Improving surveillance for zoonoses of elusive origins
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Zoonotic pathogens pose a significant risk to human health, with spillover into human populations contributing to chronic disease, sporadic epidemics, and occasional pandemics. Our ability to identify which animal populations serve as primary reservoirs for these pathogens remains incomplete. This challenge is compounded when prevalence reaches detectable levels only at specific times of year. In these cases, statistical models designed to predict the timing of peak prevalence could guide field sampling for active infections. In this talk Dr Stephanie Seifert will discuss a general model that leverages routinely collected serosurveillance data to optimize sampling for elusive pathogens by predicting time windows of peak prevalence.
Stephanie Seifert, PhD completed her doctoral thesis research in the Ecology and Evolution of Infectious Disease Systems laboratory at UPenn. She then completed her postdoctoral training at the NIH in the Laboratory of Virology/Virus Ecology Section where she studied the circulation of filoviruses and henipaviruses in African bats. This work included field sampling in the Republic of Congo and experimental work in the BSL4 at Rocky Mountain Laboratories. Dr Seifert has been active in outbreak response including EVD outbreaks in DRC and the COVID-19 pandemic. She received the NIH 2020 Merit Award for her participation in the early COVID-19 response including characterizing within-host evolution of SARS-CoV-2 in one of the first persistently infected patients.
Join the EPPIcenter online in welcoming Dr Seifert
The EPPIcenter at UCSF aims to advance the understanding of infectious diseases to reduce global morbidity and mortality. We believe that the greatest success in the fight against infectious diseases will come through a highly interdisciplinary, systems epidemiology approach, connecting traditionally siloed theoretical work, technology development, generation and collection of empiric data, and analysis using statistical and mathematical modeling.
Add to Calendar2024-11-21 19:00:002024-11-21 20:00:00EPPIcenter Seminar: Stephanie Seifert
Zoonotic pathogens pose a significant risk to human health, with spillover into human populations contributing to chronic disease, sporadic epidemics, and occasional pandemics. Our ability to identify which animal populations serve as primary reservoirs for these pathogens remains incomplete. This challenge is compounded when prevalence reaches detectable levels only at specific times of year. In these cases, statistical models designed to predict the timing of peak prevalence could guide field sampling for active infections. In this talk Dr Stephanie Seifert will discuss a general model that leverages routinely collected serosurveillance data to optimize sampling for elusive pathogens by predicting time windows of peak prevalence.
Stephanie Seifert, PhD completed her doctoral thesis research in the Ecology and Evolution of Infectious Disease Systems laboratory at UPenn. She then completed her postdoctoral training at the NIH in the Laboratory of Virology/Virus Ecology Section where she studied the circulation of filoviruses and henipaviruses in African bats. This work included field sampling in the Republic of Congo and experimental work in the BSL4 at Rocky Mountain Laboratories. Dr Seifert has been active in outbreak response including EVD outbreaks in DRC and the COVID-19 pandemic. She received the NIH 2020 Merit Award for her participation in the early COVID-19 response including characterizing within-host evolution of SARS-CoV-2 in one of the first persistently infected patients.
Join the EPPIcenter online in welcoming Dr Seifert
The EPPIcenter at UCSF aims to advance the understanding of infectious diseases to reduce global morbidity and mortality. We believe that the greatest success in the fight against infectious diseases will come through a highly interdisciplinary, systems epidemiology approach, connecting traditionally siloed theoretical work, technology development, generation and collection of empiric data, and analysis using statistical and mathematical modeling.
[email protected]Department of MedicineAmerica/Los_Angelespublic
