ECOP-MS19

Population Dynamics Across Interacting Networks or Scales

Thursday, June 17 at 09:30am (PDT)
Thursday, June 17 at 05:30pm (BST)
Friday, June 18 01:30am (KST)

SMB2021 Follow Thursday (Friday) during the "MS19" time block.

Note: this minisymposia has multiple sessions. The second session is MS20-ECOP (click here).

Necibe Tuncer (Florida Atlantic University, USA), Hayriye Gulbudak ( University of Louisiana at Lafayette, USA), Cameron Browne (University of Louisiana at Lafayette, USA)

Description:

Modeling the complexity of populations and ecosystems requires innovative applications of dynamical systems and differential equations. Of particular interest are multi-scale or multi-species models where components, in themselves representing commonly studied systems in mathematical biology, are coupled together to form complex systems. For example, ecosystems may be viewed as high-dimensional networks of interacting species. Rapidly evolving and diverse interacting populations, such as a viral ``quasi-species'' and host immune response, quickly build a dynamic network of multiple variants whose structure can possibly be predicted through analytical or computational tools. Another layer of complexity to consider can be connecting the interdependent scales of within-host (immunology) and between-host (epidemiology) for infectious diseases. Modeling populations across networks or scales can bring genetic, biological or spatial structure into the equation, and motivates novel application of partial or high-dimensional ordinary differential equations. In this special session, we collect a variety of speakers who model population dynamics across interacting networks or scales.

Glenn Webb

(Vanderbilt University, USA)

"A COVID-19 epidemic model predicting the effectiveness of vaccination in the US"

A model of a COVID-19 epidemic is used to predict the effectiveness of vaccination in the US. The model incorporates key features of COVID-19 epidemics: asymptomatic and symptomatic infectiousness, reported and unreported cases data, and social measures implemented to decrease infection transmission. The model analyzes the effectiveness of vaccination in terms of vaccination efficiency, vaccination scheduling, and relaxation of social measures that decrease disease transmission. The model demonstrates that the subsiding of the epidemic as vaccination is implemented depends strongly on the scale of relaxation of social measures that reduce disease transmission.

Cameron Browne

(University of Louisiana at Lafayette, USA)

"Connecting predator prey dynamics and population genetics in an evolving virus immune network"

Integrating population evolution and dynamics offers a promising avenue for understanding rapidly evolving pathogens. For example, during HIV infection, the virus can escape several immune response populations via resistance mutations at distinct epitopes (proteins coded in viral genome), precipitating a dynamic network of interacting virus and immune variants. Understanding the main factors shaping viral resistance pathways and immune dynamics is crucial for designing effective vaccines and immunotherapies. While the virus-immune interactions may be quite complex, I will talk about my recent work to link pathogen population genetics with dynamics theoretically and through data to characterize their evolution. We start with a general differential equation ecosystem model of multiple virus and immune populations, and then prove that different stable and persistent patterns emerge in the virus-immune network dependent on the virus fitness landscape. Next, I will present a collaborative project where the 'eco-evolutionary' modeling framework is connected to genomic and population data. We describe the interaction between several immune cell populations and viral 'quasi-species' sampled from experiments of the simian immunodeficiency virus (SIV)-infected macaque model of HIV infection. The mathematical models can recapitulate the data and shed light on pathogen evolution, along with motivating ongoing work on jointly deciphering the population genetics and dynamics of pathogens and their complex ecosystems.

Andrea Pugliese

(University of Trento, Italy)

"mmune memory build-up in models of repeated infections; how does this affect epidemic dynamics?"

It is well known that memory cells can help to build a quick immune response in case of a new infection with the same (or similar) pathogen. This is indeed the principle at the basis of vaccination. It is also known that for certain pathogens a single vaccine dose can be insufficient to achieve a complete control of an infection, and that a second dose may be necessary. On the other hand, in several models of virus-immune interactions, the lower is the immune level before an infection, the higher it will be afterwards. This property is an important feature of the immuno-epidemiological models developed recently by Diekmann and co-workers. Recently, Zarnitsyna et al. have proposed a realistic model for immune response to infection by influenza virus that results in a progressive build-up of immune memory. In the talk, I will discuss several simplifications of the model in order to assess which components of the model are essential for its qualitative behaviour. Furthermore I will show how these features can be incorporated in a consistent multi-scale epidemic models, where the susceptible population is stratified through the number of times it has been infected. Strain coexistence is then common, and potential evolutionary consequences are explored.

Lauren M Childs

(Virginia Tech, USA)

"Trade-offs in Malaria Population Dynamics Across Scales"

Malaria is a disease endemic in areas encompassing over half the world’s population and remains detrimental to the health and livelihood of millions of individuals. Plasmodium parasites, the causative agents of malaria, have a complex life cycle requiring two hosts – a vertebrate, such as a human, and the Anopheles mosquito. During the time in each of these hosts, the population dynamics of the parasite are quite variable in density and stage. In previous work using a stochastic model of malaria population dynamics, we showed how density of parasite stages alter the timing and probability of onward transmission at the mosquito to human interface. Here, we bridge within-host modeling of parasite dynamics in the mosquito and the human to investigate maintenance of parasite diversity at the population level.

Hosted by SMB2021 Follow

Virtual conference of the Society for Mathematical Biology, 2021.