MEPI Subgroup Contributed Talks

Alexander Dolnick Meyer

University of Notre Dame

"Risk and size of Aedes-borne disease outbreaks are poorly predicted by climate-based suitability indices"

The recent geographical expansion of Aedes mosquito-borne diseases (ABDs) is a global health threat. Quantifying these pathogens’ epidemiology and identifying at-risk populations are key steps toward preparing for future ABD outbreaks. Data from past outbreaks should be central to informing these efforts, but leveraging these data toward generalizable conclusions is often difficult. Outbreak data are context-dependent and take various forms (e.g., a time-series of cases or retrospective serology data), precluding straightforward comparisons. In this presentation, we approach this problem from two angles, using chikungunya virus (CHIKV) as an example. First, we show how outbreaks with different types of data can be compared directly through the framework of Bayesian inference and mathematical modeling. We use this approach to estimate several measurements of outbreak risk and potential size, such as the basic reproduction number (R0), for 87 CHIKV outbreaks. Second, we test whether these risk estimates can be predicted using local, pre-outbreak information, including demographic factors and previously published climate-based indices of suitability for ABD transmission. Our results suggest that climate-based indices may approximate where outbreaks can occur, but do not predict R0, outbreak risk, or potential outbreak size. More broadly, we illustrate the importance of combining a biologically realistic model with various data sources when quantifying the risk of ABD transmission.
Additional authors: Sandra Mendoza Guerrero, Emergent BioSolutions; Natalie E. Dean, Emory University; Kathryn B. Anderson, SUNY Upstate Medical University; Steven T. Stoddard, Emergent BioSolutions; T. Alex Perkins, University of Notre Dame

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Arash Arjmand

University of Missouri Kansas City

"Incorporating Biosecurity Adherence into a Modeling Framework to Analyze Dynamics of Antimicrobial Resistance in Cattle Farms"

Antimicrobial Resistant Organisms (ARO) pose a significant threat to human and animal health. Adherence to biosecurity measures is critical in preventing the spread of infectious diseases and minimizing the emergence of AROs. This study aims to develop a modeling framework to quantify the effects of biosecurity adherence on the dynamics of antimicrobial-resistant bacteria in cattle farms. A deterministic Susceptible-Infected-Recovered-Susceptible (SIRS) model is formulated, accounting for drug-susceptible and drug-resistant pathogen strains capable of growth and survival within and between hosts. First, the possible outcomes of the SIRS model are analytically derived and numerically verified as a benchmark. Then, the SIRS model is further extended by stochastically incorporating cattle-farmworker-environment interactions. Using numerical simulations and sensitivity analysis, the likelihood of ARO emergence is investigated under different degrees of compliance with biosecurity measures, such as cattle quarantine, hand hygiene, equipment disinfection, animal health check-ups, and proper use of antibiotics. The present work is the first step toward understanding the influence of biosecurity adherence on human and animal health.
Additional authors: Dr. Majid Bani-Yaghoub, University of Missouri Kansas City (UMKC)

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Aurod Ounsinegad

Tarleton State University

"Dynamics of Eastern Equine Encephalitis Infection Rates: A Mathematical Approach"

The Eastern Equine Encephalitis virus (EEEV) is an erratic and deadly neurological disease that spans the northeastern coast of the United States and Canada. An analysis of the migration patterns of both the mosquito vector and the avian host species was conducted to determine the rate at which the virus is spread between the Black-Tailed Mosquito (Culiseta melanura) and select avian species. It was found that certain species of avians shared similar, or even identical, migration patterns with the Black-Tailed Mosquito. A system of ordinary differential equations (ODEs) was developed and analyzed to gain insight into the transmission dynamics of EEE between the two host classes. A host stage-structured model was incorporated where the avian host group is split into two categories, adults, and hatch-year avians. By using this, the extent to which fluctuations occurred in transmission rates according to host/vector abundances, mosquito biting rate, and type of host was explored. Elasticity analysis was then conducted on all parameters that form the basic reproductive number (ℛ0­) to find the parameters that cause the greatest change in ℛ0. The hypothesis that is evaluated is that hatch-year avians are more readily exposed to the mosquito vector as they lack a defense mechanism, unlike their adult counterpart, allowing for a better understanding of how hatch-year avians drive the infection.
Additional authors: Dr. Christopher Mitchell and Dr. Nicholas Komar

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Cormac LaPrete

University of Utah

"Characterizing spatiotemporal variation in transmission heterogeneity during the 2022 Mpox outbreak in the USA"

Transmission heterogeneity plays a critical role in the dynamics of an epidemic. During an outbreak of an emerging infectious disease, efforts to characterize transmission heterogeneity are generally limited to quantifications during a small outbreak or a limited number of generations of a larger outbreak. Understanding how transmission heterogeneity itself varies over the course of a large enduring outbreak not only improves understanding of observed disease dynamics but also informs public health strategy and response. In this study, we employ a simple method, adaptable to other emerging infectious disease outbreaks, to quantify the spatiotemporal variation in transmission heterogeneity for the 2022 mpox outbreak in the United States. In line with past research on mpox and following reports of potential superspreading events early in this outbreak, we expected to find high transmission heterogeneity as quantified by the dispersion parameter of the offspring distribution, k. Our methods use maximum likelihood estimation to fit a negative binomial distribution to transmission chain offspring distributions informed by a large mpox contact tracing dataset. We find that, while estimates of transmission heterogeneity varied across the outbreak with spatiotemporal pockets of high heterogeneity, overall transmission heterogeneity was low. When testing our methods on simulated data from an outbreak with high transmission heterogeneity, k estimate accuracy depended on the contact tracing data accuracy and completeness. Since the actual contact tracing data had high incompleteness, our values of k estimated from the empirical data may therefore be artificially high. However, it is also possible that our estimates accurately reflect low transmission heterogeneity for the United States mpox outbreak, which could differ substantially from the patterns observed elsewhere.
Additional authors: Jay Love, Division of Epidemiology, University of Utah; Theresa R. Sheets, Department of Mathematics, University of Utah; George G. Vega Yon, Division of Epidemiology, University of Utah; Alun Thomas, Division of Epidemiology, University of Utah; Matthew H. Samore, Division of Epidemiology, University of Utah; Lindsay T. Keegan, Division of Epidemiology, University of Utah; Frederick R. Adler, Department of Mathematics, School of Biological Sciences, University of Utah; Rachel Slayton, CDC; Ian Spicknall, CDC; Damon J.A. Toth, Division of Epidemiology, University of Utah

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