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Haiti Mass Vaccination campaign

TODO calibration

  1. Change FOI
  2. Past vaccination campaings ?ß
  3. Lanch big calibration

TODO experiment

  1. Tipping point for vaccination
  2. Difference rainfall scenerio

Code: Shell script

  1. run.sh: run the calibration for a departement and a run level.
  2. generate.sh: run the calibration or just some generation (like pomp object, parameter file) for all departements at a run level.

Code: R scripts

  1. pomp_cholera_juba.R: build the POMP object containing data and code for simulation
  2. run_mif_cholera.R: fit the models using multiple iterated filtering
  3. analysis_haitiOCV.R: plot some information about calibration.
  4. forecast_haitiOCV.R: code to project the model. Take files haiti-data/proj/rainfall.csv for rainfall and file covar_mob.csv for the mobility covariate.
  5. trials.R: A little scratchpad ! (now to plot also diagnosis)

Code: Python notebook

  1. forecast.ipynb: Code to forecast and project vaccination scenarios
  2. rainfall.ipynb: Create a rainfall projection file, and run some checks if we add new data.
  3. data_analysis.ipynb: Data analysis on the initial data.

Code: C functions

  1. sirb_model_vacc.c: The pomp model containing the transitions, able to support two vaccination campaign.
  2. v_eff.c: Function for the two doses vaccine efficacity

Data

In the fromAzman folder:

  1. cases_corrected.csv: Cases corrected with some NAs on big drops
  2. rainfall.csv: remote sensing estimate of daily rainfall (TRMM data) In the proj folder:
  3. rainfall.csv: used for projection. At root level:
  4. input_parameters.yaml: fixed parameters for the SIRB model in YAML format

Useful commands

Package to install

install.packages(c("tictoc", "pomp", "tidyverse", "magrittr", "ggthemes", "GGally", "foreach", "itertools", "lubridate", "dplyr", "purrr", "readr", "stringr", "tibble", "doMC","doSNOW","truncnorm", "zoo")  , dependencies=TRUE)

Show errors in R

options(show.error.locations=TRUE)
options(error=recover)