Amazonian Triatomine Biodiversity and the Transmission of Chagas Disease in French Guiana: In Medio Stat Sanitas

The effects of biodiversity on the transmission of infectious diseases now stand as a cornerstone of many public health policies. The upper Amazonia and Guyana shield are hot-spots of biodiversity that offer genuine opportunities to explore the relationship between the risk of transmission of Chagas disease and the diversity of its triatomine vectors. Over 730 triatomines were light-trapped in four geomorphological landscapes shaping French-Guiana, and we determined their taxonomic status and infection by Trypanosoma cruzi. We used a model selection approach to unravel the spatial and temporal variations in species abundance, diversity and infection. The vector community in French-Guiana is typically made of one key species (Panstrongylus geniculatus) that is more abundant than three secondary species combined (Rhodnius pictipes, Panstrongylus lignarius and Eratyrus mucronatus), and four other species that complete the assemblage. Although the overall abundance of adult triatomines does not vary across French-Guiana, their diversity increases along a coastal-inland gradient. These variations unravelled a non-monotonic relationship between vector biodiversity and the risk of transmission of Chagas disease, so that intermediate biodiversity levels are associated with the lowest risks. We also observed biannual variations in triatomine abundance, representing the first report of a biannual pattern in the risk of Chagas disease transmission. Those variations were highly and negatively correlated with the average monthly rainfall. We discuss the implications of these patterns for the transmission of T. cruzi by assemblages of triatomine species, and for the dual challenge of controlling Amazonian vector communities that are made of both highly diverse and mostly intrusive species.     

Spatial Heterogeneity,Host Movement and Mosquito-Borne Disease Transmission

Mosquito-borne diseases are a global health priority disproportionately affecting low-income populations in tropical and sub-tropical countries. These pathogens live in mosquitoes and hosts that interact in spatially heterogeneous environments where hosts move between regions of varying transmission intensity. Although there is increasing interest in the implications of spatial processes for mosquito-borne disease dynamics, most of our understanding derives from models that assume spatially homogeneous transmission. Spatial variation in contact rates can influence transmission and the risk of epidemics, yet the interaction between spatial heterogeneity and movement of hosts remains relatively unexplored. Here we explore, analytically and through numerical simulations, how human mobility connects spatially heterogeneous mosquito populations, thereby influencing disease persistence (determined by the basic reproduction number R ₀), prevalence and their relationship. We show that, when local transmission rates are highly heterogeneous, R ₀ declines asymptotically as human mobility increases, but infection prevalence peaks at low to intermediate rates of movement and decreases asymptotically after this peak. Movement can reduce heterogeneity in exposure to mosquito biting. As a result, if biting intensity is high but uneven, infection prevalence increases with mobility despite reductions in R ₀. This increase in prevalence decreases with further increase in mobility because individuals do not spend enough time in high transmission patches, hence decreasing the number of new infections and overall prevalence. These results provide a better basis for understanding the interplay between spatial transmission heterogeneity and human mobility, and their combined influence on prevalence and R ₀.     

提高医院传染病管理的实践

目的：总结解放军第302医院在防控传染病方面所践采取的措施,提高医院传染病管理相关工作。方法：完善传染病管理组织,明确责任;认真落实传染病管理相关制度;加强相关知识培训,强化医护人员责任意识,提高能动性、自觉性。总结分析以上相关措施实施后,2005年-2010年期间传染病报告情况。结果：2005年-2010年每年传染病疫情报告卡填写完整率逐渐提高,医院传染病报告漏报率呈逐年下降趋势,近两年呈现填写完整无漏报的情况。结论：提高医院传染病管理的相关措施的实施,使医护人员对的传染病防控意识有所提高,做到早发现、早报告、早隔离、早治疗,降低传染病传播风险,有效减少了医院交叉感染的发生,保障人们的生命健康和社会的发展稳定。     

Inferring the Source of Transmission with Phylogenetic Data

Identifying the source of transmission using pathogen genetic data is complicated by numerous biological, immunological, and behavioral factors. A large source of error arises when there is incomplete or sparse sampling of cases. Unsampled cases may act as either a common source of infection or as an intermediary in a transmission chain for hosts infected with genetically similar pathogens. It is difficult to quantify the probability of common source or intermediate transmission events, which has made it difficult to develop statistical tests to either confirm or deny putative transmission pairs with genetic data. We present a method to incorporate additional information about an infectious disease epidemic, such as incidence and prevalence of infection over time, to inform estimates of the probability that one sampled host is the direct source of infection of another host in a pathogen gene genealogy. These methods enable forensic applications, such as source-case attribution, for infectious disease epidemics with incomplete sampling, which is usually the case for high-morbidity community-acquired pathogens like HIV, Influenza and Dengue virus. These methods also enable epidemiological applications such as the identification of factors that increase the risk of transmission. We demonstrate these methods in the context of the HIV epidemic in Detroit, Michigan, and we evaluate the suitability of current sequence databases for forensic and epidemiological investigations. We find that currently available sequences collected for drug resistance testing of HIV are unlikely to be useful in most forensic investigations, but are useful for identifying transmission risk factors.     

Transmission of infectious diseases en route to habitat hotspots

Background

The spread of infectious diseases in wildlife populations is influenced by patterns of between-host contacts. Habitat “hotspots” - places attracting a large numbers of individuals or social groups - can significantly alter contact patterns and, hence, disease propagation. Research on the importance of habitat hotspots in wildlife epidemiology has primarily focused on how inter-individual contacts occurring at the hotspot itself increase disease transmission. However, in territorial animals, epidemiologically important contacts may primarily occur as animals cross through territories of conspecifics en route to habitat hotspots. So far, the phenomenon has received little attention. Here, we investigate the importance of these contacts in the case where infectious individuals keep visiting the hotspots and in the case where these individuals are not able to travel to the hotspot any more.

Methodology and Principal Findings

We developed a simulation epidemiological model to investigate both cases in a scenario when transmission at the hotspot does not occur. We find that (i) hotspots still exacerbate epidemics, (ii) when infectious individuals do not travel to the hotspot, the most vulnerable individuals are those residing at intermediate distances from the hotspot rather than nearby, and (iii) the epidemiological vulnerability of a population is the highest when the number of hotspots is intermediate.

Conclusions and Significance

By altering animal movements in their vicinity, habitat hotspots can thus strongly increase the spread of infectious diseases, even when disease transmission does not occur at the hotspot itself. Interestingly, when animals only visit the nearest hotspot, creating additional artificial hotspots, rather than reducing their number, may be an efficient disease control measure.     

Cross-Species Pathogen Transmission and Disease Emergence in Primates

Many of the most virulent emerging infectious diseases in humans, e.g., AIDS and Ebola, are zoonotic, having shifted from wildlife populations. Critical questions for predicting disease emergence are: (1) what determines when and where a disease will first cross from one species to another, and (2) which factors facilitate emergence after a successful host shift. In wild primates, infectious diseases most often are shared between species that are closely related and inhabit the same geographic region. Therefore, humans may be most vulnerable to diseases from the great apes, which include chimpanzees and gorillas, because these species represent our closest relatives. Geographic overlap may provide the opportunity for cross-species transmission, but successful infection and establishment will be determined by the biology of both the host and pathogen. We extrapolate the evolutionary relationship between pathogen sharing and divergence time between primate species to generate “hotspot” maps, highlighting regions where the risk of disease transfer between wild primates and from wild primates to humans is greatest. We find that central Africa and Amazonia are hotspots for cross-species transmission events between wild primates, due to a high diversity of closely related primate species. Hotspots of host shifts to humans will be most likely in the forests of central and west Africa, where humans come into frequent contact with their wild primate relatives. These areas also are likely to sustain a novel epidemic due to their rapidly growing human populations, close proximity to apes, and population centers with high density and contact rates among individuals.     

Sheep Movement Networks and the Transmission of Infectious Diseases

Background and Methodology

Various approaches have been used to investigate how properties of farm contact networks impact on the transmission of infectious diseases. The potential for transmission of an infection through a contact network can be evaluated in terms of the basic reproduction number, R ₀. The magnitude of R ₀ is related to the mean contact rate of a host, in this case a farm, and is further influenced by heterogeneities in contact rates of individual hosts. The latter can be evaluated as the second order moments of the contact matrix (variances in contact rates, and co-variance between contacts to and from individual hosts). Here we calculate these quantities for the farms in a country-wide livestock network: >15,000 Scottish sheep farms in each of 4 years from July 2003 to June 2007. The analysis is relevant to endemic and chronic infections with prolonged periods of infectivity of affected animals, and uses different weightings of contacts to address disease scenarios of low, intermediate and high animal-level prevalence.

Principal Findings and Conclusions

Analysis of networks of Scottish farms via sheep movements from July 2003 to June 2007 suggests that heterogeneities in movement patterns (variances and covariances of rates of movement on and off the farms) make a substantial contribution to the potential for the transmission of infectious diseases, quantified as R ₀, within the farm population. A small percentage of farms (<20%) contribute the bulk of the transmission potential (>80%) and these farms could be efficiently targeted by interventions aimed at reducing spread of diseases via animal movement.     

A Novel Statistical Model to Estimate Host Genetic Effects Affecting Disease Transmission

There is increasing recognition that genetic diversity can affect the spread of diseases, potentially affecting plant and livestock disease control as well as the emergence of human disease outbreaks. Nevertheless, even though computational tools can guide the control of infectious diseases, few epidemiological models can simultaneously accommodate the inherent individual heterogeneity in multiple infectious disease traits influencing disease transmission, such as the frequently modeled propensity to become infected and infectivity, which describes the host ability to transmit the infection to susceptible individuals. Furthermore, current quantitative genetic models fail to fully capture the heritable variation in host infectivity, mainly because they cannot accommodate the nonlinear infection dynamics underlying epidemiological data. We present in this article a novel statistical model and an inference method to estimate genetic parameters associated with both host susceptibility and infectivity. Our methodology combines quantitative genetic models of social interactions with stochastic processes to model the random, nonlinear, and dynamic nature of infections and uses adaptive Bayesian computational techniques to estimate the model parameters. Results using simulated epidemic data show that our model can accurately estimate heritabilities and genetic risks not only of susceptibility but also of infectivity, therefore exploring a trait whose heritable variation is currently ignored in disease genetics and can greatly influence the spread of infectious diseases. Our proposed methodology offers potential impacts in areas such as livestock disease control through selective breeding and also in predicting and controlling the emergence of disease outbreaks in human populations.     

The Improbable Transmission of Trypanosoma cruzi to Human: The Missing Link in the Dynamics and Control of Chagas Disease

Chagas disease has a major impact on human health in Latin America and is becoming of global concern due to international migrations. Trypanosoma cruzi, the etiological agent of the disease, is one of the rare human parasites transmitted by the feces of its vector, as it is unable to reach the salivary gland of the insect. This stercorarian transmission is notoriously poorly understood, despite its crucial role in the ecology and evolution of the pathogen and the disease. The objective of this study was to quantify the probability of T. cruzi vectorial transmission to humans, and to use such an estimate to predict human prevalence from entomological data. We developed several models of T. cruzi transmission to estimate the probability of transmission from vector to host. Using datasets from the literature, we estimated the probability of transmission per contact with an infected triatomine to be 5.8×10⁻⁴ (95%CI: [2.6 ; 11.0]×10⁻⁴). This estimate was consistent across triatomine species, robust to variations in other parameters, and corresponded to 900–4,000 contacts per case. Our models subsequently allowed predicting human prevalence from vector abundance and infection rate in 7/10 independent datasets covering various triatomine species and epidemiological situations. This low probability of T. cruzi transmission reflected well the complex and unlikely mechanism of transmission via insect feces, and allowed predicting human prevalence from basic entomological data. Although a proof of principle study would now be valuable to validate our models'' predictive ability in an even broader range of entomological and ecological settings, our quantitative estimate could allow switching the evaluation of disease risk and vector control program from purely entomological indexes to parasitological measures, as commonly done for other major vector borne diseases. This might lead to different quantitative perspectives as these indexes are well known not to be proportional one to another.     

The Risk of Disease to Great Apes: Simulating Disease Spread in Orang-Utan (Pongo pygmaeus wurmbii) and Chimpanzee (Pan troglodytes schweinfurthii) Association Networks

All great ape species are endangered, and infectious diseases are thought to pose a particular threat to their survival. As great ape species vary substantially in social organisation and gregariousness, there are likely to be differences in susceptibility to disease types and spread. Understanding the relation between social variables and disease is therefore crucial for implementing effective conservation measures. Here, we simulate the transmission of a range of diseases in a population of orang-utans in Sabangau Forest (Central Kalimantan) and a community of chimpanzees in Budongo Forest (Uganda), by systematically varying transmission likelihood and probability of subsequent recovery. Both species have fission-fusion social systems, but differ considerably in their level of gregariousness. We used long-term behavioural data to create networks of association patterns on which the spread of different diseases was simulated. We found that chimpanzees were generally far more susceptible to the spread of diseases than orang-utans. When simulating different diseases that varied widely in their probability of transmission and recovery, it was found that the chimpanzee community was widely and strongly affected, while in orang-utans even highly infectious diseases had limited spread. Furthermore, when comparing the observed association network with a mean-field network (equal contact probability between group members), we found no major difference in simulated disease spread, suggesting that patterns of social bonding in orang-utans are not an important determinant of susceptibility to disease. In chimpanzees, the predicted size of the epidemic was smaller on the actual association network than on the mean-field network, indicating that patterns of social bonding have important effects on susceptibility to disease. We conclude that social networks are a potentially powerful tool to model the risk of disease transmission in great apes, and that chimpanzees are particularly threatened by infectious disease outbreaks as a result of their social structure.     

Relating Phylogenetic Trees to Transmission Trees of Infectious Disease Outbreaks

Transmission events are the fundamental building blocks of the dynamics of any infectious disease. Much about the epidemiology of a disease can be learned when these individual transmission events are known or can be estimated. Such estimations are difficult and generally feasible only when detailed epidemiological data are available. The genealogy estimated from genetic sequences of sampled pathogens is another rich source of information on transmission history. Optimal inference of transmission events calls for the combination of genetic data and epidemiological data into one joint analysis. A key difficulty is that the transmission tree, which describes the transmission events between infected hosts, differs from the phylogenetic tree, which describes the ancestral relationships between pathogens sampled from these hosts. The trees differ both in timing of the internal nodes and in topology. These differences become more pronounced when a higher fraction of infected hosts is sampled. We show how the phylogenetic tree of sampled pathogens is related to the transmission tree of an outbreak of an infectious disease, by the within-host dynamics of pathogens. We provide a statistical framework to infer key epidemiological and mutational parameters by simultaneously estimating the phylogenetic tree and the transmission tree. We test the approach using simulations and illustrate its use on an outbreak of foot-and-mouth disease. The approach unifies existing methods in the emerging field of phylodynamics with transmission tree reconstruction methods that are used in infectious disease epidemiology.     

Infectious Prions in Pre-Clinical Deer and Transmission of Chronic Wasting Disease Solely by Environmental Exposure

Key to understanding the epidemiology and pathogenesis of prion diseases, including chronic wasting disease (CWD) of cervids, is determining the mode of transmission from one individual to another. We have previously reported that saliva and blood from CWD-infected deer contain sufficient infectious prions to transmit disease upon passage into naïve deer. Here we again use bioassays in deer to show that blood and saliva of pre-symptomatic deer contain infectious prions capable of infecting naïve deer and that naïve deer exposed only to environmental fomites from the suites of CWD-infected deer acquired CWD infection after a period of 15 months post initial exposure. These results help to further explain the basis for the facile transmission of CWD, highlight the complexities associated with CWD transmission among cervids in their natural environment, emphasize the potential utility of blood-based testing to detect pre-clinical CWD infection, and could augur similar transmission dynamics in other prion infections.     

Shifts from native to invasive small mammals across gradients from tropical forest to urban habitat in Borneo

Urbanization has paved the way for the spread of commensal rodents at global scale. However, it is largely unknown how these species use tropical anthropogenic landscapes originally covered with forests and inhabited by diverse small mammal assemblages. We surveyed non-flying small mammals in various urban and suburban habitat types and adjacent forest in the tropical town of Kota Kinabalu in Borneo. We used occupancy and polynomial regression models to determine variation in species occurrences along gradients of land-use intensity. Müller’s sundamys (Sundamys muelleri) was the only native small mammal species found in urban and suburban landscapes with a continuous decrease in occurrence probability from forests to urban habitats. The invasive Asian black rat (Rattus rattus species complex) and the invasive Asian house shrew (Suncus murinus) had the highest occurrence probabilities in habitats of intermediate land-use intensity, but Asian black rats are also likely to occasionally invade forested habitats and occupied urban habitats in sympatry with the Norway rat (Rattus norvegicus). In urban and suburban habitats, fallow land possibly favoured the occurrence of S. muelleri and S. murinus. Other native small mammal species (Muridae, Sciuridae, Tupaiidae) were found only in forested areas. Our study shows that native small mammals found in forest are largely replaced by invasive species in urban and suburban habitats. Due to their occurrence in habitats of various land use intensities, S. muelleri and R. rattus comprise central links between forest wildlife and urban species, an association that is important to consider in studies of parasite and disease transmission dynamics.     

The Effects of Host Diversity on Vector-Borne Disease: The Conditions under Which Diversity Will Amplify or Dilute the Disease Risk

Multihost vector-borne infectious diseases form a significant fraction of the global infectious disease burden. In this study we explore the relationship between host diversity, vector behavior, and disease risk. To this end, we have developed a new dynamic model which includes two distinct host species and one vector species with variable preferences. With the aid of the model we were able to compute the basic reproductive rate, R ₀, a well-established measure of disease risk that serves as a threshold parameter for disease outbreak. The model analysis reveals that the system has two different qualitative behaviors: (i) the well-known dilution effect, where the maximal R₀ is obtained in a community which consists a single host (ii) a new amplification effect, denoted by us as diversity amplification, where the maximal R₀ is attained in a community which consists both hosts. The model analysis extends on previous results by underlining the mechanism of both, diversity amplification and the dilution, and specifies the exact conditions for their occurrence. We have found that diversity amplification occurs where the vector prefers the host with the highest transmission ability, and dilution is obtained when the vector does not show any preference, or it prefers to bite the host with the lower transmission ability. The mechanisms of dilution and diversity amplification are able to account for the different and contradictory patterns often observed in nature (i.e., in some cases disease risk is increased while in other is decreased when the diversity is increased). Implication of the diversity amplification mechanism also challenges current premises about the interaction between biodiversity, climate change, and disease risk and calls for retrospective thinking in planning intervention policies aimed at protecting the preferred host species.     

Dissociation of Prion Protein Amyloid Seeding from Transmission of a Spongiform Encephalopathy

Misfolding and aggregation of proteins are common pathogenic mechanisms of a group of diseases called proteinopathies. The formation and spread of proteinaceous lesions within and between individuals were first described in prion diseases and proposed as the basis of their infectious nature. Recently, a similar “prion-like” mechanism of transmission has been proposed in other neurodegenerative diseases such as Alzheimer''s disease. We investigated if misfolding and aggregation of corrupted prion protein (PrP^TSE) are always associated with horizontal transmission of disease. Knock-in transgenic mice (101LL) expressing mutant PrP (PrP-101L) that are susceptible to disease but do not develop any spontaneous neurological phenotype were inoculated with (i) brain extracts containing PrP^TSE from healthy 101LL mice with PrP plaques in the corpus callosum or (ii) brain extracts from mice overexpressing PrP-101L with neurological disease, severe spongiform encephalopathy, and formation of proteinase K-resistant PrP^TSE. In all instances, 101LL mice developed PrP plaques in the area of inoculation and vicinity in the absence of clinical disease or spongiform degeneration of the brain. Importantly, 101LL mice did not transmit disease on serial passage, ruling out the presence of subclinical infection. Thus, in both experimental models the formation of PrP^TSE is not infectious. These results have implications for the interpretation of tests based on the detection of protein aggregates and suggest that de novo formation of PrP^TSE in the host does not always result in a transmissible prion disease. In addition, these results question the validity of assuming that all diseases due to protein misfolding can be transmitted between individuals.     

Real time bayesian estimation of the epidemic potential of emerging infectious diseases

Background

Fast changes in human demographics worldwide, coupled with increased mobility, and modified land uses make the threat of emerging infectious diseases increasingly important. Currently there is worldwide alert for H5N1 avian influenza becoming as transmissible in humans as seasonal influenza, and potentially causing a pandemic of unprecedented proportions. Here we show how epidemiological surveillance data for emerging infectious diseases can be interpreted in real time to assess changes in transmissibility with quantified uncertainty, and to perform running time predictions of new cases and guide logistics allocations.

Methodology/Principal Findings

We develop an extension of standard epidemiological models, appropriate for emerging infectious diseases, that describes the probabilistic progression of case numbers due to the concurrent effects of (incipient) human transmission and multiple introductions from a reservoir. The model is cast in terms of surveillance observables and immediately suggests a simple graphical estimation procedure for the effective reproductive number R (mean number of cases generated by an infectious individual) of standard epidemics. For emerging infectious diseases, which typically show large relative case number fluctuations over time, we develop a Bayesian scheme for real time estimation of the probability distribution of the effective reproduction number and show how to use such inferences to formulate significance tests on future epidemiological observations.

Conclusions/Significance

Violations of these significance tests define statistical anomalies that may signal changes in the epidemiology of emerging diseases and should trigger further field investigation. We apply the methodology to case data from World Health Organization reports to place bounds on the current transmissibility of H5N1 influenza in humans and establish a statistical basis for monitoring its evolution in real time.     

On the Identifiability of Transmission Dynamic Models for Infectious Diseases

Understanding the transmission dynamics of infectious diseases is important for both biological research and public health applications. It has been widely demonstrated that statistical modeling provides a firm basis for inferring relevant epidemiological quantities from incidence and molecular data. However, the complexity of transmission dynamic models presents two challenges: (1) the likelihood function of the models is generally not computable, and computationally intensive simulation-based inference methods need to be employed, and (2) the model may not be fully identifiable from the available data. While the first difficulty can be tackled by computational and algorithmic advances, the second obstacle is more fundamental. Identifiability issues may lead to inferences that are driven more by prior assumptions than by the data themselves. We consider a popular and relatively simple yet analytically intractable model for the spread of tuberculosis based on classical IS6110 fingerprinting data. We report on the identifiability of the model, also presenting some methodological advances regarding the inference. Using likelihood approximations, we show that the reproductive value cannot be identified from the data available and that the posterior distributions obtained in previous work have likely been substantially dominated by the assumed prior distribution. Further, we show that the inferences are influenced by the assumed infectious population size, which generally has been kept fixed in previous work. We demonstrate that the infectious population size can be inferred if the remaining epidemiological parameters are already known with sufficient precision.     

Alternative Feeding Strategies and Potential Disease Transmission in Wisconsin White-Tailed Deer

ABSTRACT We conducted experimental feeding using 3 feeding methods (pile, spread, trough) and 2 quantities (rationed, ad libitum) of shelled corn to compare deer activity and behavior with control sites and evaluate potential direct and indirect transmission of infectious disease in white-tailed deer (Odocoileus virginianus) in central Wisconsin, USA. Deer use was higher at 2 of the feeding sites than at natural feeding areas (P ≤ 0.02). Deer spent a higher proportion of time (P < 0.01) feeding at pile (49%) and spread (61%) treatments than at natural feeding areas (36%). We found higher deer use for rationed than ad libitum feeding quantities and feeding intensity was greatest at rationed piles and lowest at ad libitum spreads. We also observed closer pairwise distances (≤0.3 m) among deer when corn was provided in a trough relative to spread (P=0.03). Supplemental feeding poses risks for both direct and indirect disease transmission due to higher deer concentration and more intensive use relative to control areas. Concentrated feeding and contact among deer at feeding sites can also increase risk for disease transmission. Our results indicated that restrictions on feeding quantity would not mitigate the potential for disease transmission. None of the feeding strategies we evaluated substantially reduced the potential risk for disease transmission and banning supplemental feeding to reduce transmission is warranted.     

Primate Conservation: The Prevention of Disease Transmission

We address the strategies to prevent disease transmission from human to non-human primates in natural settings. Some field research methods, such as gaining close proximity for observation, provisioning for habituation, or reintroducing for repopulation, may place primate subjects at risk for acquiring human-carried diseases. Additional risks arise through inadequate waste disposal or nonhygienic conditions of humans residing at the study site. We describe several disease outbreaks at primate field sites, emphasizing the need for proper protocols to diagnose, to treat, and to prevent recurrence. Finding solutions to the disease transmission problem requires effecting change in the behavior and policies of many individuals, including field researchers, veterinarians, human health care providers, park personnel, government officials, local villagers, and tourists. The prevention of exposure to infectious disease is an important, fundamental aspect of primate conservation; the assurance of good health and longevity in wild primate populations is paramount to the more traditional conservation issues of poaching control and forest protection.