent knowledge of the life cycle of the infectious agent.

Direct quantitation of rapid elimination of viral antigen-positive lymphocytes by antiviral CD8 + T cells in vivo

Lysis of infected cells by CD8+ T cells is an important mechanism for the control of virus infections, but remains difficult to quantify in vivo. Here, we study the elimination kinetics of viral antigen‐positive lymphocytes by antiviral CD8+ T cells using flow cytometry and mathematical analysis. In mice acutely infected with lymphocytic choriomeningitis virus, more than 99.99 % of target cells were eliminated each day, corresponding to a half‐life of 1.4 h. Even in mice exposed to virus 300 days previously, and with no ex vivo killing activity, 84 % of the target cells were eliminated per day. Unexpectedly, the elimination kinetics of antigen‐positive lymphocytes was not significantly impaired in mice deficient in either perforin‐, CD95 ligand‐ or TNF‐mediated cytotoxicity. For viruses with a particular tropism for lymphocytes, such as Epstein‐Barr virus or HIV, our results illustrate how effectively CD8+ T cell‐mediated elimination of target cells can potentially contribute to virus control and immunosuppression.

Recasting the theory of mosquito-borne pathogen transmission dynamics and control

Mosquito-borne diseases pose some of the greatest challenges in public health, especially in tropical and sub-tropical regions of the world. Efforts to control these diseases have been underpinned by a theoretical framework developed for malaria by Ross and Macdonald, including models, metrics for measuring transmission, and theory of control that identifies key vulnerabilities in the transmission cycle. That framework, especially Macdonalds formula for R0 and its entomological derivative, vectorial capacity, are now used to study dynamics and design interventions for many mosquito-borne diseases. A systematic review of 388 models published between 1970 and 2010 found that the vast majority adopted the Ross–Macdonald assumption of homogeneous transmission in a well-mixed population. Studies comparing models and data question these assumptions and point to the capacity to model heterogeneous, focal transmission as the most important but relatively unexplored component in current theory. Fine-scale heterogeneity causes transmission dynamics to be nonlinear, and poses problems for modeling, epidemiology and measurement. Novel mathematical approaches show how heterogeneity arises from the biology and the landscape on which the processes of mosquito biting and pathogen transmission unfold. Emerging theory focuses attention on the ecological and social context for mosquito blood feeding, the movement of both hosts and mosquitoes, and the relevant spatial scales for measuring transmission and for modeling dynamics and control.

Density-dependent intraspecific competition in the larval stage of Aedes aegypti (Diptera: Culicidae): revisiting the current paradigm.

ABSTRACT Density-dependent intraspecific competition has been considered an important determinant of the dynamics of larval stages of Aedes aegypti. A model was published in 1984 providing a mathematical description of this density dependence, based on field data, that has since been widely used. This description, however, is based on the strong assumption that all mortality is density-dependent. We re-examine the data without this premise and find a reduced importance of density dependence, as well as a different functional form. Based on these discrepancies, we emphasize that the characterization of density dependence in the larval stages of Ae. aegypti should be based on a more complete dataset, and we use artificially generated data to explore how such additional information could help developing a better description of this density dependence. We review other empirical studies on larval competition, discuss the need for further dedicated studies, and provide a few simple guidelines for the design of such studies.

A sensitivity matrix based methodology for inverse problem formulation

Abstract We propose an algorithm to select parameter subset combinations that can be estimated using an ordinary least-squares (OLS) inverse problem formulation with a given data set. First, the algorithm selects the parameter combinations that correspond to sensitivity matrices with full rank. Second, the algorithm involves uncertainty quantification by using the inverse of the Fisher Information Matrix. Nominal values of parameters are used to construct synthetic data sets, and explore the effects of removing certain parameters from those to be estimated using OLS procedures. We quantify these effects in a score for a vector parameter defined using the norm of the vector of standard errors for components of estimates divided by the estimates. In some cases the method leads to reduction of the standard error for a parameter to less than 1% of the estimate.

Graph fission in an evolving voter model

We consider a simplified model of a social network in which individuals have one of two opinions (called 0 and 1) and their opinions and the network connections coevolve. Edges are picked at random. If the two connected individuals hold different opinions then, with probability 1 - α, one imitates the opinion of the other; otherwise (i.e., with probability α), the link between them is broken and one of them makes a new connection to an individual chosen at random (i) from those with the same opinion or (ii) from the network as a whole. The evolution of the system stops when there are no longer any discordant edges connecting individuals with different opinions. Letting ρ be the fraction of voters holding the minority opinion after the evolution stops, we are interested in how ρ depends on α and the initial fraction u of voters with opinion 1. In case (i), there is a critical value αc which does not depend on u, with ρ ≈ u for α > αc and ρ ≈ 0 for α < αc. In case (ii), the transition point αc(u) depends on the initial density u. For α > αc(u), ρ ≈ u, but for α < αc(u), we have ρ(α,u) = ρ(α,1/2). Using simulations and approximate calculations, we explain why these two nearly identical models have such dramatically different phase transitions.