Genotype and sex-based host variation in behaviour and susceptibility drives population disease dynamics

Host heterogeneity in pathogen transmission is widespread and presents a major hurdle to predicting and minimizing disease outbreaks. Using Drosophila melanogaster infected with Drosophila C virus as a model system, we integrated experimental measurements of social aggregation, virus shedding, and disease-induced mortality from different genetic lines and sexes into a disease modelling framework. The experimentally measured host heterogeneity produced substantial differences in simulated disease outbreaks, providing evidence for genetic and sex-specific effects on disease dynamics at a population level. While this was true for homogeneous populations of single sex/genetic line, the genetic background or sex of the index case did not alter outbreak dynamics in simulated, heterogeneous populations. Finally, to explore the relative effects of social aggregation, viral shedding and mortality, we compared simulations where we allowed these traits to vary, as measured experimentally, to simulations where we constrained variation in these traits to the population mean. In this context, variation in infectiousness, followed by social aggregation, was the most influential component of transmission. Overall, we show that host heterogeneity in three host traits dramatically affects population-level transmission, but the relative impact of this variation depends on both the susceptible population diversity and the distribution of population-level variation.

Download Full-text

Population-Level Disease Dynamics Reflect Individual Heterogeneities in Transmission

10.1101/735480 ◽

2019 ◽

Author(s):

Jonathon A. Siva-Jothy ◽

Lauren A. White ◽

Meggan E. Craft ◽

Pedro F. Vale

Keyword(s):

Social Network ◽

Disease Transmission ◽

Network Connectivity ◽

Population Level ◽

Disease Dynamics ◽

Virus Shedding ◽

Transmission Potential ◽

Social Aggregation ◽

Individual Host ◽

Host Heterogeneity

AbstractHost heterogeneity in disease transmission is widespread and presents a major hurdle to predicting and minimizing pathogen spread. Using the Drosophila melanogaster model system infected with Drosophila C virus, we integrate experimental measurements of individual host heterogeneity in social aggregation, virus shedding, and disease-induced mortality into an epidemiological framework that simulates outbreaks of infectious disease. We use these simulations to calculate individual variation in disease transmission and apportion this variation to specific components of transmission: social network degree distribution, infectiousness, and infection duration. The experimentally-observed variation produces substantial differences in individual transmission potential, providing evidence for genetic and sex-specific effects on disease dynamics at a population level. Manipulating variation in social network connectivity, infectiousness, and infection duration in simulated populations reveals that these components affect disease transmission in clear and distinct ways. We consider the implications of this genetic and sex-specific variation in disease transmission and discuss implications for appropriate control methods given the relative contributions made by social aggregation, virus shedding, and infection duration to transmission in other host-pathogen systems.

Download Full-text

Dissecting genetic and sex-specific sources of host heterogeneity in pathogen shedding and spread

PLoS Pathogens ◽

10.1371/journal.ppat.1009196 ◽

2021 ◽

Vol 17 (1) ◽

pp. e1009196

Author(s):

Jonathon A. Siva-Jothy ◽

Pedro F. Vale

Keyword(s):

Viral Load ◽

Individual Variation ◽

Disease Transmission ◽

Genetic Background ◽

Pathogen Transmission ◽

Published Data ◽

Virus Shedding ◽

Transmission Potential ◽

Sources Of Variation ◽

Host Heterogeneity

Host heterogeneity in disease transmission is widespread but precisely how different host traits drive this heterogeneity remains poorly understood. Part of the difficulty in linking individual variation to population-scale outcomes is that individual hosts can differ on multiple behavioral, physiological and immunological axes, which will together impact their transmission potential. Moreover, we lack well-characterized, empirical systems that enable the quantification of individual variation in key host traits, while also characterizing genetic or sex-based sources of such variation. Here we used Drosophila melanogaster and Drosophila C Virus as a host-pathogen model system to dissect the genetic and sex-specific sources of variation in multiple host traits that are central to pathogen transmission. Our findings show complex interactions between genetic background, sex, and female mating status accounting for a substantial proportion of variance in lifespan following infection, viral load, virus shedding, and viral load at death. Two notable findings include the interaction between genetic background and sex accounting for nearly 20% of the variance in viral load, and genetic background alone accounting for ~10% of the variance in viral shedding and in lifespan following infection. To understand how variation in these traits could generate heterogeneity in individual pathogen transmission potential, we combined measures of lifespan following infection, virus shedding, and previously published data on fly social aggregation. We found that the interaction between genetic background and sex explained ~12% of the variance in individual transmission potential. Our results highlight the importance of characterising the sources of variation in multiple host traits to understand the drivers of heterogeneity in disease transmission.

Download Full-text

Deposition of pathogenic Mycoplasma gallisepticum onto bird feeders: host pathology is more important than temperature-driven increases in food intake

Biology Letters ◽

10.1098/rsbl.2013.0594 ◽

2013 ◽

Vol 9 (5) ◽

pp. 20130594 ◽

Cited By ~ 19

Author(s):

James S. Adelman ◽

Amanda W. Carter ◽

William A. Hopkins ◽

Dana M. Hawley

Keyword(s):

Food Intake ◽

Ambient Temperature ◽

Disease Transmission ◽

Population Level ◽

Mycoplasma Gallisepticum ◽

Pathogen Transmission ◽

Disease Dynamics ◽

Transmission Potential ◽

Thermoneutral Zone ◽

Bird Feeders

Although ambient temperature has diverse effects on disease dynamics, few studies have examined how temperature alters pathogen transmission by changing host physiology or behaviour. Here, we test whether reducing ambient temperature alters host foraging, pathology and the potential for fomite transmission of the bacterial pathogen Mycoplasma gallisepticum (MG), which causes seasonal outbreaks of severe conjunctivitis in house finches ( Haemorhous mexicanus ). We housed finches at temperatures within or below the thermoneutral zone to manipulate food intake by altering energetic requirements of thermoregulation. We predicted that pathogen deposition on bird feeders would increase with temperature-driven increases in food intake and with conjunctival pathology. As expected, housing birds below the thermoneutral zone increased food consumption. Despite this difference, pathogen deposition on feeders did not vary across temperature treatments. However, pathogen deposition increased with conjunctival pathology, independently of temperature and pathogen load, suggesting that MG could enhance its transmission by increasing virulence. Our results suggest that in this system, host physiological responses are more important for transmission potential than temperature-dependent alterations in feeding. Understanding such behavioural and physiological contributions to disease transmission is critical to linking individual responses to climate with population-level disease dynamics.

Download Full-text

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

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2020.1829 ◽

2020 ◽

Vol 287 (1935) ◽

pp. 20201829 ◽

Cited By ~ 1

Author(s):

Daniel J. Becker ◽

Ellen D. Ketterson ◽

Richard J. Hall

Keyword(s):

Mechanistic Model ◽

Temporal Patterns ◽

Population Level ◽

Pathogen Transmission ◽

Infection Prevalence ◽

Disease Dynamics ◽

Latent Infections ◽

Spatial And Temporal Patterns ◽

Long Distance ◽

Spatio Temporal

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.

Download Full-text

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

10.1101/2020.05.12.091736 ◽

2020 ◽

Cited By ~ 1

Author(s):

Daniel J. Becker ◽

Ellen D. Ketterson ◽

Richard J. Hall

Keyword(s):

Mechanistic Model ◽

Population Level ◽

Pathogen Transmission ◽

Infection Prevalence ◽

Disease Dynamics ◽

Latent Infections ◽

Migratory Species ◽

Spatial And Temporal Patterns ◽

Long Distance ◽

Highly Virulent

AbstractAnnual 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 spatiotemporal 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.

Download Full-text

The role of habitat configuration in shaping animal population processes: a framework to generate quantitative predictions

Oecologia ◽

10.1007/s00442-021-04967-y ◽

2021 ◽

Author(s):

Peng He ◽

Pierre-Olivier Montiglio ◽

Marius Somveille ◽

Mauricio Cantor ◽

Damien R. Farine

Keyword(s):

Population Level ◽

R Package ◽

Habitat Configuration ◽

Modelling Framework ◽

Alternative Habitat ◽

Animal Populations ◽

Research Questions ◽

Habitat Networks ◽

Animal Habitat

AbstractBy shaping where individuals move, habitat configuration can fundamentally structure animal populations. Yet, we currently lack a framework for generating quantitative predictions about the role of habitat configuration in modulating population outcomes. To address this gap, we propose a modelling framework inspired by studies using networks to characterize habitat connectivity. We first define animal habitat networks, explain how they can integrate information about the different configurational features of animal habitats, and highlight the need for a bottom–up generative model that can depict realistic variations in habitat potential connectivity. Second, we describe a model for simulating animal habitat networks (available in the R package AnimalHabitatNetwork), and demonstrate its ability to generate alternative habitat configurations based on empirical data, which forms the basis for exploring the consequences of alternative habitat structures. Finally, we lay out three key research questions and demonstrate how our framework can address them. By simulating the spread of a pathogen within a population, we show how transmission properties can be impacted by both local potential connectivity and landscape-level characteristics of habitats. Our study highlights the importance of considering the underlying habitat configuration in studies linking social structure with population-level outcomes.

Download Full-text

Protective population behavior change in outbreaks of emerging infectious disease

BMC Infectious Diseases ◽

10.1186/s12879-021-06299-x ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Evans K. Lodge ◽

Annakate M. Schatz ◽

John M. Drake

Keyword(s):

Behavior Change ◽

Ebola Virus ◽

Virus Disease ◽

Population Level ◽

Emerging Infections ◽

Emerging Pathogens ◽

Susceptible Population ◽

Zoonotic Infections ◽

Zoonotic Viruses ◽

And Behavior

Abstract Background During outbreaks of emerging and re-emerging infections, the lack of effective drugs and vaccines increases reliance on non-pharmacologic public health interventions and behavior change to limit human-to-human transmission. Interventions that increase the speed with which infected individuals remove themselves from the susceptible population are paramount, particularly isolation and hospitalization. Ebola virus disease (EVD), Severe Acute Respiratory Syndrome (SARS), and Middle East Respiratory Syndrome (MERS) are zoonotic viruses that have caused significant recent outbreaks with sustained human-to-human transmission. Methods This investigation quantified changing mean removal rates (MRR) and days from symptom onset to hospitalization (DSOH) of infected individuals from the population in seven different outbreaks of EVD, SARS, and MERS, to test for statistically significant differences in these metrics between outbreaks. Results We found that epidemic week and viral serial interval were correlated with the speed with which populations developed and maintained health behaviors in each outbreak. Conclusions These findings highlight intrinsic population-level changes in isolation rates in multiple epidemics of three zoonotic infections with established human-to-human transmission and significant morbidity and mortality. These data are particularly useful for disease modelers seeking to forecast the spread of emerging pathogens.

Download Full-text

PDE limits of stochastic SIS epidemics on networks

Journal of Complex Networks ◽

10.1093/comnet/cnaa043 ◽

2020 ◽

Vol 8 (4) ◽

Author(s):

F Di Lauro ◽

J-C Croix ◽

L Berthouze ◽

I Z Kiss

Keyword(s):

Network Inference ◽

Mean Field ◽

Population Level ◽

Death Process ◽

Disease Dynamics ◽

Epidemic Outbreak ◽

Numerical Tests ◽

Mean Field Models ◽

Stochastic Epidemic ◽

Fokker Planck Equations

Abstract Stochastic epidemic models on networks are inherently high-dimensional and the resulting exact models are intractable numerically even for modest network sizes. Mean-field models provide an alternative but can only capture average quantities, thus offering little or no information about variability in the outcome of the exact process. In this article, we conjecture and numerically demonstrate that it is possible to construct partial differential equation (PDE)-limits of the exact stochastic susceptible-infected-susceptible epidemics on Regular, Erdős–Rényi, Barabási–Albert networks and lattices. To do this, we first approximate the exact stochastic process at population level by a Birth-and-Death process (BD) (with a state space of $O(N)$ rather than $O(2^N)$) whose coefficients are determined numerically from Gillespie simulations of the exact epidemic on explicit networks. We numerically demonstrate that the coefficients of the resulting BD process are density-dependent, a crucial condition for the existence of a PDE limit. Extensive numerical tests for Regular, Erdős–Rényi, Barabási–Albert networks and lattices show excellent agreement between the outcome of simulations and the numerical solution of the Fokker–Planck equations. Apart from a significant reduction in dimensionality, the PDE also provides the means to derive the epidemic outbreak threshold linking network and disease dynamics parameters, albeit in an implicit way. Perhaps more importantly, it enables the formulation and numerical evaluation of likelihoods for epidemic and network inference as illustrated in a fully worked out example.

Download Full-text

Covariation between the physiological and behavioral components of pathogen transmission: host heterogeneity determines epidemic outcomes

Oikos ◽

10.1111/oik.04527 ◽

2017 ◽

Vol 127 (4) ◽

pp. 538-552 ◽

Cited By ~ 12

Author(s):

Lauren A. White ◽

James D. Forester ◽

Meggan E. Craft

Keyword(s):

Pathogen Transmission ◽

Host Heterogeneity

Download Full-text

Temporal Evolution of Immunity Distributions in a Population with Waning and Boosting

Complexity ◽

10.1155/2018/9264743 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13

Author(s):

M. V. Barbarossa ◽

M. Polner ◽

G. Röst

Keyword(s):

Mathematical Model ◽

Steady State ◽

Temporal Evolution ◽

Disease Outbreaks ◽

Population Level ◽

Coupled System ◽

Disease Outbreak ◽

Periodic Disease ◽

Evolution Of Immunity

We investigate the temporal evolution of the distribution of immunities in a population, which is determined by various epidemiological, immunological, and demographical phenomena: after a disease outbreak, recovered individuals constitute a large immune population; however, their immunity is waning in the long term and they may become susceptible again. Meanwhile, their immunity can be boosted by repeated exposure to the pathogen, which is linked to the density of infected individuals present in the population. This prolongs the length of their immunity. We consider a mathematical model formulated as a coupled system of ordinary and partial differential equations that connects all these processes and systematically compare a number of boosting assumptions proposed in the literature, showing that different boosting mechanisms lead to very different stationary distributions of the immunity at the endemic steady state. In the situation of periodic disease outbreaks, the waveforms of immunity distributions are studied and visualized. Our results show that there is a possibility to infer the boosting mechanism from the population level immune dynamics.

Download Full-text