Public health trends of foodborne diseases

Public health trends of foodborne diseasesSalmonella, Campylobacter and Listeria in Belgium as examples

Niko Speybroeck1, Brecht Devleesschauwer2, Lamarana Diallo1,Dirk Berkvens3, Sophie Bertrand4, Olivier Vandenberg5, Juanita

Haagsma6, Arie Havelaar2, Luc Vanholme7, Sophie Quoilin5, PatrickBrandt8, and Charline Maertens de Noordhout1

1Université catholique de Louvain, Brussels 2University of Florida, Florida 3Institute of Tropical Medicine,Antwerp 4Institute of Public Health, Brussels 5Saint-Pierre University hospital, Brussels 6Erasmus,Rotterdam 7Federal Agency for the Safety of the Food Chain, Brussels 8University of Texas, Dallas

Symposium of the Scientific Committee of the Belgian Food Safety Agency11/03/2016

(Belgian Food Safety Agency Symposium) Public health trends of foodborne diseases March 2016 1 / 29

Salmonellosis, Campylobacteriosis, Listeriosis


Salmonellosis, Campylobacteriosis, Listeriosis

The Question...


Temporal: Time


S...


Stationarity & Campylobacteriosis

Cas

es

1995 2000 2005 2010

200

400

600

800

1000


Stationarity & Campylobacteriosis

Cas

es

1995 2000 2005 2010

200

400

600

800

1000


Stationarity: take first differences & transform data

45

67

8

dt = yt – yt-1 = 1

1 1 1 1


Correlation ⇒Auto-Correlation


Auto-correlation

Our count of Campylobacter cases at time t

Yt = 0.7× Yt−1 + εt

Related to the value one month earlierand some errorThis is an AR(1) model; AR standing for Auto-RegressiveWe try to remain with error that is white noise


Auto-correlation


Yt = 0.7× Yt−1 + εt



Auto-correlation


Yt = 0.7× Yt−1 + εt



Auto-correlation


Yt = 0.7× Yt−1 + εt



Auto-correlation


Yt = 0.7× Yt−1 + εt



Fitting Time Series models: ACF


ACF and Partial ACF patterns of AR(1)


Stationarity & correlation structure

200

600

1000

data

−20

00

200

400

seas

onal

400

600

tren

d

−20

00

1995 2000 2005 2010

rem

aind

er

time

Time series models require experience: natural ordering,auto-correlation, trends & seasonal variations.


Fitting Time Series models: Basic Steps

1 Plot data. Identify patterns.2 If non-stationary data: take first differences & if necessary, transform

data to stabilize the variance until data are stationary (stable process).3 Examine AutoCorrelation Function (ACF)/Partial AutoCorrelation

Function (PACF): AutoRegressive (AR) or Moving Average (MA) modelappropriate?

4 Try chosen model(s), and use AIC to search for better model.5 Check residuals by plotting ACF of residuals. Goal of model selections:

capture cyclical and trend aspects of each series so that residuals areuncorrelated: forecasting possible.

6 Once residuals look like white noise, calculate forecasts.


From data to forecasting

year months cases2011 1 22011 2 62011 3 32011 4 142011 5 52011 6 42011 7 102011 8 6

......

...

1995 2005 2015

500

1000

1500

Observations by month of Salmonella (2001-2012), Campylobacter(1993-2013) & Listeria (2011-2013).

Start: watching the Time Series plots.


The data: Salmonella

050

010

0015

0020

00C

ases

Sal

mon

ella

Jan 2001

Jan 2002

Jan 2003

Jan 2004

Jan 2005

Jan 2006

Jan 2007

Jan 2008

Jan 2009

Jan 2010

Jan 2011

Jan 2012

Month

Figure: Salmonella Time Series


The data: Campylobacter

200

400

600

800

1000

Cas

es C

ampy

loba

cter

Jan 1992

Jan 1994

Jan 1996

Jan 1998

Jan 2000

Jan 2002

Jan 2004

Jan 2006

Jan 2008

Jan 2010

Jan 2012

Jan 2014

Month

Figure: Campylobacter Time Series


The data: Listeria

05

1015

Cas

es L

iste

ria

Jan 2011

Apr 2011

Jul 2011

Oct 2011

Jan 2012

Apr 2012

Jul 2012

Oct 2012

Jan 2013

Apr 2013

Jul 2013

Oct 2013

Month

Figure: Listeria Time Series


Exploratory work0

500

1000

1500

2000

Cas

es S

alm

onel

la

Jan 2001

Jan 2002

Jan 2003

Jan 2004

Jan 2005

Jan 2006

Jan 2007

Jan 2008

Jan 2009

Jan 2010

Jan 2011

Jan 2012

Month

200

400

600

800

1000

Cas

es C

ampy

loba

cter

Jan 1992

Jan 1994

Jan 1996

Jan 1998

Jan 2000

Jan 2002

Jan 2004

Jan 2006

Jan 2008

Jan 2010

Jan 2012

Jan 2014

Month

05

1015

Cas

es L

iste

ria

Jan 2011

Apr 2011

Jul 2011

Oct 2011

Jan 2012

Apr 2012

Jul 2012

Oct 2012

Jan 2013

Apr 2013

Jul 2013

Oct 2013

Month

Salmonella: Seasonal, with breaks in trend and levels.Campylobacter: Highly seasonal, with a local, time-varying trend.Listeria: Rare events, with the possible need of a Poisson model.


Models Selected0

500

1000

1500

2000

Cas

es S

alm

onel

la

Jan 2001

Jan 2002

Jan 2003

Jan 2004

Jan 2005

Jan 2006

Jan 2007

Jan 2008

Jan 2009

Jan 2010

Jan 2011

Jan 2012

Month

200

400

600

800

1000

Cas

es C

ampy

loba

cter

Jan 1992

Jan 1994

Jan 1996

Jan 1998

Jan 2000

Jan 2002

Jan 2004

Jan 2006

Jan 2008

Jan 2010

Jan 2012

Jan 2014

Month

05

1015

Cas

es L

iste

ria

Jan 2011

Apr 2011

Jul 2011

Oct 2011

Jan 2012

Apr 2012

Jul 2012

Oct 2012

Jan 2013

Apr 2013

Jul 2013

Oct 2013

Month

After evaluating the nature of the trends and other characteristics of each timeseries, the following models were used for forecasting:

Bai-Perron breakpoints model: estimates the optimal number ofbreakpoints in an autoregresive plus trend model for Salmonella.Dynamic Linear Model (DLM): estimates the time-varying trend,seasonal, and autoregressive dynamics for Campylobacter.Poisson autoregression model, PAR(p): estimates the trend, seasonal,and autoregressive dynamics for Listeria.


Salmonella: Bai-Perron breakpoints model

Time

Sal

mon

ella

Cas

es

2002 2004 2006 2008 2010 2012

500

1500

Figure: Salmonella time series observed and predicted values.

Time Segment Estimated Autoregression

2001(1)–2003(11) 144.35(51.9)

+ 1.197(0.104)

St−1 − 0.831(0.076)

St−2 + 0.322(0.081)

St−12 + 2106.1(456.2)

t

T

2003(12)–2005(10) 626.36(114.7)

+ 0.059(0.193)

St−1 + 0.047(0.130)

St−2 + 0.539(0.077)

St−12 − 2261.68(375.7)

t

T

2005(11)–2013(12) 44.56(50.39)

+ 0.666(0.137)

St−1 − 0.238(0.120)

St−2 + 0.361(0.093)

St−12 + 14.54(44.90)

t

T

Trend upward [2001(1)-2003(11)], downward [2003(12)-2005(10)] & statistically insignificant after 2005(11).


Salmonella predictions

2005 2010 2015 2020

500

1000

1500

Figure: Salmonella forecasts, 2001-2020.

Predicted monthly number of cases for 2020 of of 212 (95% CI: 43-381) forsalmonellosis compared to 264 cases in 2012.


Salmonella

050

010

0015

00C

ases

Sal

mon

ello

sis

Jan 2001

Jan 2002

Jan 2003

Jan 2004

Jan 2005

Jan 2006

Jan 2007

Jan 2008

Jan 2009

Jan 2010

Jan 2011

Jan 2012

Month

Typhimurium EnteritidisOthers


Campylobacter: Dynamic Linear ModelCampylobacter Cases

1995 2000 2005 2010

200

600

1000

DataFilter TrendSmoothed Trend

Seasonal Component

1995 2000 2005 2010

−30

00

200

Residuals

1995 2000 2005 2010

−2

24


Dynamic Linear Model: Trying to follow the coast-line


Campylobacter predictions

1995 2005 2015

500

1000

1500

Figure: Campylobacter forecasts, 1993-2020.

Predicted monthly number of cases for 2020 of 1081 (95% CI: 430-1741) forcampylobacteriosis compared to 633 cases in 2012.


Listeria: Poisson autoregression model

24

68

1014

2011 2012 2013 2014

Figure: Listeriosis cases and predictions (PAR(2) model).

Lt = −0.28(0.13)

Lt−1 − 0.24(0.15)

Lt−2 + (1 + 0.28(0.13)

− 0.24(0.15)

) exp(1.83(0.06)

)


Listeria predictions

2011 2012 2013 2014 2015

24

68

1014

Figure: Listeriosis cases and forecast, 2001-2020, with 95% interval.

Predicted monthly number of cases for 2020 of 6 (90% CI: 2-11) for listeriosiscompared to 6 cases in 2012.


Summary

Seasonality & trends complex and can vary little (Salmonella) or a greatdeal (Campylobacter).

Campylobacteriosis↗Listeriosis −→Salmonellosis↘

Evolution of Disease Burden.

Biggest performance increase from good data.Listeriosis: Need 50+ observations for time series modelling.Forgotten covariates? Need for a full analysis.Underreporting.

Other Public Health trends to be investigated: cancers, allergies, ...See References for more in-depth insights.


For Further Reading I

C. Chatfield.The Analysis of Time Series, 5th ed.Chapman & Hall, New York, 1996.

T.C. Mills.Time series techniques for economists.Cambridge University Press, 1990.

R.H. Shumway and D.S. Stoffer.Time Series Analysis and its Applications. With R Examples, 2nd ed.Springer, 2006.

R.J. Hyndman, and G. AthanasopoulosForecasting: principles and practice.OTexts, Melbourne, Australia, 2014.

G. Petris, S. Petrone, and P. Campagnoli.Dynamic Linear Models with R.Springer, 2009.


For Further Reading II

J.M. Collard, et al.Drastic decrease of Salmonella Enteritidis isolated from humans inBelgium in 2005, shift in phage types and influence on foodborneoutbreaks.Epidemiology and Infection, 136: 771-781, 2008.

J. Bai, and P. Perron.Estimating and testing linear models with multiple structural changes.Econometrica, 66: 47-78, 1998.

P.T. Brandt, and J.T. Williams.A linear Poisson autoregressive model: The Poisson AR(p) model.Political Analysis 9: 164-184, 2001.


Documents

Public health trends of foodborne diseases