MEPI-MS19

Women in Mathematical Epidemiology

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

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

Note: this minisymposia has multiple sessions. The second session is MS18-MEPI (click here).

Katharine Gurski (Howard University, United States), Kathleen Hoffman (University of Maryland, Baltimore County, United States)

Description:

These special sessions highlight the work of female mathematicians working in the field of epidemiology who are also in the AWM Women in Mathematical Biology (WIMB) research network. The global pandemic has put a spotlight on epidemiological research and the value of mathematical models to predict the time course of the disease dynamics in the presence of vaccines, treatments, and behavior adaptation such as social distancing. These special sessions feature female researchers in epidemiology working on a wide variety of diseases such as HIV, COVID-19, MERS, Ebola, and Malaria. The types of models considered span ODE models, PDE models, and stochastic models. population models, agent-based models as well as within host models and between host models. These special sessions provide a broad sampling of current work in mathematical epidemiology and demonstrates the growth of the Women in Mathematical Biology network.

Christina Edholm

(Scripps College, United States)

"Stochastic Models and Superspreaders: Effects of Environmental Variability"

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.

Angela Peace

(Texas Tech University, United States)

"Spatial influences on Ebola and MERS epidemic dynamics: an agent-based modeling approach"

For many communicable diseases, superspreaders are defined as specific infected individuals that transmit disproportionately to more susceptible individuals than other infected individuals, which may result from increased contact with susceptible individuals, higher pathogen shedding or increased strain virulence. Epidemiological studies show that epidemics such as EBOV and MERS were largely driven and sustained by superspreaders that are ubiquitous throughout the outbreak. Hence understanding the dynamics of superspreaders can facilitate devising individually-targeted control measures. Studies have identified host heterogeneity (e.g.,~behavioral and immunological differences), population density and urbanization as underlying factors in disease outbreaks, therefore to capture disease transmission dynamics, we need a spatial modeling approach which can incorporate social phenomenons associated with human interactions. We developed an agent-based model (ABM) for simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) during an epidemic. We show that ABMs of infectious disease dynamics can provide additional insights by incorporating individual heterogeneity and spatial information.

Carrie Manore

(Los Alamos National Laboratory, United States)

"COVID-19 modeling and forecasting to inform decision makers"

We will present mathematical and statistical models for COVID-19 spread and impacts including hospital capacity, case counts, and deaths. Different methods are needed for supporting decision making depending on if we need accurate forecasting or exploration of 'what-if' scenarios. We will show how detailed agent based models, differential equation, and high level statistical models can be used together to support modeling of an ongoing pandemic.

Sylvia Gutowska

(University of Maryland, Baltimore County, United States)

"Effects of PrEP on the spread of HIV in the MSM population"

talk will describe the convergence-divergence model and discuss some

Hosted by SMB2021 Follow

Virtual conference of the Society for Mathematical Biology, 2021.