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ISDS 2016 Conference Abstracts

Value of evidence from syndromic surveillance with 
delayed reporting
Rahel Struchen*1, 3, Flavie Vial2 and Gunnar Andersson4
1Federal Food Safety and Veterinary Office, Bern, Switzerland; 2Epi-Connect, Skogas, Sweden; 3Veterinary Public Health Institute, 
Bern, Switzerland; 4National Veterinary Institute, Uppsala, Sweden

Objective
We apply an empirical Bayesian framework to perform change 

point analysis on multiple cattle mortality data streams, accounting 
for delayed reporting of syndromes.

Introduction
Taking into account reporting delays in surveillance systems is 

not methodologically trivial. Consequently, most use the date of the 
reception of data, rather than the (often unknown) date of the health 
event itself. The main drawback of this approach is the resulting 
reduction in sensitivity and specificity1. Combining syndromic 
data from multiple data streams (most health events may leave a 
“signature” in multiple data sources) may be performed in a Bayesian 
framework where the result is presented in the form of a posterior 
probability for a disease2.

Methods
We used a historical national database on Swiss cattle mortality to 

model daily baseline counts of two syndromic time series3. Reporting 
delay was defined as the number of days between reported occurrence 
and reporting date. The cumulative probability distribution of the 
estimated reporting delays was used to calculate for each day the 
proportion of cases that were reported either on the same day or with 
a delay of 1 to 14 days.

We evaluated outbreak detection performance under three 
scenarios: (A) delayed data reporting occurs but is not accounted 
for; (B) delayed data reporting occurs and is accounted for; and (C) 
absence of delayed data reporting (i.e. an ideal system). Outputs 
are presented as the value of evidence (V) in favour of an ongoing 
outbreak accumulated over n points in time (30 days in this case).  
At each time t, V is defined as the ratio between the posterior and 
prior odds for H1 versus H0:

[insert equation 1 here]
Using sensitivity, time to detection and in-control run length, 

performance of the (V-based) system on large and small non-specific 
outbreaks was measured.

Results
The evolution of V based on the information available on the 1st, 

5th and 10th day after the onset of an outbreak can be visualised in 
Fig. 1. After 5 days, V shows evidence in favour of an outbreak for 
both syndromes combined, as well as for on-farm deaths alone, only in 
the “Delay aware” and “No delay” scenarios. The development of V 
for the perinatal deaths alone highlights the importance of considering 
multiple syndromic data streams for outbreak detection, as it speaks 
in favour of an outbreak at a later stage than on-farm deaths alone or 
both syndromes combined.

Conclusions
Our empirical Bayes approach is an attractive alternative to 

multivariate CUSUM algorithms offering a logical approach to 
weighting variables and incorporating additional information such as 
delayed reporting, and a performance on a comparable level to an 
ideal (no delay) system. Outbreaks are detected earlier and with only 

a marginal loss of specificity compared to a system where reporting 
delay is present but unaccounted for.

We also found that the accumulation of evidence from several 
days resulted in a significantly better outbreak detection timeliness, 
for a given specificity; or a similar timeliness, but higher specificity, 
compared to an algorithm4 that only looks for days with unusual high 
number of counts.

Fig. 1: Evolution of V over three time points (t) for the three scenarios. 
Outbreak starts at t=651. Number of observed perinatal (circle) and on-farm 
deaths (cross), V for both (solid grey) and individual syndromes (dotted grey 
and black respectively), prior probability that an outbreak is ongoing (grey 
dashed) and posterior probability that an outbreak is ongoing given the 
evidence (black dashed). Horizontal grey solid line shows V=1.

Keywords
empirical Bayes; Reporting delay; Multivariate surveillance

References
1. Farrington C P & Andrews N J. Monit. Heal. Popul. 2004. Oxford

University Press.
2. Andersson M G, Faverjon C, Vial F, Legrand L & Leblond A.

Using bayes’ rule to define the value of evidence from syndromic
surveillance. PLoS One. 2014, 9, e111335.

3. Struchen R, Reist M, Zinsstag J & Vial F. Investigating the potential
of reported cattle mortality data in Switzerland for syndromic 
surveillance. Prev. Vet. Med. 2015, 121, 1–7.

4. Salmon M, Schumacher D, Stark K & Höhle M. Bayesian outbreak
detection in the presence of reporting delays. Biom. J. 2015, 57, 
1051–67.

*Rahel Struchen
E-mail: rahel.struchen@blv.admin.ch

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 9(1):e28, 2017