Integrating genetic and epidemiological data to determine transmission pathways of foot-and-mouth disease virus.

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Integrating genetic and epidemiological data to determine transmission pathways of foot-and-mouth

disease virus.

Gael Thébaud, E.M. Cottam, - Wadsworth, J., J. Gloster, L. Mansley, D.J. Paton, D.P. King, D.T. Haydon

To cite this version:

Gael Thébaud, E.M. Cottam, - Wadsworth, J., J. Gloster, L. Mansley, et al.. Integrating genetic and epidemiological data to determine transmission pathways of foot-and-mouth disease virus.. Mathe-matics and InforMathe-matics in Evolution and Phylogeny, Jun 2008, Montpellier, France. �hal-02817901�

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Integrating genetic and

epidemiological data to determine

virus transmission pathways

1 _{UMR BGPI (INRA-Montpellier)}

2 _{Division of Environmental and}

Evolutionary Biology (University of Glasgow)

3 _{Institute for Animal Health}

(Pirbright)

4 _{Animal Health Divisional Office}

(Perth)

Eleanor COTTAM 2,3_{, Gaël THÉBAUD} 1,2_{, Jemma WADSWORTH}3_,

John GLOSTER 3_{, Leonard MANSLEY}4_{, David PATON}3_,

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Molecular epidemiology and directionality

Introduction

• Direction of transmission :

– reference (more or less implicit)

to additional information

• Genetic sequences:

– phylogeny

– clades / groups / types

• Comparison between genetic

similarity and

– geographic proximity – ecological zone

– host species – ...

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• type of source and target individuals • transmission distances

• important or missing sources • likely transmission modes

• evolution during 1 transmission cycle

Accessible information

epidemiology

evolution

Why is directionality interesting?

Introduction

• Implications: - logical: - legal: source “target” cause≈ consequences≈ responsible≈ victim≈

In theory, complete description of the epidemic In practice, data sets concerning few individuals

• parameterise

epidemiological models (e.g., network models)

• limit virus propagation

• multiscale models

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Questions on FMDV

Introduction

• At which scale is there some viral genetic

polymorphism?

– animal, farm, disease focus?

• Can we use the observed polymorphism

to identify transmission chains? How?

• What is the reliability of veterinary

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Biological system

• Foot-and-mouth disease virus outbreak (2001)

• 20 complete genomes (~10 kb each)

– 5 initial infections with a known history

– 15 farms from the same focus (Durham County)

10 km A _K B D E C G I J M N O L P F 10 km A _K B D E C G I J M N O L P F

• Positive-strand RNA virus:

– High mutation rate (~10-4 errors/nucleotide/replication) – Limited recombination

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• Known root • 2 independent introductions • 4 groups 0 10 20 30 40 50 60 70 80 90 100 110 Day of outbreak A B C D M E N G I J F K L 1 2 3 5 4 _O P 0 10 20 30 40 50 60 70 80 90 100 110 Day of outbreak A B C D M E N G I J F K L 1 2 3 5 4 _O P 24 n uc leot ide s ubs ti tu ti ons S A R/ 19/ 200 0 24 n uc leot ide s ubs ti tu ti ons S A R/ 19/ 200 0 TCS 10 km A _K B D E C G I J M N O L P F 10 km A _K B D E C G I J M N O L P F

Genetic data

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Genetic data

0 10 20 30 40 50 60 70 80 90 100 110 Day of outbreak A B C D M E N G I J F K L 1 2 3 5 4 _O P 0 10 20 30 40 50 60 70 80 90 100 110 Day of outbreak A B C D M E N G I J F K L 1 2 3 5 4 _O P 24 n uc leot ide s ubs t ti ons itu S A R/ 19/ 0 200 24 n uc leot ide s ubs t ti ons itu S A R/ 19/ 0 200 TCS • Known root • 2 independent introductions • 4 groups How to identify transmission history?

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• 1 known chain of transmissions 0 10 20 30 40 50 60 70 80 90 100 110 A B C D M E N G I J F K L 1 2 3 5 4 _O P 0 10 20 30 40 50 60 70 80 90 100 110 A B C D M E N G I J F K L 1 2 3 5 4 _O P 24 nucl eoti de s ubs tituti ons S A R/ 19/2000 ₂₄ nucl eoti de s ubs tituti ons S A R/ 19/2000 • 3 obvious transmissions

Which is the most likely transmission tree?

• What about the

other ones?? ? ? ? ? ? ? ? ?

Which is the most likely farm for each node ?

• Known root

Use of contact tracing data

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Epidemiology

• L : Probability density

for latency (Γ)

• I_i : Probability density

for the infection date of farm i • F_i (t) : Probability for farm i to be infectious at date t 27 J anu ar y 2 001 0 3 Fe br uar y 200 1 1 0 Fe br uar y 200 1 1 7 Fe br uar y 200 1 2 4 Fe br uar y 200 1 03 M ar c h 2001 10 M ar c h 2001 17 M ar c h 2001 24 M ar c h 2001 31 M ar c h 20 01 07 A p ri l 2 001 14 A p ri l 2 001 21 A p ri l 2 001 28 A p ri l 2 001 05 M a y 200 1 12 M a y 200 1 19 M a y 200 1 26 M a y 200 1 02 J une 200 1 1 2 3 4 5 B C A D E F G I J K L M N O P 09 J une 200 1 27 J anu ar y 2 001 0 3 Fe br uar y 200 1 1 0 Fe br uar y 200 1 1 7 Fe br uar y 200 1 2 4 Fe br uar y 200 1 03 M ar c h 2001 10 M ar c h 2001 17 M ar c h 2001 24 M ar c h 2001 31 M ar c h 20 01 07 A p ri l 2 001 14 A p ri l 2 001 21 A p ri l 2 001 28 A p ri l 2 001 05 M a y 200 1 12 M a y 200 1 19 M a y 200 1 26 M a y 200 1 02 J une 200 1 1 2 3 4 5 B C A D E F G I J K L M N O P 09 J une 200 1

Animal movement ban

27 J anuar y 2001 0 3 Feb ruar y 20 01 1 0 Feb ruar y 20 01 1 7 Feb ruar y 20 01 2 4 Feb ruar y 20 01 03 M ar c h 20 01 10 M ar c h 20 01 17 M ar c h 20 01 24 M ar c h 20 01 31 M ar c h 2001 0 7 A pr il 2001 1 4 A pr il 2001 2 1 A pr il 2001 2 8 A pr il 2001 05 M a y 2001 12 M a y 2001 19 M a y 2001 26 M a y 2001 02 J une 2 001 09 J une 2 001 27 J anuar y 2001 0 3 Feb ruar y 20 01 1 0 Feb ruar y 20 01 1 7 Feb ruar y 20 01 2 4 Feb ruar y 20 01 03 M ar c h 20 01 10 M ar c h 20 01 17 M ar c h 20 01 24 M ar c h 20 01 31 M ar c h 2001 0 7 A pr il 2001 1 4 A pr il 2001 2 1 A pr il 2001 2 8 A pr il 2001 05 M a y 2001 12 M a y 2001 19 M a y 2001 26 M a y 2001 02 J une 2 001 09 J une 2 001 27 J 2 001 1 2 3 4 5 B C A D E F G I J K L M N O P 27 J 2 001 1 2 3 4 5 B C A D E F G I J K L M N O P

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Epidemiology

• λ_ij: Likelihood of

i Å { j rather than

another observed farm }

27 J anu ar y 2 001 0 3 Fe br uar y 200 1 1 0 Fe br uar y 200 1 1 7 Fe br uar y 200 1 2 4 Fe br uar y 200 1 03 M ar c h 2001 10 M ar c h 2001 17 M ar c h 2001 24 M ar c h 2001 31 M ar c h 20 01 07 A p ri l 2 001 14 A p ri l 2 001 21 A p ri l 2 001 28 A p ri l 2 001 05 M a y 200 1 12 M a y 200 1 19 M a y 200 1 26 M a y 200 1 02 J une 200 1 1 2 3 4 5 B C A D E F G I J K L M N O P 09 J une 200 1 27 J anu ar y 2 001 0 3 Fe br uar y 200 1 1 0 Fe br uar y 200 1 1 7 Fe br uar y 200 1 2 4 Fe br uar y 200 1 03 M ar c h 2001 10 M ar c h 2001 17 M ar c h 2001 24 M ar c h 2001 31 M ar c h 20 01 07 A p ri l 2 001 14 A p ri l 2 001 21 A p ri l 2 001 28 A p ri l 2 001 05 M a y 200 1 12 M a y 200 1 19 M a y 200 1 26 M a y 200 1 02 J une 200 1 1 2 3 4 5 B C A D E F G I J K L M N O P 09 J une 200 1

∑ ∑

∑

≠ = = = ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ ⋅ ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ ⋅ = n i k k k C C t i j C C t i ij I t F t I t F t k j i j 1 ) , min( 0 ) , min( 0 ) ( ) ( ) ( ) ( λ

Animal movement ban

• L : Probability density

for latency (Γ)

• I_i : Probability density

for the infection date of farm i

• F_i (t) : Probability for

farm i to be infectious at date t

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• λ_ij: likelihood of i Å { j rather than another observed farm }

•

λ

_ij can be computed for each transmission

• Thus, for a complete transmission tree (k),

λ

_k = Π

λ

_ij

E L E L 1 {L,E} G I J F G I J F 2 _{_F_,_G_} {1,2,K} K K 1728 trees Loglikelihood F requenc y -250 -200 -150 -100 0 5 1 0 15 20 2 5 30

λ

• And

λ

_k can be computed for any tree

… if all the possible trees can be enumerated

Æ Algorithm defining the possible trees by recurrence from the leaves back to the root

Genetics + epidemiology

All differing from contact tracing results

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• Rescaled likelihood:

λ

’_k =

λ

_k / Σ

λ

_k

• Which group of trees

represent 95% of the rescaled likelihood?

Which is the most likely group of trees?

4 trees

most likely tree

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( # ) Number of distinct sources among the 4 most likely trees [ # ] Likelihood of the most probable transmission

Genetics + epidemiology

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0 10 20 30 40 50 60 70 80 90 100 110 A B C D M E N G I J F K L 1 2 3 5 4 _O P 0 10 20 30 40 50 60 70 80 90 100 110 A B C D M E N G I J F K L 1 2 3 5 4 _O P 24 nu c leot ide s ubs ti tut ions S A R/ 19/ 2000 24 nu c leot ide s ubs ti tut ions S A R/ 19/ 2000 A K O L F

Genetics + epidemiology

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Genetics + epidemiology

Spatial pattern

Short distance transmission 10 km A _K B D E C G I J M N O L P F 10 km A _{K B} D E C G I J M N O L P F MeanDistSim Fr eq ue nc y 4000 6000 8000 10000 0 5 00 0 1 00 00 1 50 00 2 00 00 P_(1-sided) = 1.2 x 10-3 Mean distance 4850 m 7472 m

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Conclusions

• The whole set of possible transmission trees is identified based on genetic data

• Their relative likelihood is evaluated based on epidemiological data

• Interesting method for real-time forensic applications

• Identifying the tree root

• Dealing with censoring / sampling issues • Weighting different sources of information

Difficulties

Summary

Cottam E.M. et al. (2008) Integrating genetic and epidemiological data to determine transmission pathways of foot-and-mouth disease virus. Proc. R. Soc. B 275: 887-895.