jeanpaulrsoucy / backandnow

Bayesian back-calculation and nowcasting for line list data

backandnow:

Bayesian back-calculation and nowcasting for line list data during the COVID-19 pandemic

simulation

• cluster.R: Parallel processing for the simulation (complete line list data).
• cluster_ss.R: Parallel processing for the simulation (delayed surveillance initiation and real time estimation).
• rt.rds: Daily reproductive numbers used in the simulation.
• The output folder: Raw simulation output files and processing code.

Source code

• backnow.cpp: Bayesian back-calculation and nowcasting.

Final Result: clean output used in the paper

• result.R: Code for generating all the figures and tables.
• count.rds: Daily case count estimates for complete line list data.
• count_ss.rds: Daily case count estimates for delayed surveillance initiation.
• count_ss1.rds: Daily case count estimates for real time estimation.
• rest.rds: Time-varying reproductive number estimates for complete line list data.
• rest_ss.rds: Time-varying reproductive number estimates for delayed surveillance initiation.
• rest_ss1.rds: Time-varying reproductive number estimates for real time estimation.
• eva1.rds: Coverage rates and RMSE for complete line list data.
• eva2.rds: Coverage rates and RMSE for delayed surveillance initiation.
• eva3.rds: Coverage rates and RMSE for real time estimation.
• geweke.rds: Geweke's diagnostics

Daily flow rates in Massachusetts

• pop_flow_county_byday_dataframe.csv: Data containing average daily flow rates over all Massachusetts counties.