MS06 - MEPI-10

Mathematical Epidemiology: Infectious disease modeling across time, space, and scale (Part 2)

Thursday, July 17 at 10:20am

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Note: this minisymposia has multiple sessions. The other session is: and Part-1.

Meredith Greer, Prashant Kumar Srivastava, Michael Robert (Bates College), Prashant Kumar Srivastava (Indian Institute of Technology, Patna) and Michael Robert (Virginia Tech)

Description:

Work within the mathematical epidemiology subgroup focuses on critical questions about the emergence, spread, and control of infectious diseases at multiple scales, and to study these questions, we must develop and implement a variety of tools. In this mini-symposium, we feature work across a broad spectrum of infectious disease modeling research and highlight work that members of the SMB Mathematical Epidemiology subgroup have been doing over the past year. This minisymposium will feature work addressing issues in parameter estimation, population heterogeneity, and modeling control efforts, among other important topics, and feature the work of a diverse group of mathematical biologists who are implementing traditional and novel methods to study questions in mathematical epidemiology.

Lihong Zhao

Kennesaw State University

"Modeling the Dynamics of Legionnaries' Disease and Management Strategies"

Some pathogens can survive and replicate in abiotic environment outside the host systems and rely on the interaction with an environmental reservoir to transmit and infect hosts. Mathematical modeling can provide insights into the complex and often unknown dynamics of environmentally transmitted diseases. One such pathogen is the bacteria Legionella, the inhalation of this bacteria suspended in aerosolized water can lead to an atypical pneumonia which is known as the Legionnaries' disease (LD). In 2018, nearly 10,000 LD cases were reported in the United States. The true incidence should be higher as LD is underdiagnosed and underreported. In this talk, we will present the model we developed to examine the factors that may have contributed to the increase in LD outbreaks, and the insights into management strategies using control theory.

Tinashe Byron Gashirai (Postdoctoral Fellow)

University of Idaho

"A theory of risk perception in shaping human behavior to policy compliance during outbreaks"

The interplay of perceived risk of infection and protective behavior of the host in response to an emerging infection is complex and difficult to abstract. We therefore present a simple human behavior model based on the hypothesis that the human host engages in positive adaptive behavior when the disease prevalence reaches a certain threshold. Our mathematical analysis shows that the recruitment rate of susceptible individuals and the prevalence that triggers protective behavior influence the persistence or extinction of the disease. Moreover, abrupt changes in the transmission rate due to risk perception modulated host behavior may result in backward bifurcation. This complicates the control of the disease since the basic reproduction number fails to predict the occurrence of an epidemic. This study highlights the importance of understanding the role of complacency in engaging human adaptive response and risk perception in combating disease spread.

Claudia Pio Ferreira

Unesp, IBB

"Mathematical epidemiology and control of hospital-associated infections"

Healthcare-associated infections cause significant patient morbidity and mortality, and contribute to growing healthcare costs. Active surveillance systems, hospital staff compliance, including hand hygiene, and a rational use of antimicrobials are among the important measures to mitigate the spread of healthcare-associated infection within and between hospitals. Focusing on the role of patient movement within and between hospitals on the transmission and incidence of enterobacteria producing the K. pneumoniae Carbapenemase, we developed a metapopulation model where the connections among hospitals are made using a theoretical hospital network based on Brazilian hospital sizes and locations. The pathogen reproductive number, R_0 was calculated in different scenarios defined by both the links between hospital environments and between different hospitals. Furthermore, the efficacy of infection prevention and control on several hospital networks is assessed. Overall, the obtained results emphasize the importance of data collection on infection transmission and patient transfers, and show that the allocation of control units based on the R_0 of the hospitals may work better than the network-topology-based allocations.

#SMB2025 Follow

Annual Meeting for the Society for Mathematical Biology, 2025.