DSA with multiple strategies apparently causes mixup in variable naming

[dtordrup]

This one is relatively complex to explain. I have a HEEMOD model with three strategies. I am running DSA with eight parameters, and ask the plot to evaluate parameter values.

The plot produces three tornado charts. The first one shows values for one parameter that this parameter never has:

As shown in the table below, GMA only ever has values <1.0:

model_time	markov_cycle	strategy	IntEff	GSA	GMA	SDE	AnnualInflow	FER	AGE	MRC	MRA	MRE	GPTo15	FGMTo15
1	1	BAU	1	0.0009076	0.0698816	0.1	362646	0.1636240	0.0152313	0.0019086	0.0006646	0.0114879	0.0578403	0.0578403
2	2	BAU	1	0.0009076	0.0698816	0.1	368449	0.1610451	0.0147803	0.0019086	0.0006646	0.0114879	0.0564110	0.0564110
3	3	BAU	1	0.0009076	0.0698816	0.1	374337	0.1584236	0.0143553	0.0019086	0.0006646	0.0114879	0.0549809	0.0549809
4	4	BAU	1	0.0009076	0.0698816	0.1	380326	0.1559283	0.0139540	0.0019086	0.0006646	0.0114879	0.0535546	0.0535546
5	5	BAU	1	0.0009076	0.0698816	0.1	386419	0.1534090	0.0135745	0.0019086	0.0006646	0.0114879	0.0521357	0.0521357
6	6	BAU	1	0.0009076	0.0698816	0.1	392612	0.1509063	0.0164400	0.0019086	0.0006646	0.0114879	0.0585914	0.0585914
7	7	BAU	1	0.0009076	0.0698816	0.1	398888	0.1484725	0.0159704	0.0019086	0.0006646	0.0114879	0.0574208	0.0574208
8	8	BAU	1	0.0009076	0.0698816	0.1	405230	0.1461343	0.0155270	0.0019086	0.0006646	0.0114879	0.0562278	0.0562278
9	9	BAU	1	0.0009076	0.0698816	0.1	411621	0.1439530	0.0151075	0.0019086	0.0006646	0.0114879	0.0550174	0.0550174
10	10	BAU	1	0.0009076	0.0698816	0.1	418031	0.1418221	0.0147100	0.0019086	0.0006646	0.0114879	0.0537945	0.0537945
11	11	BAU	1	0.0009076	0.0698816	0.1	424428	0.1397342	0.0171021	0.0019086	0.0006646	0.0114879	0.0592888	0.0592888
12	12	BAU	1	0.0009076	0.0698816	0.1	430777	0.1376777	0.0166366	0.0019086	0.0006646	0.0114879	0.0582802	0.0582802
13	13	BAU	1	0.0009076	0.0698816	0.1	437055	0.1356471	0.0161958	0.0019086	0.0006646	0.0114879	0.0572419	0.0572419
14	14	BAU	1	0.0009076	0.0698816	0.1	443247	0.1336998	0.0157778	0.0019086	0.0006646	0.0114879	0.0561796	0.0561796
15	15	BAU	1	0.0009076	0.0698816	0.1	449348	0.1317626	0.0153808	0.0019086	0.0006646	0.0114879	0.0550988	0.0550988
16	16	BAU	1	0.0009076	0.0698816	0.1	455343	0.1298349	0.0178158	0.0019086	0.0006646	0.0114879	0.0596123	0.0596123
17	17	BAU	1	0.0009076	0.0698816	0.1	461212	0.1279127	0.0173670	0.0019086	0.0006646	0.0114879	0.0596571	0.0596571
18	18	BAU	1	0.0009076	0.0698816	0.1	466932	0.1259925	0.0169291	0.0019086	0.0006646	0.0114879	0.0597136	0.0597136
19	19	BAU	1	0.0009076	0.0698816	0.1	472489	0.1241020	0.0165019	0.0019086	0.0006646	0.0114879	0.0597859	0.0597859
20	20	BAU	1	0.0009076	0.0698816	0.1	477878	0.1222069	0.0160852	0.0019086	0.0006646	0.0114879	0.0598762	0.0598762
21	21	BAU	1	0.0009076	0.0698816	0.1	483100	0.1203147	0.0187895	0.0019086	0.0006646	0.0114879	0.0599857	0.0599857
22	22	BAU	1	0.0009076	0.0698816	0.1	488151	0.1184312	0.0183706	0.0019086	0.0006646	0.0114879	0.0601135	0.0601135
23	23	BAU	1	0.0009076	0.0698816	0.1	493027	0.1165613	0.0179585	0.0019086	0.0006646	0.0114879	0.0602587	0.0602587
24	24	BAU	1	0.0009076	0.0698816	0.1	497724	0.1147208	0.0175536	0.0019086	0.0006646	0.0114879	0.0604202	0.0604202
25	25	BAU	1	0.0009076	0.0698816	0.1	502259	0.1128953	0.0171558	0.0019086	0.0006646	0.0114879	0.0605953	0.0605953
26	26	BAU	1	0.0009076	0.0698816	0.1	506649	0.1110946	0.0200350	0.0019086	0.0006646	0.0114879	0.0607804	0.0607804
27	27	BAU	1	0.0009076	0.0698816	0.1	510914	0.1093291	0.0196405	0.0019086	0.0006646	0.0114879	0.0609717	0.0609717
28	28	BAU	1	0.0009076	0.0698816	0.1	515082	0.1076089	0.0192502	0.0019086	0.0006646	0.0114879	0.0611665	0.0611665
29	29	BAU	1	0.0009076	0.0698816	0.1	519178	0.1059444	0.0188645	0.0019086	0.0006646	0.0114879	0.0613630	0.0613630
30	30	BAU	1	0.0009076	0.0698816	0.1	523220	0.1043297	0.0184840	0.0019086	0.0006646	0.0114879	0.0615605	0.0615605

A similar thing happens for plot #3:

And as can be seen, the MRA parameter never assumes values other than <1.0:

model_time	markov_cycle	strategy	IntEff	GSA	GMA	SDE	AnnualInflow	FER	AGE	MRC	MRA	MRE	GPTo15	FGMTo15
1	1	InterventionNoFGM	0	0.0009076	0.0698816	0.1	362646	0.1636240	0.0152313	0.0019086	0.0006646	0.0114879	0.0578403	0.0666667
2	2	InterventionNoFGM	0	0.0009076	0.0698816	0.1	368449	0.1610451	0.0147803	0.0019086	0.0006646	0.0114879	0.0564110	0.0714286
3	3	InterventionNoFGM	0	0.0009076	0.0698816	0.1	374337	0.1584236	0.0143553	0.0019086	0.0006646	0.0114879	0.0549809	0.0769231
4	4	InterventionNoFGM	0	0.0009076	0.0698816	0.1	380326	0.1559283	0.0139540	0.0019086	0.0006646	0.0114879	0.0535546	0.0833333
5	5	InterventionNoFGM	0	0.0009076	0.0698816	0.1	386419	0.1534090	0.0135745	0.0019086	0.0006646	0.0114879	0.0521357	0.0909091
6	6	InterventionNoFGM	0	0.0009076	0.0698816	0.1	392612	0.1509063	0.0164400	0.0019086	0.0006646	0.0114879	0.0585914	0.1000000
7	7	InterventionNoFGM	0	0.0009076	0.0698816	0.1	398888	0.1484725	0.0159704	0.0019086	0.0006646	0.0114879	0.0574208	0.1111111
8	8	InterventionNoFGM	0	0.0009076	0.0698816	0.1	405230	0.1461343	0.0155270	0.0019086	0.0006646	0.0114879	0.0562278	0.1250000
9	9	InterventionNoFGM	0	0.0009076	0.0698816	0.1	411621	0.1439530	0.0151075	0.0019086	0.0006646	0.0114879	0.0550174	0.1428571
10	10	InterventionNoFGM	0	0.0009076	0.0698816	0.1	418031	0.1418221	0.0147100	0.0019086	0.0006646	0.0114879	0.0537945	0.1666667
11	11	InterventionNoFGM	0	0.0009076	0.0698816	0.1	424428	0.1397342	0.0171021	0.0019086	0.0006646	0.0114879	0.0592888	0.2000000
12	12	InterventionNoFGM	0	0.0009076	0.0698816	0.1	430777	0.1376777	0.0166366	0.0019086	0.0006646	0.0114879	0.0582802	0.2500000
13	13	InterventionNoFGM	0	0.0009076	0.0698816	0.1	437055	0.1356471	0.0161958	0.0019086	0.0006646	0.0114879	0.0572419	0.3333333
14	14	InterventionNoFGM	0	0.0009076	0.0698816	0.1	443247	0.1336998	0.0157778	0.0019086	0.0006646	0.0114879	0.0561796	0.5000000
15	15	InterventionNoFGM	0	0.0009076	0.0698816	0.1	449348	0.1317626	0.0153808	0.0019086	0.0006646	0.0114879	0.0550988	0.9980914
16	16	InterventionNoFGM	0	0.0009076	0.0698816	0.1	455343	0.1298349	0.0178158	0.0019086	0.0006646	0.0114879	0.0596123	0.0000000
17	17	InterventionNoFGM	0	0.0009076	0.0698816	0.1	461212	0.1279127	0.0173670	0.0019086	0.0006646	0.0114879	0.0596571	0.0000000
18	18	InterventionNoFGM	0	0.0009076	0.0698816	0.1	466932	0.1259925	0.0169291	0.0019086	0.0006646	0.0114879	0.0597136	0.0000000
19	19	InterventionNoFGM	0	0.0009076	0.0698816	0.1	472489	0.1241020	0.0165019	0.0019086	0.0006646	0.0114879	0.0597859	0.0000000
20	20	InterventionNoFGM	0	0.0009076	0.0698816	0.1	477878	0.1222069	0.0160852	0.0019086	0.0006646	0.0114879	0.0598762	0.0000000
21	21	InterventionNoFGM	0	0.0009076	0.0698816	0.1	483100	0.1203147	0.0187895	0.0019086	0.0006646	0.0114879	0.0599857	0.0000000
22	22	InterventionNoFGM	0	0.0009076	0.0698816	0.1	488151	0.1184312	0.0183706	0.0019086	0.0006646	0.0114879	0.0601135	0.0000000
23	23	InterventionNoFGM	0	0.0009076	0.0698816	0.1	493027	0.1165613	0.0179585	0.0019086	0.0006646	0.0114879	0.0602587	0.0000000
24	24	InterventionNoFGM	0	0.0009076	0.0698816	0.1	497724	0.1147208	0.0175536	0.0019086	0.0006646	0.0114879	0.0604202	0.0000000
25	25	InterventionNoFGM	0	0.0009076	0.0698816	0.1	502259	0.1128953	0.0171558	0.0019086	0.0006646	0.0114879	0.0605953	0.0000000
26	26	InterventionNoFGM	0	0.0009076	0.0698816	0.1	506649	0.1110946	0.0200350	0.0019086	0.0006646	0.0114879	0.0607804	0.0000000
27	27	InterventionNoFGM	0	0.0009076	0.0698816	0.1	510914	0.1093291	0.0196405	0.0019086	0.0006646	0.0114879	0.0609717	0.0000000
28	28	InterventionNoFGM	0	0.0009076	0.0698816	0.1	515082	0.1076089	0.0192502	0.0019086	0.0006646	0.0114879	0.0611665	0.0000000
29	29	InterventionNoFGM	0	0.0009076	0.0698816	0.1	519178	0.1059444	0.0188645	0.0019086	0.0006646	0.0114879	0.0613630	0.0000000
30	30	InterventionNoFGM	0	0.0009076	0.0698816	0.1	523220	0.1043297	0.0184840	0.0019086	0.0006646	0.0114879	0.0615605	0.0000000

I therefore inspected the printed result of the DSA, and noticed the following (** added to highlight parameter names and (wrong) values):

                                                                                                                                 .par_value_eval  
InterventionNoFGM, AnnualInflow = 0.5 * getAnnualInflowArray(modelTimeHorizon, countryISO3, baselineYear, secondarySexRatioFemale)      0.0818120054
Intervention50, **AnnualInflow** = 0.5 * getAnnualInflowArray(modelTimeHorizon, countryISO3, baselineYear, secondarySexRatioFemale)    **181323.0000000000** <- this is correct
BAU, AnnualInflow = 0.5 * getAnnualInflowArray(modelTimeHorizon, countryISO3, baselineYear, secondarySexRatioFemale)                    0.0250000000
InterventionNoFGM, AnnualInflow = 1.5 * getAnnualInflowArray(modelTimeHorizon, countryISO3, baselineYear, secondarySexRatioFemale)      0.2454360162
Intervention50, **AnnualInflow** = 1.5 * getAnnualInflowArray(modelTimeHorizon, countryISO3, baselineYear, secondarySexRatioFemale)    **543969.0000000000** <- also correct
BAU, AnnualInflow = 1.5 * getAnnualInflowArray(modelTimeHorizon, countryISO3, baselineYear, secondarySexRatioFemale)                    0.4000000000
InterventionNoFGM, FER = 0.5 * getAnnualFertilityTransitionArray(modelTimeHorizon, baselineYear, countryISO3)                           0.0002268884
Intervention50, FER = 0.5 * getAnnualFertilityTransitionArray(modelTimeHorizon, baselineYear, countryISO3)                              0.0028719702
BAU, FER = 0.5 * getAnnualFertilityTransitionArray(modelTimeHorizon, baselineYear, countryISO3)                                         0.0001661492
InterventionNoFGM, FER = 1.5 * getAnnualFertilityTransitionArray(modelTimeHorizon, baselineYear, countryISO3)                           0.0036302144
Intervention50, FER = 1.5 * getAnnualFertilityTransitionArray(modelTimeHorizon, baselineYear, countryISO3)                              0.0459515236
BAU, FER = 1.5 * getAnnualFertilityTransitionArray(modelTimeHorizon, baselineYear, countryISO3)                                         0.0026583879
InterventionNoFGM, GMA = 0.25 * getModerateFGMAnnualRisk(incidenceFGM, countryISO3)                                                     0.0001661492
Intervention50, GMA = 0.25 * getModerateFGMAnnualRisk(incidenceFGM, countryISO3)                                                        0.0818120054
BAU, **GMA** = 0.25 * getModerateFGMAnnualRisk(incidenceFGM, countryISO3)                                                              **181323.0000000000** <- this is wrong
InterventionNoFGM, GMA = 4 * getModerateFGMAnnualRisk(incidenceFGM, countryISO3)                                                        0.0026583879
Intervention50, GMA = 4 * getModerateFGMAnnualRisk(incidenceFGM, countryISO3)                                                           0.2454360162
BAU, **GMA** = 4 * getModerateFGMAnnualRisk(incidenceFGM, countryISO3)                                                                 **543969.0000000000** <- also wrong
InterventionNoFGM, GSA = 0.25 * getSevereFGMAnnualRisk(incidenceFGM, countryISO3)                                                       0.0250000000
Intervention50, GSA = 0.25 * getSevereFGMAnnualRisk(incidenceFGM, countryISO3)                                                          0.0174704068
BAU, GSA = 0.25 * getSevereFGMAnnualRisk(incidenceFGM, countryISO3)                                                                     0.0002268884
InterventionNoFGM, GSA = 4 * getSevereFGMAnnualRisk(incidenceFGM, countryISO3)                                                          0.4000000000
Intervention50, GSA = 4 * getSevereFGMAnnualRisk(incidenceFGM, countryISO3)                                                             0.2795265096
BAU, GSA = 4 * getSevereFGMAnnualRisk(incidenceFGM, countryISO3)                                                                        0.0036302144
InterventionNoFGM, **MRA** = 0.25 * getMortalityRateAdults(countryISO3)                                                                **181323.0000000000** <- this is wrong
Intervention50, MRA = 0.25 * getMortalityRateAdults(countryISO3)                                                                        0.0250000000
BAU, MRA = 0.25 * getMortalityRateAdults(countryISO3)                                                                                   0.0174704068
InterventionNoFGM, **MRA** = 4 * getMortalityRateAdults(countryISO3)                                                                   **543969.0000000000** <- also wrong
Intervention50, MRA = 4 * getMortalityRateAdults(countryISO3)                                                                           0.4000000000
BAU, MRA = 4 * getMortalityRateAdults(countryISO3)                                                                                      0.2795265096
InterventionNoFGM, MRE = 0.25 * getMortalityRateElderly(countryISO3)                                                                    0.0028719702
Intervention50, MRE = 0.25 * getMortalityRateElderly(countryISO3)                                                                       0.0001661492
BAU, MRE = 0.25 * getMortalityRateElderly(countryISO3)                                                                                  0.0818120054
InterventionNoFGM, MRE = 4 * getMortalityRateElderly(countryISO3)                                                                       0.0459515236
Intervention50, MRE = 4 * getMortalityRateElderly(countryISO3)                                                                          0.0026583879
BAU, MRE = 4 * getMortalityRateElderly(countryISO3)                                                                                     0.2454360162
InterventionNoFGM, SDE = 0.25 * getType3DeinfibulationRate(countryISO3)                                                                 0.0174704068
Intervention50, SDE = 0.25 * getType3DeinfibulationRate(countryISO3)                                                                    0.0002268884
BAU, SDE = 0.25 * getType3DeinfibulationRate(countryISO3)                                                                               0.0028719702
InterventionNoFGM, SDE = 4 * getType3DeinfibulationRate(countryISO3)                                                                    0.2795265096
Intervention50, SDE = 4 * getType3DeinfibulationRate(countryISO3)                                                                       0.0036302144
BAU, SDE = 4 * getType3DeinfibulationRate(countryISO3)                                                                                  0.0459515236

So somehow the evaluated values for some of my parameters are being mixed up. All of the 181323 and 543969 values should be associated with the "AnnualInflow = .." parameters, i.e. first six rows in the printout just above. I haven't checked whether all other parameters are correct, these examples jump out because they are large numbers, but a few of the I can immediately tell are not right, e.g:

InterventionNoFGM, GMA = 0.25 * getModerateFGMAnnualRisk(incidenceFGM, countryISO3)                                                     0.0001661492
Intervention50, GMA = 0.25 * getModerateFGMAnnualRisk(incidenceFGM, countryISO3)                                                        0.0818120054
BAU, GMA = 0.25 * getModerateFGMAnnualRisk(incidenceFGM, countryISO3)                                                              181323.0000000000
InterventionNoFGM, GMA = 4 * getModerateFGMAnnualRisk(incidenceFGM, countryISO3)                                                        0.0026583879
Intervention50, GMA = 4 * getModerateFGMAnnualRisk(incidenceFGM, countryISO3)                                                           0.2454360162
BAU, GMA = 4 * getModerateFGMAnnualRisk(incidenceFGM, countryISO3)                                                                 543969.0000000000

<- The value of GMA is constant, so of course 0.25*getModerate... and 4*getModerate... should be the same values.

Possibly importantly, many of the parameters depend on model time, and have separate values for each cycle (see the parameter table above). I have not used the model_time variable to create these parameters with simple arithmetic (as in many heemod examples), but defined these parameters using arrays of type numeric(..) and size = model time horizon. The parameter values shown for "AnnualInflow = ..." are the evaluated values for the fist model year (0.5 * 362,646).

Of course, it may be just a simple ordering issue...

[dtordrup]

I had a thorough check, and pretty much all of the parameter values are out of place. Below, I indicate after each row which parameter the value belongs to:

                                                                                                                                   .par_value_eval  
InterventionNoFGM, AnnualInflow = 0.5 * getAnnualInflowArray(modelTimeHorizon, countryISO3, baselineYear, secondarySexRatioFemale)      0.0818120054 <- FER
Intervention50, AnnualInflow = 0.5 * getAnnualInflowArray(modelTimeHorizon, countryISO3, baselineYear, secondarySexRatioFemale)    181323.0000000000 <- AnnualInflow
BAU, AnnualInflow = 0.5 * getAnnualInflowArray(modelTimeHorizon, countryISO3, baselineYear, secondarySexRatioFemale)                    0.0250000000 <- SDE
InterventionNoFGM, AnnualInflow = 1.5 * getAnnualInflowArray(modelTimeHorizon, countryISO3, baselineYear, secondarySexRatioFemale)      0.2454360162 <- FER
Intervention50, AnnualInflow = 1.5 * getAnnualInflowArray(modelTimeHorizon, countryISO3, baselineYear, secondarySexRatioFemale)    543969.0000000000 <- AnnualInflow
BAU, AnnualInflow = 1.5 * getAnnualInflowArray(modelTimeHorizon, countryISO3, baselineYear, secondarySexRatioFemale)                    0.4000000000 <- SDE
InterventionNoFGM, FER = 0.5 * getAnnualFertilityTransitionArray(modelTimeHorizon, baselineYear, countryISO3)                           0.0002268884 <- GSA
Intervention50, FER = 0.5 * getAnnualFertilityTransitionArray(modelTimeHorizon, baselineYear, countryISO3)                              0.0028719702 <- MRE
BAU, FER = 0.5 * getAnnualFertilityTransitionArray(modelTimeHorizon, baselineYear, countryISO3)                                         0.0001661492 <- MRA
InterventionNoFGM, FER = 1.5 * getAnnualFertilityTransitionArray(modelTimeHorizon, baselineYear, countryISO3)                           0.0036302144 <- GSA
Intervention50, FER = 1.5 * getAnnualFertilityTransitionArray(modelTimeHorizon, baselineYear, countryISO3)                              0.0459515236 <- MRE
BAU, FER = 1.5 * getAnnualFertilityTransitionArray(modelTimeHorizon, baselineYear, countryISO3)                                         0.0026583879 <- MRA 
InterventionNoFGM, GMA = 0.25 * getModerateFGMAnnualRisk(incidenceFGM, countryISO3)                                                     0.0001661492 <- MRE
Intervention50, GMA = 0.25 * getModerateFGMAnnualRisk(incidenceFGM, countryISO3)                                                        0.0818120054 <- FER 
BAU, GMA = 0.25 * getModerateFGMAnnualRisk(incidenceFGM, countryISO3)                                                              181323.0000000000 <- AnnualInflow
InterventionNoFGM, GMA = 4 * getModerateFGMAnnualRisk(incidenceFGM, countryISO3)                                                        0.0026583879 <- MRA
Intervention50, GMA = 4 * getModerateFGMAnnualRisk(incidenceFGM, countryISO3)                                                           0.2454360162 <- FER
BAU, GMA = 4 * getModerateFGMAnnualRisk(incidenceFGM, countryISO3)                                                                 543969.0000000000 <- AnnualInflow
InterventionNoFGM, GSA = 0.25 * getSevereFGMAnnualRisk(incidenceFGM, countryISO3)                                                       0.0250000000 <- SDE
Intervention50, GSA = 0.25 * getSevereFGMAnnualRisk(incidenceFGM, countryISO3)                                                          0.0174704068 <- GMA
BAU, GSA = 0.25 * getSevereFGMAnnualRisk(incidenceFGM, countryISO3)                                                                     0.0002268884 <- GSA
InterventionNoFGM, GSA = 4 * getSevereFGMAnnualRisk(incidenceFGM, countryISO3)                                                          0.4000000000 <- SDE
Intervention50, GSA = 4 * getSevereFGMAnnualRisk(incidenceFGM, countryISO3)                                                             0.2795265096 <- GMA
BAU, GSA = 4 * getSevereFGMAnnualRisk(incidenceFGM, countryISO3)                                                                        0.0036302144 <- GSA 
InterventionNoFGM, MRA = 0.25 * getMortalityRateAdults(countryISO3)                                                                181323.0000000000 <- AnnualInflow
Intervention50, MRA = 0.25 * getMortalityRateAdults(countryISO3)                                                                        0.0250000000 <- DSE
BAU, MRA = 0.25 * getMortalityRateAdults(countryISO3)                                                                                   0.0174704068 <- GMA
InterventionNoFGM, MRA = 4 * getMortalityRateAdults(countryISO3)                                                                   543969.0000000000 <- AnnualInflow
Intervention50, MRA = 4 * getMortalityRateAdults(countryISO3)                                                                           0.4000000000 <- DSE
BAU, MRA = 4 * getMortalityRateAdults(countryISO3)                                                                                      0.2795265096 <- GMA
InterventionNoFGM, MRE = 0.25 * getMortalityRateElderly(countryISO3)                                                                    0.0028719702 <- MRE
Intervention50, MRE = 0.25 * getMortalityRateElderly(countryISO3)                                                                       0.0001661492 <- MRE
BAU, MRE = 0.25 * getMortalityRateElderly(countryISO3)                                                                                  0.0818120054 <- FER
InterventionNoFGM, MRE = 4 * getMortalityRateElderly(countryISO3)                                                                       0.0459515236 <- MRE
Intervention50, MRE = 4 * getMortalityRateElderly(countryISO3)                                                                          0.0026583879 <- MRA
BAU, MRE = 4 * getMortalityRateElderly(countryISO3)                                                                                     0.2454360162 <- FER
InterventionNoFGM, SDE = 0.25 * getType3DeinfibulationRate(countryISO3)                                                                 0.0174704068 <- GMA 
Intervention50, SDE = 0.25 * getType3DeinfibulationRate(countryISO3)                                                                    0.0002268884 <- GSA
BAU, SDE = 0.25 * getType3DeinfibulationRate(countryISO3)                                                                               0.0028719702 <- MRE
InterventionNoFGM, SDE = 4 * getType3DeinfibulationRate(countryISO3)                                                                    0.2795265096 <- GMA
Intervention50, SDE = 4 * getType3DeinfibulationRate(countryISO3)                                                                       0.0036302144 <- GSA
BAU, SDE = 4 * getType3DeinfibulationRate(countryISO3)                                                                                  0.0459515236 <- MRE

It would be useful to know, for a work-around, whether these values are only used for the labels on the tornado charts, or if they are also used as part of the plots or the underlying calculations?