forked from mrc-ide/EPIONCHO.IBM
-
Notifications
You must be signed in to change notification settings - Fork 0
/
testing_variable_ints.R
289 lines (212 loc) · 11.6 KB
/
testing_variable_ints.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
# ========================================================================================================= #
# current code for simple intervention: treat start ( in years), treat stop and interval (e.g., 1 = 1 year) #
DT.t <- 1/366
#time.its <- round(time.its / (DT))
#year_its <- seq(0, time.its, 366)
#length of simulation in years
timesteps.t = 80
#should treatment be given, when and how often
give.treat.t = 0
treat.start.t = 1; treat.stop.t = 16
treat.start.t = 1/366
#if(give.treat == 1)
#{
treat.stop.t <- round(treat.stop.t / (DT.t))
treat.start.t <- round( (treat.start.t) / (DT.t)) + 1
treat.start.t <- round( (treat.start) / (DT)) + 1
#}
times.of.treat.in.t <- seq(treat.start.t, treat.stop.t - (treat.int.t / DT.t), treat.int.t / DT.t)
times.of.treat.in.t <- seq(treat.start.t, treat.stop.t, treat.int.t / DT.t)
# ============================================================== #
# additional code to specify more complex intervention histories #
treat.start.t = 31; treat.stop.t = 46
treat.timing.in.t <- c(1,2,3,4,5,6,7,8,9,10,10.5,11,11.5,12, 13, 14, 15)
treat.timing.in.t2 <- treat.timing.in.t + (treat.start.t - 1)
times.of.treat.in.t2 <- round((treat.timing.in.t2) / (DT.t)) + 1
# =============================================================== #
# Test in model (variable timing/frequency) #
devtools::load_all()
# ============================ #
#length of simulation in years
timesteps = 47
#should treatment be given, when and how often
give.treat.in = 1
treat.strt = 31; treat.stp = 46
#trt.int = 1
treat.timing.in <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10.5, 11, 11.5, 12, 13, 14, 15)
# =========================== #
# specify large gap in middle #
timesteps = 45
#should treatment be given, when and how often
give.treat.in = 1
treat.strt = 31; treat.stp = 45
treat.strt = 30; treat.stp = 44
#trt.int = 1
treat.timing.in <- c(1, 2, 3, 4, 10, 11, 11.5, 12, 13, 14, 15)
#annual biting rate, which determines infection prevalence
ABR.in = 10000
output_MDAvariablefreq2 <- ep.equi.sim(time.its = timesteps,
ABR = ABR.in,
N.in = 440,
treat.timing = treat.timing.in,
treat.prob = 0.65,
give.treat = give.treat.in,
treat.start = treat.strt,
treat.stop = treat.stp,
pnc = 0.05,
min.mont.age = 5,
vector.control.strt = NA,
delta.hz.in = 0.186,
delta.hinf.in = 0.003,
c.h.in = 0.005,
gam.dis.in = 0.3,
run_equilibrium = TRUE,
equilibrium = NA,
print_progress = TRUE)
names(output_MDAvariablefreq2)
tme <- seq(1, 45*366-1)/366
plot(tme, output_MDAvariablefreq2$mf_prev, type = 'l', xlab = 'time (years)', ylab = 'microfilarial prevalence', ylim = c(0, 1))
plot(tme, output_MDAvariablefreq2$mf_prev, type = 'l', xlab = 'time (years)', ylab = 'microfilarial prevalence', ylim = c(0, 1), xlim = c(30,47), xaxt = "n")
axis(1, at = seq(1, 45, by = 1), las=2)
# ========================================================================= #
# Test in model (both VC & variable timing/frequency) #
devtools::load_all()
# ============================ #
#length of simulation in years
timesteps = 81
#should treatment be given, when and how often
give.treat.in = 1
treat.strt = 61; treat.stp = 80
#trt.int = 1
treat.timing.in <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20)
#annual biting rate, which determines infection prevalence
ABR.in = 10000
output_MDAvariablefreq_VC <- ep.equi.sim(time.its = timesteps,
ABR = ABR.in,
N.in = 440,
treat.timing = treat.timing.in,
treat.prob = 0.65,
give.treat = give.treat.in,
treat.start = treat.strt,
treat.stop = treat.stp,
pnc = 0.05,
min.mont.age = 5,
#vector.control.strt = NA,
vector.control.strt = 40,
vector.control.duration = 20,
vector.control.efficacy = 0.7,
delta.hz.in = 0.186,
delta.hinf.in = 0.003,
c.h.in = 0.005,
gam.dis.in = 0.3,
run_equilibrium = TRUE,
equilibrium = NA,
print_progress = TRUE)
names(output_MDAvariablefreq_VC)
tme <- seq(1, 81*366-1)/366
plot(tme, output_MDAvariablefreq_VC$ABR_recorded, type = 'l', xlab = 'time (years)', ylab = 'ABR', ylim = c(0, 10000))
plot(tme, output_MDAvariablefreq_VC$mf_prev, type = 'l', xlab = 'time (years)', ylab = 'microfilarial prevalence', ylim = c(0, 1))
plot(tme, output_MDAvariablefreq_VC$mf_prev, type = 'l', xlab = 'time (years)', ylab = 'microfilarial prevalence', ylim = c(0, 1), xlim = c(40,81), xaxt = "n")
axis(1, at = seq(1, 81, by = 1), las=2)
# ================================================================================================================================== #
# Variable coverage (vector of coverages) #
test.function.extracting.cov <- function(i, treat.prob.variable.t, times.of.treat.in.t) {
if(all(!is.na(treat.prob.variable.t))){
if(any(i == times.of.treat.in.t)) {
index.iter.treat <- match(i, times.of.treat.in.t)
treat.prob.out <- treat.prob.variable.t[index.iter.treat]} # find element where iteration number matches a time in times.of.treat vector
#if(any(i == times.of.treat.in.t)) {treat.prob.out <- treat.prob.variable.t[index.iter.treat]}
return(treat.prob.out)
}
}
treat.prob.variable.t <- c(0.65, 0.7, 0.8, 0.65)
times.of.treat.in.t <- c(2, 7, 20, 100)
test.function.extracting.cov(i = 5, treat.prob.variable.t = treat.prob.variable.t, times.of.treat.in.t = times.of.treat.in.t)
# ================== #
# test in main model #
devtools::load_all()
timesteps = 45
#should treatment be given, when and how often
# give.treat.in = 1
# treat.strt = 3; treat.stp = 15
# #trt.int = 1
# treat.timing.in <- c(3, 6, 9) # not years, this will instead directly infer the
# # (normally this would be years when MDA occur then converetd)
# # so this is directly the iters rather than the years (ONLY FOR TESTING!)
#
# treat.prob.variable.in <- c(0.65, 0.75, 0.8)
#should treatment be given, when and how often
give.treat.in = 1
treat.strt = 31; treat.stp = 45
treat.timing.in <- c(1, 2, 3, 4, 10, 11, 11.5, 12, 13, 14, 15)
treat.prob.variable.in <- c(0.65, 0.75, 0.8, 0.85, 0.9, 0.85, 0.5, 0.65, 0.9, 0.8, 0.6)
timesteps = 52
treat.strt = 31
treat.stp = 51 # note this must be year after last treatment year in vector of treatment round timings,
# so if last one is at 20 years, last year of treatment is 31 + (20 - 1) = year 50.
# therefore treat.stop needs to be at year 51.
treat.timing.in <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20) # last round at 20 year will be 50 years in above
treat.prob.variable.in <- c(0.65, 0.75, 0.8, 0.85, 0.9, 0.85, 0.5, 0.65, 0.9, 0.8, 0.6, 0.95, 0.95, 0.9, 0.8, 0.5, 0.55, 0.85, 0.9)
#annual biting rate, which determines infection prevalence
ABR.in = 10000
output_MDAvarCov <- ep.equi.sim(time.its = timesteps,
ABR = ABR.in,
N.in = 440,
treat.timing = treat.timing.in,
treat.prob.variable = treat.prob.variable.in,
give.treat = give.treat.in,
treat.start = treat.strt,
treat.stop = treat.stp,
pnc = 0.05,
min.mont.age = 5,
vector.control.strt = NA,
delta.hz.in = 0.186,
delta.hinf.in = 0.003,
c.h.in = 0.005,
gam.dis.in = 0.3,
run_equilibrium = TRUE,
equilibrium = NA,
print_progress = TRUE)
names(output_MDAvarCov)
tme <- seq(1, 52*366-1)/366
plot(tme, output_MDAvarCov$coverage.recorded, type = 'l', xlab = 'time (years)', ylab = 'coverage (0-1)', ylim = c(0, 1), xaxt = "n")
axis(1, at = seq(1, 52, by = 1), las=2)
plot(tme, output_MDAvarCov$mf_prev, type = 'l', xlab = 'time (years)', ylab = 'microfilarial prevalence', ylim = c(0, 1))
plot(tme, output_MDAvarCov$mf_prev, type = 'l', xlab = 'time (years)', ylab = 'microfilarial prevalence', ylim = c(0, 1), xlim = c(30,51), xaxt = "n")
axis(1, at = seq(1, 52, by = 1), las=2)
# =============================== #
# testing fre, cov & VC #
timesteps = 52
treat.strt = 31
treat.stp = 51 # note this must be year after last treatment year in vector of treatment round timings,
# so if last one is at 20 years, last year of treatment is 31 + (20 - 1) = year 50.
# therefore treat.stop needs to be at year 51.
treat.timing.in <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20) # last round at 20 year will be 50 years in above
treat.prob.variable.in <- c(0.65, 0.75, 0.8, 0.85, 0.9, 0.85, 0.5, 0.65, 0.9, 0.8, 0.6, 0.95, 0.95, 0.9, 0.8, 0.5, 0.55, 0.85, 0.9)
#annual biting rate, which determines infection prevalence
ABR.in = 10000
output_MDA_freqcovVC <- ep.equi.sim(time.its = timesteps,
ABR = ABR.in,
N.in = 440,
treat.timing = treat.timing.in,
treat.prob.variable = treat.prob.variable.in,
give.treat = give.treat.in,
treat.start = treat.strt,
treat.stop = treat.stp,
pnc = 0.05,
min.mont.age = 5,
#vector.control.strt = NA,
vector.control.strt = 30,
vector.control.duration = 10,
vector.control.efficacy = 0.7,
delta.hz.in = 0.186,
delta.hinf.in = 0.003,
c.h.in = 0.005,
gam.dis.in = 0.3,
run_equilibrium = TRUE,
equilibrium = NA,
print_progress = TRUE)
names(output_MDA_freqcovVC)
tme <- seq(1, 52*366-1)/366
plot(tme, output_MDA_freqcovVC$coverage.recorded, type = 'l', xlab = 'time (years)', ylab = 'coverage (0-1)', ylim = c(0, 1), xaxt = "n")
axis(1, at = seq(1, 52, by = 1), las=2)