2019-EEID US-UK Heterogeneities, Diversity and the Evolution of Infectious Disease
Lead Research Organisation:
Heriot-Watt University
Department Name: S of Mathematical and Computer Sciences
Abstract
Infectious disease transmission within populations rarely approximates as a homogenous, random "well mixed" process. Individual hosts vary in their susceptibility and transmissibility through genetic and epigenetic effects, their condition and their immune memory. In addition, variation in disease contacts is typical within populations due to how individuals are arranged in space, how they move and how they interact socially generating population structure even in the absence of heterogeneities in the environment. These individual heterogeneities and the heterogeneity in transmission due to population structure interact with variation in specificity between host genotypes and parasites strains in most if not all systems. We have an incomplete theory on what each of these three sources of heterogeneities - individual, population and interaction - have on the epidemiology of disease. For example, the role of 'superspreaders' in the SARS is a classic example of the importance of heterogeneity in disease contacts to epidemic outcomes. Furthermore, we have relatively little understanding of the implications of such heterogeneity to the evolution of infectious disease beyond theory and limited experimental tests on the role of regular spatial structure. In particular, how different heterogeneities interact to determine the evolution of disease virulence and host defense is little studied. New theory is therefore required to test their implications to the long-term evolutionary outcomes, but perhaps more importantly we need to understand transient short-term responses. The key intellectual challenges that this proposal will address are to (1) extend existing theory to understand the impact of population and individual level heterogeneities and their interaction on the evolution of host and parasite traits, (2) develop new theoretical methods to predict the transient evolutionary behavior, (3) test these predictions in a tractable laboratory model system and (4) apply the theory to agricultural systems to understand the role of agri-evolutionary feedbacks and management imposed heterogeneities on the evolution infectious disease.
Technical Summary
Infectious disease transmission within populations rarely approximates as a homogenous, random "well mixed" process. Individual hosts vary in their susceptibility and transmissibility through genetic and epigenetic effects, their condition and their immune memory. In addition, variation in disease contacts is typical within populations due to how individuals are arranged in space, how they move and how they interact socially generating population structure even in the absence of heterogeneities in the environment. We have an incomplete theory on what each of these three sources of heterogeneities - individual, population and interaction - have on the epidemiology of disease.
Our understanding of the role that population structure plays in shaping the evolution of infectious disease remains superficial for a number of reasons: (1) the existing theory remains poorly tested, (2) the focus has been on relatively simple spatial rather than more realistic population structures, (3) the theory has focused on optimal trait evolution rather than on host-parasite diversity or disease emergence and (4) there is little application of the theory, particularly on agricultural systems, and therefore the relevance of the theory to real diseases is unclear.
We propose general theory that examines the impact of heterogeneities and diversity on the evolution of infectious disease. We will test the predictions of the general theory in manipulative evolution experiments using our established laboratory spatial host-parasite experimental system (Plodia interpunctella/PiGV system). We will apply the theory to agricultural systems to understand the role of agri-evolutionary feedbacks and management imposed heterogeneities on the evolution infectious disease.
Our understanding of the role that population structure plays in shaping the evolution of infectious disease remains superficial for a number of reasons: (1) the existing theory remains poorly tested, (2) the focus has been on relatively simple spatial rather than more realistic population structures, (3) the theory has focused on optimal trait evolution rather than on host-parasite diversity or disease emergence and (4) there is little application of the theory, particularly on agricultural systems, and therefore the relevance of the theory to real diseases is unclear.
We propose general theory that examines the impact of heterogeneities and diversity on the evolution of infectious disease. We will test the predictions of the general theory in manipulative evolution experiments using our established laboratory spatial host-parasite experimental system (Plodia interpunctella/PiGV system). We will apply the theory to agricultural systems to understand the role of agri-evolutionary feedbacks and management imposed heterogeneities on the evolution infectious disease.
People |
ORCID iD |
| Andrew White (Principal Investigator) |
Publications
Evensen C
(2024)
Multispecies interactions and the community context of the evolution of virulence.
in Proceedings. Biological sciences
Howell E
(2024)
Immune interactions and heterogeneity in transmission drives the pathogen-mediated invasion of grey squirrels in the UK.
in The Journal of animal ecology
Northrup GR
(2024)
The evolutionary dynamics of hyperparasites.
in Journal of theoretical biology
O'Neill X
(2023)
The Impact of Host Abundance on the Epidemiology of Tick-Borne Infection.
in Bulletin of mathematical biology
O'Neill X
(2023)
The evolution of parasite virulence under targeted culling and harvesting in wildlife and livestock.
in Evolutionary applications
O'Neill X
(2021)
The Influence of Latent and Chronic Infection on Pathogen Persistence
in Mathematics
Saad-Roy CM
(2024)
The evolution of post-infection mortality.
in Proceedings. Biological sciences
Slade A
(2023)
Indirect effects of pine marten recovery result in benefits to native prey through suppression of an invasive species and a shared pathogen
in Ecological Modelling
| Description | The award has just begun and is assessing the processes that shape the evolution of infectious disease characteristics. |
| Exploitation Route | The results could have impact for infectious disease management. |
| Sectors | Agriculture Food and Drink Environment Healthcare |
| Description | EPSRC Centre for Doctoral Training in Mathematical Modelling, Analysis and Computation (MAC-MIGS) |
| Amount | £6,380,000 (GBP) |
| Funding ID | EP/L016508/01 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 01/2019 |
| End | 03/2028 |
| Title | Data from: Multispecies interactions and the community context of the evolution of virulence |
| Description | Pairwise host-parasite relationships are typically embedded in broader networks of ecological interactions, which have the potential to shape parasite evolutionary trajectories. Understanding this "community context" of pathogen evolution is vital for wildlife, agricultural, and human systems alike, as pathogens typically infect multiple hosts - and these hosts may have independent ecological relationships. Here we introduce an eco-evolutionary model examining ecological feedbacks across a range of host-host interactions. Specifically, we analyze a model of the evolution of virulence of a parasite infecting two hosts exhibiting competitive, mutualistic, or exploitative relationships. We first find that parasite specialism is necessary for inter-host interactions to impact parasite evolution. Furthermore, we find generally that increasing competition between hosts leads to higher shared parasite virulence, while increasing mutualism leads to lower virulence. In exploitative host-host interactions, the particular form of parasite specialization is critical - for instance, specialization in terms of onward transmission, host tolerance, or intra-host pathogen growth rate lead to distinct evolutionary outcomes under the same host-host interactions. Our work provides testable hypotheses for multi-host disease systems, predicts how changing interaction networks may impact virulence evolution, and broadly demonstrates the importance of looking beyond pairwise relationships to understand evolution in realistic community contexts. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2024 |
| Provided To Others? | Yes |
| Impact | NA |
| URL | https://datadryad.org/stash/dataset/doi:10.5061/dryad.c866t1gfm |
| Description | Modelling the impact of post-acute infection |
| Organisation | University of California, Berkeley |
| Department | Department of Integrative Biology |
| Country | United States |
| Sector | Academic/University |
| PI Contribution | I was invited to take part in a research collaboration to develop mathematical models to examine the impact of post-acute infection. Background to the research project: The recent SARS-CoV-2 pandemic has highlighted how pathogens may lead to an increase in mortality after a host recovers from a focal infection (e.g. long COVID). The risk from non-communicable diseases that follow acute infections has historically been ignored in relation to modern public health policy - but increasing evidence suggest that such infection-associated chronic conditions are widespread and substantial. Action is urgently required to understand the combined effect of acute initial infections and associated subsequent chronic infection with a call for simultaneous health policy that accounts for both infectious stages to be prioritised by national governments. Established epidemiological theory assumes that disease induced mortality is apparent during the initial, acute, pathogen infection only and these models form the basis to understanding different infections and for directing public health and disease management measures. The risk of subsequent chronic infections - post infection mortality - has rarely been considered and is likely to change the epidemiological and evolutionary dynamics. Working with Dr Chadi Saad-Roy (a Miller Fellow at UC Berkeley) and with Prof Mike Boots (my main collaborator on the EEID grant: BB/V00378X/1) we have undertaken joint work leading to a publication (Saad-Roy, C.M., White, A. and Boots, M., 2024. The evolution of post-infection mortality. Proceedings B). The work highlights settings where post infection mortality can evolve that may explain its ubiquity. I contributed to the development of the modelling framework, mathematical analysis and production of the publication. This is a new and important research topic and I expect the collaboration to continue. This collaboration was made possible due to the collaborative visits that I make to UC Berkeley. These visits are a key, overarching, aim of the USA-UK EEID funding scheme that bring researchers together with the objective of using the USA-UK collaboration as the impetus for further collaborative projects. |
| Collaborator Contribution | The collaborative partners at UC Berkeley (Dr Chadi Saad-Roy, Prof Mike Boots) initiated this research project. I was invited to share my expertise in mathematical modelling the epidemiological and evolutionary dynamics of infectious disease systems. |
| Impact | https://doi.org/10.1098/rspb.2024.1854 The collaboration is multi-disciplinary combining biological and mathematical sciences. |
| Start Year | 2024 |
| Description | The impact of varroa mite infection on honey bee colony viability |
| Organisation | University of California, Berkeley |
| Department | Department of Integrative Biology |
| Country | United States |
| Sector | Academic/University |
| PI Contribution | I was invited to take part in a research collaboration to develop mathematical models to examine the impact varroa mite infection on honey bee colony viability. Background to the project: Honey bees are vital to global agricultural systems due to their role in pollination. For example, in the USA, each year 81 billion honey bees from 1.6 million hives pollinate over 2.5 trillion almond blooms in what is the largest insect migration on the planet. However, commercial bee populations have suffered recent, catastrophic, losses of bee colonies - and this has jeopardised bee industry pollination efficacy and therefore agriculture crop success. The exact cause of colony collapse disorder is not known but it is strongly linked to the presence of a macroparasitic infestation of varroa mite (which may have fitness costs to bees and may vector other infectious disease). Recent work, led by Dr Nina Sokolov, UC Berkeley and Dr Lewis Bartlett, University of Georgia has highlighted the potential role of varroa mite resistance and tolerance on the long-term sustainability of honey bee colonies. To understand the importance of these different host phenotypes on honey bee epidemiological dynamics requires the development of new model frameworks that combine honey bee demographics, macroparasite infection and phenotype competition. I have been invited to join a collaborative project with Dr Bartlett and my collaborators at UC Berkeley to develop and analyse these model frameworks. Initial work has been completed and we are aiming for an publication in 2025. This work has the potential to have impact on the management of honeybees colonies that improves their viability and therefore protect their important status in food production. This is a new and important research topic and I expect the collaboration to continue. This collaboration was made possible due to the collaborative visits that I make to UC Berkeley. These visits are a key, overarching, aim of the USA-UK EEID funding scheme that bring researchers together with the objective of using the USA-UK collaboration as the impetus for further collaborative projects. |
| Collaborator Contribution | The collaborative partners at UC Berkeley (Prof Mike Boots, Dr Nina Sokolov) have an established collaboration with Dr Lewis Bartlett at the University of Georgia. This team recently published a review on 'Avoiding the tragedies of parasite tolerance in Darwinian beekeeping', Proceedings B, 2025. During one of my research visits to UC Berkeley we discussed this work and this was the impetus to start some mathematical modelling work to assess the role of varroa mite resistance and tolerance on the long-term sustainability of honey bee colonies. |
| Impact | We are aiming for an initial publication in 2025 The collaboration is multi-disciplinary combining biological and mathematical sciences. |
| Start Year | 2024 |
| Description | The impact of varroa mite infection on honey bee colony viability |
| Organisation | University of Georgia |
| Country | United States |
| Sector | Academic/University |
| PI Contribution | I was invited to take part in a research collaboration to develop mathematical models to examine the impact varroa mite infection on honey bee colony viability. Background to the project: Honey bees are vital to global agricultural systems due to their role in pollination. For example, in the USA, each year 81 billion honey bees from 1.6 million hives pollinate over 2.5 trillion almond blooms in what is the largest insect migration on the planet. However, commercial bee populations have suffered recent, catastrophic, losses of bee colonies - and this has jeopardised bee industry pollination efficacy and therefore agriculture crop success. The exact cause of colony collapse disorder is not known but it is strongly linked to the presence of a macroparasitic infestation of varroa mite (which may have fitness costs to bees and may vector other infectious disease). Recent work, led by Dr Nina Sokolov, UC Berkeley and Dr Lewis Bartlett, University of Georgia has highlighted the potential role of varroa mite resistance and tolerance on the long-term sustainability of honey bee colonies. To understand the importance of these different host phenotypes on honey bee epidemiological dynamics requires the development of new model frameworks that combine honey bee demographics, macroparasite infection and phenotype competition. I have been invited to join a collaborative project with Dr Bartlett and my collaborators at UC Berkeley to develop and analyse these model frameworks. Initial work has been completed and we are aiming for an publication in 2025. This work has the potential to have impact on the management of honeybees colonies that improves their viability and therefore protect their important status in food production. This is a new and important research topic and I expect the collaboration to continue. This collaboration was made possible due to the collaborative visits that I make to UC Berkeley. These visits are a key, overarching, aim of the USA-UK EEID funding scheme that bring researchers together with the objective of using the USA-UK collaboration as the impetus for further collaborative projects. |
| Collaborator Contribution | The collaborative partners at UC Berkeley (Prof Mike Boots, Dr Nina Sokolov) have an established collaboration with Dr Lewis Bartlett at the University of Georgia. This team recently published a review on 'Avoiding the tragedies of parasite tolerance in Darwinian beekeeping', Proceedings B, 2025. During one of my research visits to UC Berkeley we discussed this work and this was the impetus to start some mathematical modelling work to assess the role of varroa mite resistance and tolerance on the long-term sustainability of honey bee colonies. |
| Impact | We are aiming for an initial publication in 2025 The collaboration is multi-disciplinary combining biological and mathematical sciences. |
| Start Year | 2024 |
| Description | The interplay between social networks and infectious disease |
| Organisation | University of California, Berkeley |
| Department | Department of Integrative Biology |
| Country | United States |
| Sector | Academic/University |
| PI Contribution | The research work that is being undertaken as park of the EEID grant: BB/V00378X/1, that is assessing the evolution of virulence in heterogeneous spatial networks led to discussions with Dr Matt Silk at the University of Edinburgh. Dr Silk (currently a Royal Society University Research Fellow) is an ecologist who works on the social dynamics, population biology and epidemiology of wildlife populations. This has led to a research collaboration to examine the interplay between social networks and infectious disease. This project is supported by PhD student, jointly supervised by myself, Dr O'Neill (Heriot Watt University and a named PDRA on the EEID grant: BB/V00378X/1) and Dr Silk, and funded by the Maxwell Institute Graduate School in Analysis and its Applications, Centre for Doctoral Training, EPSRC, EP/L016508/01. Background to the research project: Social contact networks are integral to the transmission of infectious pathogens and therefore network properties can drive the epidemiological dynamics with implications for individual and population level health. Much of the analysis of the interplay between contact structure and infectious disease has taken place on static contact networks and has highlighted, for example, the key role of superspreaders in infectious outbreaks. However, social networks are not static, they are dynamic and change due to individual choice and loss and gain of individuals (birth and death). Birth and death processes are not typically represented when examining social network properties but can have a major effect, for instance when a key individual dies. As part of this collaboration we have developed a new mathematical frameworks that combines social network models with stochastic models that represent the demographic properties of birth and death. Initial work has led to important insights on the level of social organisation that is expected for species with fast or slow pace of life and this will be novelly extended to provide a mathematical explanation of the 'friendship paradox' (on average, my friends have more friends than me). We expect this work to lead to publications in 2025/26. An important next step is to develop new model frameworks that include the infectious disease properties of transmission, recovery, immunity and virulence. We postulate that by including a dynamic population, interesting, novel phenomena may emerge, such as cycles of social network evolution and infection levels driven by disease-induced death removing well connected individuals. The network structure of natural systems is a key part of what makes some species competent hosts of endemic and emergent pathogens and therefore this work will allow disease management techniques to be better targeted. This collaboration has emerged due to the research work in EEID grant: BB/V00378X/1 |
| Collaborator Contribution | The main collaborative partner is at the University of Edinburgh where Dr Silk's expertise in social dynamics and epidemiology will be key to determining the research objectives for this project. The project will also involve collaboration with UC Berkeley (Prof Mike Boot, my main collaborator on the EEID grant: BB/V00378X/1) that will provide expertise in the epidemiology and evolution of infectious disease on heterogeneous networks. |
| Impact | We expect initial publications in 2025/26. The collaboration is multi-disciplinary combining biological and mathematical sciences. |
| Start Year | 2024 |
| Description | The interplay between social networks and infectious disease |
| Organisation | University of Edinburgh |
| Department | School of Biological Sciences |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The research work that is being undertaken as park of the EEID grant: BB/V00378X/1, that is assessing the evolution of virulence in heterogeneous spatial networks led to discussions with Dr Matt Silk at the University of Edinburgh. Dr Silk (currently a Royal Society University Research Fellow) is an ecologist who works on the social dynamics, population biology and epidemiology of wildlife populations. This has led to a research collaboration to examine the interplay between social networks and infectious disease. This project is supported by PhD student, jointly supervised by myself, Dr O'Neill (Heriot Watt University and a named PDRA on the EEID grant: BB/V00378X/1) and Dr Silk, and funded by the Maxwell Institute Graduate School in Analysis and its Applications, Centre for Doctoral Training, EPSRC, EP/L016508/01. Background to the research project: Social contact networks are integral to the transmission of infectious pathogens and therefore network properties can drive the epidemiological dynamics with implications for individual and population level health. Much of the analysis of the interplay between contact structure and infectious disease has taken place on static contact networks and has highlighted, for example, the key role of superspreaders in infectious outbreaks. However, social networks are not static, they are dynamic and change due to individual choice and loss and gain of individuals (birth and death). Birth and death processes are not typically represented when examining social network properties but can have a major effect, for instance when a key individual dies. As part of this collaboration we have developed a new mathematical frameworks that combines social network models with stochastic models that represent the demographic properties of birth and death. Initial work has led to important insights on the level of social organisation that is expected for species with fast or slow pace of life and this will be novelly extended to provide a mathematical explanation of the 'friendship paradox' (on average, my friends have more friends than me). We expect this work to lead to publications in 2025/26. An important next step is to develop new model frameworks that include the infectious disease properties of transmission, recovery, immunity and virulence. We postulate that by including a dynamic population, interesting, novel phenomena may emerge, such as cycles of social network evolution and infection levels driven by disease-induced death removing well connected individuals. The network structure of natural systems is a key part of what makes some species competent hosts of endemic and emergent pathogens and therefore this work will allow disease management techniques to be better targeted. This collaboration has emerged due to the research work in EEID grant: BB/V00378X/1 |
| Collaborator Contribution | The main collaborative partner is at the University of Edinburgh where Dr Silk's expertise in social dynamics and epidemiology will be key to determining the research objectives for this project. The project will also involve collaboration with UC Berkeley (Prof Mike Boot, my main collaborator on the EEID grant: BB/V00378X/1) that will provide expertise in the epidemiology and evolution of infectious disease on heterogeneous networks. |
| Impact | We expect initial publications in 2025/26. The collaboration is multi-disciplinary combining biological and mathematical sciences. |
| Start Year | 2024 |