-
Notifications
You must be signed in to change notification settings - Fork 0
/
cwd.html
884 lines (793 loc) · 32.2 KB
/
cwd.html
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
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<meta name="generator" content="pandoc" />
<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
<meta name="author" content="" />
<title>SpeedGoat Help</title>
<script src="site_libs/header-attrs-2.26/header-attrs.js"></script>
<script src="site_libs/jquery-3.6.0/jquery-3.6.0.min.js"></script>
<meta name="viewport" content="width=device-width, initial-scale=1" />
<link href="site_libs/bootstrap-3.3.5/css/flatly.min.css" rel="stylesheet" />
<script src="site_libs/bootstrap-3.3.5/js/bootstrap.min.js"></script>
<script src="site_libs/bootstrap-3.3.5/shim/html5shiv.min.js"></script>
<script src="site_libs/bootstrap-3.3.5/shim/respond.min.js"></script>
<style>h1 {font-size: 34px;}
h1.title {font-size: 38px;}
h2 {font-size: 30px;}
h3 {font-size: 24px;}
h4 {font-size: 18px;}
h5 {font-size: 16px;}
h6 {font-size: 12px;}
code {color: inherit; background-color: rgba(0, 0, 0, 0.04);}
pre:not([class]) { background-color: white }</style>
<script src="site_libs/jqueryui-1.13.2/jquery-ui.min.js"></script>
<link href="site_libs/tocify-1.9.1/jquery.tocify.css" rel="stylesheet" />
<script src="site_libs/tocify-1.9.1/jquery.tocify.js"></script>
<script src="site_libs/navigation-1.1/tabsets.js"></script>
<link href="site_libs/highlightjs-9.12.0/textmate.css" rel="stylesheet" />
<script src="site_libs/highlightjs-9.12.0/highlight.js"></script>
<style type="text/css">
code{white-space: pre-wrap;}
span.smallcaps{font-variant: small-caps;}
span.underline{text-decoration: underline;}
div.column{display: inline-block; vertical-align: top; width: 50%;}
div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
ul.task-list{list-style: none;}
</style>
<style type="text/css">code{white-space: pre;}</style>
<script type="text/javascript">
if (window.hljs) {
hljs.configure({languages: []});
hljs.initHighlightingOnLoad();
if (document.readyState && document.readyState === "complete") {
window.setTimeout(function() { hljs.initHighlighting(); }, 0);
}
}
</script>
<style type = "text/css">
.main-container {
max-width: 940px;
margin-left: auto;
margin-right: auto;
}
img {
max-width:100%;
}
.tabbed-pane {
padding-top: 12px;
}
.html-widget {
margin-bottom: 20px;
}
button.code-folding-btn:focus {
outline: none;
}
summary {
display: list-item;
}
details > summary > p:only-child {
display: inline;
}
pre code {
padding: 0;
}
</style>
<style type="text/css">
.dropdown-submenu {
position: relative;
}
.dropdown-submenu>.dropdown-menu {
top: 0;
left: 100%;
margin-top: -6px;
margin-left: -1px;
border-radius: 0 6px 6px 6px;
}
.dropdown-submenu:hover>.dropdown-menu {
display: block;
}
.dropdown-submenu>a:after {
display: block;
content: " ";
float: right;
width: 0;
height: 0;
border-color: transparent;
border-style: solid;
border-width: 5px 0 5px 5px;
border-left-color: #cccccc;
margin-top: 5px;
margin-right: -10px;
}
.dropdown-submenu:hover>a:after {
border-left-color: #adb5bd;
}
.dropdown-submenu.pull-left {
float: none;
}
.dropdown-submenu.pull-left>.dropdown-menu {
left: -100%;
margin-left: 10px;
border-radius: 6px 0 6px 6px;
}
</style>
<script type="text/javascript">
// manage active state of menu based on current page
$(document).ready(function () {
// active menu anchor
href = window.location.pathname
href = href.substr(href.lastIndexOf('/') + 1)
if (href === "")
href = "index.html";
var menuAnchor = $('a[href="' + href + '"]');
// mark the anchor link active (and if it's in a dropdown, also mark that active)
var dropdown = menuAnchor.closest('li.dropdown');
if (window.bootstrap) { // Bootstrap 4+
menuAnchor.addClass('active');
dropdown.find('> .dropdown-toggle').addClass('active');
} else { // Bootstrap 3
menuAnchor.parent().addClass('active');
dropdown.addClass('active');
}
// Navbar adjustments
var navHeight = $(".navbar").first().height() + 15;
var style = document.createElement('style');
var pt = "padding-top: " + navHeight + "px; ";
var mt = "margin-top: -" + navHeight + "px; ";
var css = "";
// offset scroll position for anchor links (for fixed navbar)
for (var i = 1; i <= 6; i++) {
css += ".section h" + i + "{ " + pt + mt + "}\n";
}
style.innerHTML = "body {" + pt + "padding-bottom: 40px; }\n" + css;
document.head.appendChild(style);
});
</script>
<!-- tabsets -->
<style type="text/css">
.tabset-dropdown > .nav-tabs {
display: inline-table;
max-height: 500px;
min-height: 44px;
overflow-y: auto;
border: 1px solid #ddd;
border-radius: 4px;
}
.tabset-dropdown > .nav-tabs > li.active:before, .tabset-dropdown > .nav-tabs.nav-tabs-open:before {
content: "\e259";
font-family: 'Glyphicons Halflings';
display: inline-block;
padding: 10px;
border-right: 1px solid #ddd;
}
.tabset-dropdown > .nav-tabs.nav-tabs-open > li.active:before {
content: "\e258";
font-family: 'Glyphicons Halflings';
border: none;
}
.tabset-dropdown > .nav-tabs > li.active {
display: block;
}
.tabset-dropdown > .nav-tabs > li > a,
.tabset-dropdown > .nav-tabs > li > a:focus,
.tabset-dropdown > .nav-tabs > li > a:hover {
border: none;
display: inline-block;
border-radius: 4px;
background-color: transparent;
}
.tabset-dropdown > .nav-tabs.nav-tabs-open > li {
display: block;
float: none;
}
.tabset-dropdown > .nav-tabs > li {
display: none;
}
</style>
<!-- code folding -->
<style type="text/css">
#TOC {
margin: 25px 0px 20px 0px;
}
@media (max-width: 768px) {
#TOC {
position: relative;
width: 100%;
}
}
@media print {
.toc-content {
/* see https://github.com/w3c/csswg-drafts/issues/4434 */
float: right;
}
}
.toc-content {
padding-left: 30px;
padding-right: 40px;
}
div.main-container {
max-width: 1200px;
}
div.tocify {
width: 20%;
max-width: 260px;
max-height: 85%;
}
@media (min-width: 768px) and (max-width: 991px) {
div.tocify {
width: 25%;
}
}
@media (max-width: 767px) {
div.tocify {
width: 100%;
max-width: none;
}
}
.tocify ul, .tocify li {
line-height: 20px;
}
.tocify-subheader .tocify-item {
font-size: 0.90em;
}
.tocify .list-group-item {
border-radius: 0px;
}
.tocify-subheader {
display: inline;
}
.tocify-subheader .tocify-item {
font-size: 0.95em;
}
</style>
</head>
<body>
<div class="container-fluid main-container">
<!-- setup 3col/9col grid for toc_float and main content -->
<div class="row">
<div class="col-xs-12 col-sm-4 col-md-3">
<div id="TOC" class="tocify">
</div>
</div>
<div class="toc-content col-xs-12 col-sm-8 col-md-9">
<div class="navbar navbar-default navbar-fixed-top" role="navigation">
<div class="container">
<div class="navbar-header">
<button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-bs-toggle="collapse" data-target="#navbar" data-bs-target="#navbar">
<span class="icon-bar"></span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
<a class="navbar-brand" href="index.html">SpeedGoat</a>
</div>
<div id="navbar" class="navbar-collapse collapse">
<ul class="nav navbar-nav">
<li>
<a href="index.html">Home</a>
</li>
<li class="dropdown">
<a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
Manuals
<span class="caret"></span>
</a>
<ul class="dropdown-menu" role="menu">
<li>
<a href="01-ipm.html">IPM</a>
</li>
<li>
<a href="02-sight.html">Sightability</a>
</li>
<li>
<a href="03-surv.html">Survival</a>
</li>
<li>
<a href="04-dataentry.html">Data Entry</a>
</li>
<li>
<a href="cwd.html">CWD</a>
</li>
<li>
<a href="05-glossary.html">Glossary</a>
</li>
</ul>
</li>
<li class="dropdown">
<a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
Models
<span class="caret"></span>
</a>
<ul class="dropdown-menu" role="menu">
<li>
<a href="jasper_ipm_description.html">Caribou</a>
</li>
</ul>
</li>
<li>
<a href="about.html">About</a>
</li>
</ul>
<ul class="nav navbar-nav navbar-right">
</ul>
</div><!--/.nav-collapse -->
</div><!--/.container -->
</div><!--/.navbar -->
<div id="header">
</div>
<p><br></p>
<div id="weighted-surveillance" class="section level1">
<h1>Weighted Surveillance</h1>
<p><br></p>
<div id="introduction" class="section level2">
<h2>Introduction</h2>
<p>This application provides tools for planning weighted surveillance
sampling for chronic wasting disease (CWD), and estimating underlying
prevelance after sampling has been completed. Models are available for
white-tailed deer (<em>Odocoileus virginianus</em>), mule deer
(<em>Odocoileus hemionus</em>), and elk (<em>Cervus canadensis</em>)
populations, and an additional model allows combining samples from elk
and mule deer in a single analysis.</p>
<p>Weighted surveillance is based on the simple principle that within a
population there exists heterogeneity among individuals with regard to
disease risk. To maximize efficiency and potentially increase the
likelihood of detecting new disease foci, weighted surveillance programs
exploit this heterogeneity by focusing disease detection efforts in
groups most at risk. The underlying methods were orginially developed by
<a
href="https://cpw.state.co.us/Documents/Research/WildlifeHealth/Publications/WeightedSurveillanceApproachCWD.pdf">Walsh
et al. (2010)</a> using a Frequentist statistical approach. These
results were then refined and recast into a Bayesian statistical
framework by <a
href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0089843">Heisey
et al. (2014)</a>. It is important to note that the use of weighted
surveillance techniques requires that prior information is available to
estimate heterogeneity in individual risk. A general discussion of CWD
surveillance is given in <a
href="https://pubs.usgs.gov/of/2012/1036/pdf/ofr2012_1036.pdf">Walsh et
al. (2012)</a>.</p>
<p>The first tool, called <a
href="#design-tool"><strong>Design</strong></a>, is used for planning
weighted surveillance activities. The user specifies how much confidence
they need, and what minimum prevalence they would like to detect in the
group selected as a reference class (e.g., I would like a 95% confidence
of detecting at least one case if the prevalence is at least 1%). This
information then provides the total number of points required to meet
the specified confidence for the chosen minimum prevalence. The user can
then select from the potential sources of surveillance samples, and vary
the number of samples arising from each source. This provides a means of
setting sampling objectives for each source to ensure the requisite
number of points is reached. This tool can also be used to evaluate in
real-time how close a program is to achieving its goal given the number
of samples collected from each source to date.</p>
<p>The second tool, called <a
href="#estimation-tool"><strong>Estimation</strong></a>, is for use
after sampling for CWD detection has occurred and no positive cases were
found. It provides the means to estimate the potential underlying
prevalence rate of CWD given the number of negative samples collected
during surveillance. Of particular interest is the upper bound of the
credible interval, which means there is, for example, a 95% probability
that true prevalence is at or below the reported level given your
sampling effort and lack of cases detected.</p>
<p>It’s important to note that these tools are entirely independent -
you can use either one however you wish but there’s no need to use both,
and changes made in one tool won’t have any affect in the other.</p>
<p>The techniques and theory underpinning them used in this application
are described in <a
href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0089843">Heisey
et al. (2014)</a>. This application takes advantage of the <a
href="https://www.R-project.org/">R</a> statistical software for
estimation. Heterogeneity of risk classes are based on the chronic
wasting disease information collected by the <a
href="https://dnr.wi.gov/topic/wildlifehabitat/cwd.html">Wisconsin
Department of Natural Resources</a>. <a
href="https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/1365-2664.13178">Jennelle
et al. (2018)</a> provide a description of the Wisconsin estimates and
provide a case study of the application of weighted surveillance to CWD
detection. The elk and mule deer weights are based on results reported
in <a
href="https://pubs.usgs.gov/of/2012/1036/pdf/ofr2012_1036.pdf">Walsh et
al. (2012)</a>.</p>
<p><br></p>
</div>
<div id="help" class="section level2">
<h2>Help</h2>
<p>Blue question marks in the various sections of the app link directly
to the relevant sections of this document. If you have trouble with the
site or have questions that aren’t covered here you can contact
SpeedGoat, the site’s maintainer, by clicking on the life ring in the
top-right of your screen. You can also issue a <a
href="https://github.com/SpeedGoat/DiseaseMonitoring/issues/"><em>bug
report</em></a> by clicking on the bug icon in the header. Finally, if
you have an idea for a new feature or analysis please let us know by
clicking on the lightbulb icon and issuing a <a
href="https://github.com/SpeedGoat/DiseaseMonitoring/issues/"><em>feature
request</em></a>. The help icons are shown in the image below:</p>
<p><img src = "www/weightedsurveil/cwd_help.png"></p>
<p><br> <br></p>
</div>
<div id="design-tool" class="section level2">
<h2>Design Tool</h2>
<p>The <strong>Design</strong> tool is used for planning weighted
surveillance activities. If you’ve already collected your samples and
just want to analyze the results you can skip the design tab and go
straight to the <a href="#estimation-tool">Estimation tool</a>.</p>
<p>To begin using the <strong>Design</strong> tool click on CWD and then
Design in the sidebar.</p>
<p><img src = "www/weightedsurveil/cwd_design.png"></p>
<p><br></p>
<div id="design-setup" class="section level3">
<h3>Design Setup</h3>
<p>The setup pane on the Design tab allows you to specify the monitoring
goals of your surveillance program. It includes three sliders which can
be used to set target sampling effort based on the desired outcome.</p>
<p><img src = "www/weightedsurveil/cwd_design_setup.png"></p>
<ol style="list-style-type: decimal">
<li>Confidence - Use this slider to set the confidence you desire for
detecting at least one case given the selected prevalence. The
confidence level can also be thought of as 1 - alpha.</li>
<li>Prevalence - Use this slider to set the minimum target prevalence
for which you would like the specified confidence of detecting at least
one case in the reference class (see <a href="#points">Points</a>).</li>
<li>Sensitivity - The last slider is used to specify the diagnostic test
sensitivity, which is typically assumed to be 1 unless live-testing is
conducted.</li>
</ol>
<p>For example, using the default values for White-tailed deer the
target points will be based on having a 95% confidence of detecting at
least one case given a latent prevalence of 0.05 in hunter-harvested
yearling males.</p>
<p><br></p>
</div>
<div id="points" class="section level3">
<h3>Points</h3>
<p>While moving the sliders in the input pane you should notice that the
value displayed in the <em>Target Points</em> box at the top of the
screen changes.</p>
<p><img src = "www/weightedsurveil/cwd_design_pts.png"></p>
<p>The <em>Target Points</em> value represents the number of samples you
would need to collect from the reference class in order to achieve the
goals you set in the input tab. Given the example of White-tailed deer
above, you would need 58 samples from hunter-harvested yearling males to
achieve 95% confidence of detection given a prevalence rate of 0.05.
Which group of animals represents the reference class varies by species
and disease, and samples from the reference class are always assigned a
weight of 1. Samples from other classes are assigned different weights
relative to the reference class and therefore will contribute more or
less to hitting your target.</p>
<p><br></p>
</div>
<div id="effort-data-entry" class="section level3">
<h3>Effort Data Entry</h3>
<p><img src = "www/weightedsurveil/cwd_design_tbl.png"></p>
<p>The <em>Sampling Effort</em> table displays the Collection Method,
Age and Sex of classes available in the model, along with the weights
(relative to the reference class) assigned to each class. Enter the
number of samples you propose to collect for each class in the last
column and the <em>Points Entered</em> and <em>Points Remaining</em>
boxes at the top of the screen will update. Along with the gauge widget
on the right side, these boxes reflect how the sampling effort you’ve
entered relates to your monitoring target. You can also explore how the
different collection methods and demographic groups contribute to
achieving your monitoring target using the donut charts at the bottom
right of the page.</p>
<p><br> <br></p>
</div>
</div>
<div id="estimation-tool" class="section level2">
<h2>Estimation Tool</h2>
<p>The <strong>Estimation</strong> tool is used to estimate the
potential underlying prevalence rate of CWD after sampling has occurred
and no positive cases were found.</p>
<p>To begin using the <strong>Estimation</strong> tool click on CWD and
then Estimation in the sidebar. If you want to enter your own diagnostic
test characteristics start on the <em>Diagnostic Tests</em> tab, or if
you want to use the defaults provided you can skip straight to the
<em>Samples</em> tab.</p>
<p><br></p>
<div id="diagnostic-test-characteristics" class="section level3">
<h3>Diagnostic Test Characteristics</h3>
<p>The <em>Diagnostic Tests</em> tab allows you to save different test
characteristics to be referenced when you enter your sampling data.</p>
<p><img src = "www/weightedsurveil/cwd_tests.png"></p>
<p>The following methods are available for generating a posterior
distibution for diagnostic test characteristics.</p>
<ol style="list-style-type: decimal">
<li><p>The ‘Default’ method uses the posterior distributions of the
sensitivity and specificity based on the results in Vet Rec 151:610-612
for IHC on retropharyngeal lymph nodes and a Jeffreys prior (i.e., mean
sensitiviy = 98.6% and mean specificity >99%).</p></li>
<li><p>The ‘Use data’ method allows the user to specify their results
for each test, and requires the number of true and false positives and
negatives. The posterior distributions for the test characteristics are
then estimated using a Jeffreys prior.</p></li>
<li><p>The ‘Quantile’ method allows the user to specify the mean and 95%
credible bounds for the posterior distribution of the test
characteristics. These are used to generate a suitable Beta
distribution.</p></li>
</ol>
<p>The default method has already been entered for you, so if you plan
on using the default test characteristics for all your samples you can
move on to the <em>Samples</em> tab.</p>
<p>To enter your own test characteristics select the appropriate option
for both sensitivity and specificity and enter the corresponding values.
You can preview the distributions created based on your inputs in the
plots to the right. When you’re happy with the distributions enter a
name for the test and click the <em>Save Test</em> button to add it.
Test names must be unique, and you can’t overwrite a test once you’ve
saved it, so if you need to make changes just save a new test and make
sure to select the correct one when entering your samples. Test
characteristics can vary by collection method, age, and sex if needed,
e.g. in cases where different tests are used depending on the collection
method or when test characteristics vary based on the age or sex of the
subject.</p>
<p><img src = "www/weightedsurveil/cwd_tests_custom.png"></p>
<p><br></p>
</div>
<div id="sampling-data-entry" class="section level3">
<h3>Sampling Data Entry</h3>
<p>In order to run a model you need to enter the number of negative
samples collected for each class using the <em>Samples</em> tab.</p>
<p><img src = "www/weightedsurveil/cwd_samples.png"></p>
<p>First select the species and collection method near the top of the
Samples tab. Depending on the collection method additional options
regarding age and sex of subjects may become available. Then select the
appropriate test used for this collection method. Finally enter the
number of samples you collected and click the <em>Add Samples</em>
button to add these samples to your dataset.</p>
<p>Data you add through these inputs will appear in the <em>Entered
Data</em> table to the right. If you make a mistake or change your mind
you can select one or more rows in the table and use the <em>Delete
Selected Rows</em> button to remove them from the dataset, or you can
<em>Delete All Rows</em> to start over.</p>
<p><img src = "www/weightedsurveil/cwd_samples_de.png"></p>
<p><br></p>
</div>
<div id="model-settings" class="section level3">
<h3>Model Settings</h3>
<p>If you enter enter samples from elk or mule deer an additional
dropdown will appear allowing you to choose between a single-species
model or a combined model. You can adjust model settings using the
<em>MCMC</em> button at the bottom of the Samples tab. Number of chains,
burn-in length, and model iterations can all be adjusted if needed.</p>
<p>As of writing the priors available to the models are fixed and cannot
be manipulated by the user. Heterogeneity of risk classes are based on
the chronic wasting disease information described in <a
href="https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/1365-2664.13178">Jennelle
et al. 2018</a> and <a
href="https://pubs.usgs.gov/of/2012/1036/pdf/ofr2012_1036.pdf">Walsh et
al. 2012</a>. The former provides a description of the Wisconsin
estimates and a recent case study of the application of weighted
surveillance to CWD detection. You can view the data by clicking the
<em>Priors</em> button.</p>
<p><br></p>
</div>
<div id="running-a-model" class="section level3">
<h3>Running a model</h3>
<p>When you’ve entered the appropriate information for each group of
samples collected, checked that everything looks right in the
<em>Samples</em> table, and adjusted the model settings if needed, click
the <em>Fit Model</em> button to fit a model for your data. After a
short time you’ll be prompted with a message letting you know if the
model run was successful. If so a new <em>Output</em> tab will appear in
the sidebar where you can view your results.</p>
<p><br> <br></p>
</div>
</div>
<div id="output-tab" class="section level2">
<h2>Output Tab</h2>
<p>The <em>Output</em> tab is only available after you’ve entered data
in the <a href="#sampling-data-entry">Samples tab</a> and clicked the
button to fit a model.</p>
<p><br></p>
<div id="estimates-table" class="section level3">
<h3>Estimates Table</h3>
<p>The estimates table displays model outputs (estimated latent
prevalence) for all classes (Collection Method/Age/Sex) in a single
table. Mean, median, standard deviation, and credible intervals (90% and
95%) are reported for each class. Rows are color coded based on the
number of samples entered for each class.</p>
<p><img src = "www/weightedsurveil/cwd_output_tbl.png"></p>
<p><br></p>
</div>
<div id="output-plots" class="section level3">
<h3>Output Plots</h3>
<p>The plots page shows a posterior distribution plot for estimated
latent prevalence of CWD by class, along with diagnostic plots for
assessing model performance. By default a trace plot depicting every
100th iteration is displayed, but you can change the plotting interval
or switch to an autocorrelation plot using the button at the top right
of the Diagnostics section.</p>
<p>Change the data displayed in both plots by using the dropdown above
the plots to select a different class. Use the buttons and sliders
within the plots to zoom in to a finer scale or save as an image file.
You can also download density plots for all classes in a single click
using the <em>Download All</em> button.</p>
<p><img src = "www/weightedsurveil/cwd_output_plots.png"></p>
<p><br></p>
</div>
<div id="interpretation" class="section level3">
<h3>Interpretation</h3>
<p>The interpretation page provides some guidance for explaining the
model output with regard to your actual population. By default the
reference class is displayed, but you can also view an interpretation
for a different class by changing the selection in the dropdown near the
top of the page.</p>
<p><img src = "www/weightedsurveil/cwd_output_interp.png"></p>
<p><br> <br></p>
</div>
<div id="report" class="section level3">
<h3>Report</h3>
<p>Click the <em>Download Report</em> button to generate a full report
of your data. The report includes all of the test and sample data you
entered, the settings used, and the output tables and plots. Reports are
available in html and Microsoft Word formats.</p>
<p><br> <br></p>
</div>
</div>
<div id="batch-estimation" class="section level2">
<h2>Batch Estimation</h2>
<p>The batch estimation tool is designed to help users who need to
estimates from many models. The tool has a friendly spreadsheet that may
be downloaded by the user to enter their data and then uploaded to the
site for estimation. To use batch estimation begin by clicking the
<em>Batch Estimation</em> link in the sidebar.</p>
<div id="template" class="section level3">
<h3>Template</h3>
<p>The first step is to download the batch estimation template. The
template is a spreadsheet that can be used to enter data for multiple
models. The template can be downloaded by clicking the <em>Download
Template</em> link in the box titled <em>Upload Data</em>.</p>
<p><img src = "www/weightedsurveil/cwd_batch_template.png"></p>
<p>The template spreadsheet has 4 sheets: <em>Data</em>,
<em>Priors</em>, <em>Instructions</em>, and <em>Example</em>. The
<em>Data</em> sheet is where you will enter your data. The
<em>Priors</em> sheet is where you can view the priors that will be used
in the model. The <em>Instructions</em> sheet provides guidance on how
to use the template. The <em>Example</em> sheet provides an example
dataset.</p>
<p>The Data sheet has 7 columns that align with the models expectations
and how other aspects of the website function. The first column
<em>Batch</em> is used to group samples. Samples with the same value in
the <em>Batch</em> column will be estimated together. The
<em>Species</em> column is used to specify the species of the samples.
The <em>Collection Method</em> column is used to specify the method used
to collect the samples. The <em>Age</em> and <em>Sex</em> columns are
used to specify the age and sex of animals sampled. The <em>Samples</em>
column is used to specify the number of negative samples obtained for
the combination of species, collection method, age, and sex. This column
allows only whole numbers greater than or equal to 0. Finally the
<em>Test</em> column is used to specify the diagnostic test to be used.
Enter <em>Default</em> to use the default test. If a custom test is
required then specify that in the website and enter the name of the test
in the <em>Test</em> column exactly as it is in the website. For more
information about diagnostic tests see the <a
href="#diagnostic-tests">Diagnostic Tests</a> section.</p>
<p>Note when entering data that not all combinations of species,
collection method, age, and sex are valid. If any invalid combinations
are present in your data when you upload it the website will issue a
warning. Those entries with invalid combinations or those entries for
which no prior exists will be removed prior to running any models.</p>
<p>When the data are entered and all entries checked upload the
spreadsheet by clicking the <em>Browse…</em> button and selecting the
file from your computer. Once the file is selected click the
<em>Open</em> button to upload the data. The website will check the data
and issue a warning if any invalid entries are present. The data will be
shown to the user under the <em>Data</em> tab, while errors will be
shown under the <em>Sample Class Issues</em> and <em>Test Issues</em>
tabs.</p>
</div>
<div id="model-selection" class="section level3">
<h3>Model Selection</h3>
<p>After the data are uploaded the user must select the model to be used
for estimation. The model can be selected by clicking the <em>Select
Model</em> dropdown and selecting the desired model. The user can adjust
model settings and MCMC parameters as needed. The settings are the same
as those described in the <a href="#model-settings">Model Settings</a>
section.</p>
<p><img src = "www/weightedsurveil/cwd_batch_setup.png"></p>
</div>
<div id="running-a-batch-model" class="section level3">
<h3>Running a Batch Model</h3>
<p>When satisfied that all inputs are correct click the <em>Fit
Model</em> button to run the model. The website will run the model and
display the results in a newly created <em>Output</em> link in the
sidebar on the left. The website will also display a progress bar to
show the user how far along the model is in the estimation process and
notify the user of success or failure of the model runs.</p>
</div>
<div id="batch-estimation-output" class="section level3">
<h3>Batch Estimation Output</h3>
<p>After the model has run the website will display a results table and
report generation option in the <em>Output</em> tab located in the
sidebar on the left. Users are encouraged to download the results as a
report by navigating to the Reports tab at the top of their screen and
clicking the <em>Download Report</em> button. Reports will delivered as
a compressed file with one report per batch that contains all of the
plots and interpretation users expect from a single model run. For more
details on the outputs see the <a href="#estimates-table">Estimates
Table</a>, <a href="#output-plots">Output Plots</a>, and <a
href="#interpretation">Interpretation</a> sections.</p>
<p><img src = "www/weightedsurveil/cwd_batch_results.png"></p>
<p><br> <br></p>
</div>
</div>
<div id="suggestions" class="section level2">
<h2>Suggestions</h2>
<p>If you have suggestions or corrections that you would like to propose
to this document please either file a bug report at <a
href="https://github.com/SpeedGoat/DiseaseMonitoring/issues/new?assignees=Huh&labels=bug&template=bug_report.md&title=">this
link</a> or make a contribution and issue a pull request via the <a
href="https://github.com/SpeedGoat/Help_Pages">Huh/Help_Pages</a> GitHub
repository.</p>
<hr />
<p>Developed by <a href="https://www.speedgoat.io">SpeedGoat</a></p>
<p><a
href="https://www.speedgoat.io"><img src = "www/SG_Horizontal_Transparent_RGB.png" height = "122" width = "162"></a></p>
<p><br></p>
<hr />
<p><br></p>
<div style="padding-bottom:100px">
</div>
</div>
</div>
</div>
</div>
</div>
<script>
// add bootstrap table styles to pandoc tables
function bootstrapStylePandocTables() {
$('tr.odd').parent('tbody').parent('table').addClass('table table-condensed');
}
$(document).ready(function () {
bootstrapStylePandocTables();
});
</script>
<!-- tabsets -->
<script>
$(document).ready(function () {
window.buildTabsets("TOC");
});
$(document).ready(function () {
$('.tabset-dropdown > .nav-tabs > li').click(function () {
$(this).parent().toggleClass('nav-tabs-open');
});
});
</script>
<!-- code folding -->
<script>
$(document).ready(function () {
// temporarily add toc-ignore selector to headers for the consistency with Pandoc
$('.unlisted.unnumbered').addClass('toc-ignore')
// move toc-ignore selectors from section div to header
$('div.section.toc-ignore')
.removeClass('toc-ignore')
.children('h1,h2,h3,h4,h5').addClass('toc-ignore');
// establish options
var options = {
selectors: "h1,h2,h3",
theme: "bootstrap3",
context: '.toc-content',
hashGenerator: function (text) {
return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_');
},
ignoreSelector: ".toc-ignore",
scrollTo: 0
};
options.showAndHide = false;
options.smoothScroll = true;
// tocify
var toc = $("#TOC").tocify(options).data("toc-tocify");
});
</script>
<!-- dynamically load mathjax for compatibility with self-contained -->
<script>
(function () {
var script = document.createElement("script");
script.type = "text/javascript";
script.src = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
document.getElementsByTagName("head")[0].appendChild(script);
})();
</script>
</body>
</html>