New surveillance methods Anette Hulthanette.hulth@folkhalsomyndigheten.se

2014-11-21

Outline

• why surveillance?

• syndromic surveillance

• web query-based surveillance in Sweden

• computer supported outbreak detection

• mobile reporting in India & South Africa

Why is infectious diseasesurveillance needed?

1. to prevent and control the spread of infectious diseases

2. to facilitate planning for the health-care sector

3. to evaluate taken counter-measures

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Examples of surveillance activities

• follow and predict infectious disease trends

• give early warning

• detect outbreaks

• analyse infectivity

• analyse geographic and demographic spread

• identify burden of disease

• identify risk groups for serious illness

• identify therapy and vaccine failure

• follow and evaluate prevention and intervention measures

• identify knowledge gaps

• …

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Conventional surveillance

• notifiable diseases- clinical reports- positive laboratory samples

• “random sample” surveillance

• requires that a doctor is seen

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Disease severity

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•real-time or near real-time collection, analysis, interpretation, and dissemination of health-related data

•aims at early identification of impact – or absence of impact – of potential human or veterinary public-health threats

•is based on non-specific clinical signs, symptoms and proxy measures, not on laboratory-confirmed diagnoses

•examples: absenteeism, drug sales, animal production collapse

Syndromic surveillance (i)

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Syndromic surveillance (ii)•the data are usually collected for purposes other than

surveillance

•are automatically collected so as not to impose an additional burden on data providers

•tends to be non-specific but sensitive and rapid

•can augment and complement the information provided by laboratory based surveillance systems

Definition by:

Triple S Project. Assessment of Syndromic Surveillance in Europe. Lancet. 2011 Nov 26; 378(9806):1833–4

http://www.syndromicsurveillance.eu

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GET WELL: Generating Epidemiological Trends from WEb Logs, Like

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The web site and the query logs

• 1177 Vårdguiden’s website 1177.se

• the web site is owned by all counties in Sweden

• the majority wants to read about a disease or a treatment

• ~6.5 million queries in 2013

• one query log is created per day

• logs are automatically and daily transferred to the agency

• we have logs for parts of Sweden from July 2005 and for the whole country from December 2010

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Influenza

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Web-query based influenza surveillance

Q 6F726E6CCCC9BBAA04E094EE63BB4432 1311373322 * hidden:meta:category:PageType;ContactCard meta:category:CareType;distriktssköterskemottagning meta:category:CareType;jourmottagning Z 0 1 –S * meta:category:caretype;beroendemottagning_akut_vuxen meta:category:caretype;beroende_alkohol svQ FE21B01963B1DF2452D03E6BEB39DBD4 1311373323 inkontinens hidden:meta:category:PageType;Article = 18 1 –N –svQ FE21B01963B1DF2452D03E6BEB39DBD4 1311373323 inkontinens hidden:meta:category:PageType;ContactCard = 49 1 –N –sv Q FE21B01963B1DF2452D03E6BEB39DBD4 1311373323 inkontinens hidden:meta:category:PageType;FAQ 1 1 –N –svQ FE21B01963B1DF2452D03E6BEB39DBD4 1311373323 inkontinens hidden:meta:category:PageType;Blog = 0 1 –N –svQ B30303BA42C746C3BC375DB06778E140 1311373329 * hidden:meta:category:PageType;ContactCard (meta:hsa.xcoordinate>6566347 AND meta:hsa.xcoordinate<6567347 AND meta:hsa.ycoordinate>1619565 AND meta:hsa.ycoordinate<1620565) 13 1-N -svQ 7797DC56D04E7A3BD389C6AEA75808AF 1311373331* hidden:meta:category:PageType;ContactCard meta:category:CareType;arbetsterapi meta:category:CareType;jourmottagning Z 0 1 -S * meta:category:CareType;arbetsterapi meta:category:caretype;arbetsterapi, * meta:category:caretype;logopedi_språk_och_talstörning_barn_ungdom sv =Q 7797DC56D04E7A3BD389C6AEA75808AF 1311373331 * hidden:meta:category:PageType;ContactCard meta:category:CareType;arbetsterapi meta:category:CareType;jourmottagning Z 0 1 –S * meta:category:CareType;arbetsterapi meta:category:caretype;arbetsterapi, * meta:category:caretype;logopedi_språk_och_talstörning_barn_ungdom

1. Query2. Log

3. Sent to Public Health Agency

4. Automated analysis 5. Graphical output 6. E-mail 7. Published online

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Web-based influenza surveillance

• search logs from 2005

• sentinel GP network for influenza

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Investigated query types

• in total 20 types, some of which are overlapping

• influenza: on its own and in various constellations

• seven symptoms in Swedish, roughly correspond-ing to: cough, sore throat, shortness of breath, coryza (head cold), fever, head ache, muscle pain

• queries that might interfere, such as influenza vaccination and stomach flu

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Resulting statistical model

Given a number of queries for each of the20 types, the model will estimate the proportion of persons in Sweden with influenza-like illness

Wehrens R, Mevik B-H (2007) PLS: Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR). R package version 2.1–0.

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The estimations are part of the routine influenza surveillance

•automatically generated weekly reports •published on the agency’s web site

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Webbsök – evaluation•Analysis of seasons 2009/2010 –

2013/2014

•High correlation with traditional surveillance (laboratory-based and sentinel reporting)– Correlation coeff. 0.84–0.94, including

the A(H1N1) pandemic season

•Estimated peak weeks 1-2 weeks earlier 4 out of 5 seasons, 1 week later season 2010-2011

The A(H1N1) influenza pandemic

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Qualitative evaluation

• The largest contribution of the web-query based surveillance was as an additional source and a complement to the traditional surveillance:

“One surveillance system is not enough for getting a true picture, and the more sources point in the same direction, the more reliable is the interpretation of the influenza surveillance data”

• The automatic dispatch was much appreciated

• The emails (sent three and a half days before the time when the traditional surveillance was compiled) was valuable as an early signal of what to expect from the traditional surveillance, although it was the trend rather than the height of the curve that was deemed more important

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Winter vomiting disease

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Web queries vs lab tests

The onset of vomiting in society precedes the onset of positive lab tests in health care settings.

The difference is on average 2–3 weeks (2006/07 – 2012/13).

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Serfling RE. Methods for current statistical analysis of excess pneumonia–influenza deaths. Public Health Rep. 1963;78:494–506.

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Dissemination

• information is included in the weekly report on calici virus

• the health care sector is informed when there is a signal

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Comparing GET WELL to a general purpose

search engine

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So what about Google?•Google search data have been used for: – dengue (Chan et al 2011)– influenza (Eysenbach 2006; Ginsberg et al 2009; Wilson et al 2009;

Valdivia et al 2010)– listeriosis (Wilson & Brownstein 2009)– malaria (Ocampo et al 2013)– salmonella (Brownstein et al 2009)– scarlet fever (Samaras et al 2012)– West Nile virus and RSV (Carneiro & Mylonakis 2009)– MRSA (Dukic et al 2011)– French data: ILI, gastroenteritis, chickenpox (Pelat et al 2009)– Spanish data: ILI, chickenpox (Valdivia & Monge-Corella 2010)

•Except Google Flu trends, these are all retrospective studies

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References using Google data• Brownstein JS, Freifeld CC, Madoff LC. Digital disease detection–harnessing the Web for public

health surveillance. N Engl J Med 2009;360:2153–5, 2157.• Carneiro HA, Mylonakis E. Google trends: a web-based tool for real-time surveillance of disease

outbreaks. Clin Infect Dis 2009;49:1557–64.• Chan EH, Sahai V, Conrad C, Brownstein JS. Using web search query data to monitor dengue

epidemics: a new model for neglected tropical disease surveillance. PLoS Negl Trop Dis 2011 May;5(5):e1206.

• Dukic VM, David MZ, Lauderdale DS. Internet queries and methicillin-resistant Staphylococcus aureus surveillance. Emerg Infect Dis 2011 Jun;17(6):1068-70.

• Eysenbach G. Infodemiology: tracking flu-related searches on the web for syndromic surveillance. AMIA Annu Symp Proc 2006, 244-8.

• Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature 2009;457:1012-4.

• Ocampo AJ, Chunara R, Brownstein JS. Using search queries for malaria surveillance, Thailand. Malaria Journal 2013;12:390.

• Pelat C, Turbelin C, Bar-Hen A, Flahault A, Valleron AJ. More diseases tracked by using Google trends. Emerg Infect Dis 2009;15(8):1327-8.

• Samaras L, García-Barriocanal E, Sicilia MA. Syndromic surveillance models using Web data: the case of scarlet fever in the UK. Inform Health Soc Care 2012 Mar;37(2):106-24.

• Valdivia A, Lopez-Alcalde J, Vicente M, Pichiule M, Ruiz M, Ordobas M. Monitoring influenza activity in Europe with Google Flu Trends: comparison with the findings of sentinel physician networks - results for 2009-10. Euro Surveill 2010;15(29): pii=19621.

• Valdivia A, Monge-Corella S. Diseases Tracked by Using Google Trends, Spain. Emerg Infect Dis 2010 January;16(1): 168.

• Wilson K, Brownstein JS. Early detection of disease outbreaks using the Internet. CMAJ 2009;180(8):829-31.

• Wilson N, Mason K, Tobias M, Peacey M, Huang QS, Baker M. Interpreting “Google Flu Trends” data for pandemic H1N1 influenza: The New Zealand experience. Euro Surveill 2009;14(44):pii = 19386.

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Google versus a medical website

•Google trends provides regional information

•International comparisons can easily be made

•A medical website has higher specificity– less affected by media attention– most queries refer to human illness

•We have access to the raw data– data may be transformed as wished– total control of the extraction process (e.g. matching stems, not

words)

•Should public health institutes really rely on private actors for the public health surveillance?

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Concluding part web query-based

surveillance

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Problematic aspects with the source

• No demographic information provided

• Geographic information limited

• Visitors are not representative of the Swedish population

• Unknown level of noise

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Strengths with web query based surveillance

• the data are nearly real time

• the data reflect a point in time close to onset (provided that the person looking for information is actually ill)

• easy to set up automatic surveillance

• a system based on web queries can easily be adapted to various diseases or other public health issues

• can help the agency to get the epidemiological message through

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Web queries in summary

Web queries give a unique access to ill individuals who are not (yet) seeking care.

They are a cheap and labour efficient source and are a valuable complement to the conventional surveillance.

References web query-based surveillance in Sweden• Hulth A, Rydevik G, Linde A. Web queries as a source for syndromic surveillance.

PLoS ONE 2009;4(2):e4378

• Hulth A, Andersson Y, Hedlund K-O, Andersson M. Eye-opening approach to norovirus surveillance. Emerg Infect Dis 2010;16(8)

• Hulth A, Rydevik G. GET WELL: an automated surveillance system for gaining new epidemiological knowledge. BMC Public Health 2011;11:252

• Hulth A, Rydevik G. Web query-based surveillance in Sweden during the influenza A(H1N1)2009 pandemic, April 2009 to February 2010. Euro Surveill. 2011;16(18):pii=19856

• Lindh J, Magnusson M, Grünewald M, Hulth A. Head lice surveillance on a deregulated OTC-sales market: A study using web query data. PLoS ONE 2012;7(11):e48666.

• Andersson T, Bjelkmar P, Hulth A, Lindh J, Stenmark S, Widerström M. Syndromic surveillance for local outbreak detection and awareness: evaluating outbreak signals of acute gastroenteritis in telephone triage, web-based queries and over-the-counter pharmacy sales. Epidemiol Infect. 2013 May;15:1-11. [Epub ahead of print].

• Edelstein M, Wallensten A, Zetterqvist I, Hulth A. Detecting the norovirus season in Sweden using search engine data – meeting the needs of hospital infection control teams. PLoS ONE 2014;9(6):e100309.

• Ma T, Englund H, Bjelkmar P, Wallensten A, Hulth A. Syndromic surveillance for influenza activity in Sweden: Evaluation of three tools. Epidemiol Infect. Accepted.

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Computer Assisted Search for Epidemics

Computer supported outbreak detection of the notifiable diseases in Sweden

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What is CASE (i)?

•A platform for computer supported outbreak detection, developed at SMI (now converted to Public Health Agency of Sweden)

•Makes analyses on data from SmiNet (our database for notifiable diseases)

•Statistical algorithms are used to detect signals about potential outbreaks

•Runs every night on selected notifiable diseases

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What is CASE (ii)?

•40 diseases/subtypes are currently activated

•Most epidemiologists working with communicable disease surveillance at the agency are active (n=12)

•HIV, other sexually transmitted diseases and other blood-borne diseases, food- and waterborne diseases and zoonoses, vaccine preventable diseases, and antimicrobial resistance

•Detected signals are sent by email to the epidemiologist(s) in charge of the diagnosis in question

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How may CASE give a signal about an outbreak?

•CASE has currently five different statistical algorithms implemented

•The selection of algorithm for a particular diagnosis or subtype depends on what kind of increase you want to detect

•More than one algorithm can be used for the same diagnosis/subtype

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Usability evaluation

A questionnaire is sent to the users of CASE every second year in order to:

• Get an idea of how useful they find the system;

• Find out more on how the collaboration between the CASE group and the epidemiologists works;

• Get information on what further development and improvements that should be given priority.

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Some results from 2013

•7/9 stated that CASE saves time in their daily work

•5/9 stated that they had been made aware of an outbreak by CASE on at least one occasion

•0/9 stated that CASE miss outbreaks, but 4 said that they don’t know

•9/9 stated that CASE confirms signals from other surveillance sources

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CASE - conclusions

•CASE is a useful tool in the routine surveillance work

•Possible reasons for this: – CASE is flexible and allows for different parameter

settings for different diseases– a close collaboration between the CASE group and the

epidemiologists – continuous development of the system where it is

adapted to the actual needs of the epidemiologists in charge of the surveillance

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CASE: availability• Source code licenced under GNU General Public Licence

• Runs on Linux and Windows

• https://case.folkhalsomyndigheten.se/

• Contact: case@folkhalsomyndigheten.se

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References computer supported outbreak detection at the agency

•Cakici B, Hebing K, Grünewald M, Saretok P, Hulth A. CASE: a framework for computer supported outbreak detection. BMC Med Inform Decis Mak. 2010;10:14.

•Hulth A, Andrews N, Ethelberg S, Dreesman J, Faensen D, van Pelt W, Schnitzler J. Practical usage of computer-supported outbreak detection in five European countries. Euro Surveill. 2010;15(36).

•Kling AM, Hebing K, Grünewald M, Hulth A. Two years of computer supported outbreak detection in Sweden: the user’s perspective. J Health Med Informat. 2012;3:108.