Identifying the Food and Location Source of Large-Scale Outbreaks of - PowerPoint PPT Presentation

Identifying the Food and Location Source of Large-Scale Outbreaks of Foodborne Disease ABIGAIL HORN P o s t d o c t o r a l S c i e n t i s t , G e r m a n F e d e r a l I n s t i t u t e f o r R i s k A s s e s s m e n t ( B f R ) B f R C o - a u t h o r s : M a t t h i a s F i l t e r , M a r c e l F u h r m a n n , A n n e m a r i e K ä s b o h r e r , A r m i n W e i s e r O t h e r C o - a u t h o r s : H a n n o F r i e d r i c h ( K L U ) , A n d r e a s B a l s t e r ( K L U ) , E l e n a P o l o z o v a ( M I T ) I A F P 2 0 1 8 | T e c h n i c a l S e s s i o n I – M o d e l i n g a n d R i s k A s s e s s m e n t A U G U S T 9 , 2 0 1 8

Motivation: Foodborne Disease Outbreaks Impact of 2011 sprout E.coli /EHEC outbreak: • 4321 illnesses (3816 in Germany) • 54 deaths • 16 countries with cases • 9 weeks to identify source Frank, C., Werber, D., Cramer, J. P., Askar, M., Faber, M., an der Heiden, M., et al (2011). “Epidemic profile of Shiga-toxin producing Escherichia coli O104: H4 outbreak in Germany.” New England Journal of Medicine , 365(19), 1771-1780.

Could cases have been averted? May 2 Outbreak Identified Date of illness onset CDC. E.coli Germany outbreak update. cdc.gov. Archived from the original on 27 June 2011. Bundesinstitut für Risikobewertung (BfR). “Fenugreek seeds with high probability for EHEC O104: H4 responsible outbreak” 30 June 2011. Frank, Christina, et al. "Epidemic profile of Shiga-toxin–producing Escherichia coli O104: H4 outbreak in Germany." New England Journal of Medicine 365.19 (2011): 1771-1780.

Could cases have been averted? June 10 RKI confirms sprouts as May 2 food source Outbreak Identified Date of illness onset CDC. E.coli Germany outbreak update. cdc.gov. Archived from the original on 27 June 2011. Bundesinstitut für Risikobewertung (BfR). “Fenugreek seeds with high probability for EHEC O104: H4 responsible outbreak” 30 June 2011. Frank, Christina, et al. "Epidemic profile of Shiga-toxin–producing Escherichia coli O104: H4 outbreak in Germany." New England Journal of Medicine 365.19 (2011): 1771-1780.

Could cases have been averted? June 10 June 30 RKI confirms BfR confirms sprouts as organic farm in May 2 food source Uelzen as Outbreak Identified location source Date of illness onset CDC. E.coli Germany outbreak update. cdc.gov. Archived from the original on 27 June 2011. Bundesinstitut für Risikobewertung (BfR). “Fenugreek seeds with high probability for EHEC O104: H4 responsible outbreak” 30 June 2011. Frank, Christina, et al. "Epidemic profile of Shiga-toxin–producing Escherichia coli O104: H4 outbreak in Germany." New England Journal of Medicine 365.19 (2011): 1771-1780.

Identifying the Location Source Food Supply Network Model

Identifying the Location Source Food Supply Illness Case Network Model Report Data Reported Cases

Identifying the Location Source Food Supply Source Localization Illness Case Network Model Algorithm Report Data Top Ranked Source Reported Cases

Identifying the Location Source Food Supply Source Localization Illness Case Network Model Model Report Data Top Ranked Source 0.30 Illustrative PMF and Source Ranking 0.25 probability 0.20 0.15 0.10 0.05 0.00 5 3 15 9 8 4 1 13 12 11 2 6 10 7 14 Source ID Reported Cases

German food supply network model

German spatial commodity flow model Agriculture 50 Export countries 402 regions Imports Processing Exports 402 regions Trade 50 Import 225 Individual countries Warehouses Consumption 402 regions Balster, Andreas and Hanno Friedrich (2018). "Dynamic freight flow modelling for risk evaluation in food supply“. In: Transportation Research Part E. https://doi.org/10.1016/j.tre.2018.03.002

51 commodity groups: industrial interactions Balster, Andreas and Hanno Friedrich (2018). "Dynamic freight flow modelling for risk evaluation in food supply“. In: Transportation Research Part E. https://doi.org/10.1016/j.tre.2018.03.002

Inputs: Spatial production & consumption data t/km ² ≤ 10 ≤ 100 ≤ 200 ≤ 500 > 500 100% 30% Production of sugar beets Production of sugar Production of confectionaries … § 30.000.000 t/year § 5.000.000 t/year § 3.000.000 t/year …

Modeling process: Estimating links Modeling Links: 1) Between categories of food: 452 Research to identify industrial regions interactions 51 commodity groups E.g. Sugar, milk products, eggs, grains à 59 groups of actors production of confectionaries 2) Between regions: Estimate flows using gravity model T ij = A i B j O i D j exp( − β ⋅ d ij ) (2) >> Calibration necessary: German Federal Transport Master Plan data (1)

Identifying the outbreak location source

Network Source Localization Problem Statement Assume • Network model • Probabilistic model of transmission process Outbreak process • At time t s a single source s* is contaminated, all others susceptible. Contamination spreads through the network resulting in cases of illness Observe reports of illness • At a subset of network node locations Objective • Estimate the source location given the locations of observations of illness , based on the network structure and the transmission process

Features of Existing Source Identification Approaches (And Literature Gap) Source Features of Existing Source Identification Approaches identification (1) (2) (3) (4) (5) (6) methodology of Assumes Assumes Ignores Only shortest Only Assumes existing work SI(R) complete weights paths dominant observation model/status observations paths times Rumor centrality X X X ( 14 ) Betweenness X X X centrality ( 15 ) Eigenvector X X centrality ( 16,17 ) Message X X passing ( 18 ) Belief X X propagation ( 19 ) Gaussian X X ( 20,21 ) Four-metric X X X ( 22 ) Monte Carlo X X X ( 22 ) Jordan centrality X X ( 24,25 ) Effective X Distance ( 26,27 ) Horn, A., Friedrich, H. “Locating the Source of Large-scale Diffusion of Foodborne Contamination,” preprint . https://arxiv.org/abs/1805.03137

Distinguishing features of foodborne disease transmission 1) A transport (diffusion), not epidemiological (contagion), transmission process 2) Observations are sparse: most nodes not observable 3) Observations are far from the source 4) Similar path lengths 5) Multiple candidate paths Horn, A., Friedrich, H. “Locating the Source of Large-scale Diffusion of Foodborne Contamination,” preprint . https://arxiv.org/abs/1805.03137

Example: Multiple candidate paths Uelzen: Paderborn Uelzen: Frankfurt Shorter distances à High probability “designated” path

Example: Multiple candidate paths Uelzen: Frankfurt Longer distances à Multiple similar probability paths

Random Walk Contamination Transmission Model Random Walk model of contamination diffusion through the network The Markov transition probabilities, ( ) = p ij P X n + 1 = j X n = i taken together define the Markov transition matrix for the process, ⎡ ⎤ Sub-matrix P P ⎢ Q R ⎥ representing P = The transition probabilities p ij are transitions from ⎢ ⎥ O I R defined in terms of the weights as ⎣ ⎦ transient to absorbing nodes V w ij [ ] , i ≠ j p ij = ∈ 0,1 ∑ V w ij Transitions between Sub-matrix transient nodes representing j Absorption Horn, A., Friedrich, H. “Locating the Source of Large-scale Diffusion of Foodborne Contamination,” preprint . https://arxiv.org/abs/1805.03137

Maximum Likelihood Source Estimator s ∈Ω 1. Use network structure to filter the feasible source set s ∈Ω 2. Determine the probability that any feasible source is the true Θ source s * , given the observation set Probabilistically optimal solution: • Bayesian inference : ( ) P Θ ( ) = P s ∗ = s ( ) P Θ s ∗ = s P s ∗ = s Θ ( ) Prior probability Transmission model • Maximum likelihood estimator: ( ) ( ) P Θ s ∗ = s P s ∗ = s s = argmax ˆ s ∈ Ω

Source Estimator: Main Result Source estimator: ( ) − 1 P ∏ ⎡ ⎤ s = argmax I − P ˆ ⎣ ⎦ so Q R s ∈ Ω o ∈ O Horn, A., Friedrich, H. “Locating the Source of Large-scale Diffusion of Foodborne Contamination,” preprint . https://arxiv.org/abs/1805.03137

Source Estimator: Main Result Source estimator: ( ) − 1 P ∏ ⎡ ⎤ s = argmax I − P ˆ ⎣ ⎦ so Q R s ∈ Ω o ∈ O Considering all Probability of traveling through contaminated nodes o the network from s to o Horn, A., Friedrich, H. “Locating the Source of Large-scale Diffusion of Foodborne Contamination,” preprint . https://arxiv.org/abs/1805.03137

Source Estimator: Main Result Source estimator: ( ) − 1 P ∏ ⎡ ⎤ s = argmax I − P ˆ ⎣ ⎦ so Q R s ∈ Ω o ∈ O Considering all Probability of traveling through contaminated nodes o the network from s to o Contribution: Accounts for all possible combinations of paths of travel through the network à Increases accuracy in non-tree-like networks à The probabilistically exact (maximum likelihood) solution Horn, A., Friedrich, H. “Locating the Source of Large-scale Diffusion of Foodborne Contamination,” preprint . https://arxiv.org/abs/1805.03137