The following is a tentative program schedule for the minisymposium. Please check back here for the most up-to-date information.
In order to help participants find the Biology building (which is where all in-person talks will take place), please see this map. For further clarity, see the map below (which has the biology building highlighted).
Friday, August 20, 2021 [Location: BIOL 101 and 102]
1:15 - 1:25 PM
Welcome and opening remarks
1:30 - 2:20 PM
Alun Lloyd — Stochasticity and Heterogeneity in the Aedes aegypti/Dengue Transmission System: Implications for Spread and Control of Infection
Slides: PDF file
Abstract: The Aedes aegypti mosquito is the vector for several infections of public health concern, including dengue, chikungunya, Zika and yellow fever. The mosquito lives in close proximity to humans, typically only disperses over short distances and its population density is often highly heterogeneous across space. As a result, the transmission dynamics of the infections it vectors are subject to significant heterogeneity which must be accounted for when modelling the spread and control of these infections. Through a series of vignettes, we will discuss some of this modelling, utilizing a number of different mathematical and simulation frameworks---from deterministic and stochastic multi-patch models through to cohort or individual-based simulation models. Pros and cons of the various approaches will be discussed.
2:30 - 3:20 PM
Christina Edholm — Heterogeneity in Transmission for Superspreaders
Abstract: The importance of host transmissibility in disease emergence has been demonstrated in historical and recent pandemics that involve infectious individuals, known as superspreaders, who are capable of transmitting the infection to a large number of susceptible individuals. To investigate the impact of superspreaders on epidemic dynamics, we formulate deterministic and stochastic models that incorporate differences in superspreaders versus nonsuperspreaders. In particular, continuous-time Markov chain models are used to investigate epidemic features associated with the presence of superspreaders in a population. We parameterize the models for two case studies, Middle East respiratory syndrome (MERS) and Ebola. In this talk, we will explore how superspreaders and environmental variability impact important epidemiological measures via mathematical analysis and numerical simulations.
3:30 - 5:30 PM
In-person poster session
Saturday, August 21, 2021 [Location: BIOL 101 and 102]
8:30 - 9:20 AM
Spencer Hall — Heterogeneity in food web modules of disease: a look at infection classes and resources, host evolution and resources, and stage-structure and predators
Abstract: Heterogeneity in disease systems, like in all of ecology, abounds. If we embrace that heterogeneity, we can understand and predict outcomes that otherwise seem surprising. To illustrate, I'll show three examples. First, heterogeneity of hosts due to infection can make stable host-resource systems oscillate but oscillatory host-resource system stable. I show this mathematically, by tracing the implications of disease-mediated trophic cascades onto resource density-dependence to stability via feedback loops. Next, I show how those disease-mediated cascades can lead to evolution of increased host susceptibility (not resistance) – and larger (not smaller) disease epidemics. I show these results with an experiment and model of adaptive dynamics. Finally, with field, experiments, and models, I illustrate how predators which cull juveniles can shift populations towards adult stages. Such 'culling the young' then can create larger, not smaller epidemics. All three examples illustrate how host heterogeneity (infection, genetic, stage) and resources, together, can produce outcomes in disease systems that seem hard that would seem to contradict the norm.
9:30 - 10:20 AM
Jude Kong — Phytoplankton competition for nutrients and light in a stratified lake: a mathematical model connecting epilimnion and hypolimnion
Slides: PDF file
Abstract: In this talk, I will present several mathematical models describing the vertical distribution of phytoplankton in the water column. In particular, I will introduce a new mathematical model connecting epilimnion and hypolimnion to describe the growth of phytoplankton limited by nutrients and light in a stratified lake. Stratification separates the lake with a horizontal plane called thermocline into two zones: epilimnion and hypolimnion. The epilimnion is the upper zone which is warm (lighter) and well-mixed; and the hypolimnion is the bottom colder zone which is usually dark and relatively undisturbed. The growth of phytoplankton in the water column depends on two essential resources: nutrients and light. The critical thresholds for settling speed of phytoplankton cells in the thermocline and the loss rate of phytoplankton are established, which determine the survival or extirpation of phytoplankton in epilimnion and hypolimnion. This is a joint work with Jimin Zhang (Heilongjiang University), Junping Shi (William & Mary) and Hao Wang (University of Alberta).
10:30 - 11:00 AM
Break
11:00 - 11:50 AM
Mark Lewis — Mathematical Analysis of Animal Movement Patterns
Abstract: Animal movement patterns have long been the subject of mathematical and ecological interest. How do individual behavioral decision rules translate into macroscale patterns of space use such as foraging, patrolling or territories? I will show how mechanistic models, using random walks, stochastic processes, first passage time analysis and partial differential equations can be used to connect underlying processes to the observed patterns. Here interactions are complex and may involve memory of past events, as well as a cognitive map. I will make applications to a spectrum of different emerging patterns, ranging from territories in Amazonian birds to patrolling in wolves to seasonal movement patterns in grizzly bears.
12:00 - 1:30 PM
Lunch break
1:30 - 2:50 PM
Joel Brown — Using Evolutionary Game Theory to Model, Understand and Treat Cancer
Abstract: Here I present cancer and therapy as an evolutionary game. Instead of a disease of unregulated proliferation or a disease of the genes, cancer is a speciation event where a multicellular organism gives rise to a new single celled pathogen. The cancer cell lineage goes from being a part of the whole organism’s traits to becoming a self-defined fitness function. Cancer happens when the cell becomes the unit of natural selection. Within their tumor ecosystem, cancer cells experience and contribute to heterogeneity that selects for the evolution and coexistence of different cancer “species”. The cancer cells engage in public goods games, tragedies of the commons and competition for scarce resources. Between patients with the same cancer, one sees a high degree of functional and morphological convergent evolution. Tumor growth, metastases and ill-health to the patient all become emergent properties of cancer’s eco-evolutionary dynamics. Treatment becomes part of the game where the physician can and should be the leader in a Stackelberg Game. This gives rise to evolutionarily informed therapies where the goal is to anticipate and steer the cancer’s ecology and evolution. A clinical trial of advanced metastatic prostate cancer has more than doubled progression free survival. Additional clinical trials show a lockstep between mathematical modelling and clinical application. In two forms, adaptive therapy aims to contain an otherwise incurable stage of cancer, and, more recently, extinction therapy applies ecological and evolutionary principles to engineer the cure of otherwise incurable cancers by engaging the cancer in a kind of chess match.
3:00 - 3:50 PM
Elizabeth Hobson — The evolution of decision-making, social cognition, and complex sociality
Abstract: In many social species individuals create their social worlds through interaction decisions and are then subject to and constrained by these social constructs, which can affect an individual’s future actions. Understanding how much individuals “know” about their social worlds is critical in understanding these potential feedbacks. However, it is difficult to determine how much information individuals have about the social structures in which they live. I present new computational methods that make detecting the presence and use of social information more tractable and serve as social assays to categorize the social dominance patterns used to direct aggression within dominance hierarchies. Using a historical dataset containing 85 species, I will show how custom-built reference models can allow us to detect the presence and use of information and heterogeneity in rank-based aggression patterns. These approaches, and a taxonomically broad perspective, provide new opportunities to investigate the effect of social information on individual behavior within conflict, and has the potential to provide rigorous evidence for the evolutionary patterns underlying social cognition.
4:00 - 5:30 PM
Remote poster session
Registered Participants
The following is a list of registered participants for the minisymposium. Those who are presented posters have their titles and abstracts listed, as well.
Participant
Poster Title and Abstract (if applicable)
Odelola Veronica Abimbola
Osun State University
Abdulakeem Adams
Federal University of Agriculture, Abeokuta Nigeria
Antifungal activities of Wood Ash extract of Delonix regia and Mangifera indica against selected fungal plant pathogens
Abstract: Abstract to come...
Adeola Adeboje
University of Kansas
Samuel Adeniyi Adeleye
Rutgers University, New-Brunswick
Title (TBA)
Abstract: Abstract to come...
Bukola Muibat Adenuga
University of Ibadan
Modeling the Growth of Four Rabbit Breeds
Abstract: Abstract to come...
Akande Oluwatosin Adetoye
Ladoke Akintola University of Technology Ogbomoso, Nigeria
Joshua Oluwasegun Agbomola
Tai Solarin University of Education
Bifurcation analysis and dynamical behavior of an Ebola virus model with Saturated incidence
Elements of disease in a changing world: modelling feedbacks between infectious disease and ecosystems
Abstract: An overlooked effect of ecosystem eutrophication is the potential to alter disease dynamics in primary producers, inducing disease-mediated feedbacks that alter net primary productivity and elemental recycling. Models in disease ecology rarely track organisms past death, yet death from infection can alter important ecosystem processes including elemental recycling rates and nutrient supply to living hosts. In contrast, models in ecosystem ecology rarely track disease dynamics, yet elemental nutrient pools (e.g. nitrogen, phosphorus) can regulate important disease processes including pathogen reproduction and transmission. Thus, both disease and ecosystem ecology stand to grow as fields by exploring questions that arise at their intersection. However, we currently lack a framework explicitly linking these disciplines. We developed a stoichiometric model using elemental currencies to track primary producer biomass (carbon) in vegetation and soil pools, and to track prevalence and the basic reproduction number (R0) of a directly transmitted pathogen. This model, parameterized for a deciduous forest, demonstrates that anthropogenic nutrient supply can interact with disease to qualitatively alter both ecosystem and disease dynamics. Using this element-focused approach, we identify knowledge gaps and generate predictions about the impact of anthropogenic nutrient supply rates on infectious disease and feedbacks to ecosystem carbon and nutrient cycling.
Oladejo Ayobukola
Federal University of Technology, Akure
Salmonella Infections: The Increased Incidence of Foodborne illness outbreak, Antimicrobial-Resistance, Effects on the economy and Prevention
Abstract: Abstract to come...
Bamigbade Gafar Babatunde
Crescent University, Abeokuta, Ogun State, Nigeria
Antifungal Activities of Lactic Acid Bacteria Isolated during Fermentation of Cassava
Abstract: Abstract to come...
Harsimran Bains
Texas Tech University
Mathew Beauregard
Stephen F. Austin State University
Basic Proof of Finite Speed of Propagation in One Dimensional Degenerate Einstein Brownian Motion Model
Abstract: We use the generalization of Einstein’s paradigm of Brownian motion for diffusion when the parameter of the time interval of free jump degenerates to derive a system of one dimensional degenerate nonlinear partial differential equation. The solution of the system represents the number of particles per unit volume during the diffusion process as the time interval of free jumps degenerates. Specifically, in these equations, the time interval depends on the solution of the equations. We will demonstrate the finite speed of propagation of the system by using the construction of Christov-Hevage-Ibraguimov-Islam and the subsequent methods of Kompaneets–Zel’dovich–Barenblatt.
Research supported by REU NSF grant \#DMS-2050133.
Stochastic Modelling of Event-Based SARS-CoV-2 Superspreading
Abstract: The novel coronavirus SARS-CoV-2 emerged in China’s Hubei province in the winter of 2019 and subsequently spread throughout the world, causing over 180 million cases and 3 million deaths to-date. However, SARS-CoV-2 outbreak profiles vary by region. Superspreading – both individual- and event-based – spurs SARS-CoV-2 spread and may contribute to variability. While individual-based superspreading involves individuals who cause disproportionately more infections over their infectious lifetime, event-based superspreading involves public events and/or social gatherings that result in multiple infections. Here, we adopt an event-based framework. Superspreading events (SSEs) are incorporated into a continuous-time Markov chain model in such a way that their influence on outbreak dynamics may be investigated relative to that of non-SSEs. The model further incorporates heterogeneities in transmission from and recovery of asymptomatic versus symptomatic individuals. We explore three versions of this model – two with hospitalization and quarantine and one without – by varying the SSE-related and non-SSE-related infection rates and find that SSE-dominated outbreaks are more variable than non-SSE-dominated outbreaks. This variability has important public-health implications, as it limits prediction robustness and complicates management strategies. These implications suggest a heightened need for targeted control of SSEs.
Traveling Band Model for E. coli Transport in the Presence of Limited Immovable Food using the Einstein Paradigm of Brownian Motion
Abstract: In the presence of a limited amount of sugar, E. coli bacteria will travel in bands towards their food. Using the Einstein paradigm of Brownian motion and making hypotheses about the expected free jumps of these bacteria and the crowd transport that affects their motion, a predator-prey PDE model can be created to model the changes in the concentrations per unit volume of the bacteria and of a limited amount of food. Implementing a traveling band structure will provide us with an ODE model that can be used to find closed form solutions for each of the concentrations. By analyzing the graphs of these solutions, the traveling band structure can be observed in the changes in the two concentrations as the E. coli bacteria travel along the tube and consume the sugar.
Mitigating the Losses of COVID-19 variants breakthrough following vaccination
Abstract: Due to high infectiousness, Coronavirus Disease (COVID-19), which began as a small outbreak in Wuhan, China in December 2019, became a global pandemic within months. In absence of pharmaceutical treatment, various non-pharmaceutical interventions (NPI’s) to contain the spread of COVID -19 brought the entire world to a halt [1][2]. After a year of seemingly returning to normalcy with the launch of the world’s quickest vaccine development [3], the advent of more infectious and vaccine resistant COVID variants is bringing the situation back to where it was a year ago. In the light of this new situation, we conducted a study to examine the impact of the variants in the United States in a number of plausible scenarios. We developed and analyzed a compartmental system of ordinary differential equations tracking COVID transmission under vaccination implementation to simulate the future scenarios.
Immune priming and the limited diversity of resistance genes in host mixtures
Abstract:Host mixtures are a promising method for agroecological plant
disease control. Plant immunity is key to the success of host mixtures
against polymorphic pathogen populations. This results from
priming-induced cross-protection, whereby plants able to resist
infection by specific pathogen genotypes become more resistant to other
pathogen genotypes. Strikingly, this phenomenon was thus far absent from
mathematical models aiming at designing host mixtures. First, we developed an epidemiological model in n dimensions to explore the effect of host mixtures with a large resistant host genotypes on the equilibrium prevalence of the disease. A significant amount of resistance genes must be deployed to achieve low disease prevalence. Next, as in a simple case of gene-for-gene interaction model, priming minimizes the prevalence of the disease by cross-protection process (Clin et al, 2020), consider it in a n dimensional model could reduce the number of plant genotypes needed to drop the prevalence below an acceptable level. Given the limited availability of resistance genes in cultivars, this mechanism of plant immunity would make the use of host mixtures more realistic.
Peaceman Model for Well-Block and Steady-State Einstein Paradigm of Brownian Motion
Abstract: We started by exploring Einstein's Paradigm of Brownian Motion, and used it to create a model for flow towards a well in a circular reservoir in the steady-state, or linear case, with which we compared to Peaceman's model for flow in a well-block. Using Darcy's Law, we found the equation for rate of production, and through discrete finite approximation, equated it to Peaceman's equation for rate of production, where pressure on the exterior of the reservoir is given, and pressure on the well is related to the rate of production where the value for the radius of the well is adjusted to allow for this. We then used Forchheimer's equation to find a formula for rate of production of the non-linear case, and again compared it to Peaceman's equation. In our equation, we find that the radius of the well is dependent on the exterior pressure, well pressure, and radius of the total reservoir, with which the value for the radius of the well converges to the value of the well radius found from using Darcy's Law. In the future we will apply these formulas to realistic reservoir simulations to further test the capabilities of our model for modeling various biological processes.
Olanrewaju Dada
Mountain Top University, Makogi
Sachith Eranga Dassanayaka
Texas Tech University
Jummy David
York University
The effect of human mobility on the spatial spread of airborne diseases: an epidemic model with indirect transmission
Stoichiometric regulation of immune responses in primary producers
Abstract: All organisms require carbon and nutrients such as nitrogen for their growth and reproduction. In the presence of pathogens, host defense has been shown to increase with enhanced nutrient availability. Thus, availability of nitrogen may stimulate a host by enhancing its growth as well as immunity response. However, at the same time, nutrient availability may promote infection as higher host growth trades-off with reduced resistance as well as through enhanced pathogen performance. We explore the role of nitrogen availability on infection dynamics of a primary producer host and its pathogen using a stoichiometry-based disease model. Specifically, we test how changes in nitrogen investments in host immune response will alter host biomass build-up and pathogen infection rates.
Godservice Eziefule
University of Port Harcourt
Amos Tochukwu Ezeh
University of Ilorin
Tao Feng
Yangzhou University
Khaled Furati
King Fahd University of Petroleum and Minerals
HPV vaccination strategy: modeling and implications
Abstract: Vaccination is effective in preventing human papillomavirus (HPV) infection. It is
imperative to investigate who should be vaccinated and what the best vaccine distri-
bution strategy is. In this paper, we use a dynamic model to assess HPV vaccination
strategies in a heterosexual population combined with gay, bisexual, and other men
who have sex with men (MSM). The basic reproduction numbers for heterosexual
females, heterosexual males and MSM as well as their average for the total popula-
tion are obtained. We also derive a threshold parameter, based on basic reproduction
numbers, for model analysis. From the analysis and numerical investigations, we have
several conclusions. (1) To eliminate HPV infection, the priority of vaccination should
be given to MSM, especially in countries that have already achieved high coverage
in females. The heterosexual population gets great benefit but MSM only get minor
benefit from vaccinating heterosexual females or males. (2) The best vaccination strat-
egy is to vaccinate MSM firstly as many as possible, then heterosexual females, lastly
heterosexual males. (3) Given a fixed vaccination coverage of MSM, distributing the
remaining vaccines to only heterosexual females or males leads to a similar preva-
lence in the total population. This prevalence is lower than that when vaccines are
distributed to both genders. The evener the distribution, the higher the prevalence in
the total population. (4) Vaccination becomes less effective in reducing the prevalence
as more vaccines are given. It is more effective to allocate vaccines to a region with
lower vaccination coverage. This study provides information that may help policy-
makers formulate guidelines for vaccine distribution to reduce HPV prevalence on the
basis of vaccine availability and prior vaccination coverage. Whether these guidelines
are affected when the objective is to reduce HPV-associated cancer incidence remains
to be further studied.
Simplified Stoichiometric Model of Nutrient-Mediated Pathogen Dynamics
Abstract: Organisms can be thought about in terms of their carbon biomass and nitrogen content. When disease spreads in an ecosystem, it can play a large role in the reproduction and nutrient uptake of the organisms. Our work combines a disease model and a nutrient model in order to build a more complete representation of an ecosystem. We begin with a five-equation stoichiometric system that we simplify to a two-equation system by assuming that nutrient dynamics occur on a much faster timescale than population dynamics. Our goal is to understand how nutrient availability affects long term behavior of a disease population.
Abstract: Usutu virus is a mosquito-borne virus maintained in wild bird populations, which leads to mosquito infections, and occasional spillover in humans. It has been hypothesized that increased Usutu virus replication in birds and/or decreased bird immune competence leads to increased mosquitoes infection and increased transmission to humans. To provide insight into the intrinsic complexity of host-virus processes in birds, we developed mathematical models of Usutu virus infection and fitted them against four Usutu virus strains data from chicken infections. We have also investigated the effect of antibody on virus dynamics by fitting the models to chickens that were genetically engineered to have low and high antibody count. Parameter distributions for virus production, virus replication, host responses, and basic reproduction number were generated using non-linear mixed-effects models. We observed differences in virulence amongst the four virus strains, and found that birds with high antibody count have higher infected cell killing and higher virus clearance rates, indicative of non-neutralizing antibody function. These results can be used to better determine which virus strain is the most likely to spillover in the human population.
Exploring the Contribution of “Silent Spreaders” to COVID-19 Disease Dynamics in British Columbia, Canada
Abstract: The dynamic nature of the COVID-19 pandemic has demanded a public health response that is constantly evolving due to the novelty of the virus. Many jurisdictions in the US, Canada, and across the world have adopted social distancing and recommended the use of face masks. Considering these measures, it is prudent to better understand the contributions of subpopulations—such as “silent spreaders”—to disease transmission. Silent spreaders are those who are undiagnosed or never develop symptoms throughout the entire disease duration. As a result, they do not experience disease-induced mortality and are less likely to get tested or self-isolate compared to symptomatic spreaders who show symptoms and have the potential to experience disease-induced mortality. To capture differences between sub-populations, our mathematical model divides silent spreaders and symptomatic spreaders into two separate classes. We then fit the model to the number of confirmed cases, deaths, and recoveries to derive transmission rates, death rates, and other relevant parameters for outbreaks. Then, we used these parameters as the baseline to explore how disease dynamics change due to demographic and environmental variations. Through modelling, we hope to identify shifts in disease dynamics resulting from the implementation of public health restrictions and the degree of adherence to social distancing.
Wisdom Hyginus
Federal University of Technology Owerri
Ifeanyi Ijeomah
Abia State University, Uturu
Saddam Muhammad Ishaq
University of Chinese Academy of Sciences
In silico comparative analysis of BRCA2 gene in some selected animal species in Africa
Abstract:Background: BRCA2 genes are not only found in humans, but in other animal species. BRCA2 gene plays a vital role in maintaining the stability of a cell's genetic information. BRCA2 is considered as a gatekeeper gene; however, if mutated or abnormally expressed, it causes the destruction of normal cell structure and promotes the growth of cancer cells.
Aim & Objective: This study aimed to assess the differences and similarities of BRCA2 genes from different animal species in Africa. Insilico genomics analysis of BRCA2 gene of different animal species in Africa was the objective achieved in this study in view of providing insights on its comparative genomics features.
Materials and Methods: Fifteen nucleotide sequences of BRCA2 gene of different mammals and bird species were retrieved from National Centre for Biotechnology Information (NCBI). Multiple sequence alignment was done with MEGA 7.0 software, while identity and similarities were determined by constructing a pairwise comparison. Conserved domains on the sequences were identified with NCB1-CDD.
Results: BRCA2 gene was found to be present not only in humans, but other lower animals and birds across African countries. The phylogenetic tree for Homo sapiens BRCA2 gene in Tunisia belongs to the same ecological niche with the Theropithecus gelada BRCA2 gene in Ethiopia and BRCA2 from the same African region has high bootstrap, implying that they share the same homology. Conserved regions identified in the all the sequences were absent in Miniopterus natalensis and most present in Chrysochloris asiatica, Theropithecus gelada, Apaloderma vittatum, Pterocles gutturalis, Rousettus aegyptiacus, Homo sapiens, Echinops telfairi, and Cavia porcellus.
Conclusion: Based on the findings obtained from this study, BRCA2 gene in humans and other lower animals, particularly from same region, share the same homology and similarities.
Deciphering the Role of Immune Responses & the Route of Infection In-Host Foot-and-Mouth Disease (FMD) Progression
Abstract: Foot-and mouth disease viruses are highly contagious, globally distributed pathogens that can infect a broad range of cloven-hoofed ungulates, including livestock and wild species. Viral production, transmission rates, and disease outcomes vary among host species, and viral serotypes. Variation in within-host viral dynamics may underpin these differences among host species and viral serotypes; as such, understanding dynamic interactions between viral replication and the hosts' immune responses may provide mechanistic insight into variable disease dynamics and host outcomes for this important pathogen. Within-host dynamics of FMDV have been investigated in livestock but have not been explored in its wild reservoir host, African buffalo. Here we combine data on viral dynamics, innate and adaptive immune responses of buffalo experimentally infected with southern African territories serotypes of FMDV (SAT1, 2, 3) with non-linear ODE models, practical identifiability analysis, and uncertainty quantification, to ask (i) How does the route of infection affect within-host viral and immune dynamics? (ii) How do viral and immune dynamics vary among viral serotypes SAT1, SAT2, SAT3? (iii) What immune parameters are most informative for predicting within-host viral dynamics? Our models show that there is significant variation across serotype for contact infected hosts and that needle infected hosts are not suitable for understanding infection dynamics across scales. These results correspond with transmission parameters at the population scale. Together, our results present one of the first cases where data and models dovetail across organizational scales: variation among within-host viral and immune dynamics are consilient with contrasting transmission dynamics at the population scale.
Integrating Adverse Outcome Pathways for Drug-Induced Liver Injury
Abstract: The development of high throughput in vitro assays has had profound impact on toxicological assessment. It has the potential to lead to more efficient, accurate, and less animal-intensive testing. However, how to take advantage of the numerous in vitro tests in actual risk assessment processes is still a significant challenge. On the other hand, the adverse outcome pathway (AOP) framework has emerged as a rich source for mechanistic knowledge and as a potential tool to select and structure in vitro assays in predictive models for toxicity. In this study, we utilize knowledge encoded in AOPs to build a predictive model for drug-induced liver injury (DILI) using a Bayesian network approach. DILI is an important cause for the abandonment of promising drug candidates as well as drugs withdraws from the market. Due to its importance, there is substantial work to define AOPs that lead to liver toxicity. We reviewed AOPwiki and related literature to construct a comprehensive AOP network regarding DILI, which represents our current knowledge for molecular events that lead to liver injury by drugs and other chemicals. This constitutes a graphical guide to develop in vitro assays to detect liver injury. As the basic structure of AOP networks are directed acyclic graphs, they provide a natural opportunity to construct models with Bayesian networks. In this poster, we present a Bayesian network model based on DILI AOP networks using L1000 and Tox21 data for gene expression and nuclear receptor binding. Due to the incorporation of significant expert knowledge in the form of AOP, the Bayesian network model has the advantage of being parsimonious and requires only a small number of assays to predict the risk of toxicity.
Simplified Stoichiometric Model of Nutrient-Mediated Pathogen Dynamics
Abstract: Organisms can be thought about in terms of their carbon biomass and nitrogen content. When disease spreads in an ecosystem, it can play a large role in the reproduction and nutrient uptake of the organisms. Our work combines a disease model and a nutrient model in order to build a more complete representation of an ecosystem. We begin with a five-equation stoichiometric system that we simplify to a two-equation system by assuming that nutrient dynamics occur on a much faster timescale than population dynamics. Our goal is to understand how nutrient availability affects long term behavior of a disease population.
Christopher Mitchell
Tarleton State University
Okeh Mmaduabuchi
Federal university of technology Owerri
Assessing the impact of adherence to Non-pharmaceutical interventions and indirect transmission on the dynamics of COVID-19: a mathematical modeling study
Data-driven deep learning algorithm for Asymptomatic COVID-19 model with varying mitigation measures and transmission rate
Abstract: The fight against COVID-19 has been largely successful due to the adoption of several pharmaceutical and non-pharmaceutical mitigation measures, these measures directly impact the transmission of COVID-19. As a result, in this study, we develop an asymptomatic mathematical model, and an Epidemiology Informed Neural Network algorithm is introduced to learn the nonlinear time-varying transmission rate from data. The accuracy of the algorithm developed for the model is demonstrated using error metrics in a data-driven simulation of COVID-19.
Dynamics of Eastern Equine Encephalitis Infection Rates: A Mathematical Approach
Abstract: The Eastern Equine Encephalitis Virus (EEEV) is an erratic and deadly neurological disease that spans across the northeastern coast of the United States. To determine the rate at which the virus is spread between the Black-Tailed Mosquito (Culiseta melanura) and select avian species we began by analyzing the migration patterns of both the mosquito and the avian species. It was found that certain species of avians shared similar, or even identical, flight patterns with the Black-Tailed Mosquito. Through this research, we develop and analyze a system of Ordinary Differential Equations (ODEs) to gain insight into how and when transmission of the virus to avians is at its highest. We incorporate a host stage-structured model where the avian host group is split into two categories, adults and young-of-the-year birds (YOY). Using this we explored the extent to which fluctuations occurred in transmission rates according to host/vector abundances, mosquito biting rate, and type of host. We evaluate the hypothesis that YOY avians are more readily exposed to the mosquito vector as they lack a defense mechanism, unlike their adult counterpart using the compartmental model.
Simplified Stoichiometric Model of Nutrient-Mediated Pathogen Dynamics
Abstract: Organisms can be thought about in terms of their carbon biomass and nitrogen content. When disease spreads in an ecosystem, it can play a large role in the reproduction and nutrient uptake of the organisms. Our work combines a disease model and a nutrient model in order to build a more complete representation of an ecosystem. We begin with a five-equation stoichiometric system that we simplify to a two-equation system by assuming that nutrient dynamics occur on a much faster timescale than population dynamics. Our goal is to understand how nutrient availability affects long term behavior of a disease population.
Tick-mouse Lyme disease models with seasonal variations in the tick and mouse populations
Abstract: Lyme disease is the most prevalent vector-borne disease in the United States impacting the Northeast and Midwest at the highest rates and recently has been established in southeastern and south-central regions of Canada. Lyme disease is passed by the black-legged tick, Ixodes scapularis, infected with the Borrelia burgdorferi bacterium. One of the hosts commonly fed on by I. scapularis is Peromyscus leucopus colloquially known as a white-footed mouse. Understanding this parasite-host interaction is critical because P. leucopus is one of the most competent reservoirs for Lyme disease. The cycle of infection is driven by larvae feeding on infected mice that molt into infected nymphs and then transmit the disease to another susceptible host such as a mouse or human. Lyme disease in humans is generally caused by the bite of an infected nymph. We aim to accurately model how seasonal variation in tick births, deaths, and feedings impact the infection cycle seen in the tick-mouse cycle. To account for the seasonal changes, we formulate a deterministic model using delay differential equations with periodic parameters that depend on the spring, summer, fall, or winter seasons. Then we approximate this model using ordinary differential equations with multiple latent stages to represent the delays in molting or reproduction. Finally, we extend the ordinary differential equations into a stochastic model, a continuous-time Markov chain. In addition, we calculate and discuss the relevance of the basic reproduction number, R0, for the mouse-tick cycle and determine the sensitivity of R0 with respect to changes in model parameters.
Tedi Ramaj
Western University
A Mathematical Model of Melanoma Treatment via Oncolytic Virotherapy
