Amy York
Biographic Data
| ID | 7938560 |
|---|---|
| NAME | Amy York |
| GIVEN NAMES | Amy |
| FAMILY NAME | York |
| SIGNATURE | YORK A |
| AFFILIATIONS | Griffith University |
| ORCID | 0000-0001-5738-7048 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 1 |
| EDITOR COUNT | 1 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Integrating Surveillance and Stakeholder Insights to Predict Influenza Epidemics: A Bayesian Network Study in Queensland, Australia
Seasonal influenza continues to pose a substantial and recurrent public health challenge in Queensland, driven by annual variability in transmission and uncertainty in climatic, demographic, and behavioural determinants. Predictive modelling is constrained by data limitations and parameter uncertainty. In response, this study developed a Bayesian network (BN) model to estimate the probability of influenza epidemics in Queensland, Australia. The m…
Privacy and Safety in Online Learning
This collection features essays, case studies, and pedagogical approaches that explore how educators managed the privacy, security, and safety concerns that rushed into our lives as we shifted into emergency remote learning in 2020. While the COVID-19 pandemic brought this concern into focus, privacy issues with online learning continue to exist alongside us and our students. This book provides readers insight into the current state of privacy is…
No prominent works on this page.
Privacy and Safety in Online Learning
This collection features essays, case studies, and pedagogical approaches that explore how educators managed the privacy, security, and safety concerns that rushed into our lives as we shifted into emergency remote learning in 2020. While the COVID-19 pandemic brought this concern into focus, privacy issues with online learning continue to exist alongside us and our students. This book provides readers insight into the current state of privacy is…
Integrating Surveillance and Stakeholder Insights to Predict Influenza Epidemics: A Bayesian Network Study in Queensland, Australia
Seasonal influenza continues to pose a substantial and recurrent public health challenge in Queensland, driven by annual variability in transmission and uncertainty in climatic, demographic, and behavioural determinants. Predictive modelling is constrained by data limitations and parameter uncertainty. In response, this study developed a Bayesian network (BN) model to estimate the probability of influenza epidemics in Queensland, Australia. The m…
Artificial Intelligence (1 works) · Bayes' theorem (1 works) · Bayesian network (1 works) · Bayesian probability (1 works) · Computer Science (1 works) · Coronavirus disease 2019 (COVID-19) (1 works) · COVID-19 epidemiological studies (1 works) · Credible interval (1 works) · Data-Driven Disease Surveillance (1 works) · Discriminative model (1 works)