ALT University of Virginia's Biocomplexity Institute (UVA-BII) in collaboration with Emory University is proud to support the US-UK prize challenge as a part of the Summit for Democracy 2023. The challenge asked participants to develop privacy-enhancing technologies for two important societal applications: financial transactions and pandemics. UVA-BII supported the challenge by providing synthetic data (aka digital twin) representing a realistic pandemic in the UK and in the state of Virginia. We have made the data used as a part of the competition open to the broader scientific community. Find more information about the challenge here: https://petsprizechallenges.com and a description of the data and how to access it here: https://prepare-vo.org/synthetic-pandemic-outbreaks We hope that the data will help the community develop and test broad-ranging methods in network science, epidemiology, and data privacy. Thanks to our colleagues at NSF, NIST, OSTP, DrivenData, and UK’s CDEI!

ALT Adapting connections: integrating adaptive behavior into epidemiological multilayer network models // Fabio Sanchez // University of Costa Rica // GPCE Seminar Series on Thursday, 3/14 11:30am ET // Find more on computational-epidemiology.org The evolution of infectious diseases requires innovative modeling strategies that accurately capture the complex, adaptive behaviors underlying human interactions and their implications for disease transmission. We introduce a novel approach to epidemiological modeling by integrating adaptive behavior into multilayer network models, offering a more nuanced understanding of how diseases spread through human populations. By examining multiple dimensions of human contact networks, from physical interactions to digital communications, the goal is to highlight how individuals modify their behaviors in response to perceived disease risks and how these changes can influence the trajectory and evolution of an epidemic.

ALT One Health, Food Safety, Food Security, and AMR-complexity and trade-offs // Johanna Lindahl // Uppsala University, Swedish University of Agricultural Sciences, International Livestock Research Institute // GPCE Seminar Series on Thursday, 11/28 11:30am ET // Find more on computational-epidemiology.org Policymakers frequently need to make decisions, but in complex systems, there are seldom easy solutions to problems. This presentation will cover One Health, with a particular focus on food production and antimicrobial resistance (AMR).

ALT One Health, Food Safety, Food Security, and AMR-complexity and trade-offs // Johanna Lindahl // Uppsala University, Swedish University of Agricultural Sciences, International Livestock Research Institute // GPCE Seminar Series on Thursday, 11/28 11:30am ET // Find more on computational-epidemiology.org Policymakers frequently need to make decisions, but in complex systems, there are seldom easy solutions to problems. This presentation will cover One Health, with a particular focus on food production and antimicrobial resistance (AMR).

ALT Adaptive group testing strategy for infectious diseases using social contact graph partition // Jingyi Zhang & Lenwood Heath // Virginia Tech // GPCE Seminar Series on Thursday, 11/9 11:30am ET // Find more on computational-epidemiology.org During epidemics, mass testing stands as a frontline defense, enabling timely identification and isolation of infected individuals while safeguarding hte community. The traditional Dorfma’s group testing technique, while reducing test counts, neglects factors like infection dynamics, testing capacity challenges, and community-based transmission patterns in a person-to-person contact network. To address these challenges, we propose the adaptive group testing (AGT) strategy, based on graph partitioning. In comparative evaluations against six synthetic and seven real social contact networks, AGT consistently outperformed Dorfman’s method, requiring fewer tests and better at controlling disease spread.

ALT ALT: Adaptive group testing strategy for infectious diseases using social contact graph partition // Jingyi Zhang & Lenwood Heath // Virginia Tech // GPCE Seminar Series on Thursday, 11/9 11:30am ET // Find more on computational-epidemiology.org During epidemics, mass testing stands as a frontline defense, enabling timely identification and isolation of infected individuals while safeguarding hte community. The traditional Dorfma’s group testing technique, while reducing test counts, neglects factors like infection dynamics, testing capacity challenges, and community-based transmission patterns in a person-to-person contact network. To address these challenges, we propose the adaptive group testing (AGT) strategy, based on graph partitioning. In comparative evaluations against six synthetic and seven real social contact networks, AGT consistently outperformed Dorfman’s method, requiring fewer tests and better at controlling disease spread.

ALT Adaptive group testing strategy for infectious diseases using social contact graph partition // Jingyi Zhang & Lenwood Heath // Virginia Tech // GPCE Seminar Series on Thursday, 11/9 11:30am ET // Find more on computational-epidemiology.org During epidemics, mass testing stands as a frontline defense, enabling timely identification and isolation of infected individuals while safeguarding hte community. The traditional Dorfma’s group testing technique, while reducing test counts, neglects factors like infection dynamics, testing capacity challenges, and community-based transmission patterns in a person-to-person contact network. To address these challenges, we propose the adaptive group testing (AGT) strategy, based on graph partitioning. In comparative evaluations against six synthetic and seven real social contact networks, AGT consistently outperformed Dorfman’s method, requiring fewer tests and better at controlling disease spread.

ALT Modeling social complexity in epidemiology: risk perception and adaptive human behavior // Baltazar Espinoza // GPCE Seminar Series on Thursday, 9/28 11:30am ET // Find more on computational-epidemiology.org To study the complex dynamics between behavioral adaptations and epidemic progression, we use a mechanistic modeling framework that explicitly incorporates the interdependence between the (individual-level) adaptive behavioral choices and the (population-level) progression of the infection process. We found that individuals’ risk perceptions in the presence of asymptomatic individuals modulate final epidemic size. Moreover, under behavioral polarization, privately determined behavioral responses may increase the final epidemic size, compared to the homogeneous behavior scenario. Finally, we explored testing as a potential source of misinformation about infection risk that may lead to harmful decisions that increase epidemic burden.

ALT Modeling social complexity in epidemiology: risk perception and adaptive human behavior // Baltazar Espinoza // GPCE Seminar Series on Thursday, 9/28 11:30am ET // Find more on computational-epidemiology.org To study the complex dynamics between behavioral adaptations and epidemic progression, we use a mechanistic modeling framework that explicitly incorporates the interdependence between the (individual-level) adaptive behavioral choices and the (population-level) progression of the infection process. We found that individuals’ risk perceptions in the presence of asymptomatic individuals modulate final epidemic size. Moreover, under behavioral polarization, privately determined behavioral responses may increase the final epidemic size, compared to the homogeneous behavior scenario. Finally, we explored testing as a potential source of misinformation about infection risk that may lead to harmful decisions that increase epidemic burden.