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H1N1 project Plagiarism Midterm Mini-project: Using cross sectional serology to estimate influenza infection rates Alex Cook Week 7 Alex Cook, ST5219, Bayesian Hierarchical Modelling 1/17
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H1N1 project Plagiarism Midterm
Mini-project:
Using cross sectional serology to estimate
influenza infection rates
Alex Cook
Week 7
Alex Cook, ST5219, Bayesian Hierarchical Modelling 1/17
H1N1 project Plagiarism Midterm
H1N1-2009
Influenza pandemics emerge every generation or so
They are associated with increased mortality andmorbidity
A novel strain of influenza A (H1N1) emerged in NorthAmerica in 2009 & quickly spread around the world
First influenza pandemic since the 1960s
Alex Cook, ST5219, Bayesian Hierarchical Modelling 2/17
H1N1 project Plagiarism Midterm
H1N1-2009
Estimating attack rates
Important: to understand
the total amount of disease caused by the first wave,
the differential attack rates in different groups, especiallybetween different age groups,
denominators for measures of severity, such as the casefatality and hospitalisation rates.
Alex Cook, ST5219, Bayesian Hierarchical Modelling 3/17
H1N1 project Plagiarism Midterm
Influenza serology
Researchers in various countries carried out serologicalstudies to estimate infection rates
Singapore implemented longitudinal serology
A more common study design was cross sectional
Longitudinal
Follows the same group ofpeople, taking multiplemeasurements at differenttime points
Cross-sectional
At different time points, getdifferent groups of people,taking one measurementfrom each
Alex Cook, ST5219, Bayesian Hierarchical Modelling 4/17
H1N1 project Plagiarism Midterm
Influenza serology
Researchers in various countries carried out serologicalstudies to estimate infection rates
Singapore implemented longitudinal serology
A more common study design was cross sectional
Longitudinal
Follows the same group ofpeople, taking multiplemeasurements at differenttime points
Cross-sectional
At different time points, getdifferent groups of people,taking one measurementfrom each
Alex Cook, ST5219, Bayesian Hierarchical Modelling 4/17
H1N1 project Plagiarism Midterm
Influenza serology
Researchers in various countries carried out serologicalstudies to estimate infection rates
Singapore implemented longitudinal serology
A more common study design was cross sectional
Longitudinal
Follows the same group ofpeople, taking multiplemeasurements at differenttime points
Cross-sectional
At different time points, getdifferent groups of people,taking one measurementfrom each
Alex Cook, ST5219, Bayesian Hierarchical Modelling 4/17
H1N1 project Plagiarism Midterm
Serological response
Antibody level usually expressed as a range (1:x) to(1:2x) where x ∈ {5, 10, 20, 40, . . .}Antibodies overcome the virus at the lower end but notthe higher end of this range
Higher x indicates more antibodies ⇒ more evidence ofinfection
With cross-sectional serological samples, it is common toascribe infection to anyone with a titre of more than 1:40
With longitudinal surveys, look for a fourfold rise in titresas evidence of infection
For this project, we’ll pretend Singapore did crosssectional rather than longitudinal
Three time points: June, the middle of August and thestart of October
Alex Cook, ST5219, Bayesian Hierarchical Modelling 5/17
H1N1 project Plagiarism Midterm
Serological response
Antibody level usually expressed as a range (1:x) to(1:2x) where x ∈ {5, 10, 20, 40, . . .}Antibodies overcome the virus at the lower end but notthe higher end of this range
Higher x indicates more antibodies ⇒ more evidence ofinfection
With cross-sectional serological samples, it is common toascribe infection to anyone with a titre of more than 1:40
With longitudinal surveys, look for a fourfold rise in titresas evidence of infection
For this project, we’ll pretend Singapore did crosssectional rather than longitudinal
Three time points: June, the middle of August and thestart of October
Alex Cook, ST5219, Bayesian Hierarchical Modelling 5/17
H1N1 project Plagiarism Midterm
Serological response
Antibody level usually expressed as a range (1:x) to(1:2x) where x ∈ {5, 10, 20, 40, . . .}Antibodies overcome the virus at the lower end but notthe higher end of this range
Higher x indicates more antibodies ⇒ more evidence ofinfection
With cross-sectional serological samples, it is common toascribe infection to anyone with a titre of more than 1:40
With longitudinal surveys, look for a fourfold rise in titresas evidence of infection
For this project, we’ll pretend Singapore did crosssectional rather than longitudinal
Three time points: June, the middle of August and thestart of October
Alex Cook, ST5219, Bayesian Hierarchical Modelling 5/17
H1N1 project Plagiarism Midterm
Serological response
Antibody level usually expressed as a range (1:x) to(1:2x) where x ∈ {5, 10, 20, 40, . . .}Antibodies overcome the virus at the lower end but notthe higher end of this range
Higher x indicates more antibodies ⇒ more evidence ofinfection
With cross-sectional serological samples, it is common toascribe infection to anyone with a titre of more than 1:40
With longitudinal surveys, look for a fourfold rise in titresas evidence of infection
For this project, we’ll pretend Singapore did crosssectional rather than longitudinal
Three time points: June, the middle of August and thestart of October
Alex Cook, ST5219, Bayesian Hierarchical Modelling 5/17
H1N1 project Plagiarism Midterm
Serological response
Antibody level usually expressed as a range (1:x) to(1:2x) where x ∈ {5, 10, 20, 40, . . .}Antibodies overcome the virus at the lower end but notthe higher end of this range
Higher x indicates more antibodies ⇒ more evidence ofinfection
With cross-sectional serological samples, it is common toascribe infection to anyone with a titre of more than 1:40
With longitudinal surveys, look for a fourfold rise in titresas evidence of infection
For this project, we’ll pretend Singapore did crosssectional rather than longitudinal
Three time points: June, the middle of August and thestart of October
Alex Cook, ST5219, Bayesian Hierarchical Modelling 5/17
H1N1 project Plagiarism Midterm
Your task
Develop a model for the data that can be used to estimate anoverall infection rate for Singapore
The data have been uploaded tohttp://blog.nus.edu.sg/alexcook/files/2010/06/sero.txt
Write a short report describing how you developed your model,the assumptions underlying it, how you fit it, and yourfindings, in particular your estimate of the overall attack rate.
Alex Cook, ST5219, Bayesian Hierarchical Modelling 6/17
H1N1 project Plagiarism Midterm
The data per individual
the time of recruitment (0 for June, 1 for August or 2 forOctober, soon after the end of the outbreak);
his or her age (the participants are all adults);
the maximum bound on the titre (e.g. a “20” indicatesthe titre is between 1:10 and 1:20);
an indicator for whether the participant had live virusidentified by RT-PCR
1 the participant definitively had infection regardless oftitres
0 the participant may or may not have been infected
(there is only a small window of opportunity to catch livevirus so a negative reading doesn’t provide much evidenceagainst infection)
Alex Cook, ST5219, Bayesian Hierarchical Modelling 7/17
H1N1 project Plagiarism Midterm
The data per individual
the time of recruitment (0 for June, 1 for August or 2 forOctober, soon after the end of the outbreak);
his or her age (the participants are all adults);
the maximum bound on the titre (e.g. a “20” indicatesthe titre is between 1:10 and 1:20);
an indicator for whether the participant had live virusidentified by RT-PCR
1 the participant definitively had infection regardless oftitres
0 the participant may or may not have been infected
(there is only a small window of opportunity to catch livevirus so a negative reading doesn’t provide much evidenceagainst infection)
Alex Cook, ST5219, Bayesian Hierarchical Modelling 7/17
H1N1 project Plagiarism Midterm
The data per individual
the time of recruitment (0 for June, 1 for August or 2 forOctober, soon after the end of the outbreak);
his or her age (the participants are all adults);
the maximum bound on the titre (e.g. a “20” indicatesthe titre is between 1:10 and 1:20);
an indicator for whether the participant had live virusidentified by RT-PCR
1 the participant definitively had infection regardless oftitres
0 the participant may or may not have been infected
(there is only a small window of opportunity to catch livevirus so a negative reading doesn’t provide much evidenceagainst infection)
Alex Cook, ST5219, Bayesian Hierarchical Modelling 7/17
H1N1 project Plagiarism Midterm
The data per individual
the time of recruitment (0 for June, 1 for August or 2 forOctober, soon after the end of the outbreak);
his or her age (the participants are all adults);
the maximum bound on the titre (e.g. a “20” indicatesthe titre is between 1:10 and 1:20);
an indicator for whether the participant had live virusidentified by RT-PCR
1 the participant definitively had infection regardless oftitres
0 the participant may or may not have been infected
(there is only a small window of opportunity to catch livevirus so a negative reading doesn’t provide much evidenceagainst infection)
Alex Cook, ST5219, Bayesian Hierarchical Modelling 7/17
H1N1 project Plagiarism Midterm
Practicalities
Deadline: Friday 12th November at 19.05
No word limit (probably ≤5pp)
Feel free to
take any approach you wishdiscuss with others (but see later)
No need rush lah. Future lectures may be relevant.
Alex Cook, ST5219, Bayesian Hierarchical Modelling 8/17
H1N1 project Plagiarism Midterm
Practicalities
Deadline: Friday 12th November at 19.05
No word limit (probably ≤5pp)
Feel free to
take any approach you wishdiscuss with others (but see later)
No need rush lah. Future lectures may be relevant.
Alex Cook, ST5219, Bayesian Hierarchical Modelling 8/17
H1N1 project Plagiarism Midterm
Practicalities
Deadline: Friday 12th November at 19.05
No word limit (probably ≤5pp)
Feel free to
take any approach you wishdiscuss with others (but see later)
No need rush lah. Future lectures may be relevant.
Alex Cook, ST5219, Bayesian Hierarchical Modelling 8/17
H1N1 project Plagiarism Midterm
Practicalities
Deadline: Friday 12th November at 19.05
No word limit (probably ≤5pp)
Feel free to
take any approach you wishdiscuss with others (but see later)
No need rush lah. Future lectures may be relevant.
Alex Cook, ST5219, Bayesian Hierarchical Modelling 8/17
H1N1 project Plagiarism Midterm
Plagiarism
Do not plagiarise!
Plagiarism is failure to acknowledge the ideas of others
Alex Cook, ST5219, Bayesian Hierarchical Modelling 9/17
H1N1 project Plagiarism Midterm
Plagiarism
Alex Cook, ST5219, Bayesian Hierarchical Modelling 10/17
H1N1 project Plagiarism Midterm
Plagiarism
NUS penalties. . . (Google: NUS plagiarism penalties)
Reprimand
Failure of the Assignment / Letter of Censure
Failure of the Module / Dismissal from the Programme
Alex Cook, ST5219, Bayesian Hierarchical Modelling 11/17
H1N1 project Plagiarism Midterm
Plagiarism
NUS penalties. . . (Google: NUS plagiarism penalties)
Reprimand
Failure of the Assignment / Letter of Censure
Failure of the Module / Dismissal from the Programme
Alex Cook, ST5219, Bayesian Hierarchical Modelling 11/17
H1N1 project Plagiarism Midterm
Plagiarism
NUS penalties. . . (Google: NUS plagiarism penalties)
Reprimand
Failure of the Assignment / Letter of Censure
Failure of the Module / Dismissal from the Programme
Alex Cook, ST5219, Bayesian Hierarchical Modelling 11/17
H1N1 project Plagiarism Midterm
Avoiding plagiarism
(from Cook et al (2008) Biometrics 64:860–8)
Alex Cook, ST5219, Bayesian Hierarchical Modelling 12/17
H1N1 project Plagiarism Midterm
Avoiding plagiarism
(from Cook et al (2008) Biometrics 64:860–8)
Alex Cook, ST5219, Bayesian Hierarchical Modelling 13/17
H1N1 project Plagiarism Midterm
Avoiding plagiarism
(from Cook et al (2008) Biometrics 64:860–8)
Alex Cook, ST5219, Bayesian Hierarchical Modelling 14/17
H1N1 project Plagiarism Midterm
Avoiding plagiarism
If you discuss with others must acknowledge
If others provide input into work must acknowledge
If you read an idea somewhere must cite
If you use what someone else wrote must cite
If something is not common knowledge and you state itas if true, must cite or demonstrate
Alex Cook, ST5219, Bayesian Hierarchical Modelling 15/17
H1N1 project Plagiarism Midterm
Avoiding plagiarism
If you discuss with others must acknowledge
If others provide input into work must acknowledge
If you read an idea somewhere must cite
If you use what someone else wrote must cite
If something is not common knowledge and you state itas if true, must cite or demonstrate
Alex Cook, ST5219, Bayesian Hierarchical Modelling 15/17
H1N1 project Plagiarism Midterm
Avoiding plagiarism
If you discuss with others must acknowledge
If others provide input into work must acknowledge
If you read an idea somewhere must cite
If you use what someone else wrote must cite
If something is not common knowledge and you state itas if true, must cite or demonstrate
Alex Cook, ST5219, Bayesian Hierarchical Modelling 15/17
H1N1 project Plagiarism Midterm
Avoiding plagiarism
If you discuss with others must acknowledge
If others provide input into work must acknowledge
If you read an idea somewhere must cite
If you use what someone else wrote must cite
If something is not common knowledge and you state itas if true, must cite or demonstrate
Alex Cook, ST5219, Bayesian Hierarchical Modelling 15/17
H1N1 project Plagiarism Midterm
Avoiding plagiarism
If you discuss with others must acknowledge
If others provide input into work must acknowledge
If you read an idea somewhere must cite
If you use what someone else wrote must cite
If something is not common knowledge and you state itas if true, must cite or demonstrate
Alex Cook, ST5219, Bayesian Hierarchical Modelling 15/17
H1N1 project Plagiarism Midterm
Citing others’ work
In text:
“Klein & Moeschberger (2005) showed . . . ”
It has been shown (Arenz et al., 2004) that . . . ”
Matched in references:
Klein, J.P. & M.L. Moeschberger (2005). Survivalanalysis: techniques for censored and truncated data.Springer, London, UK.
Arenz, S., R. Ruckerl, B. Koletzko & R. von Kries (2004).“Breast-feeding and childhood obesity—a systematicreview.” International Journal of Obesity, 28:1247–1256.
For more info: google the Harvard referencing system
Alex Cook, ST5219, Bayesian Hierarchical Modelling 16/17
H1N1 project Plagiarism Midterm
Citing others’ work
In text:
“Klein & Moeschberger (2005) showed . . . ”
It has been shown (Arenz et al., 2004) that . . . ”
Matched in references:
Klein, J.P. & M.L. Moeschberger (2005). Survivalanalysis: techniques for censored and truncated data.Springer, London, UK.
Arenz, S., R. Ruckerl, B. Koletzko & R. von Kries (2004).“Breast-feeding and childhood obesity—a systematicreview.” International Journal of Obesity, 28:1247–1256.
For more info: google the Harvard referencing system
Alex Cook, ST5219, Bayesian Hierarchical Modelling 16/17
H1N1 project Plagiarism Midterm
Citing others’ work
In text:
“Klein & Moeschberger (2005) showed . . . ”
It has been shown (Arenz et al., 2004) that . . . ”
Matched in references:
Klein, J.P. & M.L. Moeschberger (2005). Survivalanalysis: techniques for censored and truncated data.Springer, London, UK.
Arenz, S., R. Ruckerl, B. Koletzko & R. von Kries (2004).“Breast-feeding and childhood obesity—a systematicreview.” International Journal of Obesity, 28:1247–1256.
For more info: google the Harvard referencing system
Alex Cook, ST5219, Bayesian Hierarchical Modelling 16/17
H1N1 project Plagiarism Midterm
Midterm test
Time 7.30 to 9.30
Should have three things
White answer bookYellow distributions table (2pp)White question paper (4pp)
9 minutes per 5 marks
I will clarify questions only if I believe the wording of thequestion is ambiguous
I will not answer questions such as “what do you want meto write here?”
Closed book exam
Alex Cook, ST5219, Bayesian Hierarchical Modelling 17/17
