Fitting and forecasting of daily influenza hospital admission data using a flexible time-dependent transmission term
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MAIN CODE
flu_hosp_forecast.R
S[I]2RH fitting and forecasting code - using daily hospitalization data
COMPILING THE FORTRAN CODE
The R code requires the compilation of fortran code
After downloading the repository please go to the gsrc subdirectory
%cd gsrc
and use the provided python script to compile the code
%./compile.py
If successul three dynamic (.so) libraries will be created:
detsirh.so, stosirh.so and mcmc.so
DATA
The code assumes a certain format for the data and an example is provided in the data sub-directory
If you choose to change the location of the data file you will need to update the 'data_path' variable in the flu_hosp_forecast.R code
DETAILS ABOUT THE MODEL
The number of values that the time-dependent transmission term is set by the parameter 'nb' Reasonable values are 1-4 For more on our model for R(t) and the fitting procedure see our publications:
Consistent pattern of epidemic slowing across many geographies led to longer, flatter initial waves of the COVID-19 pandemic Michal Ben-Nun, Pete Riley, James Turtle, Steven Riley
Research Article | published 15 Aug 2022 PLOS Computational Biology
https://doi.org/10.1371/journal.pcbi.1010375
Forecasting national and regional influenza-like illness for the USA Michal Ben-Nun, Pete Riley, James Turtle, David P. Bacon, Steven Riley
Research Article | published 23 May 2019 PLOS Computational Biology
https://doi.org/10.1371/journal.pcbi.1007013
Accurate influenza forecasts using type-specific incidence data for small geographic units James Turtle, Pete Riley, Michal Ben-Nun, Steven Riley
Research Article | published 29 Jul 2021 PLOS Computational Biology
https://doi.org/10.1371/journal.pcbi.1009230
HELP and SUPPORT
For questions and/or help please email us: [email protected]
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