Use of laboratory data for near realtime disease reporting, prediction, and understanding complex problems

Transcription

1 Use of laboratory data for near realtime disease reporting, prediction, and understanding complex problems Zvonimir Poljak 2017 CAHLN Meeting Guelph, ON June 6 th, 2017

2 Epidemiological research Driven by end users: -moving target Driven by: -data quality -data organization -available expertise -available technology Usefulness = Relevance Validity Time 2 Driven by researchers: -validity -selection bias -confounding bias -information bias -research ethics Use of different approaches and methods Usefulness = Equation Modified from Unknown Author (Discovery Training) Validity2 Time

3 Outline Pathogens of interest Lab surveillance Incidence and prevalence Outbreak investigation Brief overview of complex problems Final thoughts

4 H. parasuis E. coli.28 L. intracellularis Salmonella M. hyopneumoniae S. suis.59 Bacteria of interest Most sought Bacteria research information - choice options 3 App B hyo E coli Hps L ill M hyo Salm S suis Other Source: Sheila Kaey, ongoing PhD work

5 Viruses of interest Most Sought Virus Research Information-Up to 3 options PRRS IAV-S PED PCV-2 SVA ROTA ASF FMD CSF PRV OTHER Source: Sheila Kaey, ongoing PhD work

6 Data and levels of analysis Y pig-herd-province = X pig-herd-province Without sufficient information, laboratory data could only be analyzed at the ecological (high) level Y pig-herd-province = X pig-herd-province

7 Near real-time reporting of Influenza 18-page pdf report Generated semi-automatically on a weekly basis knitr package in R as the framework No. of submissions & no. of +ve submissions Weekly and monthly time series Objectives: Provide description of all data Highlight more recent ones Decompose time series (assess trend and seasonality) Identify time periods with unusually disease frequency Forecast number of cases Source: Tatiana Petukhova, Postdoctoral work

8 Source: Tatiana Petukhova, Postdoctoral work

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10 Feedback Preference for 1-page Ideally, would have prevalence and incidence Most important pathogens addressed together

11 Proportion Reporting preferences With Synthesis Preferred Research Reading Format to Keep Current - up to 2 choices % % Body of Evidence 31% Individual Paper 15% BoeNarRev Boe3pgSum Boe1pgSum Other IndResPap IndPap1pgSumm Source: Sheila Kaey, ongoing PhD work

12 Reporting preferences Time available per week for keeping current with research 24% 29% 31% 15% >0-30 minutes >30 minutes - 1 hour >1 hour - 3 hours >3 hours Source: Sheila Kaey, ongoing PhD work

13 Influenza V 2.0 Source: Tatiana Petukhova, Postdoctoral work

14 Feedback Preference for 1-page Ideally would have prevalence and incidence Need to trace diagnostic status of herds Need to have interpretation of diagnostic results Emerging (PEDV) Endemic (PRRSV) Different approaches Most important pathogens addressed together Variability of data sources

15 Estimation of incidence With sufficient identifiers, laboratory and epidemiological data could be analyzed to estimate herd level disease frequency Y pig-herd-province = X pig-herd-province For incidence: when a negative herd becomes a positive herd

16 PEDV: Surveillance components for detection of new cases - Incidence Lab-confirmed cases, declared by OMAFRA as new cases and entered into OSHAB database Lab-confirmed cases, only declared by OMAFRA as new cases Presumed positive cases mostly due to animal movement. Not tested in the laboratory Lab-confirmed cases, not considered as primary cases by OMAFRA, recorded as incident cases by OSHAB Source: Toluwalope Ajayi, ongoing MSc work Positive submissions not contributing to incidence

17 Source: Toluwalope Ajayi, ongoing MSc work PEDV Incidence

18 Estimation of prevalence With sufficient identifiers, laboratory and epidemiological data could be analyzed to estimate herd level disease frequency Y pig-herd-province = X pig-herd-province For incidence: when a negative herd becomes a positive herd For prevalence: when a positive herd becomes a negative herd

19 Source: Toluwalope Ajayi, ongoing MSc work PEDV prevalence

20 Data quantity and quality Lab data Unique identifiers AND interpretation of diagnostic tests (surveillance components?) Processes that result in diagnostic submissions Data useful for disease frequency Disease frequency in the source population Accurate assessment of disease frequency in the source population

21 Research: contributors to new cases PEDV pig-herd-province = X pig-herd-province N new cases in a week Trend in the no. of new cases Zero, Low, Moderate, High Environmental factors Temperature Humidity Precipitation (Mean, Min, Max) Prevalence Herds N infected sows Lags (1-5)

22 Random forest No new cases in the next week General trend in cases over the next 4 weeks Source: Toluwalope Ajayi, ongoing MSc work

23 Measuring PRRSV frequency PRRSV pig-herd-province = X pig-herd-province New clinical cases: -any level of clinical disease in a sow herd -PCR confirmation -ongoing weekly reporting New infection (new genotype): -Any PRRSV detection in a previously naïve herd -New genotype in herd that is known PRRSV +ve -Molecular information required Prevalence: -Reporting when a herd becomes ve could be complicated process linked with veterinary interpretation of diagnostic, clinical and epidemiological information Source: Juliana Ferreira, Postdoctoral work

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26 Animal movement Most likely virus movement

27 Final thoughts Quality of laboratory data Unique ID is necessary But often not sufficient to determine disease frequency Efficiency of gathering data from different sources Many opportunities for improvement New reporting mechanisms knitr Functionality from the shinny package Reporting and epidemiological research could be overlapping

28 Acknowledgements Financial support OMAFRA Production Systems & Emergency Management KTT NSERC Discovery Swine Cluster 2 Swine Innovation Porc Agriculture and Agrifood Canada Ontario Pork Data and collaborations Animal Health Laboratory Ontario Swine Health Advisory Board Swine Health Ontario Ontario Animal Health Network