Publications
Selected publications
- Simulation to optimize the laboratory diagnosis of bacteremia. (Journal article - 2024)
- Bayesian Calibration to Address the Challenge of Antimicrobial Resistance: A Review (Journal article - 2024)
- Refining epidemiological forecasts with simple scoring rules (Journal article - 2022)
- Enhanced SMC<sup>2</sup>: Leveraging Gradient Information from Differentiable Particle Filters Within Langevin Proposals (Conference Paper - 2024)
- An O(log<sub>2</sub> N) SMC<sup>2</sup> Algorithm on Distributed Memory with an Approx. Optimal L-Kernel (Conference Paper - 2023)
- Inference of Stochastic Disease Transmission Models Using Particle-MCMC and a Gradient Based Proposal (Conference Paper - 2022)
- Efficient Learning of the Parameters of Non-Linear Models using Differentiable Resampling in Particle Filters (Journal article - 2022)
2024
Simulation to optimize the laboratory diagnosis of bacteremia.
Gerada, A., Roberts, G., Howard, A., Reza, N., Velluva, A., Rosato, C., . . . Hope, W. (2024). Simulation to optimize the laboratory diagnosis of bacteremia.. Microbiology spectrum, 12(11), e0144924. doi:10.1128/spectrum.01449-24
Enhanced SMC<sup>2</sup>: Leveraging Gradient Information from Differentiable Particle Filters Within Langevin Proposals
Rosato, C., Murphy, J., Varsi, A., Horridge, P., & Maskell, S. (2024). Enhanced SMC<sup>2</sup>: Leveraging Gradient Information from Differentiable Particle Filters Within Langevin Proposals. In 2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) (pp. 1-8). IEEE. doi:10.1109/mfi62651.2024.10705779
Enhanced SMC$^2$: Leveraging Gradient Information from Differentiable Particle Filters Within Langevin Proposals
Bayesian Calibration to Address the Challenge of Antimicrobial Resistance: A Review
Rosato, C., Green, P. L., Harris, J., Maskell, S., Hope, W., Gerada, A., & Howard, A. (2024). Bayesian Calibration to Address the Challenge of Antimicrobial Resistance: A Review. IEEE Access, 12, 100772-100791. doi:10.1109/access.2024.3427410
2023
An O(log<sub>2</sub> N) SMC<sup>2</sup> Algorithm on Distributed Memory with an Approx. Optimal L-Kernel
Rosato, C., Varsi, A., Murphy, J., & Maskell, S. (2023). An O(log<sub>2</sub> N) SMC<sup>2</sup> Algorithm on Distributed Memory with an Approx. Optimal L-Kernel. In 2023 IEEE Symposium Sensor Data Fusion and International Conference on Multisensor Fusion and Integration (SDF-MFI) (pp. 1-8). IEEE. doi:10.1109/sdf-mfi59545.2023.10361452
Disease Surveillance using Bayesian Methods
Rosato, C. (2023, October 11). Disease Surveillance using Bayesian Methods.
Extracting Self-Reported COVID-19 Symptom Tweets and Twitter Movement Mobility Origin/Destination Matrices to Inform Disease Models
Rosato, C., Moore, R. E., Carter, M., Heap, J., Harris, J., Storopoli, J., & Maskell, S. (2023). Extracting Self-Reported COVID-19 Symptom Tweets and Twitter Movement Mobility Origin/Destination Matrices to Inform Disease Models. Information, 14(3), 170. doi:10.3390/info14030170
2022
Refining epidemiological forecasts with simple scoring rules
Moore, R. E., Rosato, C., & Maskell, S. (2022). Refining epidemiological forecasts with simple scoring rules. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 380(2233). doi:10.1098/rsta.2021.0305
Inference of Stochastic Disease Transmission Models Using Particle-MCMC and a Gradient Based Proposal
Rosato, C., Harris, J., Panovska-Griffiths, J., & Maskell, S. (2022). Inference of Stochastic Disease Transmission Models Using Particle-MCMC and a Gradient Based Proposal. In 2022 25TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION 2022). Retrieved from https://www.webofscience.com/
Efficient Learning of the Parameters of Non-Linear Models using Differentiable Resampling in Particle Filters
Maskell, S., Devlin, L., Beraud, V., Horridge, P., & Rosato, C. (2022). Efficient Learning of the Parameters of Non-Linear Models using Differentiable Resampling in Particle Filters. IEEE Transactions on Signal Processing.