Quantifying transmission dynamics of acute hepatitis C virus infections in a heterogeneous population using sequence data

Opioid substitution and syringes exchange programs have drastically reduced hepatitis C virus (HCV) spread in France but HCV sexual transmission in men having sex with men (MSM) has recently arisen as a significant public health concern. The fact that the virus is transmitting in a heterogeneous population, with different transmission routes, makes prevalence and incidence rates poorly informative. However, additional insights can be gained by analyzing virus phylogenies inferred from dated genetic sequence data. By combining a phylodynamics approach based on Approximate Bayesian Computation (ABC) and an original transmission model, we estimate key epidemiological parameters of an ongoing HCV epidemic among MSMs in Lyon (France). We show that this new epidemic is largely independent of the previously observed non-MSM HCV epidemics and that its doubling time is ten times lower (0.44 years versus 4.37 years). These results have practical implications for HCV control and illustrate the additional information provided by virus genomics in public health.     

The assessment of fluctuating odontometric asymmetry from incomplete hominid fossil data

Problems of small sample size and incompleteness of fossil data in the evaluation of metric asymmetry may be overcome by the application of cluster sampling techniques together with factorial analyses of variance. Degrees of fluctuating odontometric asymmetry are reported in Australopithecus, Homo erectus and Homo neanderthalensis. In all three populations mesiodistal dimensions were shown to be more asymmetric than buccolingual dimensions, with maxillary teeth exhibiting significantly greater degrees of asymmetry than mandibular teeth. Neanderthal teeth were significantly more metrically asymmetric than those of either Australopithecus or H. erectus, with population differences in asymmetry centered in the maxillary teeth.     

Gene network inference from incomplete expression data: transcriptional control of hematopoietic commitment

MOTIVATION: The topology and function of gene regulation networks are commonly inferred from time series of gene expression levels in cell populations. This strategy is usually invalid if the gene expression in different cells of the population is not synchronous. A promising, though technically more demanding alternative is therefore to measure the gene expression levels in single cells individually. The inference of a gene regulation network requires knowledge of the gene expression levels at successive time points, at least before and after a network transition. However, owing to experimental limitations a complete determination of the precursor state is not possible. RESULTS: We investigate a strategy for the inference of gene regulatory networks from incomplete expression data based on dynamic Bayesian networks. This permits prediction of the number of experiments necessary for network inference depending on parameters including noise in the data, prior knowledge and limited attainability of initial states. Our strategy combines a gradual 'Partial Learning' approach based solely on true experimental observations for the network topology with expectation maximization for the network parameters. We illustrate our strategy by extensive computer simulations in a high-dimensional parameter space in a simulated single-cell-based example of hematopoietic stem cell commitment and in random networks of different sizes. We find that the feasibility of network inferences increases significantly with the experimental ability to force the system into different initial network states, with prior knowledge and with noise reduction. AVAILABILITY: Source code is available under: www.izbi.uni-leipzig.de/services/NetwPartLearn.html SUPPLEMENTARY INFORMATION: Supplementary Data are available at Bioinformatics online.     

Combining viral genetic and animal mobility network data to unravel peste des petits ruminants transmission dynamics in West Africa

Peste des petits ruminants (PPR) is a deadly viral disease that mainly affects small domestic ruminants. This disease threaten global food security and rural economy but its control is complicated notably because of extensive, poorly monitored animal movements in infected regions. Here we combined the largest PPR virus genetic and animal mobility network data ever collected in a single region to improve our understanding of PPR endemic transmission dynamics in West African countries. Phylogenetic analyses identified the presence of multiple PPRV genetic clades that may be considered as part of different transmission networks evolving in parallel in West Africa. A strong correlation was found between virus genetic distance and network-related distances. Viruses sampled within the same mobility communities are significantly more likely to belong to the same genetic clade. These results provide evidence for the importance of animal mobility in PPR transmission in the region. Some nodes of the network were associated with PPRV sequences belonging to different clades, representing potential “hotspots” for PPR circulation. Our results suggest that combining genetic and mobility network data could help identifying sites that are key for virus entrance and spread in specific areas. Such information could enhance our capacity to develop locally adapted control and surveillance strategies, using among other risk factors, information on animal mobility.     

Extracting key information from historical data to quantify the transmission dynamics of smallpox

Background  

Quantification of the transmission dynamics of smallpox is crucial for optimizing intervention strategies in the event of a bioterrorist attack. This article reviews basic methods and findings in mathematical and statistical studies of smallpox which estimate key transmission parameters from historical data.     

Modelling to explore the potential impact of asymptomatic human infections on transmission and dynamics of African sleeping sickness

Gambiense human African trypanosomiasis (gHAT, sleeping sickness) is one of several neglected tropical diseases (NTDs) where there is evidence of asymptomatic human infection but there is uncertainty of the role it plays in transmission and maintenance. To explore possible consequences of asymptomatic infections, particularly in the context of elimination of transmission—a goal set to be achieved by 2030—we propose a novel dynamic transmission model to account for the asymptomatic population. This extends an established framework, basing infection progression on a number of experimental and observation gHAT studies. Asymptomatic gHAT infections include those in people with blood-dwelling trypanosomes, but no discernible symptoms, or those with parasites only detectable in skin. Given current protocols, asymptomatic infection with blood parasites may be diagnosed and treated, based on observable parasitaemia, in contrast to many other diseases for which treatment (and/or diagnosis) may be based on symptomatic infection. We construct a model in which exposed people can either progress to either asymptomatic skin-only parasite infection, which would not be diagnosed through active screening algorithms, or blood-parasite infection, which is likely to be diagnosed if tested. We add extra parameters to the baseline model including different self-cure, recovery, transmission and detection rates for skin-only or blood infections. Performing sensitivity analysis suggests all the new parameters introduced in the asymptomatic model can impact the infection dynamics substantially. Among them, the proportion of exposures resulting in initial skin or blood infection appears the most influential parameter. For some plausible parameterisations, an initial fall in infection prevalence due to interventions could subsequently stagnate even under continued screening due to the formation of a new, lower endemic equilibrium. Excluding this scenario, our results still highlight the possibility for asymptomatic infection to slow down progress towards elimination of transmission. Location-specific model fitting will be needed to determine if and where this could pose a threat.     

Effects of tick population dynamics and host densities on the persistence of tick-borne infections

The transmission and the persistence of tick-borne infections are strongly influenced by the densities and the structure of host populations. By extending previous models and analysis, in this paper we analyse how the persistence of ticks and pathogens, is affected by the dynamics of tick populations, and by their host densities. The effect of host densities on infection persistence is explored through the analysis and simulation of a series of models that include different assumptions on tick-host dynamics and consider different routes of infection transmission. Ticks are assumed to feed on two types of host species which vary in their reservoir competence. Too low densities of competent hosts (i.e., hosts where transmission can occur) do not sustain the infection cycle, while too high densities of incompetent hosts may dilute the competent hosts so much to make infection persistence impossible. A dilution effect may occur also for competent hosts as a consequence of reduced tick to host ratio; this is possible only if the regulation of tick populations is such that tick density does not increase linearly with host densities.     

A bayesian recurrent neural network for unsupervised pattern recognition in large incomplete data sets

A recurrent neural network, modified to handle highly incomplete training data is described. Unsupervised pattern recognition is demonstrated in the WHO database of adverse drug reactions. Comparison is made to a well established method, AutoClass, and the performances of both methods is investigated on simulated data. The neural network method performs comparably to AutoClass in simulated data, and better than AutoClass in real world data. With its better scaling properties, the neural network is a promising tool for unsupervised pattern recognition in huge databases of incomplete observations.     

Effects of treatment and drug resistance on the transmission dynamics of malaria in endemic areas

We present a mathematical model for malaria treatment and spread of drug resistance in an endemic population. The model considers treated humans that remain infectious for some time and partially immune humans who are also infectious to mosquitoes although their infectiousness is always less than their non immune counterparts. The model is formulated by considering delays in the latent periods in both mosquito and human populations and in the period within which partial immunity is lost. Qualitative analysis of the model including positivity and boundedness of solutions is performed. Analysis of the reproductive numbers shows that if the treated humans become immediately uninfectious to mosquitoes then treatment will always reduce the number of sensitive infections. If however treated humans are infectious then for treatment to effectively reduce the number of sensitive infections, the ratio of the infectious period of the treated humans to the infectious period of the untreated humans multiplied by the ratio of the transmission rate from a treated human to the transmission rate of an untreated human should be less than one. Our results show that the spread of drug resistance with treatment as a control strategy depends on the ratio of the infectious periods of treated and untreated humans and on the transmission rates from infectious humans with resistant and sensitive infections. Numerical analysis is performed to assess the effects of treatment on the spread of resistance and infection. The study provides insight into the possible intervention strategies to be employed in malaria endemic populations with resistant parasites by identifying important parameters.     

Mixed-genotype infections of malaria parasites: within-host dynamics and transmission success of competing clones.

Mixed-genotype infections of microparasites are common, but almost nothing is known about how competitive interactions within hosts affect the subsequent transmission success of individual genotypes. We investigated changes in the composition of mixed-genotype infections of the rodent malaria Plasmodium chabaudi clones CR and ER by monoclonal antibody analysis of the asexual infection in mice, and by PCR amplification of clone-specific alleles in oocysts sampled from mosquitoes which had fed on these mice. Mixed-clone infections were initiated with a 9:1 ratio of the two clones, with ER as the minority in the first experiment and CR as the minority in the second experiment. When beginning as the majority, clones achieved parasite densities in mice comparable to those achieved in control (single-clone) infections. When they began as the minority, clones were suppressed to less than 10% of control parasitaemias during the early part of the infections. However, in mosquitoes, the frequency of the initially rare clone was substantially greater than it was in mice at the start of the infection or four days prior to the feed. In both experiments, the minority clone in the inocula produced as many, or more, oocysts than it did as a single-clone infection. These experiments show that asexual dominance during most of the infection is poorly correlated to transmission probability, and therefore that the assumption that within-host population size correlates to transmission probability may not be warranted. They also raise the fundamental question of why transmission rates of individual genotypes are often higher from mixed than single-clone infections.     

Potential effects of mixed infections in ticks on transmission dynamics of pathogens: comparative analysis of published records

Ticks are often infected with more than one pathogen, and several field surveys have documented nonrandom levels of coinfection. Levels of coinfection by pathogens in four tick species were analyzed using published infection data. Coinfection patterns of pathogens in field-collected ticks include numerous cases of higher or lower levels of coinfection than would be expected due to chance alone, but the vast majority of these cases can be explained on the basis of vertebrate host associations of the pathogens, without invoking interactions between pathogens within ticks. Nevertheless, some studies have demonstrated antagonistic interactions, and some have suggested potential mutualisms, between pathogens in ticks. Negative or positive interactions between pathogens within ticks can affect pathogen prevalence, and thus transmission patterns. Probabilistic projections suggest that the effect on transmission depends on initial conditions. When the number of tick bites is relatively low (e.g., for ticks biting humans) changes in prevalence in ticks are predicted to have a commensurate effects on pathogen transmission. In contrast, when the number of tick bites is high (e.g., for wild animal hosts) changes in pathogen prevalence in ticks have relatively little effect on levels of transmission to reservoir hosts, and thus on natural transmission cycles.     

Consequences of Wolbachia transmission process on the infection dynamics

In some species displaying Wolbachia-induced cytoplasmic incompatibility, the intensity of incompatibility depends on the density of symbionts in both parents. Although modalities of the transmission process are poorly known, it appears that the density of Wolbachia within the offspring of a female is variable and is correlated with that of the mother. Assuming that the infection level of an host is a continuous trait, we examine some theoretical consequences of the Wolbachia transmission process on the evolution of the infection level within a population. The hypotheses of this model concern two main points: the transmission of Wolbachia is affected by stochastic processes and a deterministic bias, and the bacterial load of the parents of a cross affects their compatibility relationships. It is shown that the variance of the number of bacteria transmitted induced by the stochastic processes tends to counteract the effect of bacterial curing on the dynamics of infection. A general consequence of the model is that the extinction of Wolbachia is possible even if there is strong incompatibility and no selective disadvantage for the host to bear the bacteria. The model indicates that the evolution of bacterial mutants does not depend on the level of incompatibility they induce, but that mutants with higher transmission variance can be selected for. Moreover, the mean infection level of the host population increases in the presence of such bacteria.     

Virulence and community dynamics of fungal species with vertical and horizontal transmission on a plant with multiple infections

The virulence evolution of multiple infections of parasites from the same species has been modeled widely in evolution theory. However, experimental studies on this topic remain scarce, particularly regarding multiple infections by different parasite species. Here, we characterized the virulence and community dynamics of fungal pathogens on the invasive plant Ageratina adenophora to verify the predictions made by the model. We observed that A. adenophora was highly susceptible to diverse foliar pathogens with mixed vertical and horizontal transmission within leaf spots. The transmission mode mainly determined the pathogen community structure at the leaf spot level. Over time, the pathogen community within a leaf spot showed decreased Shannon diversity; moreover, the vertically transmitted pathogens exhibited decreased virulence to the host A. adenophora, but the horizontally transmitted pathogens exhibited increased virulence to the host. Our results demonstrate that the predictions of classical models for the virulence evolution of multiple infections are still valid in a complex realistic environment and highlight the impact of transmission mode on disease epidemics of foliar fungal pathogens. We also propose that seedborne fungi play an important role in structuring the foliar pathogen community from multiple infections within a leaf spot.     

Analysis of data from incomplete block designs

This paper presents an organized solution to the problem of computing inter- and intrablock analyses of incomplete block designs, based on the modified maximum likelihood principle proposed by Patterson and Thompson (1971, Biometrika 58, 545-554). The calculations are set out to be easily programmed on a microcomputer. The method is attractive because it is simple, yet sufficiently general to handle a wide class of designs, including partially balanced incomplete block designs, designs with unequal block sizes, designs with missing values, and generally unbalanced split-plot experiments.     

Incorporating metadata in HIV transmission network reconstruction: A machine learning feasibility assessment

HIV molecular epidemiology estimates the transmission patterns from clustering genetically similar viruses. The process involves connecting genetically similar genotyped viral sequences in the network implying epidemiological transmissions. This technique relies on genotype data which is collected only from HIV diagnosed and in-care populations and leaves many persons with HIV (PWH) who have no access to consistent care out of the tracking process. We use machine learning algorithms to learn the non-linear correlation patterns between patient metadata and transmissions between HIV-positive cases. This enables us to expand the transmission network reconstruction beyond the molecular network. We employed multiple commonly used supervised classification algorithms to analyze the San Diego Primary Infection Resource Consortium (PIRC) cohort dataset, consisting of genotypes and nearly 80 additional non-genetic features. First, we trained classification models to determine genetically unrelated individuals from related ones. Our results show that random forest and decision tree achieved over 80% in accuracy, precision, recall, and F1-score by only using a subset of meta-features including age, birth sex, sexual orientation, race, transmission category, estimated date of infection, and first viral load date besides genetic data. Additionally, both algorithms achieved approximately 80% sensitivity and specificity. The Area Under Curve (AUC) is reported 97% and 94% for random forest and decision tree classifiers respectively. Next, we extended the models to identify clusters of similar viral sequences. Support vector machine demonstrated one order of magnitude improvement in accuracy of assigning the sequences to the correct cluster compared to dummy uniform random classifier. These results confirm that metadata carries important information about the dynamics of HIV transmission as embedded in transmission clusters. Hence, novel computational approaches are needed to apply the non-trivial knowledge collected from inter-individual genetic information to metadata from PWH in order to expand the estimated transmissions. We note that feature extraction alone will not be effective in identifying patterns of transmission and will result in random clustering of the data, but its utilization in conjunction with genetic data and the right algorithm can contribute to the expansion of the reconstructed network beyond individuals with genetic data.     

Effects of ivermectin treatment of backyard chickens on mosquito dynamics and West Nile virus transmission

BackgroundVector control strategies typically rely on pesticides to target mosquitoes involved in enzootic and zoonotic transmission of West Nile virus (WNV). Nevertheless, increasing insecticide resistance and a desire to reduce pesticide usage provide the impetus for developing alternative strategies. Ivermectin (IVM), an antiparasitic drug which is widely used in human and veterinary medicine, is a potential alternative for targeted control because Culex mosquitoes experience increased mortality following ingestion of IVM in bloodmeals.Methodology/Principal findingsWe conducted a randomized field trial to investigate the impact of treating backyard chicken flocks with IVM in urban neighborhoods across Davis, California on mosquito populations and WNV transmission dynamics. We observed a significant reduction in WNV seroconversions in treated vs. untreated chickens, suggesting a reduction in WNV transmission intensity around treated flocks. We also detected a reduction in parity rates of Cx. tarsalis near treated vs. untreated flocks and increased mortality in wild mosquitoes following a bloodmeal on treated chickens (IVM serum concentration > 5ng/mL) vs. chickens with IVM serum concentrations < 5 ng/mL. However, we did not find a significant difference in abundance or infection prevalence in mosquitoes between treatment groups associated with the reductions in seroconversions. Mosquito immigration from surrounding larval habitat, relatively low WNV activity in the study area, and variable IVM serum concentrations likely contributed to uncertainty about the impact.Conclusions/SignificanceTaken together, our results point to a reduction in WNV transmission due to the impact of IVM on Culex mosquito populations and support the ongoing investigation of oral administration of IVM to wild birds for local control of WNV transmission, although further work is needed to optimize dosing and understand effects on entomological endpoints.     

Retrospective assessment of the dynamics of nitrogen availability in pine forests of the near-Moscow region based on the data of phytoindication

Earlier published data on the long-term geobotanic monitoring in permanent pilot plots have been used to analyze changes in trophic conditions and estimate nitrogen nutrition availability to pine forests of the Serebryanyi Bor Forestry located in the Moscow oblast. The assessments of habitats based on the ecological scales of soil richness with nitrogen, which were developed by D.N. Tsyganov and G. Ellenberg, and analysis of the dynamics of stenobiont species have revealed the properties of changes in the species composition and conditions of nitrogen availability to forests for the period from 1957–1961 to 2003. The results indicate increased nitrogen availability to forests in the studied period for all permanent plots, but reflect different intensities of the trend of eutrophication in different monitoring stages. A more intense growth in soil richness with nitrogen in all permanent plots was in the period since 1957–1961 to 1989–1990. In the next years, the level of nitrogen availability to phytocenoses almost did not change. The improvement in the trophic conditions was accompanied by the growth in total species richness and increase in the number of stenobionts with high requirements to nitrogen nutrition. The results from analyzing the dynamics of stenobionts confirm the division of the period under consideration into two stages. From 1957–1961 to 1989–1990, changes in the species composition of plant communities in the pine forests under consideration were similar but differed in intensity. From 1989–1990 to 2003 (against the background of total deceleration of the eutrophication trend), the similarity of species dynamics in the communities was disturbed. The impact of air pollution by NOx on nitrogen nutrition availability to forests was examined with consideration for the fact that the studied area is within the capital agglomeration. The dynamics of the intensity of air nitrogen deposition is in good agreement with the revealed changes in the species composition of phytocenoses and nitrogen soil richness. It gives reasons to suppose that the trend of forest eutrophication in the 1960s–1980s was strengthened by the impact of increased levels of atmospheric nitrogen deposition.