Math Biology Archives for Fall 2023 to Spring 2024
When the best pandemic models are the simplest
When: Tue, August 30, 2022 - 12:30pmWhere: Kirwan Hall 3206
Speaker: James Yorke (University of Maryland College Park) - yorke.umd.edu
Abstract: As a pandemic of covid-19 spread across the globe, many complex, highly detailed models have been developed to help policy setters make better decisions. A major goal of modeling covid-19 is to advise those in power how to react, and only the simplest models can be explained to those decision-makers.
The data collection has made this epidemic among the best documented. “Best documented” does not mean “thoroughly documented”. We argue that to understand and predict the epidemic, the simplest models are best. Major documented factors are the varying strains of Covid including delta and the various omicron strains. We describe the advantages of simple models for covid-19. We say a model is simple if its only parameter is the average rate of contact between people in the population. This contact rate can vary over time, depending on choices by policy setters. Transient interventions like lockdowns and intermittent mask campaigns can make the epidemic data confusing.
Modeling Infectious Disease Dynamics: A Case Study of Malaria Immunity
When: Tue, September 6, 2022 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Lauren Childs (Department of Mathematics, Virginia Tech) - https://personal.math.vt.edu/lchilds/
Abstract: The importance of understanding, predicting, and controlling infectious disease has become increasingly evident during the current COVID-19 pandemic. In particular, the pandemic highlighted the need for interpretable, quantitative models that link mechanism with data while accounting for variability. Despite significant effort and advances, infectious disease dynamics remain incompletely understood, in part due to the lack of heterogeneity considered in immunological, ecological, and epidemiological aspects, which produce complicated, non-linear feedbacks. In this talk, we will focus on an age-structured PDE model of malaria, one of the deadliest infectious diseases globally. Our novel model of malaria specifically tracks acquisition and loss of immunity across a population. We study the role of vaccination and immunity feedback on severe disease and malaria incidence through a combination of our analytical calculation of the basic reproduction number (R0) and numerical simulations. Using demographic and immunological data, we parameterize our model to simulate realistic scenarios in Kenya. Our work sheds new light on the role of natural- and vaccine-acquired immunity in malaria dynamics in the presence of demographic effects.
Spatial spread and persistence in integro-difference equations with a strong Allee effect
When: Tue, September 13, 2022 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Dr. Bingtuan Li (Dept. of Mathematics, University of Louisville) -
Abstract: Integro-difference equations (IDEs) are of great interest in the studies of invasions of populations with discrete generations and separate growth and dispersal stages. IDEs with a strong Allee effect can generate a variety of spatiotemporal patterns in population growth and spread. In this talk, we will discuss IDEs with a monotone growth function exhibiting a strong Allee effect, and present rigorous results regarding traveling wave solutions and results on minimal patch size for population persistence. We will also talk about IDEs with a growth function having a strong Allee effect and overcompensation, and provide analytical and numerical results on oscillating traveling wave speeds as well as stationary and oscillating non-spreading solutions.
Emergent pattern formation in the evolution of penguin colonies through time
When: Tue, September 20, 2022 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Dr. Heather Lynch (Dept. of Ecology and Evolution, Stony Brook University) -
Data-driven models of phytoplankton blooms and opportunities for undergraduate research
When: Tue, September 27, 2022 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Seth Cowall (St. Mary's College of Maryland) - https://inside.smcm.edu/directory/seth-cowall
Abstract: Phytoplankton are the base of the marine food web. They are also responsible for much of the oxygen we breathe, and they remove carbon dioxide from the atmosphere. The mechanisms that govern the timing of seasonal phytoplankton blooms is a highly debated topic in oceanography. Here, we present a macroscale plankton ecology model consisting of coupled, nonlinear reaction-diffusion equations with spatially and temporally changing coefficients to offer insight into the causes of phytoplankton blooms. This model simulates biological interactions between nutrients, phytoplankton and zooplankton. It also incorporates seasonally varying solar radiation, diffusion and depth of the ocean’s upper mixed layer because of their impact on phytoplankton growth. The model’s predictions are dependent on the dynamical behavior of the model. The model is analyzed using seasonal oceanic data with the goals of understanding the model’s dependence on its parameters and of understanding seasonal changes in plankton biomass. A study of varying parameter values and the resulting effects on the solutions, the stability of the steady-states, and the timing of phytoplankton blooms is carried out. The model’s simulated blooms result from a temporary attraction to one of the model’s steady-states. An ongoing undergraduate research project seeks to improve the timing of the model’s blooms by considering some zooplankton to be mixotrophic.
Stochastic Process Models in Movement Ecology
When: Tue, October 4, 2022 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Dr. Christen Fleming (University of Maryland and Smithsonian Conservation Biology Institute) -
Abstract: The study of animal movement has exploded with the development of GPS and lithium-ion battery technologies, and the Movebank data repository alone records millions of new animal locations per day. In general, data obtained by tracking animals are irregularly sampled time-series, subject to temporal autocorrelation, measurement error, and various seasonal and non-stationary behaviors. Therefore, the natural mathematical framework for these data are continuous-time stochastic process models. Here, I discuss a number of stochastic process movement models that are both biologically useful and mathematically interesting.
Pandemic Models and Real-Time Mitigation: Translating Epidemic Principles into Practice
When: Tue, October 11, 2022 - 5:00pmWhere: Atrium, Stamp Student Union
Speaker: Dr. Joshua Weitz (Georgia Institute of Technology and the Institut de Biologie at the École Normale Supérieure) - https://research.gatech.edu/joshua-weitz
Modeling seasonal malaria transmission: A methodology connecting regional temperatures to mosquito and parasite developmental traits
When: Tue, October 18, 2022 - 12:30pmWhere: John S. Toll Physics Building 2213
Speaker: Katherine Gurski (Howard University) - https://profiles.howard.edu/katharine-gurski
Abstract: Climate changes observed over recent years have raised concerns over the potential effect on disease spread. Temperature is a factor affecting mosquito population dynamics and the rate of development of the malaria parasite within the mosquito, and hence significantly affects malaria transmission dynamics. While a sinusoidal wave is commonly used to incorporate temperature effects in malaria models, it misses the within and between seasonal monthly and yearly variations in the parameter traits and temperature profiles. We address this in our seasonal malaria framework by introducing a spline methodology that integrates temperature-dependent mosquito and parasite demographic traits with regional temperature data to create a seasonal profile unique to each locale. The embedded time-varying parameters yield a non-autonomous system of differential equations. We compute periodic and invasion threshold numbers for two strains of malaria parasites, one sensitive to drugs and the other resistant to drugs, to shed light on the impact of variable temperature on disease dynamics. We present numerical simulations illustrating how climatic temperature changes will alter the monthly entomological inoculation rate and the number of malaria infections over three years.
Dissecting the roles of vector mobility and spatial heterogeneity in vector-borne disease control using degenerate perturbation theory.
When: Tue, October 25, 2022 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Dr. Jeff Demers (University of Maryland) -
Abstract: We present and analyze a vector-borne disease model with spatially heterogeneous control accessibility inspired by governmental efforts to contain the 2016 Zika outbreak in Miami, Florida. We show that in the presence of control efforts, the system’s basic reproduction number decreases as vector mobility increases, thus seeming to imply that an increased capacity for spatial disease spread decreases the potential for system-wide epidemic outbreaks. To explain this counterintuitive result, we apply perturbation techniques typically utilized in quantum mechanics to analyze our system’s next-generation matrix. Our results provide mechanistic insight into the influences of vector motion and spatial clustering on model predictions and raise important questions regarding the interpretation of the basic reproduction number in complex spatial models for disease spread.
Disease-economy trade-offs under alternative epidemic control strategies
When: Tue, November 1, 2022 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Ana I. Bento (School of Public Health, Indiana University & Rockefeller Foundation) - https://publichealth.indiana.edu/research/faculty-directory/profile.html?user=abento
Abstract: Public policy and academic debates regarding pandemic control strategies note disease economy trade-offs, often prioritizing one outcome over the other. Using a calibrated, coupled epi-economic model of individual behavior embedded within the broader economy during a novel epidemic, we show that targeted isolation strategies can avert up to 91% of economic losses relative to voluntary isolation strategies. Unlike widely used blanket lockdowns, economic savings of targeted isolation do not impose additional disease burdens, avoiding disease-economy trade-offs. Targeted isolation achieves this by addressing the fundamental coordination failure between infectious and susceptible individuals that drives the recession. Importantly, we show testing and compliance frictions can erode some of the gains from targeted isolation but improving test quality unlocks the majority of the benefits of targeted isolation.
A Rational Basis for Hope: Human Behavior Modeling and Climate Change
When: Tue, November 8, 2022 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Louis J. Gross (Department of Ecology and Evolutionary Biology, University of Tennessee) - https://eeb.utk.edu/people/louis-gross/
Abstract: It is easy to lose confidence in the capacity for human social and political systems to respond effectively to the challenges from rising average global temperature and associated climate change. A working group of diverse researchers with backgrounds in mathematical modeling, climate science, psychology, sociology, economics, geography and ecology has addressed the question as to whether there is any rational basis to expect that human behavioral changes can sufficiently impact climate to significantly reduce future mean global temperatures. Climate models can readily make assumptions about reductions in future greenhouse gas emissions and project the implications, but they do this with no rational basis for human responses. We have developed models to build such a rational basis to link social system and human behavior with climate. This includes a model from the theory of planned behavior in social psychology, linked to extreme events obtained from a climate model, and feedback to global emissions. Results from these efforts is that there is indeed some rational basis for hope, with social, political and technical feedback processes driving future climate policies and emissions. Under some circumstances these lead to a meaningful reduction of projected future average temperature.
Note: this talk is based in part on the following papers:
Beckage, B., L. J. Gross, K. Lacasse, E. Carr, S. S. Metcalf, J. M. Winter, P. D. Howe, N. Fefferman, T. Franck, A. Zia, A. Kinzig and F. M. Hoffman. 2018. Linking models of human behavior and climate alters projected climate change. Nature Climate Change 8: 79–84. Moore, F.C., K. Lacasse, K. J. Mach, Y. A. Shin, L. J. Gross and B. Beckage. 2022. Determinants of emissions pathways in the coupled climate–social system. Nature 603: 103–111=====================================================================
Brief Bio:
Louis J. Gross is a Chancellor's Professor Emeritus and Emeritus Distinguished Professor of Ecology and Evolutionary Biology and Mathematics and Director of The Institute for Environmental Modeling at The University of Tennessee, Knoxville. He was the founding Director of the National Institute for Mathematical and Biological Synthesis, a National Science Foundation-funded center to foster research and education at the interface between math and biology. He completed a B.S. degree in mathematics at Drexel University and a Ph.D. in applied mathematics at Cornell University, and has been a faculty member at UTK since 1979. His research emphasizes applications of mathematics and computational methods in many areas of ecology, including disease ecology, landscape ecology, spatial control for natural resource management, photosynthetic dynamics, and the development of quantitative curricula for life science undergraduates. He has served as Program Chair of the Ecological Society of America, President of the Society for Mathematical Biology, Treasurer for the American Institute of Biological Sciences and twice as President of the UTK Faculty Senate. He is the 2006 Distinguished Scientist awardee of the American Institute of Biological Sciences and is a Fellow of the American Association for the Advancement of Science and of the Society for Mathematical Biology. As the volunteer sound engineer for over thirty years at the Laurel Theater, he has engineered and recorded more than a thousand performances of traditional music. His live recordings have been included on over a dozen albums, and his free annual workshops have trained several hundred individuals in the basics of live sound engineering.
PHY 1402 Modeling social complexity in epidemiology: risk perception and adaptive human behavior
When: Tue, November 15, 2022 - 12:30pmWhere: John S. Toll Physics Building 1402
Speaker: Baltazar Espinoza Cortes (Biocomplexity Institute, University of Virginia) - https://biocomplexity.virginia.edu/person/baltazar-espinoza
Abstract: The COVID-19 pandemic highlighted critical factors to be addressed in the modern study of
epidemic dynamics: human behavior, economics, bio-surveillance, social dynamics and
information sharing, are but examples of these. Non-pharmaceutical interventions (NPIs)
constitute a suite of front-line behavioral responses, however collective compliance depends on individual level decisions, which may assemble a behavioral immune system at the population scale. Yet, many epidemiological models currently in use do not include a behavioral component, and do not address the potential consequences of individuals’ adaptive behavioral responses. The simultaneously evolving processes of individuals making behavioral decisions driven by the epidemic dynamics, that in turn reshape the contagions progression, make epidemics complex adaptive systems.
To study the complex dynamics between behavioral adaptations and the epidemic progression, we use a mechanistic modeling framework that explicitly incorporates the interdependence between the (individual-level) adaptive behavioral choices and the (population-level) progression of the infection process. We found that individuals’ risk perceptions in the presence of asymptomatic individuals modulates the final epidemic size. Moreover, under behavioral polarization, privately determined behavioral responses may increase the final size of the epidemic, compared to the homogeneous behavior scenario. Finally, we explored the effect of testing as a potential source of misinformation about the risk of infection, which may lead to harmful behavioral decisions ultimately increasing epidemic burden.
References
1. Asymptomatic individuals can increase the final epidemic size under adaptive human
behavior https://www.nature.com/articles/s41598-021-98999-2
2. Heterogeneous adaptive behavioral responses may increase epidemic burden
https://www.nature.com/articles/s41598-022-15444-8
Extinction of multiple populations and a team of Lyapunov functions
When: Tue, November 22, 2022 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Naghmeh Akhavan and James Yorke (UMBC and UMD) -
Abstract: We present a general trophic Lotka-Volterra model for which we show there is a bounded trapping region to which every trajectory is attracted. We explore a situation where this model satisfies a generalized competitive exclusion principle that applies so that one or more species will die out. When a Lotka-Volterra competition model has no steady state where all the species coexist, how many species must die out? Lyapunov functions can be vital tools for determining the behavior of a system. But constructing a Lyapunov function is usually difficult. Therefore, a general Lyapunov function for the d-dimensional competitive Lotka-Volterra model is still unknown. We find a novel type of Lyapunov function to show that “excess” species die out exponentially fast.
Causal Mediation Analysis to Address Intercurrent Unblinding in HIV Prevention Research
When: Tue, November 29, 2022 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Dr. Alisa Stephens-Shields (Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine , University of Pennsylvania) - https://scholar.google.com/citations?user=jAwqfdAAAAAJ&hl=en
Abstract:
Although the primary target of inference in randomized trials is often some version of an average causal effect, recent guidance in the practice of clinical trials has emphasized alternative estimands to enhance the handling of intercurrent events, such as treatment switches or discontinuation. We use causal mediation analysis to explicate observed treatment differences in the Phambili study, a double-blind, randomized trial of an experimental vaccine to reduce HIV infection. Initial treatment comparisons revealed an elevated rate of infection among participants randomized to vaccine compared with placebo. Interpretation of this effect, however, was challenged by the intercurrent effect of post-randomization unblinding due to futility in a parallel trial. Considering post-randomization sexual behavior, we use additive hazard models to estimate direct effects of vaccine when sexual behavior is standardized to the blinded period. Our analyses demonstrate the complementary value of this estimand in understanding the vaccine effect amid unblinding.
Modeling the COVID-19 Pandemic at home and abroad : The JHUAPL-Bucky Model
When: Tue, December 6, 2022 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Shelby Wilson (Johns Hopkins University, Applied Physics Laboratory) - https://shelby-wilson.com/
Abstract: Data-driven epidemiological modeling has allowed us to exploit a wealth of data related to the Covid-19 pandemic to provide real-time forecasts of the impact of the pandemic; providing key insights to the public and policy makers both in the US and abroad. These models have been used to assess the impact of public health interventions and estimate critical resource needs under great uncertainty. During the course of the COVID-19 pandemic, JHU-APL has developed the “Bucky Model”; an infectious disease model framework that models the spread of the COVID-19 pandemic at a sub-national level. At its base, the Bucky model is a geographically distributed SEIR model. Here, we will describe the JHUAPL-Bucky model as well as discuss how the model has been operationalized to support forecasts provided to both the CDC and the United Nations Office for the Coordination of Humanitarian Aid.
Ebola Virus Disease spread and control under various challenges such as conflicts and wars
When: Tue, February 7, 2023 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Jean Lubuma (University of Witswatersrand, Johannesburg, South Africa) - https://www.wits.ac.za/staff/academic-a-z-listing/l/jeanlubumawitsacza/
Abstract: In recent years, Ebola Virus Disease (EVD) outbreaks erupted in some African regions that are affected by conflicts and wars characterized by violence, destruction of Ebola Treatment Centers and escape of patients from hospitals to the bush. We develop SIR-type models in which these disruptive events are suitably incorporated. We also include in the models compartments that account for the education of the community and for the indirect/slow transmission through the contaminated environment. We compute the basic reproduction number of each model and use it to discuss the stability of disease-free and endemic equilibria. We construct dynamically consistent nonstandard finite difference (NSFD)
schemes. Using real data of the 2018-2020 EVD outbreak in the Democratic Republic of Congo (DRC), we undertake a statistical data analytics study, which supports the theory and leads to some recommendations to control the disease.
Integration of high-resolution mobility data into mathematical models for public heath applications
When: Tue, February 14, 2023 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Giulia Pullano (Georgetown University) -
Abstract: Human mobility is a driver of epidemics. Long-range movements drive patterns of pathogen importation and exportation. Short-range mobility fuels local epidemics, by tuning population mixing and transmission rates. This is why the study of patterns of mobility can tell us why and how epidemics break out and circulate, and inform us on effective prevention and control policies. The current COVID-19 pandemic is proof of this potential. In the last decades, human mobility data coming mostly from mobile devices (phones, smartphones) have informed epidemiological studies: e.g., malaria, cholera, Ebola. But, during this pandemic, we witnessed a data-sharing revolution in which network operators such as Orange, Vodafone, Telefonica, and companies like Google, Apple, Facebook, Cuebiq, SafeGraph, Unacast, started providing their aggregated mobility data extracted from mobile phone traces in real-time. These new resources are, however, also a challenge. Epidemiological research is in the process of developing new tools, and models, to integrate these data and use them both for retrospective analysis of epidemic dynamics, and for active surveillance and epidemic monitoring, at higher spatial resolution than ever before. In my talk, I will present how to properly aggregate high-resolution mobile phone individual trajectory data at different spatial scales to improve model prediction power. I will also discuss how to integrate the resulting aggregated mobility indicators into models to characterize the mobility process behind infectious diseases spread.
The Role of Human Movement on Vector-Borne Diseases
When: Tue, February 21, 2023 - 12:30pmWhere: Kirwan Hall 3206 - https://umd.zoom.us/j/8294121960?pwd=UjR3MXpTMFBNbjhTTjc4SjNQRXVTQT09
Speaker: Omar Saucedo (Virginia Tech) - https://math.vt.edu/people/faculty/saucedo-omar.html
Abstract: In the past decade, human mobility data has become increasingly available with the introduction of smartphone devices. Not only did communication between acquaintances and access to information become easier; smartphones can provide helpful clues on the movement patterns of individuals throughout their day. Additionally, incorporating mobility data into an epidemiological model offers valuable insight for implementing mitigation strategies. In this talk, we will present analytical tools for approximating the critical epidemiological threshold, the basic reproduction number, for a SIS-SI vector-borne disease multipatch model. We will use cell phone data to estimate the movement patterns of individuals between different geographical regions with the objective of understanding how the mobility network structure influences vector-borne disease dynamics.
Mathematics of malaria transmission dynamics: current challenges and eradication prospects
When: Tue, February 28, 2023 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Abba B. Gumel (Department of Mathematics, UMD) - https://math.umd.edu/~agumel/
Abstract: Malaria, a deadly disease caused by protozoan Plasmodium parasites, is spread between humans via the bite of infected adult female Anopheles mosquitoes. Over 2.5 billion people live in geographies whose local epidemiology permits transmission of P. falciparum, responsible for most of the life-threatening form of malaria. The widescale and heavy use of insecticide-based interventions, notably long-lasting insecticidal nets and indoor residual spraying), during the period 2000-2015, resulted in a dramatic reduction in malaria incidence and burden in endemic areas, prompting a renewed quest for malaria eradication. Numerous factors, such as Anopheles resistance to all currently-available insecticides and anthropogenic climate change, potentially pose important challenges to the eradication efforts. In this talk, I will discuss a genetic-epidemiology framework for assessing the impact of insecticide resistance on malaria. Specifically, questions on whether eradication can be achieved using existing insecticide-based control resources will be addressed. If time permits, I may briefly discuss the utility of some of the gene drive-based biological interventions being proposed as a plausible alternative pathway for achieving the laudable malaria eradication goal.
Some Suggested References:
1. Iboi Enahoro, Steffen Eikenberry, Abba B. Gumel, Silvie Huijben and Krijn Paaijmans. Long-lasting insecticidal nets and the quest for malaria eradication: A mathematical modeling approach. Journal of Mathematical Biology. 81(2020): 113-158.
2. Jemal Mohammed-Awel, Iboi Enahoro and Abba Gumel. Insecticide resistance and malaria control: A genetics-epidemiology modeling approach. Mathematical Biosciences. 325(2020): 108368.
3. Kamaldeen Okuneye, Steffen Eikenberry and Abba Gumel. Weather-driven malaria transmission model with gonotrophic and sporogonic cycles. Journal of Biological Dynamics. 13(1)(2019): 288-324.
4. Steffen Eikenberry and Abba Gumel. Mathematical modeling of climate change and malaria transmission dynamics: a historical review. Journal of Mathematical Biology. 77(4)(2018): 857--933.
5. Rahim Taghikhani, Oluwaseun Sharomi and Abba B. Gumel. Dynamics of a two-sex model for the population ecology of dengue mosquitoes in the presence of Wolbachia.
Mathematical Biosciences. 328(2020): 108426
6. Iboi Enahoro, Abba Gumel and Jesse E. Taylor. Mathematical modeling of the impact of periodic release of sterile male mosquitoes and seasonality on the population abundance of malaria mosquitoes. Journal of Biological Systems. 28(2) (2020): 277-310.
A review and application of Disease Informed neural networks for efficient parameter estimation
When: Tue, March 7, 2023 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Alonso Gabriel Ogueda (George Mason University (Graduate Student)) -
Abstract: In this talk we will introduce a novel parameter estimation approach called Disease Informed Neural Networks (DINNs) that can help to predict the dynamics of infectious diseases. This approach builds on Physics-Informed Neural Networks (PINNs) that have been applied successfully to a variety of applications that can be modeled by linear and non-linear ordinary and partial differential equations. Specifically, this talk will provide a review of DINNs applied to compartmental models in epidemiology with an overview of neural networks capable of learning how diseases spread, forecasting their progression, and finding their unique parameters. Specifically, we will apply our DINNs approach to mathematical models that incorporate transportation between populations and their impact on the dynamics of infectious diseases. We show how these approaches are capable of predicting the behavior of a disease described by governing differential equations that include parameters and variables associated with the movement of the population between neighboring cities. We show that our model validates real data and also how such DINNs based methods predict optimal parameters for given datasets.
Data-driven approaches for modeling, analysis and prediction of disease dynamics
When: Tue, March 14, 2023 - 12:30pmWhere: Brin Center Seminar Room, CSIC 4th Floor
Speaker: Padmanabhan Seshaiyar (George Mason University) - https://math.gmu.edu/~pseshaiy/
Abstract: Modeling, analysis and prediction have become foundational tools to understand the early transmission dynamics of infectious diseases, to evaluate the effectiveness of non-pharmaceutical control measures and to assess the potential for sustained transmission. Along with development of mathematical modeling, there have also been a variety of approaches that have been introduced to estimate the parameters such as the transmission, infection, quarantine and recovery rates using real data sets. In this talk, we will discuss how differential equation systems that are often used to describe the dynamics of infectious diseases can be learned from data. Specifically, we will discuss data-driven approaches such as physics informed neural networks for predicting the behavior of a disease described by compartmental models that include parameters and variables. Through benchmark problems, we will show that these approaches validate real-data and demonstrate how such physics informed neural network based methods can predict optimal parameters for a given dataset. We will also discuss developments in data science education that is needed to prepare the next generation workforce on robustness and reliability of mathematical modeling in research and education.
Human Disease, Dynamics and Control, a journey
When: Tue, March 28, 2023 - 12:30pmWhere: Kirwan Hall 3206
Speaker: James Yorke (UMD) - https://yorke.umd.edu/
Abstract: I will survey my modeling of measles, mumps, chicken pox, polio, gonorrhea, and HIV/AIDS to show how models have yielded important public health insights. And how COVID-19 yields insights important to modelling. All of this work is mostly due to my collaborators.
References:
Recurrent outbreaks of measles, chicken pox, and mumps:
I. Seasonal variation in contact rates, and
W. London, M.D. and J. A. Yorke,
Amer. J. Epidemiology 98 (1973), 453-468.
Seasonality and the requirements for perpetuation and eradication of viruses in populations,
J. A. Yorke, N. Nathanson, G. Pianigiani and J. Martin,
Amer. J. Epidemiology 109 (1979), 103-123.
Gonorrhea Transmission Dynamics and Control,
H. W. Hethcote and J. A. Yorke,
Springer-Verlag Lecture Notes inBiomathematics #56, 1984.
HIV Epidemics Driven by Late Disease-Stage Transmission,
Brandy L. Rapatski, Frederick Suppe, and James A. Yorke,
JAIDS, Journal of Acquired Immune Deficiency Syndromes, 38, 2005, 241-253.
When the best pandemic models are the simplest
Sana Jahedi and James A. Yorke
Biology 2020, 9, 353;
Covid-19 work in progress with Shayak Bhattacharjee
Climate change, extreme weather events and impaired health – how do we adapt as a society?
When: Tue, April 4, 2023 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Amir Sapkota (UMD) - https://sph.umd.edu/people/amir-sapkota
Abstract: Most recent IPCC report suggests that the frequency, intensity, and duration of extreme weather events are increasing and this trend will continue into the foreseeable future because of ongoing climate change. This poses a fundamental question – how are we going to adapt to these new set of hazards as a society? Using local, national and global epidemiological data, this presentation will highlight climate change related differential health burden experienced by vulnerable communities. Building on these findings, the presentation will argue for an AI based public health early warning system with sub-seasonal to seasonal lead time to enhance public health adaptation to climate change.
The evolution and maintenance of host genetic diversity for pathogen resistance
When: Tue, April 11, 2023 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Samuel Hulse (Biology, UMD (Postdoc)) -
Abstract: In natural systems, organisms must confront a near constant deluge of harmful pathogens, both from endemic pathogens that share long coevolutionary histories, as well as from novel spillover pathogens. Previous work has shown a general pattern of correlated resistance to endemic and foreign pathogens, with pleiotropy proposed as the underlying mechanism. However, pleiotropy alone cannot explain all the resistance variation observed in natural populations, such as individuals with relatively high resistance against foreign pathogens and low resistance to endemic pathogens.
In this talk, I will discuss some of our work detailing models with multiple forms of resistance. We find that feedback between general and specific forms of resistance can recapitulate some patterns seen in natural populations. We also find that eco-evolutionary feedbacks between host and pathogen are important for host resistance at multiple loci. For example, host-pathogen coevolution can help maintain general resistance in hosts, and how this may reduce spillover risks. I will also discuss our work on the evolution of quantitative resistance, where we use a random mutation model to avoid explicit assumptions about the shape of the tradeoff function.
A PDE Model to Study Natural Selection Across Multiple Levels of Organization
When: Tue, April 18, 2023 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Daniel Cooney (University of Pennsylvania) - https://www.math.upenn.edu/people/daniel-cooney
Abstract: Natural selection in complex biological and social systems can simultaneously operate across multiple levels of organization, ranging from genes and cells to animal groups and complex human societies. Such scenarios typically present an evolutionary conflict between the incentive of individuals to cheat and the collective incentive to establish cooperation within a group. In this talk, we will explore this conflict by considering a game-theoretic model of multilevel selection in a group-structured population featuring two types of individuals, cooperators and defectors. Assuming that individuals compete based on their payoff and groups also compete based on the average payoff of group members, we consider how the distribution of cooperators within groups changes over time depending on the relative strength of within-group and between-group competition. In the limit of infinitely many groups and of infinite group size, we can describe the state of the population through the probability density of the fraction of cooperators within groups, and derive a hyperbolic partial differential equation for the changing composition of the population. We show that there exists a threshold relative selection strength such that defectors will take over the population for sufficiently weak between-group competition, while cooperation persists in the long-time population when the strength of between-group competition exceeds the threshold. Surprisingly, when groups are best off with an intermediate level of within-group cooperation, individual-level selection casts a long shadow on the dynamics of multilevel selection: no level of between-group competition can erase the effects of the individual incentive to defect. This is joint work with Yoichiro Mori.
The progression of neural representations of speech in the brain, from acoustics to semantics
When: Tue, May 2, 2023 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Jonathan Simon (UMD, Departments of Biology & Electrical and Computer Engineering & The Institute for Systems Research) - http://cansl.isr.umd.edu/simonlab
Abstract: Listening to speech drives robust neural responses throughout auditory and language processing pathways. These can be recorded non-invasively using magnetoencephalography (MEG) and electroencephalography (EEG), since the neural processing is performed using electrical current that generate recordable electromagnetic fields. The recorded fields can be well modeled as the linear superposition of responses of the different brain areas, each of which responds approximately linearly to the speech representation to which it is tuned. This talk will focus on the concomitant multi-dimensional kernel estimation, and the identification of the relevant speech representations. The earliest (lowest latency) neural representations are dominantly acoustical (“bottom up”), but later neural representations become more and more influenced by language and general cognitive (“top down”) factors, and tend to reflect the speech as it is perceived rather than as the acoustic input alone would predict.
A Reinforced Diffusion Approach to Modeling Animal Memory
When: Tue, May 9, 2023 - 12:30pmWhere: Kirwan Hall 3206
Speaker: Frank McBride (AMSC Graduate Program) -
Abstract: This talk will focus on recent work modeling the impact of memory on animal movement as reinforced diffusion, described by a system of stochastic integrodifferential equations. The novel approach expands on past work modeling animal movement as a stochastic process, expanding past models with terms to account for short-term and long-term interactions between an animal and the set of its past locations. We found that, under certain parameters, this combination of short- and long-term memory effects produces networks of relatively stable, heavily reused trails. I will also discuss applications of the model to real-world animal movement data, in which simple modifications of the model reproduce key qualitative behavior seen in experiments with argentine ant colonies and homing pigeons.
Population dynamics and demography of North American barren-ground caribou
When: Tue, May 9, 2023 - 1:00pmWhere: Kirwan Hall 3206
Speaker: Marron McConnell (BEES Graduate Program) -
Call coming
When: Mon, June 26, 2023 - 8:00amWhere: