Highlights:

• TorchSurv: A package for deep survival analysis associated with [7]
• covid19model: Model for characterizing COVID-19 spread associated with [1, 2, 3]

Other open source code:

• deep_rl_liquidation: Deep Reinforcement Learning for Online Optimal Execution Strategies associated with [6]
• phyloSI-RakaiAgeGender: Analysis of age- and time-specific HIV transmission dynamics associated with [5]
• BSplinesProjectedGPs: Regularised B-splines Projected Gaussian Process Priors associated with [4]
• US-covid19-agespecific-mortality-data: Code to retrieve age-specific COVID-19 daily deaths data from city or state Department of Health (DoH) websites, data repositories or via data requests to DoH. The data were used in [3]

References:

[1] Flaxman, Mishra, Gandy, et al. “Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe”. Nature 584 (7820), 257-261, 3026, (2020)

[2] Unwin, Mishra, Bradley, et al. “State-level tracking of COVID-19 in the United States”. Nature communications 11 (1), 6189, 156, (2020)

[3] Monod, Blenkinsop, Xi, et al. “Age groups that sustain resurging COVID-19 epidemics in the United States”. Science 371 (6536), eabe8372, 261, (2021)

[4] Monod, Blenkinsop, Brizzi, et al. “Regularised B-splines Projected Gaussian Process Priors to Estimate Time-trends in Age-specific COVID-19 Deaths”, Bayesian Analysis, 18 (3), 957-987 (2023)

[5] Monod, Brizzi, Galiwango, et al. “Longitudinal population-level HIV epidemiologic and genomic surveillance highlights growing gender disparity of HIV transmission in Uganda”. Nature Microbiology, 1-20 (2023)

[6] Micheli and Monod. “Deep Reinforcement Learning for Online Optimal Execution Strategies”. arXiv. (2024)

[7] Monod, Krusche, Cao, et al. “TorchSurv: A Lightweight Package for Deep Survival Analysis”. Journal of Open Source Software 9, 7341, (2024)