Math Biology Archives for Fall 2024 to Spring 2025
How non-Markovian stochastic processes and probability ridges help us understand space use in wild animals
When: Tue, September 5, 2023 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Bill Fagan (Dept. of Biology, University of Maryland) - https://science.umd.edu/biology/faganlab/
Abstract: Massive increases in the availability of animal tracking data presents rich opportunities for mathematical modeling and statistical analyses. This seminar will provide an overview of how non-Markovian stochastic processes are being used to understand animal movement and space use, especially the structure of individuals’ so-called ‘home ranges.’ I will focus on the area of ‘cognitive movement ecology’ that seeks to build bridges between animal movement and such biologically rich topics as memory and learning. Previous work has addressed the importance of reused pathways for movement in a range of biological systems from ants to elephants. Here I report on results from a study that brought together a global database of almost 1300 GPS movement tracks from range-resident mammalian carnivores to test—for free-ranging animals in the wild—predictions from laboratory experiments investigating aspects of animal cognition. By calculating the location of ‘probability ridges’ within each individual’s 2D home range, we developed a statistical metric that provides a concise summary of internal home range structure, identifying the extent to which travel is concentrated along favored routes. Using these ridges in a comparative analysis, we found strong evidence for a clade-level difference in the carnivores’ reliance on preferred travel routes. On average, home ranges of canids featured ~30% greater ‘ridge density’ than did felids, after fully controlling for effects of body mass, environmental covariates, hunting strategy, home range size, and phylogenetic correlation. Providing the first large-scale, ‘in the wild’ support for experimental predictions concerning evolutionary differences in the nature of spatial memory among carnivores, we found that felids tended to rely on a reduced set of heavily traveled routes for movement whereas canids employed less spatially intensive movement. These differences, which suggest a greater reliance among canids on navigation via higher-level cognitive processes such as path integration and cognitive maps, have critical implications for how predators use space and interact with mobile resource species.
Modelling the Propagation of Epidemics with Diffusion
When: Tue, September 12, 2023 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Henri Berestycki (EHESS & UMD) - http://cams.ehess.fr/henri-berestycki/
Abstract: Including diffusion in the classical framework of SIR equations in epidemiology leads to reaction-diffusion systems. In epidemiology, diffusion arises under several guises. After reviewing some classical results, Prof. Berestycki will present recent and ongoing work on spatial spreading, social diffusion, and diffusion on graphs motivated by the Covid-19 pandemic.
Early risk-assessment of pathogen genomic variants emergence
When: Tue, September 19, 2023 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Ana Bento (Rockefeller Foundation) : CANCELLED- https://www.anabento.io/
Abstract: Accurate, reliable, and timely estimates of pathogen variant risk are essential for informing public health responses. Unprecedented rates of genomic sequencing have generated new insights into variant dynamics. However, estimating the fitness advantage of a novel variant shortly after emergence, or its dynamics more generally in data-sparse settings, remains difficult. This challenge is exacerbated in countries where surveillance is limited or intermittent. To stabilize inference in these data-sparse settings, we develop a hierarchical modeling approach to estimate variant fitness advantage and prevalence by pooling data across geographic regions. We demonstrate our method by reconstructing SARS-CoV-2 BA.5 variant emergence, and assess performance using retrospective, out-of-sample validation. We show that stable and robust estimates can be obtained even when sequencing data are sparse. Finally, we discuss how this method can inform risk assessment of novel variants and provide situational awareness on circulating variants for a range of pathogens and use-cases.
Modelling inherited migratory programs using evolutionary algorithms
When: Tue, September 26, 2023 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Dr. James McLaren (University of Oldenburg, Germany) -
Abstract: Many inexperienced long-distant bird, insect and turtle migrants reach remote non-breeding
destinations independently, using inherited geomagnetic or celestial compass cues. Inexperienced migrants are also proposed to detour unfavorable regions using inherited
geomagnetic signposts to trigger switches in migratory headings (Zugknicks). However, the
overall relative feasibility among migratory compass courses and signposts (often termed clock-and-compass migration) remains uncertain, particularly at population levels. To address these unknowns, I developed a compass-based migration model incorporating spatiotemporal geomagnetic data (1900-2023) and an evolutionary algorithm, accounting for trans-generational changes in inherited geomagnetic headings and signposts through population mixing and natal dispersal. Signposted trans-hemispheric songbird migrations remained viable over the 124-year period, including through a highly geomagnetically unstable region (East-arctic North America and Greenland) and across a migratory divide maintained through dominant allelic inheritance. The key role of intrinsic variability in inheritance of headings is also discussed. Finally, I discuss how migratory orientation programs could both mediate and constrain evolution of routes in response to global climate change.
Decisions and Disease: Social Division and Vaccine Misinformation
When: Tue, October 3, 2023 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Mallory Harris (Stanford University) - https://mjharris95.github.io/
Abstract: In this talk, I will discuss my work on the interplay between
human behavior and infectious diseases: how human activity can impact
epidemic dynamics and how people respond to outbreaks. First, I will
develop a compartmental model to demonstrate how awareness-based behavior
with group-level heterogeneity in decision-making and disease risk can
produce complicated and counterintuitive epidemic dynamics. Second, I will
use a Twitter data set containing over 4.2 million posts about COVID-19
vaccines from April 2021 to characterize an understudied set of potential
anti-vaccine influencers: perceived experts (i.e., people who signal
expertise in the biomedical sciences).
The Ideal Free Distribution and Evolution of Dispersal in Integrodifference Equation Models
When: Tue, October 10, 2023 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Dr. Joy Zhou (Lafayette College) - https://yingzhou.weebly.com/
Abstract: Studying the evolution of dispersal is important for understanding how populations are distributed in space and how species adapt to changing environments spatially. In this talk, I will present some recent work using integrodifference equation models to study the evolution of dispersal. Integrodifference equations (IDEs) are discrete-time dynamical systems that describe the dynamics of a population distributed over continuous space. In an IDE, dispersal from one location to another is modeled by the kernel of an integral operator referred to as the “dispersal kernel”. We begin with a model where the dispersal kernels are fixed over time, and use pairwise invasion analysis to find which kernels correspond to evolutionarily stable strategies (each kernel is considered a dispersal “strategy”) when there is spatial heterogeneity and seasonal variation. We prove that the evolutionarily stable strategies are the ones that can produce an ideal free distribution (a result also shown in previous studies using other model frameworks). We then develop a model where the dispersal kernel evolves over time by incorporating spatial memory and learning. If the environment is temporally static, the model has an equilibrium corresponding to the ideal free distribution. The simulation results indicate some level of convergence towards this equilibrium even when the environment changes over time. Overall, the mechanism proposed in the second model shows a possible way for the dispersal kernel of a population to evolve towards one that is ideal free.
Biomathematics and the Brain: a primer for modeling neuropathology and neurodegeneration
When: Tue, October 17, 2023 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Dr. Travis Thompson (Dept of Mathematics, Texas Tech University) - https://www.mathemology.com/
Abstract: The modern medical perspective on neurological diseases has evolved, slowly, since the 20th century but recent breakthroughs in medical imaging have quickly transformed medicine into a quantitative science. Today, mathematical modeling and scientific computing allow us to go farther than observation alone. Bringing together neuroimaging and mesh generation tools together with numerical methods and scientific computing, experimental and data-informed mathematical models are leading to new clinical insights into serious diseases that affect the nervous system.
In this talk, I will discuss some foundations for modeling the brain on both short and long time scales. Topics covered will include an introduction to continuum multi-fluid porous tissue models of the brain, numerical methods for porous multifluid models and the use of high-dimensional network dynamical systems to model neurodegenerative diseases such as Alzheimer's disease. Along the way, we will see how magnetic resonance imaging can be used to extract the computational assets that are essential for patient-specific mathematical brain modeling.
Two talks: Prey-predator reversal model & modeling human behavior and epidemics (Luis Suarez and Alice Oveson, PhD students, UMD Mathematics)
When: Tue, October 24, 2023 - 12:30pmWhere: Kirwan Hall 3206
Speaker #1: Luis Suarez (PhD student, UMD Mathematics) -
Title: Prey-predator reversal within an age structured model
Abstract: There are predator-prey biological systems in which the roles of the prey and the predator changes as a function of time and/or the size of the species. We develop a mathematical model for such predator-prey reversal systems. Our mathematical model is constructed as an age-structured model, which allows us to capture the change in roles as a function of time. We study different modes of transitioning from a young population to adult population. We further study the behavior in the trophic constants of the model which allow us to obtain biological meaningful results. This is a joint work with Professors Bill Fagan, Maria Cameron, and Doron Levy.
Speaker #2 (1.00 pm -1.30 pm): Alice Oveson (PhD student, UMD Mathematics)
Title: Towards a novel behavior-epidemiology modeling framework for pandemics of respiratory pathogens
Abstract: The recent COVID-19 pandemic, caused by SARS-CoV-2, has highlighted the importance of explicitly incorporating human behavior into mathematical models for the transmission dynamics and control of emerging, resurging and re-emerging respiratory pathogens. Models that did not explicitly account for the impacts of human behavior changes during the epidemic often had limited predictive power due to distinctly human phenomena such as masking fatigue. In this lecture, I will present a new mathematical framework for incorporating human behavior changes and social influences (as a driver for positive or negative human behavior changes with respect to adherence to control and mitigation measures or overall attitude towards the pandemic) on the transmission dynamics of a respiratory pathogen of major public health significance. The two models to be presented include one probabilistic network model based on a high school contact dataset and one more classical compartmental model that brings in elements of social influence dynamics. I will discuss the merits and demerits of these various modeling types within this framework, and how they might be combined to potentially enhance the predictive capacity of the modeling framework. This is a joint work with Professors Abba Gumel and Michelle Girvan.
Eco-evolutionary dynamics of temperate viruses in periodic environments
When: Tue, October 31, 2023 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Tapan Goel (Biology - UMD) - https://scholar.google.com/citations?user=563V9eQAAAAJ&hl=en
Abstract: Temperate viruses use a variety of strategies to reproduce - ranging from lytic to lysogenic. The basic reproduction number, R0, provides a useful measure of the instantaneous fitness of different viral strategies and their dependence on the factors such as host availability, virus-host ratios and host physiology. However, periodic changes in these factors can create trade-offs between short-term and long-term fitness. In this talk, I will present a computational framework that allows us to examine generalized drivers of viral strategies in periodic environments. We use a compartmental model with a periodic filter to model the eco-evolutionary dynamics of the resource-host-virus system. The filtration step involves differential dilution of susceptible hosts, infected cells and viruses in the system. By choosing appropriate dilution factors, we can create different short-term and long-term selection pressures that may give rise to distinct (and opposing) pressures on viral strategies. In doing so, we also investigate the evolution of viral plasticity, i.e. how the probability of lysogen formation changes in response to cellular multiplicity of infection.
Complexities of the Cytoskeleton and Applications of Single Particle Tracking
When: Tue, November 7, 2023 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Dr. Keisha Cook (Department of Mathematical and Statistical Sciences,Clemson University) - https://www.clemson.edu/science/academics/departments/mathstat/about/profiles/keisha
Abstract: Biological systems are traditionally studied as isolated processes (e.g. regulatory pathways, motor protein dynamics, transport of organelles, etc.). Although more recent approaches have been developed to study whole cell dynamics, integrating knowledge across biological levels remains largely unexplored. In experimental processes, we assume that the state of the system is unknown until we sample it. Many scales are necessary to quantify the dynamics of different processes. These may include a magnitude of measurements, multiple detection intensities, or variation in the magnitude of observations. The
interconnection between scales, where events happening at one scale are directly influencing events occurring at other scales, can be accomplished using mathematical tools for integration to connect and predict complex biological outcomes. In this work, we focus on building inference methods to study the complexity of the cytoskeleton from one scale to another. We rely on single particle tracking techniques based on stochastic models and explore long-term dynamics of the systems.
Evolutionary interactions in phage treatment of bacterial infections
When: Tue, November 28, 2023 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Jacopo Marchi (University of Maryland) -
Abstract: Antibiotic resistant bacteria are a serious global health threat. As a result, bacteriophage (or 'phage' -- viruses that exclusively infect and lyse bacteria) are increasingly considered as a therapeutic alternative to treat bacterial infections. In this talk I will showcase two examples of how evolutionary processes arising from phage-bacteria interactions pose challenges to phage therapy, but also present opportunities to design successful treatments. The first study examines the efficacy of a phage 'cocktail' composed of two phages that exploit different adsorption routes, via a combination of vitro experiments and nonlinear dynamics models. We analyze a theoretical model of in vivo infection dynamics, where therapeutic phage and the innate immune system can work synergistically to prevent infection, showing if and when the phage cocktail can control the bacterial population. In the second study, I present a model of co-evolving populations of phages and bacteria, given the important role of resistance and counter-resistance in shaping therapeutic outcomes. We aim to address how cross-infection structure and phage and bacterial diversity affect one another in the presence of the immune system. Studying such a model may help us understand how to use evolutionarily aware phage as a means to steer, and potentially control, bacterial evolution in therapeutic settings.
Two Talks: Virus-Microbe Dynamics Spanning (i) Pseudolysogeny and its Impact on Populations; and (ii) Inferring Interactions in Complex Communities
When: Tue, December 5, 2023 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Akash Arani & Raunak Dey (University of Maryland) -
Abstract: Talk 1 (Arani): Mathematically modeling pseudolysogeny in M1 Salinibacter Ruber and EM1 Holosalinivirus
Abstract: Bacteriophage are conventionally thought to have two infection strategies: lysis and lysogeny. However alternative infection strategies such as pseudolsyogeny have recently been discovered and require study. Pseudolysogeny occurs when the infecting phage resides intracellularly yet extrachromosomally within its host. This allows the phage genome to passively sustain in the population through asymmetrical division amongst the host’s daughter cells. We attempt to model pseudolysogeny in M1 Salinibacter Ruber and its bacteriophage EM1 Holosalinivirus, to succeed where models that only consider lytic and lysogenic strategies have failed. We build a nonlinear ODE model that accounts for pseudolysogeny, as well as features such as lysis inhibition and multiplicity of infection. We aim to use our model’s success to highlight mechanisms of pseudolysogeny and motivate more study into alternative phage infection strategies.
Talk 2 (Dey): Inferring (higher-order) interactions in complex virus-bacteria communities
In complex natural ecosystems multiple species of viruses and their bacterial hosts coexist together. Understanding the ecological life-history traits of virus-host interactions as well as “who interacts with whom” are essential for understanding downstream ecological and evolutionary outcomes. Here we study the in vitro temporal population dynamics of a 5×5 host-virus model community for all the species together as well as when they are in isolated pairs. We find evidence suggesting that the Bayesian inferred virus-host life history traits and mechanistic models from pairwise experiments are inadequate to recapitulate the experimentally observed population dynamics of the community. This inadequacy is addressed by invoking context-dependent shifts in interactions and other emergent higher-order effects, potentially explaining coexistence of multiple virus-host species in a natural ecosystem.
Patterns and trade-offs in the evolution of bacterial pathogens and their viruses
When: Tue, January 30, 2024 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Julie Pourtois (Graduate student) (Stanford University, Department of Biology) - https://profiles.stanford.edu/julie-pourtois?tab=bio
Abstract: Filamentous bacteriophages can infect their bacterial hosts with a twist: their shape allows progeny phages to leave their bacterial host without killing it. Filamentous phages can thus produce progeny phages and get reintegrated into the bacterial genome, opening the door to both horizontal and vertical transmission. The production of these phages, however, does come at a metabolic cost for the bacterium, limiting new mutations and potential for adaptation. In this talk, I explore these dynamics in the context of chronic lung infections by Pseudomonas aeruginosa and its filamentous phage Pf. I first use genomics and phylogenetic analyses to evaluate the prevalence of horizontal and vertical Pf transmission in patients. In the second part of the talk, I then develop a mathematical model to quantify the effect of a reduced growth rate on the development of antibiotic resistance, focusing on the trade-off between antibiotic susceptibility and mutability.
Causes and consequences of imperfect coordination in collective behaviors across scales: from microbial aggregates to ungulate migration
When: Tue, February 13, 2024 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Dr. Ricardo Martinez-Garcia (Center for Advanced Systems Understanding ) - https://www.casus.science/team-members/ricardo-martinez-garcia/
Abstract: Collective behaviors, in which many individuals exhibit some degree of behavioral coordination, are frequent in nature and observed across a continuum of scales, from microbial aggregates to ungulate migrations. Intriguingly, however, such coordination is sometimes imperfect, and “out-of-sync” individuals exist in many of these systems. For example, in the model social amoeba Dictyostelium discoideum, free-living cells aggregate in a multicellular fruiting body upon starvation, while others remain in the unicellular phase. In several ungulate species, hundreds of thousands of individuals coordinate with each other to migrate across seasonal ranges, but resident populations that do not follow the migration also exist. The roots of such imperfect coordination, and hence the mechanisms underlying the emergence of out-of-sync individuals, will undoubtedly differ across systems. Nevertheless, the occurrence of imperfect coordination across such different systems and scales raises fundamental questions about its causes and consequences. Are “out-of-sync” individuals merely inevitable byproducts of large-scale coordination attempts, or can they, at least in some systems, be a variable trait that selection can shape with potential ecological consequences?
I will address this question by combining empirical data on D.discoideum imperfect aggregation and observed patterns of partial migration observed within three ungulate species. In each of these systems, we find that the number of individuals that do not engage in the collective behavior is unrelated to the total population size, suggesting that a complex individual decision-making process underlies the onset of the collective behavior. Using a minimalistic modeling framework, we propose that imperfectly synchronized collective behaviors are, in fact, a dynamic population partition process that originates from each individual making a stochastic signal-based decision. The parallelisms between these two seemingly different systems suggest that imperfectly synchronized collective behaviors could be critical to understanding social behaviors and ecological dynamics across scales. More broadly, these results suggest that, across taxa, imperfect coordination of collective behaviors might be adaptive by enabling the diversification of life-history strategies.
Modeling limitations, stressors, and constraints on population dynamics
When: Tue, February 20, 2024 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Dr. Angela Peace (Texas Tech University, Department of Mathematics and Statistics) - https://www.math.ttu.edu/~anpeace/
Abstract: Ecological processes are naturally structured and depend on the flow and balance of essential elements such as carbon (C), nitrogen (N), and phosphorus (P). The theory of Ecological Stoichiometry (ES) considers the balance of multiple chemical elements and how the relative abundance of essential elements in organisms affects ecological dynamics. Recently, the integration of ES theory into population dynamics has provided a useful lens into understanding how stoichiometric constraints can help shape food webs and nutrient cycles. Nutritional constraints are common as food resources are rarely optimally suited for grazing species. Elemental mismatches between trophic levels can influence population growth and foraging behaviors. Mathematical models developed under the framework of Ecological Stoichiometry can help shed light on population dynamics subject to stoichiometric constraints. I will give a brief overview of stoichiometric producer-grazer models (systems of ordinary differential equations) and present some commonly used functional forms for incorporating stoichiometric constraints into trophic interactions. Moving forward from here, this modeling framework has the potential to investigate the influence of stoichiometric constraints on biodiversity and nutrient cycling and how they propagate throughout larger and more complex food webs.
What drives species' range dynamics? A historical modeling study of the cabbage white butterfly Pieris rapae
When: Tue, February 27, 2024 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Dr. Judith Miller (Georgetown University, Department of Mathematics and Statistics) - https://gufaculty360.georgetown.edu/s/contact/00336000014RX8oAAG/judith-miller
Abstract: A large body of theory has identified numerous factors that can play major roles in determining the speed and ultimate extent of range expansions. Among these are dispersal patterns, traits affecting fecundity, interspecific competition and adaptation or maladaptation to local environments. Yet few empirical studies establish the reasons for the range dynamics of particular species.
We develop a detailed deterministic model of the initial spread of the cabbage white butterfly Pieris rapae in North America. We parametrize the model using climate and geographic data as well as physiological and life history parameter values from numerous studies of the species. The model does not permit adaptive evolution.
We compared numerical simulations of our model with historical data provided by Scudder (1887). This comparison shows the importance of railroads in the spread of P. rapae. It also yields an intriguing failure: the model does not capture the species' northward spread into Canada. The next step is to incorporate features absent from the present model to test whether they allow a revised model to replicate this northward spread.
Challenges of limited temporal data in clinical applications of mathematical modeling
When: Tue, March 5, 2024 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Dr. Heyrim Cho (Arizona State University, School of Mathematical and Statistical Sciences) - https://heyrim.github.io
Abstract: Recent advances in biotechnology and genome sequencing, resulting in a surge of data, are bringing in new opportunities in the mathematical modeling of biological systems. However, the amount of data that can be practically collected in everyday patients in the clinic is limited due to various reasons including the cost and the patient’s burden. Especially the amount of data that can be collected in the time domain is limited. This motivates us to transfer the mathematical and computational models to meet the challenges in clinical setting, to guide patient therapy via prediction. In this talk, I will discuss modeling approaches on the two ends of the spectrum of data. In the first part, I will discuss a Bayesian information-theoretic approach to determining effective scanning protocols for cancer patients. We propose a modified mutual information function with a temporal penalty term to account for the loss of temporal data. The effectiveness of our framework is demonstrated in determining patient scanning scheduling for prostate cancer patients. In the second part, I will discuss modeling work using single-cell gene sequencing data. Due to the high cost of obtaining gene sequencing data, temporal data is also lacking. We show that our cell state dynamics model can be used to incorporate genetic alteration at low cost, where we provide an example of modeling a hematopoiesis system and simulating abnormal differentiation that corresponds to acute myeloid leukemia.
Behavioral aspects in the individual and spatial transmission of mpox
When: Tue, March 12, 2024 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Dr. Vittoria Colizza (Head of Research at INSERM & Faculty of Medicine at Sorbonne Université) - https://www.epicx-lab.com/vittoria-colizza.html
Abstract: Starting May 2022, individuals with mpox (formerly known as monkeypox) were reported by several countries worldwide, outside of the regions of West and Central Africa where the disease is endemic and sustained by circulation in animal reservoirs. The mpox global outbreak led to a fast increase in new infected individuals through close contact transmission primarily among men-who-have-sex-with-men (MSM). Focusing on the mpox outbreak in France and using multiple data streams – including outbreak case data, detailed survey data from these cases, vaccination data, sexual behavioral data from surveys in the MSM community undertaken before and after the major mpox outbreak– we constructed different networks to integrate the behavioral aspects relevant to individual and spatial transmission. We showed that attendance to MSM commercial venues drove the rapid initial spread, followed by a rapid decline largely explained by a reduction of sexual activity induced by the perceived risk of infection, and before vaccination could reach mitigating effects at the population level. The study findings highlight the need for early awareness campaigns involving the affected community.
Biodata: Vittoria Colizza is Head of Research at INSERM (French National Institute for Health and Medical Research) & Sorbonne Université, Faculty of Medicine, working in the Pierre Louis Institute of Epidemiology and Public Health. She is an expert of modeling to evaluate the risks associated with epidemics and pandemics, anticipate their spread, and assess the effectiveness of prevention and control strategies. Her research informs public health policies, taking into consideration population behavior in terms of contacts and mobility. Colizza currently holds a position as Distinguished Visiting Fellow at the Georgetown
University Dept of Biology, the Georgetown Global Health Institute, and the Massive Data Institute in Washington DC, USA.
Trained as a physicist (PhD in Statistical and Biological Physics in 2004 at the International School for Advanced Studies in Trieste, SISSA, Italy), she worked at Indiana University (US) in the School of Informatics as post-doc (2004-2006) and visiting Assistant Professor (2007), and joined ISI Foundation (Turin, Italy, 2007-2010) after being awarded an ERC Starting
Grant in Life Sciences in 2007. In 2011 Colizza joined INSERM in Paris, and was promoted Head of Research in 2017. In 2020-2022 she was Visiting Professor at the Tokyo Institute of Technology in Japan. Colizza has been active in the response against COVID-19 pandemic, advising French public health agencies and government authorities, and international agencies. For her work on the pandemic, in 2020 she received her Knighthood of the Order of Merit of the Italian Republic by the Italian President Mr. Mattarella; in 2021 she was awarded the prix Irène Joliot Curie – Prix spécial de l’engagement by the French Academy of Sciences and the French Ministry of Research; in 2022 she was nominated Chevalier in the Order of the Légion d'honneur by the French President Mr. Macron.
An old threat in new territory: investigating dengue emergence with mathematical modeling
When: Tue, March 26, 2024 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Dr. Michael A. Robert (Department of Mathematics, Virginia Tech) - https://personal.math.vt.edu/michaelrobert/index.html
Abstract: Mosquito-borne diseases endemic to areas with tropical climates have been spreading in temperate regions of the world with greater frequency in recent years. Numerous factors contribute to this spread, including urbanization; increases in global travel; and changes in temperature, precipitation, and humidity patterns due to climate change. Mathematical modeling is a useful tool to examine how these different influences impact transmission and spread of arboviruses and for projecting how potential future changes in these factors could
affect arbovirus dynamics. Models have been employed for years to study disease dynamics, but diseases emerging in new regions present particular challenges. Here, we discuss models developed to study the introduction, emergence, and spread of dengue fever in Argentina.
Dengue, caused by a virus transmitted by Aedes aegypti mosquitoes, first emerged in temperate Argentinian cities in 2009, and multiple outbreaks of increasing incidence have occurred since. With particular focus on the role of climate in dengue emergence, we present
mathematical models designed to study meteorological influences on seasonal Aedes aegypti and dengue dynamics in temperate Argentinian cities, and we show how different seasonal patterns influence the risk of outbreaks. We also investigate potential influences of climate
change on risk of dengue transmission in the future. We discuss the implications of our work on dengue and mosquito mitigation strategies, and we address some of the issues and areas for improvement in modeling emerging arboviruses.
Modeling immunity and estimating vaccine effectiveness against fast-evolving pathogens
When: Tue, April 2, 2024 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Dr. Sarah Cobey (University of Chicago, Department of Ecology and Evolution) - https://cobeylab.uchicago.edu/people/sarah-cobey/
Abstract: It is well established that viruses such as influenza and SARS-CoV-2 evolve rapidly to escape immunity in the host population, but the state of population immunity and its response to epidemics and vaccinations remain poorly understood. The adaptive immune system is itself composed of competing, evolving populations of immune cells, and I will present a model showing how the competitive advantage of immune memory cells compared to naive cells implies a limit to the speed of viral evolution. Immune memory also produces unique signatures in the age distribution of infections, but its role in explaining curious patterns in vaccine effectiveness is uncertain. Occasionally repeat vaccinees (people vaccinated this year and last) appear to have higher infection risk than people vaccinated this year but not in recent years. I will present mechanistic models suggesting how this phenomenon might arise biologically and via statistical artifact, demonstrating that we cannot rule out subclinical infections as a possible explanation for the effect. I will share results from an ongoing randomized clinical trial and describe our plans to use mathematical models to understand how infections and vaccinations shape individual and population immunity.
Transient dynamics and the control of measles and rubella
When: Tue, April 9, 2024 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Dr. Matthew Ferrari (The Pennsylvania State University, Departments of Biology and Statistics) - https://science.psu.edu/bio/people/mjf283
Abstract: The regular and predictable cycles of measles outbreaks in the pre-vaccine era are a classic case study in the dynamics of non-linear phenomena in the natural world. These dynamics inspired the development and expansion of the Susceptible-Infectious-Recovered (SIR) class of models that are ubiquitous in the study of infectious disease spread and control today. But contemporary measles (and rubella) dynamics are dominated by transient phenomena at the local scale, even in geographies where endemic transmission is persistent. We study the dynamics of age-structured, stochastic SIR-type models to derive measures of progress towards elimination for evaluating the performance of vaccination programs in resource poor settings. We further combine subnational clinical and serological surveillance to estimate local variation in transmission and risk to guide tailored vaccination strategies to facilitate measles and rubella control and elimination. We show, using examples from measles and rubella, that designing control strategies around near-term transient dynamics yields alternative, and often more efficient, strategies than control designed for endemic equilibria.
Population Dynamics subject to nitrogen-to-carbon stoichiometric constraints
When: Tue, April 16, 2024 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Dr. Rebecca Everett (Haverford College, Department of Mathematics and Statistics) - https://scholar.google.com/citations?user=OoRq4VUAAAAJ&hl=en
Abstract: Ecological processes depend on the flow and balance of essential elements such as carbon, nitrogen, and phosphorus. The balance of these essential elements in ecological interactions is studied in the theory of Ecological Stoichiometry (ES). The integration of ES into population dynamics has provided insights into understanding how stoichiometric constraints affect food webs and nutrient cycles. I will present stoichiometric ordinary differential equation mathematical models that focus on the essential elements of nitrogen and carbon to investigate topics such as disease-ecosystem feedback loops and nitrogen fixation.
Stability of the return to normal behavior in an epidemic
When: Tue, April 23, 2024 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Dr. Tyrus Berry ( Department of Mathematical Sciences, George Mason University) - https://math.gmu.edu/~berry/
Abstract: Mathematical models have provided a general framework for understanding the dynamics and control of infectious disease. Many compartmental models are limited in that they do not account for the range of behavioral feedbacks that have been observed in the response to emerging infections. Here we expand on the SIR compartmental model framework by introducing a general class of behavioral feedbacks that encompasses both individual responses and non-pharmaceutical interventions. By linking transmission dynamics and behavior, this new class of models can capture the interplay of disease incidence, behavioral response, and controls such as vaccination. Within this wide class of behavioral models, we consider a minimal set of assumptions which we call: Reactivity, Resilience, and Boundedness. For example, Reactivity assumes that the immediate change in activity level at any given time depends only on the current activity level and the current disease incidence, but we do not assume any specific form for this reactivity function. Using these minimal assumptions on the response, we prove mathematically the existence of two new endemic equilibria depending on the vaccination rate: one in the presence of low vaccination but with reduced societal activity (the ``new normal"), and one with return to normal activity which requires a vaccination rate that is lower than would be required for disease elimination. We show how the stability of the various equilibria (disease free, old normal, and new normal) depends on Resilience and Boundedness of the response as well as the vaccination rate.
Transmission intervals and disease spread
When: Tue, April 30, 2024 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Dr. Jonathan Dushoff (McMaster University, Department of Biology) - https://mac-theobio.github.io/dushoff.html
Abstract: The spread of epidemics is structured by time distributions,
including the now-famous “serial interval” between when an individual experiences symptoms, and when the person that they infect experiences symptoms. This is often used to represent the “generation interval” between when the same two individuals were infected, but these can be importantly different. Defining these time distributions clearly, and describing how they relate to each other, and to key parameters of disease spread, poses interesting theoretical and practical questions.
I will discuss how transmission intervals link the “speed” and “strength” of epidemics, issues in their estimation, and their role in helping monitor changes in the parameters underlying disease, with examples from COVID-19, rabies and HIV.