Split school types

data/README.md

@@ -92,7 +92,7 @@ A folder containing all raw simulations used for the RESTORE reports, the report
 
 #### Belgian Census 2011
 
-+ `Pop_LPW_NL_25FEB15.xlsx` contains the working population per sex, place of residence and place of work. First, the raw spreadsheet `data/covid19_DTM/raw/census_2011/Pop_LPW_NL_25FEB15.xlsx` was modified in MS Excel and placed in the data/covid19_DTM/interim folder under the name `Pop_LPW_NL_25FEB15_delete_unknown.xlsx`. Data free for download at https://census2011.fgov.be/download/downloads_nl.html .
++ `Pop_LPW_NL_25FEB15.xlsx` contains the working population per sex, place of residence and place of work. First, the raw spreadsheet `data/covid19_DTM/raw/census_2011/Pop_LPW_NL_25FEB15.xlsx` was modified in MS Excel and placed in the data/covid19_DTM/interim folder under the name `Pop_LPW_NL_25FEB15_delete_unknown.xlsx`. Data free for download at https://census2011.fgov.be/download/downloads_nl.html.
 + `census_demo_nl_04nov14.xlsx` contains all demographic data from the 2011 Belgian census. From these data, the number of individuals in 10 year age-bins per Belgian arrondissement are calculated. The conversion notebook is `notebooks/0.1-twallema-extract-census-data.ipynb`.  Data free for download at https://census2011.fgov.be/download/downloads_nl.html .
 + `census_arbeidsmarkt_nl_24oct14.xlsx` contains all work related data from the 2011 Belgian census. Data free for download at https://census2011.fgov.be/download/downloads_nl.html .
 

data/covid19_DTM/raw/sero/Belgium COVID-19 Studies - Sciensano_Blood Donors_Tijdreeks.csv

@@ -11,9 +11,9 @@ Date,Lower CI,Upper CI,Seroprevalence
 23 Jul 2020,0.035,0.069,0.052
 5 Aug 2020,0.031,0.066,0.048
 19 Aug 2020,0.042,0.079,0.06
-2 Sept 2020,0.03,0.069,0.049
-16 Sept 2020,0.028,0.062,0.044
-30 Sept 2020,0.03,0.069,0.048
+2 Sep 2020,0.03,0.069,0.049
+16 Sep 2020,0.028,0.062,0.044
+30 Sep 2020,0.03,0.069,0.048
 14 Oct 2020,0.038,0.077,0.057
 28 Oct 2020,0.068,0.12,0.092
 11 Nov 2020,0.106,0.167,0.136
@@ -29,4 +29,4 @@ Date,Lower CI,Upper CI,Seroprevalence
 25 May 2021,0.575,0.651,0.611
 22 Jun 2021,0.759,0.815,0.787
 20 Jul 2021,0.955,0.992,0.975
-1 Aug 2021,,,
+1 Aug 2021,,,

environment.yml

@@ -1,19 +1,17 @@
 name: COVID_MODEL
 channels:
-  - defaults
   - conda-forge
+  - defaults
 dependencies:
-  - python=3.10
-  - jupyter
+  - python=3.11.4
+  - pandas  
+  - numpy  
+  - scipy  
+  - numba  
+  - xarray  
   - matplotlib
-  - numpy
-  - scipy
-  - xarray
-  - pandas
   - emcee
+  - h5py 
   - corner
-  - geopandas
-  - zarr
-  - SAlib
-  - numba
-  - h5py
+  - tqdm
+  - click

notebooks/calibration/calibrate_BASE-COVID19_SEIQRD_hybrid_vacc.py

@@ -1,10 +1,10 @@
 """
 This script contains a calibration of national COVID-19 SEIQRD model to hospitalization data in Belgium.
 
-Example function call
----------------------
+Example
+-------
 
-python calibrate_BASE-COVID19_SEIQRD_hybrid_vacc.py -e 2021-08-23 -n_ag 10 -ID test
+python calibrate_BASE-COVID19_SEIQRD_hybrid_vacc.py -e 2021-11-01 -n_ag 10 -ID test
 """
 
 __author__      = "Tijs Alleman"
@@ -19,12 +19,12 @@
 import ast
 import click
 import json
-import datetime
 import argparse
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
 import multiprocessing as mp
+from datetime import date, datetime, timedelta
 # COVID-19 code
 from covid19_DTM.models.utils import initialize_COVID19_SEIQRD_hybrid_vacc
 from covid19_DTM.data import sciensano
@@ -76,13 +76,15 @@
 # Number of age groups used in the model
 age_stratification_size=int(args.n_age_groups)
 # Date at which script is started
-run_date = str(datetime.date.today())
+run_date = str(date.today())
 # Keep track of runtime
-initial_time = datetime.datetime.now()
+initial_time = datetime.now()
 # Start and end of calibration
-start_calibration = pd.to_datetime(args.start_calibration)
+start_calibration = datetime.strptime(args.start_calibration,"%Y-%m-%d")
 if args.end_calibration:
-    end_calibration = pd.to_datetime(args.end_calibration)
+    end_calibration = datetime.strptime(args.end_calibration,"%Y-%m-%d")
+# Leap size
+tau = 0.50
 
 ##############################
 ## Define results locations ##
@@ -117,10 +119,9 @@
 ## Initialize the model ##
 ##########################
 
-model, BASE_samples_dict, initN = initialize_COVID19_SEIQRD_hybrid_vacc(age_stratification_size=age_stratification_size, update_data=False, start_date=start_calibration.strftime("%Y-%m-%d"),
+model, BASE_samples_dict, initN = initialize_COVID19_SEIQRD_hybrid_vacc(age_stratification_size=age_stratification_size, update_data=False, start_date=start_calibration,
                                                                         stochastic=True, distinguish_day_type=True)
 
-tau = 0.5
 # Deterministic
 model.parameters['beta'] = 0.027 # R0 = 3.31 --> https://pubmed.ncbi.nlm.nih.gov/32498136/
 warmup = 0# 39 # Start 5 Feb. 2020: day of first detected COVID-19 infectee in Belgium
@@ -203,7 +204,7 @@
     #out = pso.optimize(objective_function, bounds, kwargs={'simulation_kwargs':{'warmup': warmup}},
     #                   swarmsize=multiplier_pso*processes, max_iter=n_pso, processes=processes, debug=True)[0]
     # A good guess
-    theta = [0.50, 0.56, 5000, 1.40, 1.90, 0.225, 0.60]
+    theta = [0.50, 0.56, 5000, 1.45, 1.80, 0.225, 0.60]
 
     # Nelder-mead
     #step = len(bounds)*[0.05,]
@@ -220,10 +221,10 @@
         # Assign estimate
         model.parameters = assign_theta(model.parameters, pars, theta)
         # Perform simulation
-        end_visualization = '2022-07-01'
-        out = model.sim([start_calibration, pd.Timestamp(end_visualization)], warmup=warmup)
+        end_visualization = datetime(2022, 7, 1)
+        out = model.sim([start_calibration, end_visualization], warmup=warmup)
         # Visualize fit
-        ax = plot_PSO(out, data, states, start_calibration-pd.Timedelta(days=warmup), end_visualization)
+        ax = plot_PSO(out, data, states, start_calibration-timedelta(days=warmup), end_visualization)
         plt.show()
         plt.close()
 

notebooks/calibration/calibrate_BASE-COVID19_SEIQRD_spatial_hybrid_vacc.py

@@ -1,5 +1,5 @@
 """
-This script contains a calibration of the spatial COVID-19 SEIQRD model to hospitalization data in Belgium during the period 2020-03-15 until 2021-10-07.
+This script contains a calibration of the spatial COVID-19 SEIQRD model to hospitalization data in Belgium during the period 2020-03-15 until 2021-11-01.
 """
 
 __author__      = " Tijs W. Alleman, Michiel Rollier"
@@ -21,6 +21,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import multiprocessing as mp
+from datetime import date, datetime, timedelta
 # COVID-19 code
 from covid19_DTM.models.utils import initialize_COVID19_SEIQRD_spatial_hybrid_vacc
 from covid19_DTM.data import sciensano
@@ -88,15 +89,17 @@
 # Number of age groups used in the model
 age_stratification_size=int(args.n_age_groups)
 # Date at which script is started
-run_date = str(datetime.date.today())
+run_date = str(date.today())
 # Keep track of runtime
-initial_time = datetime.datetime.now()
+initial_time = datetime.now()
 # Show progress
 progress=True
 # Start and end of calibration
-start_calibration = pd.to_datetime(args.start_calibration)
+start_calibration = datetime.strptime(args.start_calibration,"%Y-%m-%d")
 if args.end_calibration:
-    end_calibration = pd.to_datetime(args.end_calibration)
+    end_calibration = datetime.strptime(args.end_calibration,"%Y-%m-%d")
+# Leap size
+tau = 0.50
 
 ##############################
 ## Define results locations ##
@@ -136,10 +139,8 @@
 ## Initialize the model ##
 ##########################
 
-model, BASE_samples_dict, initN = initialize_COVID19_SEIQRD_spatial_hybrid_vacc(age_stratification_size=age_stratification_size, agg=agg,
-                                                                                start_date=start_calibration.strftime("%Y-%m-%d"), stochastic=True,
-                                                                                distinguish_day_type=True)
-tau = 0.50
+model, BASE_samples_dict, initN = initialize_COVID19_SEIQRD_spatial_hybrid_vacc(age_stratification_size=age_stratification_size, agg=agg,start_date=start_calibration,
+                                                                                stochastic=True, distinguish_day_type=True)
 
 if agg == 'arr':
     # Switch to the provinicial initN
@@ -190,18 +191,20 @@
     processes = int(os.getenv('SLURM_CPUS_ON_NODE', mp.cpu_count()/2))
     multiplier_pso = 3
     # MCMC settings
-    multiplier_mcmc = 10
+    multiplier_mcmc = 5
     max_n = n_mcmc
-    print_n = 5
+    print_n = 10
     # Define dataset
     data=[df_hosp.loc[(slice(start_calibration,end_calibration), slice(None))],
+          df_hosp.loc[(slice(start_calibration,end_calibration), slice(None))].groupby(by=['date']).sum(),
           df_sero_herzog['abs','mean'],
           df_sero_sciensano['abs','mean'][:23],
           df_cases]
-    states = ["H_in", "R", "R", "M_in"]
-    weights = np.array([1, 1, 1, 1])
-    log_likelihood_fnc = [ll_negative_binomial, ll_negative_binomial, ll_negative_binomial, ll_negative_binomial] # For arr calibration --> use poisson
+    states = ["H_in", "H_in", "R", "R", "M_in"]
+    weights = np.array([1, 1, 1, 1, 1])
+    log_likelihood_fnc = [ll_negative_binomial, ll_negative_binomial, ll_negative_binomial, ll_negative_binomial, ll_negative_binomial] # For arr calibration --> use poisson
     log_likelihood_fnc_args = [dispersion_hosp.values,
+                               dispersion_weighted_hosp,
                                dispersion_weighted_hosp,
                                dispersion_weighted_hosp,
                                dispersion_cases]
@@ -246,7 +249,7 @@
     # out = pso.optimize(objective_function, bounds, kwargs={'simulation_kwargs':{'warmup': 0}},
     #                   swarmsize=multiplier_pso*processes, maxiter=n_pso, processes=processes, debug=True)[0]
     # A good guess
-    theta = [0.5, 0.56, 5000, 0.50, 0.4, 0.7, 1.4, 1.9, 0.225, 0.6]
+    theta = [0.5, 0.56, 5000, 0.45, 0.40, 0.70, 1.4, 1.9, 0.225, 0.6]
 
     #######################################
     ## Visualize fits on multiple levels ##
@@ -257,13 +260,13 @@
         print(theta)
         pars_PSO = assign_theta(model.parameters, pars, theta)
         model.parameters = pars_PSO
-        end_visualization = '2022-01-01'
+        end_visualization = datetime(2022, 1, 1)
         # Perform simulation with best-fit results
-        out = model.sim([start_calibration, pd.Timestamp(end_visualization)], tau=tau)
+        out = model.sim([start_calibration, end_visualization], tau=tau)
         # Aggregate Brussels and Brabant
         out = aggregate_Brussels_Brabant_Dataset(out)
         # National fit
-        data_star=[data[0].groupby(by=['date']).sum(), df_sero_herzog['abs','mean'], df_sero_sciensano['abs','mean'][:23], data[-1].groupby(by=['date']).sum(),]
+        data_star=[data[0].groupby(by=['date']).sum(), data[0].groupby(by=['date']).sum(), df_sero_herzog['abs','mean'], df_sero_sciensano['abs','mean'][:23], data[-1].groupby(by=['date']).sum(),]
         ax = plot_PSO(out, data_star, states, start_calibration, end_visualization)
         plt.show()
         plt.close()
@@ -294,7 +297,7 @@
             pars_PSO = assign_theta(model.parameters, pars, theta)
             model.parameters = pars_PSO
             # Perform simulation
-            out = model.sim([start_calibration, pd.Timestamp(end_visualization)], tau=tau)
+            out = model.sim([start_calibration, end_visualization], tau=tau)
             # Aggregate Brussels and Brabant
             out = aggregate_Brussels_Brabant_Dataset(out)
             # Visualize national fit

notebooks/calibration/emcee_sampler_to_dictionary.py

@@ -152,7 +152,7 @@
     smooth=1,
     label_kwargs=dict(fontsize=24),
     title_kwargs=dict(fontsize=14),
-    quantiles=[0.05, 0.95],
+    title_quantiles=[0.05, 0.50, 0.95],
     levels=(1 - np.exp(-0.5), 1 - np.exp(-2), 1 - np.exp(-9 / 2.)),
     plot_density=True,
     plot_datapoints=False,

notebooks/calibration/plot_fit_BASE-COVID19_SEIQRD_hybrid_vacc.py

@@ -25,7 +25,7 @@
 """
 
 __author__      = "Tijs Alleman"
-__copyright__   = "Copyright (c) 2022 by T.W. Alleman, BIOMATH, Ghent University. All Rights Reserved."
+__copyright__   = "Copyright (c) 2023 by T.W. Alleman, BIOMATH, Ghent University. All Rights Reserved."
 
 ############################
 ## Load required packages ##
@@ -37,6 +37,7 @@
 import argparse
 import numpy as np
 import pandas as pd
+from datetime import datetime, timedelta
 import matplotlib.pyplot as plt
 from covid19_DTM.models.utils import initialize_COVID19_SEIQRD_hybrid_vacc
 from covid19_DTM.data import sciensano
@@ -67,7 +68,7 @@
 ################################
 
 # Start and end of simulation
-end_sim = '2022-01-01'
+end_sim = datetime(2022,1,1)
 # Confidence level used to visualise model fit
 conf_int = 0.05
 
@@ -91,10 +92,10 @@
 samples_dict = load_samples_dict(samples_path+str(args.agg)+'_'+str(args.identifier) + '_SAMPLES_' + str(args.date) + '.json', age_stratification_size=age_stratification_size)
 warmup = samples_dict['warmup']
 # Start of calibration warmup and beta
-start_calibration = samples_dict['start_calibration']
+start_calibration = datetime.strptime(samples_dict['start_calibration'], '%Y-%m-%d')
 start_sim = start_calibration
 # Last datapoint used to calibrate warmup and beta
-end_calibration = samples_dict['end_calibration']
+end_calibration = datetime.strptime(samples_dict['end_calibration'], '%Y-%m-%d')
 # Overdispersion data
 dispersion = float(samples_dict['dispersion'])
 
@@ -160,7 +161,7 @@
 ax2.plot(df_2plot['H_in','mean'], color='blue', linewidth=1.5)
 ax2.fill_between(simtime, df_2plot['H_in','lower'], df_2plot['H_in','upper'],alpha=0.20, color = 'blue')
 ax2.scatter(df_hosp[start_calibration:end_calibration].index,df_hosp['H_in'][start_calibration:end_calibration], color='red', alpha=0.2, linestyle='None', facecolors='red', s=10)
-ax2.scatter(df_hosp[pd.to_datetime(end_calibration)+datetime.timedelta(days=1):end_sim].index,df_hosp['H_in'][pd.to_datetime(end_calibration)+datetime.timedelta(days=1):end_sim], color='black', alpha=0.2, linestyle='None', facecolors='black', s=10)
+ax2.scatter(df_hosp[pd.to_datetime(end_calibration)+timedelta(days=1):end_sim].index,df_hosp['H_in'][pd.to_datetime(end_calibration)+timedelta(days=1):end_sim], color='black', alpha=0.2, linestyle='None', facecolors='black', s=10)
 ax2 = _apply_tick_locator(ax2)
 ax2.set_xlim(start_sim,end_sim)
 ax2.set_ylabel('Incidence\nHospital (-)', fontsize=13)

notebooks/calibration/plot_fit_BASE-COVID19_SEIQRD_spatial_hybrid_vacc.py

@@ -39,10 +39,12 @@
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
+from datetime import datetime
 from covid19_DTM.data import sciensano
 from covid19_DTM.visualization.output import _apply_tick_locator 
 # Import the function to initialize the model
 from covid19_DTM.models.utils import initialize_COVID19_SEIQRD_spatial_hybrid_vacc,  output_to_visuals, add_negative_binomial
+
 #############################
 ## Handle script arguments ##
 #############################
@@ -66,8 +68,7 @@
 ################################
 
 # Start and end of simulation
-start_sim = '2020-09-01'
-end_sim = '2022-01-01'
+end_sim = datetime(2022,1,1)
 # Confidence level used to visualise model fit
 conf_int = 0.05
 
@@ -92,11 +93,12 @@
 samples_dict = load_samples_dict(samples_path+str(args.agg)+'_'+str(args.identifier) + '_SAMPLES_' + str(args.date) + '.json', age_stratification_size=age_stratification_size)
 warmup = float(samples_dict['warmup'])
 dispersion = float(samples_dict['dispersion'])
-tau = float(samples_dict['dispersion'])
+tau = float(samples_dict['tau'])
 # Start of calibration warmup and beta
-start_calibration = samples_dict['start_calibration']
+start_calibration = datetime.strptime(samples_dict['start_calibration'], '%Y-%m-%d')
+start_sim = start_calibration
 # Last datapoint used to calibrate warmup and beta
-end_calibration = samples_dict['end_calibration']
+end_calibration = datetime.strptime(samples_dict['end_calibration'], '%Y-%m-%d')
 
 ##################################################
 ## Load data not needed to initialize the model ##
@@ -138,7 +140,7 @@
 
 print('\n1) Simulating spatial COVID-19 SEIRD '+str(args.n_samples)+' times')
 start_sim = start_calibration
-out = model.sim([start_sim,end_sim], warmup=warmup, N=args.n_samples, draw_function=draw_fnc, samples=samples_dict, processes=int(args.processes), tau=0.75)
+out = model.sim([start_sim,end_sim], warmup=warmup, N=args.n_samples, draw_function=draw_fnc, samples=samples_dict, processes=int(args.processes), tau=tau)
 # Aggregate Brussels and Brabant
 out = aggregate_Brussels_Brabant_Dataset(out)
 simtime = out['date'].values