Views
Graph
Explorer
Focus
Down
Load 1 level
Load 2 levels
Load 3 levels
Load 4 levels
Load all levels
All
Dagre
Focus
Down
Load 1 level
Load 2 levels
Load 3 levels
Load 4 level
Load all levels
All
Tree
SpaceTree
Focus
Expanding
Load 1 level
Load 2 levels
Load 3 levels
Down
All
Down
Radial
Focus
Expanding
Load 1 level
Load 2 levels
Load 3 levels
Down
All
Down
Box
Focus
Expanding
Down
Up
All
Down
Page ✓
Article
Outline
Document
Down
All
Canvas
Time
Timeline
Calendar
Request email digest
Past 24 hours
Past 2 days
Past 3 days
Past week
Add
Add page
Add comment
Add citation
Edit
Edit page
Delete page
Share
Link
Bookmark
Embed
Social media
Login
Member login
Register now for a free account
🔎
Statistical Methods for model selection and parameter estimation
Klausimas
1
#91703
Immediately related elements
How this works
-
Controlling Infectious Diseases »
Controlling Infectious Diseases
Controlling Infectious Diseases☜Mapping the issues around endemic diseases—such as malaria, measles, influenza, HIV, Dengue and West Nile Virus—with the aims of identifying improved control strategies and of providing insights into to the work of mathematical biologists, epidemiologists and public health officials in this field.☜F1CEB7
▲
Research Questions »
Research Questions
Research Questions☜What are the research priorities for the field?☜FFB597
▲
Open mathematical/computational problems? »
Open mathematical/computational problems?
Open mathematical/computational problems?☜☜FFB597
■
Statistical Methods for model selection and parameter estimation
Statistical Methods for model selection and parameter estimation☜☜FFB597
●
Nonparametric approaches »
Nonparametric approaches
Nonparametric approaches☜Use information or summary statistics on disease incidence or dynamics to estimate metrics such as R0, without using a fully specified disease model☜FFB597
●
Parametric approaches »
Parametric approaches
Parametric approaches☜Given an explicit (full) model of disease, estimate all parameters (or some parameters, e.g. beta/R0 only assuming others known from independent sources) by fitting to disease dynamics☜FFB597
●
References »
References
References☜☜FFB597
●
Unobservable events »
Unobservable events
Unobservable events☜Data are based on observable events. Some key events in disease transmission are unobservable. Inference for unobservable events based on observed data (usually subsequent events of the event of interest) often subject to bias, and/or, non-identifiability issues.☜FFB597
Heading
Summary
Click the button to enter task scheduling information
Open
Details
Enter task details
Message text
Select assignee(s)
Due date (click calendar)
RadDatePicker
RadDatePicker
Open the calendar popup.
Calendar
Title and navigation
Title and navigation
<<
<
November 2024
>
<<
November 2024
S
M
T
W
T
F
S
44
27
28
29
30
31
1
2
45
3
4
5
6
7
8
9
46
10
11
12
13
14
15
16
47
17
18
19
20
21
22
23
48
24
25
26
27
28
29
30
49
1
2
3
4
5
6
7
Reminder
No reminder
1 day before due
2 days before due
3 days before due
1 week before due
Ready to post
Copy to text
Enter
Cancel
Task assignment(s) have been emailed and cannot now be altered
Lock
Cancel
Save
Comment graphing options
Choose comments:
Comment only
Whole thread
All comments
Choose location:
To a new map
To this map
New map options
Select map ontology
Options
Standard (default) ontology
College debate ontology
Hypothesis ontology
Influence diagram ontology
Story ontology
Graph to private map
Cancel
Proceed
+Komentarai (
0
)
- Komentarai
Komentuoti
Newest first
Oldest first
Show threads
+Citavimą (
0
)
- Citavimą
Pridėti citatą
List by:
Citerank
Map
+About
- About
Redagavo:
Daihai He
NodeID:
#91703
Node type:
Issue
Įvedimo data (GMT):
2/9/2011 8:29:00 PM
Paskutinės redakcijos data (GMT laikas):
3/12/2011 8:13:00 PM
Show other editors
Įeinančios sąsajos:
0
Išeinančios sąsajos:
0
Vidutinis vertinimas:
8
by
1
vartotojai
x
Select file to upload