The Context-Dependent Effects of Host Competence, Competition, and Pathogen Transmission Mode on Disease Prevalence

Evolution of pathogen tolerance and reproductive trade-off implications

We develop an epidemic model that accounts explicitly for the pathogen pool and incorporates population variations in host defense strategy, measured in disease tolerance that is assumed to be perfectly inherited by offspring. Although the proposed model is more general, it is motivated by the devastating Batrachochytrium dendrobatidis (Bd) fungus that is responsible for severe declines in amphibians. We show that the model's basic reproduction number consists of a weighted average of individual basic reproduction numbers associated to each tolerance class. If the individual basic reproduction number associated to the highest tolerance level is less than one, then any solution converges to a (non-unique) disease-free equilibrium. We show that in the absence of a trade-off, different host defense strategies can coexist as long as the disease will go extinct eventually. In contrast, if the disease persists, the set of pandemic equilibria consists of isolated vertex equilibria, implying the survival of an individual host defense strategy. The pandemic equilibrium corresponding to the highest tolerance, i.e., lowest disease-induced death rate is the only asymptotically stable pandemic equilibrium. Additionally, to investigate the impact of a trade-off, we incorporate a tolerance cost in reproduction, whereby a higher tolerance comes at the expense of a lower reproductive rate. Now, the coexistence of host defense strategies in the absence of the disease is no longer supported. However, the set of pandemic equilibria increases in richness to contain equilibria where different tolerance classes are present.

How do non-independent host movements affect spatio-temporal disease dynamics? Partitioning the contributions of spatial overlap and correlated movements to transmission risk

BACKGROUND

Despite decades of epidemiological theory making relatively simple assumptions about host movements, it is increasingly clear that non-random movements drastically affect disease transmission. To better predict transmission risk, theory is needed that quantifies the contributions of both fine-scale host space use and non-independent, correlated host movements to epidemiological dynamics.

METHODS

We developed and applied new theory that quantifies relative contributions of fine-scale space use and non-independent host movements to spatio-temporal transmission risk. Our theory decomposes pairwise spatio-temporal transmission risk into two components: (i) spatial overlap of hosts-a classic metric of spatial transmission risk - and (ii) pairwise correlations in space use - a component of transmission risk that is almost universally ignored. Using analytical results, simulations, and empirical movement data, we ask: under what ecological and epidemiological conditions do non-independent movements substantially alter spatio-temporal transmission risk compared to spatial overlap?

RESULTS

Using theory and simulation, we found that for directly transmitted pathogens even weak pairwise correlations in space use among hosts can increase contact and transmission risk by orders of magnitude compared to independent host movements. In contrast, non-independent movements had reduced importance for transmission risk for indirectly transmitted pathogens. Furthermore, we found that if the scale of pathogen transmission is smaller than the scale where host social decisions occur, host movements can be highly correlated but this correlation matters little for transmission. We applied our theory to GPS movement data from white-tailed deer (Odocoileus virginianus). Our approach predicted highly seasonally varying contributions of the spatial and social drivers of transmission risk - with social interactions augmenting transmission risk between hosts by greater than a factor of 10 in some cases, despite similar degrees of spatial overlap. Moreover, social interactions could lead to a distinct shift in the predicted locations of transmission hotspots, compared to joint space use.

CONCLUSIONS

Our theory provides clear expectations for when non-independent movements alter spatio-temporal transmission risk, showing that correlated movements can reshape epidemiological landscapes, creating transmission hotspots whose magnitude and location are not necessarily predictable from spatial overlap.

The reverse zoonotic potential of SARS-CoV-2

There has been considerable emphasis recently on the zoonotic origins of emerging infectious diseases in humans, including the SARS-CoV-2 pandemic; however, reverse zoonoses (infections transmitted from humans to other animals) have received less attention despite their potential importance. The effects can be devastating for the infected species and can also result in transmission of the pathogen back to human populations or other animals either in the original form or as a variant. Humans have transmitted SARS-CoV-2 to other animals, and the virus is able to circulate and evolve in those species. As global travel resumes, the potential of SARS-CoV-2 as a reverse zoonosis threatens humans and endangered species. Nonhuman primates are of particular concern given their susceptibility to human respiratory infections. Enforcing safety measures for all people working in and visiting wildlife areas, especially those with nonhuman primates, and increasing access to safety measures for people living near protected areas that are home to nonhuman primates will help mitigate reverse zoonotic transmission.

Rapid Evolution of Resistance and Tolerance Leads to Variable Host Recoveries following Disease-Induced Declines

AbstractRecoveries of populations that have suffered severe disease-induced declines are being observed across disparate taxa. Yet we lack theoretical understanding of the drivers and dynamics of recovery in host populations and communities impacted by infectious disease. Motivated by disease-induced declines and nascent recoveries in amphibians, we developed a model to ask the following question: How does the rapid evolution of different host defense strategies affect the transient recovery trajectories of hosts following pathogen invasion and disease-induced declines? We found that while host life history is predictably a major driver of variability in population recovery trajectories (including declines and recoveries), populations that use different host defense strategies (i.e., tolerance, avoidance resistance, and intensity-reduction resistance) experience notably different recoveries. In single-species host populations, populations evolving tolerance recovered on average four times slower than populations evolving resistance. Moreover, while populations using avoidance resistance strategies had the fastest potential recovery rates, these populations could get trapped in long transient states at low abundance prior to recovery. In contrast, the recovery of populations evolving intensity-reduction resistance strategies were more consistent across ecological contexts. Overall, host defense strategies strongly affect the transient dynamics of population recovery and may affect the ultimate fate of real populations recovering from disease-induced declines.

Impact of vector richness on the risk of vector-borne disease: The role of vector competence

A central goal of disease ecology is to identify the factors that drive the spread of infectious diseases. Changes in vector richness can have complex effects on disease risk, but little is known about the role of vector competence in the relationship between vector richness and disease risk. In this study, we firstly investigated the combined effects of vector competence, interspecific competition, and feeding interference on disease risk through a two-vector, one-host SIR-SI model, and obtained threshold conditions for the occurrence of dilution and amplification effects. Secondly, we extended the above model to the case of N vectors and assumed that all vectors were homogeneous to obtain analytic expressions for disease risk. It was found that in the two-vector model, disease risk declined more rapidly as interspecific competition of the high-competence vector increased. When vector richness increases, the positive effects of adding a high-competence vector species on disease transmission may outweigh the negative effects of feeding interference due to increased vector richness, making an amplification effect more likely to occur. While the addition of a highly competitive vector species may exacerbate the negative effects of feeding interference, making a dilution effect more likely to occur. In the N-vector model, the effect of increased vector richness on disease risk was fully driven by the strength of feeding interference and interspecific competition, and changes in vector competence only quantitatively but not qualitatively altered the vector richness-disease risk relationship. This work clarifies the role of vector competence in the relationship between vector richness and disease risk and provides a new perspective for studying the diversity-disease relationship. It also provides theoretical guidance for vector management and disease prevention strategies.

A novel method to jointly estimate transmission rate and decay rate parameters in environmental transmission models

In environmental transmission, pathogens transfer from one individual to another via the environment. It is a common transmission mechanism in a wide range of host-pathogen systems. Incorporating environmental transmission in dynamic transmission models is crucial for gauging the effect of interventions, as extrapolating model results to new situations is only valid when the mechanisms are modelled correctly. The challenge in environmental transmission models lies in not jointly identifiable parameters for pathogen shedding, decay, and transmission dynamics. To solve this unidentifiability issue, we present a stochastic environmental transmission model with a novel scaling method for shedding rate parameter and a novel estimation method that distinguishes transmission rate and decay rate parameters. The core of our scaling and estimation method is calculating exposure and relating exposure to infection risks. By scaling shedding rate parameter, we standardize exposure to pathogens contributed by one infectious individual present during one time interval to one. The standardized exposure leads to a standard definition of transmission rate parameter applicable to scenarios with different decay rate parameters. Hence, we unify direct transmission (large decay rate) and environmental transmission in a continuous manner. More importantly, our exposure-based estimation method can correctly estimate back the transmission rate and the decay rate parameters, while the commonly used trajectory-based method failed. The reason is that exposure-based method gives the correct weight to infection data from previous observation periods. The correct estimation from exposure-based method will lead to more reliable predictions of intervention impact. Using the effect of disinfection as an example, we show how incorrectly estimated parameters may lead to incorrect conclusions about the effectiveness of interventions. This illustrates the importance of correct estimation of transmission rate and decay rate parameters for extrapolating environmental transmission models and predicting intervention effects.

Host community composition, community assembly pattern, and disease transmission mode jointly determine the direction and strength of the diversity-disease relationship

Rapid global biodiversity loss and increasing emerging infectious diseases underscore the significance of identifying the diversity-disease relationship. Although experimental evidence supports the existence of dilution effects in several natural ecosystems, we still know very little about the conditions under which a dilution effect will occur. Using a multi-host Susceptible-Infected-Recovered model, we found when disease transmission was density-dependent, the diversity-disease relationship could exhibit an increasing, decreasing, or non-monotonic trend, which mainly depended on the patterns of community assembly. However, the combined effects of the host competence-abundance relationship and species extinction order may reverse or weaken this trend. In contrast, when disease transmission was frequency-dependent, the diversity-disease relationship only showed a decreasing trend, the host competence-abundance relationship and species extinction order did not alter this decreasing trend, but it could reduce the detectability of the dilution effect and affect disease prevalence. Overall, a combination of disease transmission mode, community assembly pattern, and host community composition determines the direction or strength of the diversity-disease relationship. Our work helps explain why previous studies came to different conclusions about the diversity-disease relationship and provides a deeper understanding of the pathogen transmission dynamics in actual communities.

Host competence, interspecific competition and vector preference interact to determine the vector-borne infection ecology

Understanding how ecological interactions affect vector-borne disease dynamics is crucial in the context of rapid biodiversity loss and increased emerging vector-borne diseases. Although there have been many studies on the impact of interspecific competition and host competence on disease dynamics, few of them have addressed the case of a vector-borne disease. Using a simple compartment model with two competing host species and one vector, we investigated the combined effects of vector preference, host competence, and interspecific competition on disease risk in a vector-borne system. Our research demonstrated that disease transmission dynamics in multi-host communities are more complex than anticipated. Vector preference and differences in host competence shifted the direction of the effect of competition on community disease risk, yet interspecific competition quantitatively but not qualitatively changed the effect of vector preference on disease risk. Our work also identified the conditions of the dilution effect and amplification effect in frequency-dependent transmission mode, and we discovered that adding vector preference and interspecific competition into a simple two-host-one-vector model altered the outcomes of how increasing species richness affects disease risk. Our work explains some of the variation in outcomes in previous empirical and theoretical studies on the dilution effect.

High prevalence does not necessarily equal maintenance species: Avoiding biased claims of disease reservoirs when using surveillance data

1. Many pathogens of public health and conservation concern persist in host communities. Identifying candidate maintenance and reservoir species is therefore a central component of disease management. The term maintenance species implies that if all species but the putative maintenance species were removed, then the pathogen would still persist. In the absence of field manipulations, this statement inherently requires a causal or mechanistic model to assess. 2. However, we lack a systematic understanding of i) how often conclusions are made about maintenance and reservoir species without reference to mechanistic models ii) what types of biases may be associated with these conclusions and iii) how explicitly invoking causal or mechanistic modeling can help ameliorate these biases. Filling these knowledge gaps is critical for robust inference on pathogen persistence and spillover in multihost parasite systems, with clear implications for human and wildlife health. 3. To address these gaps, we performed a literature review on the evidence previous studies have used to make claims regarding maintenance or reservoir species. We then developed a multihost-parasite model to explore and demonstrate common biases that could arise when inferring maintenance potential from observational prevalence data. Finally, we developed new theory to show how model-driven inference of maintenance species can minimize and eliminate emergent biases. 4. In our review, we found that 83% of studies used some form of observational prevalence data to draw conclusions on maintenance potential and only 6% of these studies combined observational data with mechanistic modeling. Using our model, we demonstrate how the community, spatial, and temporal context of observational data can lead to substantial biases in inferences of maintenance potential. Importantly, our theory identifies that model-driven inference of maintenance species elucidates other streams of observational data that can be leveraged to correct these biases. 5. Model-driven inference is an essential, yet underused, component of multidisciplinary studies that make inference on host reservoir and maintenance species. Better integration of wildlife disease surveillance and mechanistic models is necessary to improve the robustness and reproducibility of our conclusions regarding maintenance and reservoir species.

Variability in environmental persistence but not per capita transmission rates of the amphibian chytrid fungus leads to differences in host infection prevalence

Heterogeneities in infections among host populations may arise through differences in environmental conditions through two mechanisms. First, environmental conditions may alter host exposure to pathogens via effects on survival. Second, environmental conditions may alter host susceptibility, making infection more or less likely if contact between a host and pathogen occurs. Further, host susceptibility might be altered through acquired resistance, which hosts can develop, in some systems, through exposure to dead or decaying pathogens and their metabolites. Environmental conditions may alter the rates of pathogen decomposition, influencing the likelihood of hosts developing acquired resistance. The present study primarily tests how environmental context influences the relative contributions of pathogen survival and per capita transmission on host infection prevalence using the amphibian chytrid fungus (Batrachochytrium dendrobatidis; Bd) as a model system. Secondarily, we evaluate how environmental context influences the decomposition of Bd because previous studies have shown that dead Bd and its metabolites can illicit acquired resistance in hosts. We conducted Bd survival and infection experiments and then fit models to discern how Bd mortality, decomposition and per capita transmission rates vary among water sources [e.g. artificial spring water (ASW) or water from three ponds]. We found that infection prevalence differed among water sources, which was driven by differences in mortality rates of Bd, rather than differences in per capita transmission rates. Bd mortality rates varied among pond water treatments and were lower in ASW compared to pond water. These results suggest that variation in Bd infection dynamics could be a function of environmental factors in waterbodies that result in differences in exposure of hosts to live Bd. In contrast to the persistence of live Bd, we found that the rates of decomposition of dead Bd did not vary among water sources, which may suggest that exposure of hosts to dead Bd or its metabolites might not commonly vary among nearby sites. Ultimately, a mechanistic understanding of the environmental dependence of free-living pathogens could lead to a deeper understanding of the patterns of outbreak heterogeneity, which could inform surveillance and management strategies.

Using sensitivity analysis to identify factors promoting higher versus lower infection prevalence in multi-host communities

Relationships between host species richness and levels of disease in a focal host are likely to be context-dependent, depending on the characteristics of which particular host species are present in a community. I use a multi-host epidemiological model with environmental transmission to explore how the characteristics of the host species (e.g., competence and competitive ability), host density, and the pathogen transmission mechanism affect the proportion of infected individuals (i.e., infection prevalence) in a focal host. My sensitivity-based approach identifies the indirect pathways through which specific ecological and epidemiological processes affect focal host infection prevalence. This in turn yields predictions about the context-dependent rules governing whether increased host species richness increases (amplifies) or decreases (dilutes) infection prevalence in a focal host. For example, in many cases, amplification and dilution are predicted to occur when added host species are sources or sinks of infectious propagules, respectively. However, if the added host species have strong and asymmetric competitive effects on resident host species, then amplification and dilution are predicted to occur when the added host species have stronger competitive effects on resident host species that are sources or sinks of infectious propagules, respectively. My results also predict that greater dilution and less amplification is more likely to occur under frequency-dependent direct transmission than density-dependent direct transmission when (i) the added hosts have lower competence than resident host species and (ii) interspecific competition between the added host species and resident host species is lower; the opposite conditions promote greater amplification and less dilution under frequency-dependent direct transmission. This work helps identify and explain the mechanisms shaping the context-dependent relationships between host species richness and disease in multi-host communities.