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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,138 +1,3 @@ | ||
| # Haiti Mass Vaccination campaign | ||
| # Haiti Mass Vaccination Campaign | ||
|
|
||
|
|
||
| ## TODO | ||
|
|
||
| ### Calibration | ||
|
|
||
| - Demographic stochasticity ? | ||
| - Beta Drop to match the drop with two beta ? | ||
|
|
||
| ### Forecast and results | ||
| - Open-Source. | ||
| - Mmmh it's a bit inconsistent that mobility does not dépends on real cases. | ||
| - Replace NaN in ouest with just the data. | ||
|
|
||
|
|
||
| Find a way to get to elimination sometime, it never goes with this FOI add :/ | ||
| Finish that fast. | ||
|
|
||
| ### Reading | ||
|
|
||
| - Tidy data | ||
|
|
||
| ### DONE since feb 20. 2019 | ||
|
|
||
| - VE proportional to population U5* | ||
| - Find what's need to be calibrated | ||
| - Add changed with K to the sharelatex | ||
| - Use artibonite (Xcept rainfall) as starting point for other simulation | ||
| - add new data | ||
| - Cancel some parameters | ||
| - being able to run stuff in parallel | ||
| - Mobility a better way: | ||
| 1. Manage to recreate initial conditions: launch sim by simulation b.c pomp store state | ||
| 2. Do it in the pomp object | ||
| 3. Iterative (easier but wronger) via csv | ||
| -> I keep nobility like this because it's too long otherwise | ||
| - Build some calibration diagnosis | ||
| - Check Artibonite on echopc20 | ||
| - Produce national plots | ||
| - the ability to see simulation by simulation [WIP in trials.R] | ||
| - Why isn't the first value of the dataframe the same between q05 and q95 -> because same starting point | ||
| - I and A seems fine. but B is strange, I should correct it, the initial condition is one order of magnitude too low | ||
| - Get a more realistic reporting rate. -> Javier says 0.97 is ok | ||
| - Why all simulation begins at 0 ? | ||
| -> probably because the first day of reporting is really not taken into account (is a saturday, so 1/7 cases less.that week.) | ||
| - Move 1 day ? yes but may disrupt some reporting. But this date is not good because begining of saturday, should be end. | ||
| - For now ignore first data point | ||
| - Investigate starting parameters ro improve artinonite, especially reporting ? Javier says 97% is fine, better calibrated than anything | ||
| - Make use of CDC case definiation information | ||
| - *Get better Ouest by calibrating* | ||
| - BUG: in the pomp object used for calibration, covar is not defined at most time step. Is it a bug or is it interpolated ? | ||
| - Why West has no vaccination in graph? | ||
| - I have a new west calibration on echopc 16, use it | ||
| - Better mobility | ||
| - Why some simulation scenarios extincts and goes back up ? | ||
| - Ability to compute the time to elimination | ||
| - Switch to all incidence ! | ||
| - Add cumulative vaccine to graph | ||
| - Add national graph to graph done automatically. | ||
| - Make graph of vaccine distributed overtime: at do you think about each team including a figure with cumulative vaccines delivered over time for each | ||
|
|
||
|
|
||
| ## Results | ||
|
|
||
| ### Calibration for next round of results (April 8 2019) | ||
|
|
||
| - Big calibration in Artibonite | ||
| - Calibrate only foi and beta in all other departement, take param from artibonite | ||
|
|
||
| We have good results except in *Ouest* (see `15-05-long`) | ||
|
|
||
| ### Results for the haiti meeting in January: | ||
|
|
||
| - data used was 'output_12-20-2gammaMOD/' | ||
| - scripts on echopc40 for probablity of extinction | ||
| - foi_add was scaled :) | ||
| - see `meeting 27-01-2019 output` | ||
| - Allow run of one simulation for diagnosis | ||
| - Plot national scale. | ||
|
|
||
|
|
||
| ### As of the last big calibration we have (12-20) is: | ||
|
|
||
| - good in Artibonite, Sud, Grande Anse, Centre, Sud-Est | ||
| - bad in Nord-Est, Nord, Mord Ouest, OUest, Centre, Nippes. | ||
| -> Drop is needed in Nippes, Grande Anse, Nord, Nord-Est, Ouest, Sud, ... | ||
|
|
||
| ## Files | ||
|
|
||
| ### Code: Shell script | ||
|
|
||
| 1. `run.sh`: run the calibration for a departement and a run level. | ||
| 1. `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](https://kingaa.github.io/pomp/) object containing data and code for simulation | ||
| 2. `run_mif_cholera.R`: fit the models using [multiple iterated filtering](http://www.pnas.org/content/112/3/719) | ||
| 3. `analysis_haitiOCV.R`: plot some information about calibration. | ||
| 5. `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. | ||
| 4. `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: | ||
| 1. `rainfall.csv`: used for projection. | ||
| At root level: | ||
| 3. `input_parameters.yaml`: fixed parameters for the SIRB model in [YAML](http://yaml.org/) 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) | ||
| Partially-observed markov model for cholera in Haiti, in order to compute the extinction probablity given different vaccination scenarios. |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,138 @@ | ||
| # Haiti Mass Vaccination Campaign | ||
|
|
||
|
|
||
| ## TODO | ||
|
|
||
| ### Calibration | ||
|
|
||
| - Demographic stochasticity ? | ||
| - Beta Drop to match the drop with two beta ? | ||
|
|
||
| ### Forecast and results | ||
| - Open-Source. | ||
| - Mmmh it's a bit inconsistent that mobility does not dépends on real cases. | ||
| - Replace NaN in ouest with just the data. | ||
|
|
||
|
|
||
| Find a way to get to elimination sometime, it never goes with this FOI add :/ | ||
| Finish that fast. | ||
|
|
||
| ### Reading | ||
|
|
||
| - Tidy data | ||
|
|
||
| ### DONE since feb 20. 2019 | ||
|
|
||
| - VE proportional to population U5* | ||
| - Find what's need to be calibrated | ||
| - Add changed with K to the sharelatex | ||
| - Use artibonite (Xcept rainfall) as starting point for other simulation | ||
| - add new data | ||
| - Cancel some parameters | ||
| - being able to run stuff in parallel | ||
| - Mobility a better way: | ||
| 1. Manage to recreate initial conditions: launch sim by simulation b.c pomp store state | ||
| 2. Do it in the pomp object | ||
| 3. Iterative (easier but wronger) via csv | ||
| -> I keep nobility like this because it's too long otherwise | ||
| - Build some calibration diagnosis | ||
| - Check Artibonite on echopc20 | ||
| - Produce national plots | ||
| - the ability to see simulation by simulation [WIP in trials.R] | ||
| - Why isn't the first value of the dataframe the same between q05 and q95 -> because same starting point | ||
| - I and A seems fine. but B is strange, I should correct it, the initial condition is one order of magnitude too low | ||
| - Get a more realistic reporting rate. -> Javier says 0.97 is ok | ||
| - Why all simulation begins at 0 ? | ||
| -> probably because the first day of reporting is really not taken into account (is a saturday, so 1/7 cases less.that week.) | ||
| - Move 1 day ? yes but may disrupt some reporting. But this date is not good because begining of saturday, should be end. | ||
| - For now ignore first data point | ||
| - Investigate starting parameters ro improve artinonite, especially reporting ? Javier says 97% is fine, better calibrated than anything | ||
| - Make use of CDC case definiation information | ||
| - *Get better Ouest by calibrating* | ||
| - BUG: in the pomp object used for calibration, covar is not defined at most time step. Is it a bug or is it interpolated ? | ||
| - Why West has no vaccination in graph? | ||
| - I have a new west calibration on echopc 16, use it | ||
| - Better mobility | ||
| - Why some simulation scenarios extincts and goes back up ? | ||
| - Ability to compute the time to elimination | ||
| - Switch to all incidence ! | ||
| - Add cumulative vaccine to graph | ||
| - Add national graph to graph done automatically. | ||
| - Make graph of vaccine distributed overtime: at do you think about each team including a figure with cumulative vaccines delivered over time for each | ||
|
|
||
|
|
||
| ## Results | ||
|
|
||
| ### Calibration for next round of results (April 8 2019) | ||
|
|
||
| - Big calibration in Artibonite | ||
| - Calibrate only foi and beta in all other departement, take param from artibonite | ||
|
|
||
| We have good results except in *Ouest* (see `15-05-long`) | ||
|
|
||
| ### Results for the haiti meeting in January: | ||
|
|
||
| - data used was 'output_12-20-2gammaMOD/' | ||
| - scripts on echopc40 for probablity of extinction | ||
| - foi_add was scaled :) | ||
| - see `meeting 27-01-2019 output` | ||
| - Allow run of one simulation for diagnosis | ||
| - Plot national scale. | ||
|
|
||
|
|
||
| ### As of the last big calibration we have (12-20) is: | ||
|
|
||
| - good in Artibonite, Sud, Grande Anse, Centre, Sud-Est | ||
| - bad in Nord-Est, Nord, Mord Ouest, OUest, Centre, Nippes. | ||
| -> Drop is needed in Nippes, Grande Anse, Nord, Nord-Est, Ouest, Sud, ... | ||
|
|
||
| ## Files | ||
|
|
||
| ### Code: Shell script | ||
|
|
||
| 1. `run.sh`: run the calibration for a departement and a run level. | ||
| 1. `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](https://kingaa.github.io/pomp/) object containing data and code for simulation | ||
| 2. `run_mif_cholera.R`: fit the models using [multiple iterated filtering](http://www.pnas.org/content/112/3/719) | ||
| 3. `analysis_haitiOCV.R`: plot some information about calibration. | ||
| 5. `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. | ||
| 4. `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: | ||
| 1. `rainfall.csv`: used for projection. | ||
| At root level: | ||
| 3. `input_parameters.yaml`: fixed parameters for the SIRB model in [YAML](http://yaml.org/) 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) |