Advances in Infectious Disease Modeling

Tuesday, June 15 at 11:30am (PDT)
Tuesday, June 15 at 07:30pm (BST)
Wednesday, June 16 03:30am (KST)

SMB2021 SMB2021 Follow Tuesday (Wednesday) during the "MS08" time block.
Note: this minisymposia has multiple sessions. The second session is MS09-MEPI (click here).

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Lihong Zhao (University of California Merced, United States), Ling Xue (Harbin Engineering University, China), Suzanne Sindi (University of California Merced, United States)


This mini-symposium will bring together established and up-and-coming researchers to explore how mathematical and computational models can be applied to address public health challenges for a wide range of infectious diseases, including Wolbachia, Ebola, COVID-19, etc. The presentations will range from theoretical perspective, such as developing models to better understand the transmission dynamics and spatiotemporal patterns, to more applied aspects, such as fitting models to evaluate the impact of control strategies. Current mathematical and computational modeling challenges will also be discussed.

Folashade Agusto

(Ecology and Evolutionary Biology, University of Kansas, United States)
"Playing with fire: Modeling the effects of prescribed fire on Lyme disease"
Tick-borne illnesses are trending upward and are an increasing source of risks to people’s health in the United States; furthermore, the range of tick habitats is expanding due to climate change. Thus, it is imperative to find a practical and cost-efficient way of managing tick populations. Prescribed burns are a common form of land management, it can be cost efficient if properly managed. In this seminar, I will present a compartmental model for ticks carrying Lyme disease using an impulsive system, and then investigate the effect of prescribed fire intensity and the duration between burns. Our study found that fire intensity has a larger impact in reducing tick population than frequency between burns. Furthermore, burning at high intensity is preferable to burning at low intensity whenever possible, although high intensity burns may be unrealistic due to environmental factors. Annual burns resulted in the most significant reduction of infectious nymphs, which are the primary carriers of Lyme disease.

Fabian Santiago

(Department of Applied Mathematics, University of California Merced, United States)
"Mathematical Assessment of Intervention Strategies for Mitigating COVID-19 Transmission in a University Setting "
In March 2020, the University of California, Merced (UC Merced), along with other universities throughout the United States moved to an on-line only mode of course delivery to decrease the spread of SARS-CoV-2, the virus responsible for the COVID-19 disease. During that time, the UC Merced leadership focused on how to safely bring students back to the campus in the Fall. At UC Merced this involved using mathematical models to evaluate the effectiveness of proposed mitigation strategies for containing the spread of COVID-19 within the university setting. In this talk I will discuss the mathematical model we used to evaluate Fall 2021 re-opening strategies and present a global sensitivity analysis of the contact and infection model parameters that govern the transmission dynamics of COVID-19 within the university setting.

Zhuolin Qu

(Department of Mathematics, The University of Texas at San Antonio, United States)
"Modeling the invasion wave of Wolbachia in mosquitoes for controlling mosquito-borne diseases"
We develop and analyze partial differential equation (PDE) models to study the transmission and invasion dynamics of Wolbachia infection among the wild mosquitoes. Wolbachia is a natural bacterium that can infect mosquitoes and reduce their ability to transmit mosquito-borne diseases, such as Zika, Chikungunya, and dengue fever, and releasing Wolbachia-infected mosquitoes is a rising biological control to mitigate these diseases. Both field trials and previous modeling studies have shown that the Wolbachia infection among the mosquitoes needs to exceed a threshold level to persist in time. To give a realistic prediction of the threshold condition, it is critical to capture the spatial heterogeneity in the distributions of the infected and uninfected mosquitoes, which is created by the local introduction of the infection in the field release. We derive reaction-diffusion-type PDE models from the existing ordinary differential equation (ODE) models to better characterize the spatial invasion of Wolbachia infection into the native mosquitoes. The models account for both the complex vertical transmission parameters (inherited from the ODE models) and the horizontal transmission of infection (spatial diffusion). We analyze the threshold condition of establishing a successful invasion, the “critical bubble”, for the spatial models, and we compare it with the level in the spatially homogenous setting. We also show that the proposed PDE models can give rise to the traveling waves of Wolbachia infection. We then quantify how the magnitude of the diffusion coefficient can impact the threshold condition and the shape and velocity of the traveling front, and we numerically study different scenarios that may inform the design of the field release strategies.

Christopher Remien

(Department of Mathematics and Statistical Sciences, University of Idaho, United States)
"Reservoir population dynamics and pathogen epidemiology drive pathogen genetic diversity, spillover, and emergence"
When several factors align, pathogens that normally infect wildlife can spill over into the human population. If pathogen transmission within the human population is self-sustaining, or rapidly evolves to be self-sustaining, novel human pathogens can emerge. Although many factors influence the likelihood of spillover and emergence, the rate of contact between humans and wildlife is critical. Thus, for those pathogens inhabiting wildlife reservoirs with pronounced seasonal fluctuations in population density, it is broadly recognized that spillover risk also varies with season. What remains unknown, however, is the extent to which seasonal fluctuations in reservoir populations influence the evolutionary dynamics of pathogens in ways that affect the likelihood of emergence. Here, we use mathematical models and stochastic simulations to show that seasonal fluctuations in reservoir population densities lead to seasonal increases in genetic variation within pathogen populations and thus influence the waiting time for mutations capable of sustained human-to-human transmission. These seasonal increases in genetic variation also lead to elevated risk of emergence at predictable times of year.  

Hosted by SMB2021 Follow
Virtual conference of the Society for Mathematical Biology, 2021.