Models Of Pathogen Transmission Research Articles

Assessing the impact of preventative measures to limit the spread of Toxoplasma gondii in wild carnivores of Madagascar.

Novel multihost pathogens can threaten endangered wildlife species, as well as humans and domestic animals. The zoonotic protozoan parasite Toxoplasma gondii is transmitted by members of Felidae and can infect a large number of animal species, including humans. This parasite can have significant health consequences for infected intermediate hosts and could further endanger wild carnivore populations of Madagascar. Building on an empirical characterization of the prevalence of the pathogen in local mammals, we used mathematical models of pathogen transmission in a multihost community to compare preventative measures that aim to limit the spread of this parasite in wild carnivores. Specifically, we examined the effect of hypothetical cat vaccination and population control campaigns on reducing the risk of infection by T. gondii in wild Eupleridae. Our model predicted that the prevalence of exposure to T. gondii in cats would be around 72% and that seroprevalence would reach 2% and 43% in rodents and wild carnivores, respectively. Reducing the rodent population in the landscape by half may only decrease the prevalence of T. gondii in carnivores by 10%. Similarly, cat vaccination and reducing the population of definitive hosts had limited impact on the prevalence of T. gondii in wild carnivorans of Madagascar. A significant reduction in prevalence would require extremely high vaccination, low turnover, or both in the cat population. Other potential control methods of T. gondii in endangered Eupleridae include targeted vaccination of wild animals but would require further investigation. Eliminating the threat entirely will be difficult because of the ubiquity of cats and the persistence of the parasite in the environment.

Ectoparasite and bacterial population genetics and community structure indicate extent of bat movement across an island chain.

Few studies have examined the genetic population structure of vector-borne microparasites in wildlife, making it unclear how much these systems can reveal about the movement of their associated hosts. This study examined the complex host–vector–microbe interactions in a system of bats, wingless ectoparasitic bat flies (Nycteribiidae), vector-borne microparasitic bacteria (Bartonella) and bacterial endosymbionts of flies (Enterobacterales) across an island chain in the Gulf of Guinea, West Africa. Limited population structure was found in bat flies and Enterobacterales symbionts compared to that of their hosts. Significant isolation by distance was observed in the dissimilarity of Bartonella communities detected in flies from sampled populations of Eidolon helvum bats. These patterns indicate that, while genetic dispersal of bats between islands is limited, some non-reproductive movements may lead to the dispersal of ectoparasites and associated microbes. This study deepens our knowledge of the phylogeography of African fruit bats, their ectoparasites and associated bacteria. The results presented could inform models of pathogen transmission in these bat populations and increase our theoretical understanding of community ecology in host–microbe systems.

Stability Switches, Hopf Bifurcation and Chaotic Dynamics in Simple Epidemic Model with State-Dependent Delay

Dynamic behavior investigations of infectious disease models are central to improve our understanding of emerging characteristics of model states interaction. Here, we consider a Susceptible-Infected (SI) model with a general state-dependent delay, which covers an immuno-epidemiological model of pathogen transmission, developed in our early study, using a threshold delay to examine the effects of multiple exposures to a pathogen. The analysis in the previous work showed the appearance of forward as well as backward bifurcations of endemic equilibria when the basic reproductive ratio [Formula: see text] is less than unity. The analysis, in the present work, of the endemically infected equilibrium behavior, through the study of a second order exponential polynomial characteristic equation, concludes the existence of a Hopf bifurcation on the upper branch of the backward bifurcation diagram and gives the criteria for stability switches. Furthermore, the inclusion of state-dependent delays is shown to entirely change the dynamics of the SI model and give rise to rich behaviors including periodic, torus and chaotic dynamics.

Implications for infectious disease models of heterogeneous mixing on control thresholds.

Mixing among sub-populations, as well as heterogeneity in characteristics affecting their reproduction numbers, must be considered when evaluating public health interventions to prevent or control infectious disease outbreaks. In this overview, we apply a linear algebraic approach to re-derive some well-known results pertaining to preferential within- and proportionate among-group contacts in compartmental models of pathogen transmission. We give results for the meta-population effective reproduction number ([Formula: see text]) assuming different levels of vaccination in the sub-populations. Specifically, we unpack the dependency of [Formula: see text] on the fractions of contacts reserved for individuals within one's own subgroup and, by obtaining implicit expressions for the partial derivatives of [Formula: see text], we show that these increase as this preferential-mixing fraction increases in any sub-population.

Using 'sentinel' plants to improve early detection of invasive plant pathogens.

Infectious diseases of plants present an ongoing and increasing threat to international biosecurity, with wide-ranging implications. An important challenge in plant disease management is achieving early detection of invading pathogens, which requires effective surveillance through the implementation of appropriate monitoring programmes. However, when monitoring relies on visual inspection as a means of detection, surveillance is often hindered by a long incubation period (delay from infection to symptom onset) during which plants may be infectious but not displaying visible symptoms. 'Sentinel' plants-alternative susceptible host species that display visible symptoms of infection more rapidly-could be introduced to at-risk populations and included in monitoring programmes to act as early warning beacons for infection. However, while sentinel hosts exhibit faster disease progression and so allow pathogens to be detected earlier, this often comes at a cost: faster disease progression typically promotes earlier onward transmission. Here, we construct a computational model of pathogen transmission to explore this trade-off and investigate how including sentinel plants in monitoring programmes could facilitate earlier detection of invasive plant pathogens. Using Xylella fastidiosa infection in Olea europaea (European olive) as a current high profile case study, for which Catharanthus roseus (Madagascan periwinkle) is a candidate sentinel host, we apply a Bayesian optimisation algorithm to determine the optimal number of sentinel hosts to introduce for a given sampling effort, as well as the optimal division of limited surveillance resources between crop and sentinel plants. Our results demonstrate that including sentinel plants in monitoring programmes can reduce the expected prevalence of infection upon outbreak detection substantially, increasing the feasibility of local outbreak containment.

REVENGE OF THE TREES: ENVIRONMENTAL DETERMINANTS AND POPULATION EFFECTS OF INFECTIOUS DISEASE OUTBREAKS ON A BREEDING COLONY OF DOUBLE-CRESTED CORMORANTS (PHALACROCORAX AURITUS) OVER A PERIOD OF 21 YEARS.

During 16 of 21 consecutive annual breeding seasons, two diseases, Newcastle disease and avian cholera, killed approximately 50% of juvenile Double-crested Cormorants (Phalacrocorax auritus) in a large nesting colony in Canada. From 1994 to 2014, we recorded data annually on disease occurrence, causal pathogens, species and age classes affected, total number of breeding pairs of cormorants on the colony site, and other biological parameters. A mathematical model of pathogen transmission was constructed to assess the potential importance of transmission parameters and to test a hypothesis regarding the potential effect of the observed progressive loss of nest trees and the consequent shift from tree-nesting to ground-nesting behavior. The model indicated that juveniles from ground nests were 14 times more likely to die from epidemic disease (50.14% mortality) than were juveniles from nests in trees (3.57% mortality). Additive disease-related mortality of juvenile cormorants in the observed range of 40-60% would reduce a closed cormorant population over time. There was no directional change in the colony population during the study period, suggesting that immigration had compensated for disease-related mortality. Our results highlight the preeminent influence of environmental factors on pathogen transmission and the value of long-term data sets.

Elk migration influences the risk of disease spillover in the Greater Yellowstone Ecosystem.

Wildlife migrations provide important ecosystem services, but they are declining. Within the Greater Yellowstone Ecosystem (GYE), some elk Cervus canadensis herds are losing migratory tendencies, which may increase spatiotemporal overlap between elk and livestock (domestic bison Bison bison and cattle Bos taurus), potentially exacerbating pathogen transmission risk.We combined disease, movement, demographic and environmental data from eight elk herds in the GYE to examine the differential risk of brucellosis transmission (through aborted foetuses) from migrant and resident elk to livestock.For both migrants and residents, we found that transmission risk from elk to livestock occurred almost exclusively on private ranchlands as opposed to state or federal grazing allotments. Weather variability affected the estimated distribution of spillover risk from migrant elk to livestock, with a 7%–12% increase in migrant abortions on private ranchlands during years with heavier snowfall. In contrast, weather variability did not affect spillover risk from resident elk.Migrant elk were responsible for the majority (68%) of disease spillover risk to livestock because they occurred in greater numbers than resident elk. On a per‐capita basis, however, our analyses suggested that resident elk disproportionately contributed to spillover risk. In five of seven herds, we estimated that the per‐capita spillover risk was greater from residents than from migrants. Averaged across herds, an individual resident elk was 23% more likely than an individual migrant elk to abort on private ranchlands.Our results demonstrate links between migration behaviour, spillover risk and environmental variability, and highlight the utility of integrating models of pathogen transmission and host movement to generate new insights about the role of migration in disease spillover risk. Furthermore, they add to the accumulating body of evidence across taxa that suggests that migrants and residents should be considered separately during investigations of wildlife disease ecology. Finally, our findings have applied implications for elk and brucellosis in the GYE. They suggest that managers should prioritize actions that maintain spatial separation of elk and livestock on private ranchlands during years when snowpack persists into the risk period.

830. The effect of a long-stay patient on transmission of a pathogen to shorter-stay patients in a small multi-bed hospital unit: Implications for infection control

BackgroundObjective: To quantify the effect of a long-stay patient in a hospital unit on the likelihood of colonization of that patient and other patients in the unit.Prolonged hospital and intensive care unit (ICU) stays have been found to be risk factors for colonization and infection with bacteria such as carbapenem-resistant Enterobacteriaceae (CRE) for which there are limited treatment options and high mortality rates, making prevention of transmission and infection important public health objectives. Many studies have shown that long-stay patients (such as inpatient rehabilitation or skilled nursing facility patients) are at higher risk for hospital-acquired infections than short-stay patients, but the impact of long-stay patients on other patients in the same hospital unit has not received as much attention. Here, we consider a mathematical model of pathogen transmission within a hospital unit and assess the impact at different patient-patient transmission rates of a single long-stay patient on the probability that any other patient leaves the unit colonized with a pathogen.MethodsWe estimated the increased risk caused by a colonized long-stay patient on colonization of other patients using an ordinary differential equation Markov model with three mechanisms of colonization (pre-existing colonization, environmental transmission, and patient-patient transmission) with parameters previously estimated from a 13-bed hospital rehabilitation unit to evaluate the probability of exiting colonized.ResultsA single colonized long-stay patient increases the probability of each other patient exiting the unit colonized from 10.4% to 17.3%, a relative increase in risk of 1.4, and increases the expected number of colonized patients within the unit from 1.37 to 2.07 (not including the long-stay colonized patient).Probability of exiting colonized from a unit with a long-stay, initially colonized patient versus the probability of exiting colonized from a unit with no long-stay patient. The solid black line indicates a unit with regular turnover, whereas the dashed black line indicates a unit with a single long-stay, initially colonized patient. The vertical line shows the value of γ = 0.002 inferred from data from a 13-bed rehabilitation unit, and the shaded regime indicates the uncertainty in this estimate (0.000329, 0.003729). ConclusionColonized long-stay patients pose a risk to other uncolonized patients in the unit, especially at higher patient-patient transmission rates. Potential long-stay patients should be screened for CRE at entry and periodically during their stay because they are both at higher risk of colonization and also of transmitting bacteria to other patients. Consider increased surveillance, isolation, and/or decolonization of long-stay patients.Disclosures All Authors: No reported disclosures

Trait-based variation in host contribution to pathogen transmission across species and resource supplies.

Two key knowledge gaps currently limit the development of more predictive and general models of pathogen transmission: (1) the physiological basis of heterogeneity in host contribution to pathogen transmission (reservoir potential) remains poorly understood and (2) a general means of integrating the ecological dynamics of host communities has yet to emerge. If the traits responsible for differences in reservoir potential also modulate host community dynamics, these traits could be used to predict pathogen transmission as host communities change. In two greenhouse experiments, across 23 host species and two levels of resource supply, the reservoir potential of plant hosts increased significantly along the Leaf Economics Spectrum, a global axis of plant physiological trait covariation that features prominently in models of plant community ecology. This indicates that the traits of the Leaf Economics Spectrum underlie broad differences in reservoir potential across host species and resource supplies. Therefore, host traits could be used to integrate epidemiological models of pathogen transmission with ecological models of host community change.

Reactivation of latent infections with migration shapes population-level disease dynamics.

Annual migration is common across animal taxa and can dramatically shape the spatial and temporal patterns of infectious disease. Although migration can decrease infection prevalence in some contexts, these energetically costly long-distance movements can also have immunosuppressive effects that may interact with transmission processes in complex ways. Here, we develop a mechanistic model for the reactivation of latent infections driven by physiological changes or energetic costs associated with migration (i.e. 'migratory relapse') and its effects on disease dynamics. We determine conditions under which migratory relapse can amplify or reduce infection prevalence across pathogen and host traits (e.g. infectious periods, virulence, overwinter survival, timing of relapse) and transmission phenologies. We show that relapse at either the start or end of migration can dramatically increase prevalence across the annual cycle and may be crucial for maintaining pathogens with low transmissibility and short infectious periods in migratory populations. Conversely, relapse at the start of migration can reduce the prevalence of highly virulent pathogens by amplifying culling of infected hosts during costly migration, especially for highly transmissible pathogens and those transmitted during migration or the breeding season. Our study provides a mechanistic foundation for understanding the spatio-temporal patterns of relapsing infections in migratory hosts, with implications for zoonotic surveillance and understanding how infection patterns will respond to shifts in migratory propensity associated with environmental change. Further, our work suggests incorporating within-host processes into population-level models of pathogen transmission may be crucial for reconciling the range of migration-infection relationships observed across migratory species.

ESTIMATING AGE-SPECIFIC HAZARD RATES OF INFECTION FROM CROSS-SECTIONAL OBSERVATIONS.

Mathematical models of pathogen transmission in age-structured host populations, can be used to design or evaluate vaccination programs. For reliable results, their forces or hazard rates of infection (FOI) must be formulated correctly and the requisite contact rates and probabilities of infection on contact estimated from suitable observations. Elsewhere, we have described methods for calculating the probabilities of infection on contact from the contact rates and FOI. Here, we present methods for estimating the FOI from cross-sectional serological surveys or disease surveillance in populations with or without concurrent vaccination. We consider both continuous and discrete age, and present estimates of the FOI for vaccine-preventable diseases that confer temporary or permanent immunity.

Dynamic and integrative approaches to understanding pathogen spillover.

Dynamic and integrative approaches to understanding pathogen spillover.

Challenges and Opportunities Developing Mathematical Models of Shared Pathogens of Domestic and Wild Animals.

Diseases that affect both wild and domestic animals can be particularly difficult to prevent, predict, mitigate, and control. Such multi-host diseases can have devastating economic impacts on domestic animal producers and can present significant challenges to wildlife populations, particularly for populations of conservation concern. Few mathematical models exist that capture the complexities of these multi-host pathogens, yet the development of such models would allow us to estimate and compare the potential effectiveness of management actions for mitigating or suppressing disease in wildlife and/or livestock host populations. We conducted a workshop in March 2014 to identify the challenges associated with developing models of pathogen transmission across the wildlife-livestock interface. The development of mathematical models of pathogen transmission at this interface is hampered by the difficulties associated with describing the host-pathogen systems, including: (1) the identity of wildlife hosts, their distributions, and movement patterns; (2) the pathogen transmission pathways between wildlife and domestic animals; (3) the effects of the disease and concomitant mitigation efforts on wild and domestic animal populations; and (4) barriers to communication between sectors. To promote the development of mathematical models of transmission at this interface, we recommend further integration of modern quantitative techniques and improvement of communication among wildlife biologists, mathematical modelers, veterinary medicine professionals, producers, and other stakeholders concerned with the consequences of pathogen transmission at this important, yet poorly understood, interface.

Model of pathogen transmission between livestock and white-tailed deer in fragmented agricultural and forest landscapes

The study summarizes the current knowledge on infection and recovery of white-tailed deer and cattle, and integrates this knowledge into the Soil and Water Assessment Tool (SWAT) via a new add-on module SIR (Susceptible - Infected - Recovered) for predicting pathogen transmission between livestock and deer. New processes modeled by the SWAT-SIR model include: (a) seasonal changes in deer population and habitat; (b) resource selection and seasonal changes in foliage consumption by deer; (c) ingestion of pathogens with water, foliage and via grooming soiled hide by deer and grazing cattle; (d) infection and recovery of deer and co-grazing cattle; (e) pathogen shedding by infected animals; (f) survival of pathogens in manure; (g) kinetic release of pathogens from applied manure and fecal material. The model output is linked with ARC-GIS to allow spatial and temporal analysis of pathogen distribution across the watershed for specific land use, weather and management scenarios.

Modeling the potential impact of vaccination on the epidemiology of congenital cytomegalovirus infection

BackgroundUnderstanding the potential for vaccination to change cytomegalovirus (CMV) epidemiology is important for developing CMV vaccines and designing clinical trials. MethodsWe constructed a deterministic, age-specific and time-dependent mathematical model of pathogen transmission, parameterized using CMV seroprevalence from the United States and Brazil, to predict the impact of vaccination on congenital CMV infection. FindingsConcurrent vaccination of young children and adolescents would result in the greatest reductions in congenital CMV infections in populations with moderate and high baseline maternal seroprevalence. Such a vaccination strategy, assuming 70% vaccine efficacy, 90% coverage and 5-year duration of protection, could ultimately prevent 30–50% of congenital CMV infections. At equilibrium, this strategy could result in a 30% reduction in congenital CMV infections due to primary maternal infection in the United States but a 3% increase in Brazil. The potential for an increase in congenital CMV infections due to primary maternal infections in Brazil was not predicted with use of a vaccine that confers protection for greater than 5 years. InterpretationModeling suggests that vaccination strategies that include young children will result in greater declines in congenital CMV infection than those restricted to adolescents or women of reproductive age. Our study highlights the critical need for better understanding of the relative contribution of type of maternal infection to congenital CMV infection and disease, the main focus of vaccine prevention.

An immuno-epidemiological model with threshold delay: a study of the effects of multiple exposures to a pathogen

An immuno-epidemiological model of pathogen transmission is developed. This model incorporates two main features: (i) the epidemiological model includes within-host pathogen dynamics for an infectious disease, (ii) the susceptible individuals to the infection experience a series of exposures via the pathogen before becoming infectious. It is shown that this model leads naturally to a system of differential delay equations of the threshold type and that these equations can be transformed, in a biologically natural way, to differential equations with state-dependent delay. An interesting dynamical behavior of the model is the bistability phenomena, when the basic reproductive ratio R0 is less than unity, which raises many new challenges to effective infection control.

Social network analysis of wild chimpanzees provides insights for predicting infectious disease risk

1. Heterogeneity in host association patterns can alter pathogen transmission and strategies for control. Great apes are highly social and endangered animals that have experienced substantial population declines from directly transmitted pathogens; as such, network approaches to quantify contact heterogeneity could be crucially important for predicting infection probability and outbreak size following pathogen introduction, especially owing to challenges in collecting real-time infection data for endangered wildlife. 2. We present here the first study using network analysis to quantify contact heterogeneity in wild apes, with applications for predicting community-wide infectious disease risk. Specifically, within a wild chimpanzee community, we ask how associations between individuals vary over time, and we identify traits of highly connected individuals that might contribute disproportionately to pathogen spread. 3. We used field observations of behavioural encounters in a habituated wild chimpanzee community in Kibale National Park, Uganda to construct monthly party level (i.e. subgroup) and close-contact (i.e. ≤ 5 m) association networks over a 9-month period. 4. Network analysis revealed that networks were highly dynamic over time. In particular, oestrous events significantly increased pairwise party associations, suggesting that community-wide disease outbreaks should be more likely to occur when many females are in oestrus. 5. Bayesian models and permutation tests identified traits of chimpanzees that were highly connected within the network. Individuals with large families (i.e. mothers and their juveniles) that range in the core of the community territory and to a lesser extent high-ranking males were central to association networks, and thus represent the most important individuals to target for disease intervention strategies. 6. Overall, we show striking temporal variation in network structure and traits that predict association patterns in a wild chimpanzee community. These empirically-derived networks can inform dynamic models of pathogen transmission and have practical applications for infectious disease management of endangered wildlife species.

NosoSim: an agent-based model of nosocomial pathogens circulation in hospitals

Abstract Over the last decades, nosocomial infections have become a major Public Health problem in hospitals worldwide. In this context, it is very important to understand the underlying mechanisms of nosocomial pathogen transmission, in order to devise and assess the best control strategies. Here, we present an agent-based model of pathogen transmission in a hospital ward, NosoSim. In this model, the actions of patients and health-care workers are reproduced at all times in a spatially explicit environment, and pathogen colonization and transmission are simulated. NosoSim allows users to define simulation parameters such as: ward geography, including -but not limited to- the number of rooms; daily allocation and schedule of all present healthcare workers; patient status; microbiological and epidemiological characteristics of all circulating pathogens. After describing the model and its implementation, we illustrate its potential applications through an example addressing a real-life Public-health issue. In this application, NosoSim is used to assess the factors which promote so-called “super-spreading events” in hospital settings.

Development of a mathematical model for mechanical transmission of trypanosomes and other pathogens of cattle transmitted by tabanids

Mechanical transmission of pathogens by biting insects is a non-specific phenomenon in which pathogens are transmitted from the blood of an infected host to another host during interrupted feeding of the insects. A large range of pathogens can be mechanically transmitted, e.g. hemoparasites, bacteria and viruses. Some pathogens are almost exclusively mechanically transmitted, while others are also cyclically transmitted. For agents transmitted both cyclically and mechanically (mixed transmission), such as certain African pathogenic trypanosomes, the relative impact of mechanical versus cyclical transmission is essentially unknown. We have developed a mathematical model of pathogen transmission by a defined insect population to evaluate the importance of mechanical transmission. Based on a series of experiments aimed at demonstrating mechanical transmission of African trypanosomes by tabanids, the main parameters of the model were either quantified (host parasitaemia, mean individual insect burden, initial prevalence of infection) or estimated (unknown parameters). This model allows us to simulate the evolution of pathogen prevalence under various predictive circumstances, including control measures and could be used to assess the risk of mechanical transmission under field conditions. If adjustments of parameters are provided, this model could be generalized to other pathogenic agents present in the blood of their hosts (Bovine Leukemia virus, Anaplasma, etc.) or other biting insects such as biting muscids (stomoxyines) and hippoboscids.

MOSQUITOES DO SENESCE: DEPARTURE FROM THE PARADIGM OF CONSTANT MORTALITY

Although variation in mortality is considered by virtually all vector-borne disease specialists to be one of the most important determinants of an arthropod's capacity to transmit pathogens, the operational assumption often is that insect vector mortality is independent of age. Acceptance of the non-senescence assumption leads to the erroneous conclusion that mosquito age is unimportant, results in misleading predictions regarding disease reductions after vector control, and represses study of other aspects of mosquito biology that change with age. We brought large-scale laboratory life table techniques (N > 100,000) to bear on the question of age-dependent mortality in the mosquito vector of dengue virus, Aedes aegypti. Mortality was highly age dependent in both sexes. Mortality was low at young ages (< 10 days old), steadily increased at middle ages, and decelerated at older ages. A newly derived age-dependent model of pathogen transmission shows the importance of young mosquitoes and population age structure to transmission dynamics. Departure from the age-independent mortality paradigm encourages research on overlooked complexities in mosquito biology, the need for innovative methods to study mosquito population dynamics, and the need to study age-dependent changes for an accurate understanding of mosquito biology and pathogen transmission.