Jonathan Porter Edyta Bartkowska Rob Bonsor Andy Gawler Matilda La Trobe Rachel Long Natalie Nicholson Louise Pearce

Nick Pye Keith Salter Steve Wilde

National Laboratory Service Starcross Devon Tel 01626 892742 jonathanporterenvironment-agencygovuk

Developments in Microbial Source Tracking

Sta

rcro

ss a

nd the B

runel P

um

pin

g S

tation f

rom

the E

xe -

geogra

pho

rgu

k -

1285641

by S

ara

h C

harlesw

ort

h

Lic

ensed u

nder

CC

BY

-SA

20

via

Com

mons

Devo

n U

K locatio

n m

ap

by C

onta

ins O

rdnance S

urv

ey d

ata

copy C

row

n c

opyri

ght and d

ata

base r

ight L

icensed u

nder

CC

BY

-SA

30

via

Com

mons

Source Tracking If we know where the bacteria are coming from we can target our work bull 04-08 humans ruminants bull 08-09 dogs birds bull 10-12 cows sheep pigs

Well that just about wraps things up then All UK microbiological water quality issues can now be resolved

Source Tracking If we know where the bacteria are coming from we can target our work bull 04-08 humans ruminants bull 08-09 dogs birds bull 10-12 cows sheep pigs

Well that just about wraps things up then All UK microbiological water quality issues can now be resolved

Well that just about wraps things up then All UK microbiological water quality issues can now be resolved

Hum Rum

33 67

1 99

13 87

4 96

80 20

18 82

6 94

16 84

What does MST data tell you ndash case study

gt40 bathing water samples collected

Eight samples had high bacti counts

Agricultural sources dominated seven out of eight

We are NOT able to state (67+99+87hellip)7 = 79

of the faecal bacteria are from ruminant sources

We ARE able to state that faecal bacteria associated with ruminant animals dominated in these samples

Interpret the pattern with local knowledge

This recommendation is still in place

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses (and other pathogens) o data analysis o horses

MST for sea birds Catellicoccus marimammalium

[Catmar]

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses (and other pathogens) o data analysis o horses

MST for sea birds Catellicoccus marimammalium

[Catmar]

MST for sea birds Catellicoccus marimammalium

[Catmar]

_________________________________________

Sample Catmar _____________________________________ Water (expected positive) 435 Water (expected negative) 191

Gull 870 Goose 335 Swan 356 Pheasant 0 Duck 384 Pigeon 381 ____________________________________

Example Catmar data known samples

Data are log10 copies litre or g wet weight

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses (and other pathogens) o data analysis o horses

MST using culturable E coli

Sim

on

Davis

DF

ID

A G C G G A T A G C G G

C A T C G C C A T C G C T T

EXPECTED

MST using culturable E coli

C

T A G C G G

A T C G C C A T C G C T T

G C G G A A

BUT SOME ISOLATES

ldquoUnclassifiedrdquo isolates ndash 2 main types Animal E coli ndash 2 mismatches in probe Human E coli ndash 3 mismatches in forward primer and final base in forward primer overlaps with probe sequence One isolate ndash excellent match to E fergusonii and various Salmonella enterica serovars Clearly contains the uid gene and phenotypically β-glucuronidase positive

MST using culturable E coli

y = 08x + 09 Rsup2 = 087

150

200

250

300

350

400

450

500

550

150 200 250 300 350 400 450 500 550

Total E coli by the bathing water directive

Hu

man

+ a

nim

al

E co

li b

y D

NA

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses (and other pathogens) o data analysis o horses

MST using viruses

Adenovirus

Norovirus (GI and GII)

Enterovirus (coxsackievirus)

MST using viruses ndash data

Censored data ltX values (also applies to gtX but very few of these) Geometric mean used as general descriptor Cannot substitute zero have substituted data points one sig fig below LOD (eg lt10 becomes 9 lt1 becomes 09) Frequency of detection approach used as part of the detailed analysis

0

10

20

30

40

50

60

70

80

000-099 100-199 200-299 300-399 400-499

Overall

Winter

Summer

Ob

serv

ed f

req

uen

cy

log10 Virus gene copies per litre

River water

Viruses ndash can show exceptional host specificity - huge array of viruses as MST options - lack of basic ldquoenvironmentalrdquo data on most of them - not the sole or ultimate answer for MST but useful information - indicators vs pathogens

Md

k5

72

lic

en

se

d u

nd

er

the

Cre

ative

Com

mo

ns A

ttri

bu

tio

n-S

ha

re A

like

30

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses (and other pathogens) o data analysis o horses

MST data analysis

We often get queries asking for guidance on MST data

- Partly because itrsquos ldquonewrdquo

- Partly because it is sometimes apparently confused

(Rare we get boxes of chocolates)

Data requires more than just general descriptors

Shellfish waters south Devon Data from adjacent catchment consistently suggested ruminant sources were significant This dataset apparently much more confused

0

2

4

6

8

0 2 4 6

HuBac

RuBac

log10 Escherichia coli per 100 ml

log 1

0 M

ST m

arke

r p

er

10

0 m

l

0

20

40

60

80

100

Human

Ruminant

Early Late

M

ST m

arke

r

Explored the data looking for patterns

48h rainfall a key variable human when dry ruminant when wet but extent of change was sub-catchment specific __________________________________________ Site rainfall HumgtRum Mean E coli ( of samples) 100 ml ___________________________________________ Site 1 dry 75 243 Site 2 wet 50 329 Site 2 dry 100 076 Site 2 wet 20 157 Site 3 dry 84 169 Site 3 wet 11 208 ___________________________________________

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses (and other pathogens) o data analysis o horses

MST using culturable E coli

Sim

on

Davis

DF

ID

A G C G G A T A G C G G

C A T C G C C A T C G C T T

EXPECTED

MST using culturable E coli

C

T A G C G G

A T C G C C A T C G C T T

G C G G A A

BUT SOME ISOLATES

ldquoUnclassifiedrdquo isolates ndash 2 main types Animal E coli ndash 2 mismatches in probe Human E coli ndash 3 mismatches in forward primer and final base in forward primer overlaps with probe sequence One isolate ndash excellent match to E fergusonii and various Salmonella enterica serovars Clearly contains the uid gene and phenotypically β-glucuronidase positive

MST using culturable E coli

y = 08x + 09 Rsup2 = 087

150

200

250

300

350

400

450

500

550

150 200 250 300 350 400 450 500 550

Total E coli by the bathing water directive

Hu

man

+ a

nim

al

E co

li b

y D

NA

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses (and other pathogens) o data analysis o horses

MST using viruses

Adenovirus

Norovirus (GI and GII)

Enterovirus (coxsackievirus)

MST using viruses ndash data

Censored data ltX values (also applies to gtX but very few of these) Geometric mean used as general descriptor Cannot substitute zero have substituted data points one sig fig below LOD (eg lt10 becomes 9 lt1 becomes 09) Frequency of detection approach used as part of the detailed analysis

0

10

20

30

40

50

60

70

80

000-099 100-199 200-299 300-399 400-499

Overall

Winter

Summer

Ob

serv

ed f

req

uen

cy

log10 Virus gene copies per litre

River water

Viruses ndash can show exceptional host specificity - huge array of viruses as MST options - lack of basic ldquoenvironmentalrdquo data on most of them - not the sole or ultimate answer for MST but useful information - indicators vs pathogens

Md

k5

72

lic

en

se

d u

nd

er

the

Cre

ative

Com

mo

ns A

ttri

bu

tio

n-S

ha

re A

like

30

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses (and other pathogens) o data analysis o horses

MST data analysis

We often get queries asking for guidance on MST data

- Partly because itrsquos ldquonewrdquo

- Partly because it is sometimes apparently confused

(Rare we get boxes of chocolates)

Data requires more than just general descriptors

Shellfish waters south Devon Data from adjacent catchment consistently suggested ruminant sources were significant This dataset apparently much more confused

0

2

4

6

8

0 2 4 6

HuBac

RuBac

log10 Escherichia coli per 100 ml

log 1

0 M

ST m

arke

r p

er

10

0 m

l

0

20

40

60

80

100

Human

Ruminant

Early Late

M

ST m

arke

r

Explored the data looking for patterns

48h rainfall a key variable human when dry ruminant when wet but extent of change was sub-catchment specific __________________________________________ Site rainfall HumgtRum Mean E coli ( of samples) 100 ml ___________________________________________ Site 1 dry 75 243 Site 2 wet 50 329 Site 2 dry 100 076 Site 2 wet 20 157 Site 3 dry 84 169 Site 3 wet 11 208 ___________________________________________

MST using culturable E coli

Sim

on

Davis

DF

ID

A G C G G A T A G C G G

C A T C G C C A T C G C T T

EXPECTED

MST using culturable E coli

C

T A G C G G

A T C G C C A T C G C T T

G C G G A A

BUT SOME ISOLATES

ldquoUnclassifiedrdquo isolates ndash 2 main types Animal E coli ndash 2 mismatches in probe Human E coli ndash 3 mismatches in forward primer and final base in forward primer overlaps with probe sequence One isolate ndash excellent match to E fergusonii and various Salmonella enterica serovars Clearly contains the uid gene and phenotypically β-glucuronidase positive

MST using culturable E coli

y = 08x + 09 Rsup2 = 087

150

200

250

300

350

400

450

500

550

150 200 250 300 350 400 450 500 550

Total E coli by the bathing water directive

Hu

man

+ a

nim

al

E co

li b

y D

NA

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses (and other pathogens) o data analysis o horses

MST using viruses

Adenovirus

Norovirus (GI and GII)

Enterovirus (coxsackievirus)

MST using viruses ndash data

Censored data ltX values (also applies to gtX but very few of these) Geometric mean used as general descriptor Cannot substitute zero have substituted data points one sig fig below LOD (eg lt10 becomes 9 lt1 becomes 09) Frequency of detection approach used as part of the detailed analysis

0

10

20

30

40

50

60

70

80

000-099 100-199 200-299 300-399 400-499

Overall

Winter

Summer

Ob

serv

ed f

req

uen

cy

log10 Virus gene copies per litre

River water

Viruses ndash can show exceptional host specificity - huge array of viruses as MST options - lack of basic ldquoenvironmentalrdquo data on most of them - not the sole or ultimate answer for MST but useful information - indicators vs pathogens

Md

k5

72

lic

en

se

d u

nd

er

the

Cre

ative

Com

mo

ns A

ttri

bu

tio

n-S

ha

re A

like

30

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses (and other pathogens) o data analysis o horses

MST data analysis

We often get queries asking for guidance on MST data

- Partly because itrsquos ldquonewrdquo

- Partly because it is sometimes apparently confused

(Rare we get boxes of chocolates)

Data requires more than just general descriptors

Shellfish waters south Devon Data from adjacent catchment consistently suggested ruminant sources were significant This dataset apparently much more confused

0

2

4

6

8

0 2 4 6

HuBac

RuBac

log10 Escherichia coli per 100 ml

log 1

0 M

ST m

arke

r p

er

10

0 m

l

0

20

40

60

80

100

Human

Ruminant

Early Late

M

ST m

arke

r

Explored the data looking for patterns

48h rainfall a key variable human when dry ruminant when wet but extent of change was sub-catchment specific __________________________________________ Site rainfall HumgtRum Mean E coli ( of samples) 100 ml ___________________________________________ Site 1 dry 75 243 Site 2 wet 50 329 Site 2 dry 100 076 Site 2 wet 20 157 Site 3 dry 84 169 Site 3 wet 11 208 ___________________________________________

A G C G G A T A G C G G

C A T C G C C A T C G C T T

EXPECTED

MST using culturable E coli

C

T A G C G G

A T C G C C A T C G C T T

G C G G A A

BUT SOME ISOLATES

ldquoUnclassifiedrdquo isolates ndash 2 main types Animal E coli ndash 2 mismatches in probe Human E coli ndash 3 mismatches in forward primer and final base in forward primer overlaps with probe sequence One isolate ndash excellent match to E fergusonii and various Salmonella enterica serovars Clearly contains the uid gene and phenotypically β-glucuronidase positive

MST using culturable E coli

y = 08x + 09 Rsup2 = 087

150

200

250

300

350

400

450

500

550

150 200 250 300 350 400 450 500 550

Total E coli by the bathing water directive

Hu

man

+ a

nim

al

E co

li b

y D

NA

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses (and other pathogens) o data analysis o horses

MST using viruses

Adenovirus

Norovirus (GI and GII)

Enterovirus (coxsackievirus)

MST using viruses ndash data

Censored data ltX values (also applies to gtX but very few of these) Geometric mean used as general descriptor Cannot substitute zero have substituted data points one sig fig below LOD (eg lt10 becomes 9 lt1 becomes 09) Frequency of detection approach used as part of the detailed analysis

0

10

20

30

40

50

60

70

80

000-099 100-199 200-299 300-399 400-499

Overall

Winter

Summer

Ob

serv

ed f

req

uen

cy

log10 Virus gene copies per litre

River water

Viruses ndash can show exceptional host specificity - huge array of viruses as MST options - lack of basic ldquoenvironmentalrdquo data on most of them - not the sole or ultimate answer for MST but useful information - indicators vs pathogens

Md

k5

72

lic

en

se

d u

nd

er

the

Cre

ative

Com

mo

ns A

ttri

bu

tio

n-S

ha

re A

like

30

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses (and other pathogens) o data analysis o horses

MST data analysis

We often get queries asking for guidance on MST data

- Partly because itrsquos ldquonewrdquo

- Partly because it is sometimes apparently confused

(Rare we get boxes of chocolates)

Data requires more than just general descriptors

Shellfish waters south Devon Data from adjacent catchment consistently suggested ruminant sources were significant This dataset apparently much more confused

0

2

4

6

8

0 2 4 6

HuBac

RuBac

log10 Escherichia coli per 100 ml

log 1

0 M

ST m

arke

r p

er

10

0 m

l

0

20

40

60

80

100

Human

Ruminant

Early Late

M

ST m

arke

r

Explored the data looking for patterns

48h rainfall a key variable human when dry ruminant when wet but extent of change was sub-catchment specific __________________________________________ Site rainfall HumgtRum Mean E coli ( of samples) 100 ml ___________________________________________ Site 1 dry 75 243 Site 2 wet 50 329 Site 2 dry 100 076 Site 2 wet 20 157 Site 3 dry 84 169 Site 3 wet 11 208 ___________________________________________

C

T A G C G G

A T C G C C A T C G C T T

G C G G A A

BUT SOME ISOLATES

ldquoUnclassifiedrdquo isolates ndash 2 main types Animal E coli ndash 2 mismatches in probe Human E coli ndash 3 mismatches in forward primer and final base in forward primer overlaps with probe sequence One isolate ndash excellent match to E fergusonii and various Salmonella enterica serovars Clearly contains the uid gene and phenotypically β-glucuronidase positive

MST using culturable E coli

y = 08x + 09 Rsup2 = 087

150

200

250

300

350

400

450

500

550

150 200 250 300 350 400 450 500 550

Total E coli by the bathing water directive

Hu

man

+ a

nim

al

E co

li b

y D

NA

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses (and other pathogens) o data analysis o horses

MST using viruses

Adenovirus

Norovirus (GI and GII)

Enterovirus (coxsackievirus)

MST using viruses ndash data

Censored data ltX values (also applies to gtX but very few of these) Geometric mean used as general descriptor Cannot substitute zero have substituted data points one sig fig below LOD (eg lt10 becomes 9 lt1 becomes 09) Frequency of detection approach used as part of the detailed analysis

0

10

20

30

40

50

60

70

80

000-099 100-199 200-299 300-399 400-499

Overall

Winter

Summer

Ob

serv

ed f

req

uen

cy

log10 Virus gene copies per litre

River water

Viruses ndash can show exceptional host specificity - huge array of viruses as MST options - lack of basic ldquoenvironmentalrdquo data on most of them - not the sole or ultimate answer for MST but useful information - indicators vs pathogens

Md

k5

72

lic

en

se

d u

nd

er

the

Cre

ative

Com

mo

ns A

ttri

bu

tio

n-S

ha

re A

like

30

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses (and other pathogens) o data analysis o horses

MST data analysis

We often get queries asking for guidance on MST data

- Partly because itrsquos ldquonewrdquo

- Partly because it is sometimes apparently confused

(Rare we get boxes of chocolates)

Data requires more than just general descriptors

Shellfish waters south Devon Data from adjacent catchment consistently suggested ruminant sources were significant This dataset apparently much more confused

0

2

4

6

8

0 2 4 6

HuBac

RuBac

log10 Escherichia coli per 100 ml

log 1

0 M

ST m

arke

r p

er

10

0 m

l

0

20

40

60

80

100

Human

Ruminant

Early Late

M

ST m

arke

r

Explored the data looking for patterns

48h rainfall a key variable human when dry ruminant when wet but extent of change was sub-catchment specific __________________________________________ Site rainfall HumgtRum Mean E coli ( of samples) 100 ml ___________________________________________ Site 1 dry 75 243 Site 2 wet 50 329 Site 2 dry 100 076 Site 2 wet 20 157 Site 3 dry 84 169 Site 3 wet 11 208 ___________________________________________

ldquoUnclassifiedrdquo isolates ndash 2 main types Animal E coli ndash 2 mismatches in probe Human E coli ndash 3 mismatches in forward primer and final base in forward primer overlaps with probe sequence One isolate ndash excellent match to E fergusonii and various Salmonella enterica serovars Clearly contains the uid gene and phenotypically β-glucuronidase positive

MST using culturable E coli

y = 08x + 09 Rsup2 = 087

150

200

250

300

350

400

450

500

550

150 200 250 300 350 400 450 500 550

Total E coli by the bathing water directive

Hu

man

+ a

nim

al

E co

li b

y D

NA

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses (and other pathogens) o data analysis o horses

MST using viruses

Adenovirus

Norovirus (GI and GII)

Enterovirus (coxsackievirus)

MST using viruses ndash data

Censored data ltX values (also applies to gtX but very few of these) Geometric mean used as general descriptor Cannot substitute zero have substituted data points one sig fig below LOD (eg lt10 becomes 9 lt1 becomes 09) Frequency of detection approach used as part of the detailed analysis

0

10

20

30

40

50

60

70

80

000-099 100-199 200-299 300-399 400-499

Overall

Winter

Summer

Ob

serv

ed f

req

uen

cy

log10 Virus gene copies per litre

River water

Viruses ndash can show exceptional host specificity - huge array of viruses as MST options - lack of basic ldquoenvironmentalrdquo data on most of them - not the sole or ultimate answer for MST but useful information - indicators vs pathogens

Md

k5

72

lic

en

se

d u

nd

er

the

Cre

ative

Com

mo

ns A

ttri

bu

tio

n-S

ha

re A

like

30

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses (and other pathogens) o data analysis o horses

MST data analysis

We often get queries asking for guidance on MST data

- Partly because itrsquos ldquonewrdquo

- Partly because it is sometimes apparently confused

(Rare we get boxes of chocolates)

Data requires more than just general descriptors

Shellfish waters south Devon Data from adjacent catchment consistently suggested ruminant sources were significant This dataset apparently much more confused

0

2

4

6

8

0 2 4 6

HuBac

RuBac

log10 Escherichia coli per 100 ml

log 1

0 M

ST m

arke

r p

er

10

0 m

l

0

20

40

60

80

100

Human

Ruminant

Early Late

M

ST m

arke

r

Explored the data looking for patterns

48h rainfall a key variable human when dry ruminant when wet but extent of change was sub-catchment specific __________________________________________ Site rainfall HumgtRum Mean E coli ( of samples) 100 ml ___________________________________________ Site 1 dry 75 243 Site 2 wet 50 329 Site 2 dry 100 076 Site 2 wet 20 157 Site 3 dry 84 169 Site 3 wet 11 208 ___________________________________________

MST using culturable E coli

y = 08x + 09 Rsup2 = 087

150

200

250

300

350

400

450

500

550

150 200 250 300 350 400 450 500 550

Total E coli by the bathing water directive

Hu

man

+ a

nim

al

E co

li b

y D

NA

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses (and other pathogens) o data analysis o horses

MST using viruses

Adenovirus

Norovirus (GI and GII)

Enterovirus (coxsackievirus)

MST using viruses ndash data

Censored data ltX values (also applies to gtX but very few of these) Geometric mean used as general descriptor Cannot substitute zero have substituted data points one sig fig below LOD (eg lt10 becomes 9 lt1 becomes 09) Frequency of detection approach used as part of the detailed analysis

0

10

20

30

40

50

60

70

80

000-099 100-199 200-299 300-399 400-499

Overall

Winter

Summer

Ob

serv

ed f

req

uen

cy

log10 Virus gene copies per litre

River water

Viruses ndash can show exceptional host specificity - huge array of viruses as MST options - lack of basic ldquoenvironmentalrdquo data on most of them - not the sole or ultimate answer for MST but useful information - indicators vs pathogens

Md

k5

72

lic

en

se

d u

nd

er

the

Cre

ative

Com

mo

ns A

ttri

bu

tio

n-S

ha

re A

like

30

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses (and other pathogens) o data analysis o horses

MST data analysis

We often get queries asking for guidance on MST data

- Partly because itrsquos ldquonewrdquo

- Partly because it is sometimes apparently confused

(Rare we get boxes of chocolates)

Data requires more than just general descriptors

Shellfish waters south Devon Data from adjacent catchment consistently suggested ruminant sources were significant This dataset apparently much more confused

0

2

4

6

8

0 2 4 6

HuBac

RuBac

log10 Escherichia coli per 100 ml

log 1

0 M

ST m

arke

r p

er

10

0 m

l

0

20

40

60

80

100

Human

Ruminant

Early Late

M

ST m

arke

r

Explored the data looking for patterns

48h rainfall a key variable human when dry ruminant when wet but extent of change was sub-catchment specific __________________________________________ Site rainfall HumgtRum Mean E coli ( of samples) 100 ml ___________________________________________ Site 1 dry 75 243 Site 2 wet 50 329 Site 2 dry 100 076 Site 2 wet 20 157 Site 3 dry 84 169 Site 3 wet 11 208 ___________________________________________

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses (and other pathogens) o data analysis o horses

MST using viruses

Adenovirus

Norovirus (GI and GII)

Enterovirus (coxsackievirus)

MST using viruses ndash data

Censored data ltX values (also applies to gtX but very few of these) Geometric mean used as general descriptor Cannot substitute zero have substituted data points one sig fig below LOD (eg lt10 becomes 9 lt1 becomes 09) Frequency of detection approach used as part of the detailed analysis

0

10

20

30

40

50

60

70

80

000-099 100-199 200-299 300-399 400-499

Overall

Winter

Summer

Ob

serv

ed f

req

uen

cy

log10 Virus gene copies per litre

River water

Viruses ndash can show exceptional host specificity - huge array of viruses as MST options - lack of basic ldquoenvironmentalrdquo data on most of them - not the sole or ultimate answer for MST but useful information - indicators vs pathogens

Md

k5

72

lic

en

se

d u

nd

er

the

Cre

ative

Com

mo

ns A

ttri

bu

tio

n-S

ha

re A

like

30

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses (and other pathogens) o data analysis o horses

MST data analysis

We often get queries asking for guidance on MST data

- Partly because itrsquos ldquonewrdquo

- Partly because it is sometimes apparently confused

(Rare we get boxes of chocolates)

Data requires more than just general descriptors

Shellfish waters south Devon Data from adjacent catchment consistently suggested ruminant sources were significant This dataset apparently much more confused

0

2

4

6

8

0 2 4 6

HuBac

RuBac

log10 Escherichia coli per 100 ml

log 1

0 M

ST m

arke

r p

er

10

0 m

l

0

20

40

60

80

100

Human

Ruminant

Early Late

M

ST m

arke

r

Explored the data looking for patterns

48h rainfall a key variable human when dry ruminant when wet but extent of change was sub-catchment specific __________________________________________ Site rainfall HumgtRum Mean E coli ( of samples) 100 ml ___________________________________________ Site 1 dry 75 243 Site 2 wet 50 329 Site 2 dry 100 076 Site 2 wet 20 157 Site 3 dry 84 169 Site 3 wet 11 208 ___________________________________________

MST using viruses

Adenovirus

Norovirus (GI and GII)

Enterovirus (coxsackievirus)

MST using viruses ndash data

Censored data ltX values (also applies to gtX but very few of these) Geometric mean used as general descriptor Cannot substitute zero have substituted data points one sig fig below LOD (eg lt10 becomes 9 lt1 becomes 09) Frequency of detection approach used as part of the detailed analysis

0

10

20

30

40

50

60

70

80

000-099 100-199 200-299 300-399 400-499

Overall

Winter

Summer

Ob

serv

ed f

req

uen

cy

log10 Virus gene copies per litre

River water

Viruses ndash can show exceptional host specificity - huge array of viruses as MST options - lack of basic ldquoenvironmentalrdquo data on most of them - not the sole or ultimate answer for MST but useful information - indicators vs pathogens

Md

k5

72

lic

en

se

d u

nd

er

the

Cre

ative

Com

mo

ns A

ttri

bu

tio

n-S

ha

re A

like

30

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses (and other pathogens) o data analysis o horses

MST data analysis

We often get queries asking for guidance on MST data

- Partly because itrsquos ldquonewrdquo

- Partly because it is sometimes apparently confused

(Rare we get boxes of chocolates)

Data requires more than just general descriptors

Shellfish waters south Devon Data from adjacent catchment consistently suggested ruminant sources were significant This dataset apparently much more confused

0

2

4

6

8

0 2 4 6

HuBac

RuBac

log10 Escherichia coli per 100 ml

log 1

0 M

ST m

arke

r p

er

10

0 m

l

0

20

40

60

80

100

Human

Ruminant

Early Late

M

ST m

arke

r

Explored the data looking for patterns

48h rainfall a key variable human when dry ruminant when wet but extent of change was sub-catchment specific __________________________________________ Site rainfall HumgtRum Mean E coli ( of samples) 100 ml ___________________________________________ Site 1 dry 75 243 Site 2 wet 50 329 Site 2 dry 100 076 Site 2 wet 20 157 Site 3 dry 84 169 Site 3 wet 11 208 ___________________________________________

MST using viruses ndash data

Censored data ltX values (also applies to gtX but very few of these) Geometric mean used as general descriptor Cannot substitute zero have substituted data points one sig fig below LOD (eg lt10 becomes 9 lt1 becomes 09) Frequency of detection approach used as part of the detailed analysis

0

10

20

30

40

50

60

70

80

000-099 100-199 200-299 300-399 400-499

Overall

Winter

Summer

Ob

serv

ed f

req

uen

cy

log10 Virus gene copies per litre

River water

Viruses ndash can show exceptional host specificity - huge array of viruses as MST options - lack of basic ldquoenvironmentalrdquo data on most of them - not the sole or ultimate answer for MST but useful information - indicators vs pathogens

Md

k5

72

lic

en

se

d u

nd

er

the

Cre

ative

Com

mo

ns A

ttri

bu

tio

n-S

ha

re A

like

30

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses (and other pathogens) o data analysis o horses

MST data analysis

We often get queries asking for guidance on MST data

- Partly because itrsquos ldquonewrdquo

- Partly because it is sometimes apparently confused

(Rare we get boxes of chocolates)

Data requires more than just general descriptors

Shellfish waters south Devon Data from adjacent catchment consistently suggested ruminant sources were significant This dataset apparently much more confused

0

2

4

6

8

0 2 4 6

HuBac

RuBac

log10 Escherichia coli per 100 ml

log 1

0 M

ST m

arke

r p

er

10

0 m

l

0

20

40

60

80

100

Human

Ruminant

Early Late

M

ST m

arke

r

Explored the data looking for patterns

48h rainfall a key variable human when dry ruminant when wet but extent of change was sub-catchment specific __________________________________________ Site rainfall HumgtRum Mean E coli ( of samples) 100 ml ___________________________________________ Site 1 dry 75 243 Site 2 wet 50 329 Site 2 dry 100 076 Site 2 wet 20 157 Site 3 dry 84 169 Site 3 wet 11 208 ___________________________________________

Viruses ndash can show exceptional host specificity - huge array of viruses as MST options - lack of basic ldquoenvironmentalrdquo data on most of them - not the sole or ultimate answer for MST but useful information - indicators vs pathogens

Md

k5

72

lic

en

se

d u

nd

er

the

Cre

ative

Com

mo

ns A

ttri

bu

tio

n-S

ha

re A

like

30

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses (and other pathogens) o data analysis o horses

MST data analysis

We often get queries asking for guidance on MST data

- Partly because itrsquos ldquonewrdquo

- Partly because it is sometimes apparently confused

(Rare we get boxes of chocolates)

Data requires more than just general descriptors

Shellfish waters south Devon Data from adjacent catchment consistently suggested ruminant sources were significant This dataset apparently much more confused

0

2

4

6

8

0 2 4 6

HuBac

RuBac

log10 Escherichia coli per 100 ml

log 1

0 M

ST m

arke

r p

er

10

0 m

l

0

20

40

60

80

100

Human

Ruminant

Early Late

M

ST m

arke

r

Explored the data looking for patterns

48h rainfall a key variable human when dry ruminant when wet but extent of change was sub-catchment specific __________________________________________ Site rainfall HumgtRum Mean E coli ( of samples) 100 ml ___________________________________________ Site 1 dry 75 243 Site 2 wet 50 329 Site 2 dry 100 076 Site 2 wet 20 157 Site 3 dry 84 169 Site 3 wet 11 208 ___________________________________________

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses (and other pathogens) o data analysis o horses

MST data analysis

We often get queries asking for guidance on MST data

- Partly because itrsquos ldquonewrdquo

- Partly because it is sometimes apparently confused

(Rare we get boxes of chocolates)

Data requires more than just general descriptors

Shellfish waters south Devon Data from adjacent catchment consistently suggested ruminant sources were significant This dataset apparently much more confused

0

2

4

6

8

0 2 4 6

HuBac

RuBac

log10 Escherichia coli per 100 ml

log 1

0 M

ST m

arke

r p

er

10

0 m

l

0

20

40

60

80

100

Human

Ruminant

Early Late

M

ST m

arke

r

Explored the data looking for patterns

48h rainfall a key variable human when dry ruminant when wet but extent of change was sub-catchment specific __________________________________________ Site rainfall HumgtRum Mean E coli ( of samples) 100 ml ___________________________________________ Site 1 dry 75 243 Site 2 wet 50 329 Site 2 dry 100 076 Site 2 wet 20 157 Site 3 dry 84 169 Site 3 wet 11 208 ___________________________________________

MST data analysis

We often get queries asking for guidance on MST data

- Partly because itrsquos ldquonewrdquo

- Partly because it is sometimes apparently confused

(Rare we get boxes of chocolates)

Data requires more than just general descriptors

Shellfish waters south Devon Data from adjacent catchment consistently suggested ruminant sources were significant This dataset apparently much more confused

0

2

4

6

8

0 2 4 6

HuBac

RuBac

log10 Escherichia coli per 100 ml

log 1

0 M

ST m

arke

r p

er

10

0 m

l

0

20

40

60

80

100

Human

Ruminant

Early Late

M

ST m

arke

r

Explored the data looking for patterns

48h rainfall a key variable human when dry ruminant when wet but extent of change was sub-catchment specific __________________________________________ Site rainfall HumgtRum Mean E coli ( of samples) 100 ml ___________________________________________ Site 1 dry 75 243 Site 2 wet 50 329 Site 2 dry 100 076 Site 2 wet 20 157 Site 3 dry 84 169 Site 3 wet 11 208 ___________________________________________

Shellfish waters south Devon Data from adjacent catchment consistently suggested ruminant sources were significant This dataset apparently much more confused

0

2

4

6

8

0 2 4 6

HuBac

RuBac

log10 Escherichia coli per 100 ml

log 1

0 M

ST m

arke

r p

er

10

0 m

l

0

20

40

60

80

100

Human

Ruminant

Early Late

M

ST m

arke

r

Explored the data looking for patterns

48h rainfall a key variable human when dry ruminant when wet but extent of change was sub-catchment specific __________________________________________ Site rainfall HumgtRum Mean E coli ( of samples) 100 ml ___________________________________________ Site 1 dry 75 243 Site 2 wet 50 329 Site 2 dry 100 076 Site 2 wet 20 157 Site 3 dry 84 169 Site 3 wet 11 208 ___________________________________________

0

20

40

60

80

100

Human

Ruminant

Early Late

M

ST m

arke

r

Explored the data looking for patterns

48h rainfall a key variable human when dry ruminant when wet but extent of change was sub-catchment specific __________________________________________ Site rainfall HumgtRum Mean E coli ( of samples) 100 ml ___________________________________________ Site 1 dry 75 243 Site 2 wet 50 329 Site 2 dry 100 076 Site 2 wet 20 157 Site 3 dry 84 169 Site 3 wet 11 208 ___________________________________________

48h rainfall a key variable human when dry ruminant when wet but extent of change was sub-catchment specific __________________________________________ Site rainfall HumgtRum Mean E coli ( of samples) 100 ml ___________________________________________ Site 1 dry 75 243 Site 2 wet 50 329 Site 2 dry 100 076 Site 2 wet 20 157 Site 3 dry 84 169 Site 3 wet 11 208 ___________________________________________

__________________________________ mtBird Catmar BirdCatmar __________________________________ Duck 655 509 2918 Gull 525 914 0000 Pigeon 716 345 303848 Swan 682 588 2881 __________________________________

Exploring MST data ratios of markers

Data are log copies 1000 ml or g wet weight

Exploratory MST data analysis 2 bathing waters approx 10 km apart multiple sample sites for each bathing water E coli and IE as standard directive parameters MST various data points from _________________________________________________________ HuBac - human-specific bacteria mtHuman - human DNA RuBac ndash ruminant-specific bacteria mtBird - bird DNA Catmar - sea-bird bacterial _________________________________________________________

Local knowledge ndash birds may be important wanted to differentiate (ducks andor pheasants) from (seabirds)

Exploratory MST data analysis

Basic plots of the data looking for patterns Site-specific data has helped the investigation very strong Catmar signal at one sample point different sample site with possible pheasantduck impact mtBirdCatmar anything gt1 is non-seabird species anything lt 1 is from seabirds Local duck and pheasant samples support this claim (mtBirdCatmar 42 - 170000) Data from water samples all suggest mtBirdCatmar lt1 therefore bird pollution more likely to be seabirds

Exploratory MST data analysis

Can we produce an ldquooverallrdquo picture Probably too few data points All data combined RuBac decreased over time HuBac increased Sorting data according to HuBac when HuBac is high HuBac correlates with E coli when HuBac is low Catmar correlates with IE

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses (and other pathogens) o data analysis o horses

Linking MST data with the presence of pathogens Bear with me while I ramble

Pathogenic E coli Diversity of mechanisms causing illness Specific phage carry toxin genes Plasmid DNA carries adherence genes Toxin genes VT1 and VT2 Adherence gene(s) eae Typically E coli O157H7 in the UK are VT2 positive eae positive

Exploratory MST data analysis

Basic plots of the data looking for patterns Site-specific data has helped the investigation very strong Catmar signal at one sample point different sample site with possible pheasantduck impact mtBirdCatmar anything gt1 is non-seabird species anything lt 1 is from seabirds Local duck and pheasant samples support this claim (mtBirdCatmar 42 - 170000) Data from water samples all suggest mtBirdCatmar lt1 therefore bird pollution more likely to be seabirds

Exploratory MST data analysis

Can we produce an ldquooverallrdquo picture Probably too few data points All data combined RuBac decreased over time HuBac increased Sorting data according to HuBac when HuBac is high HuBac correlates with E coli when HuBac is low Catmar correlates with IE

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses (and other pathogens) o data analysis o horses

Linking MST data with the presence of pathogens Bear with me while I ramble

Pathogenic E coli Diversity of mechanisms causing illness Specific phage carry toxin genes Plasmid DNA carries adherence genes Toxin genes VT1 and VT2 Adherence gene(s) eae Typically E coli O157H7 in the UK are VT2 positive eae positive

Exploratory MST data analysis

Can we produce an ldquooverallrdquo picture Probably too few data points All data combined RuBac decreased over time HuBac increased Sorting data according to HuBac when HuBac is high HuBac correlates with E coli when HuBac is low Catmar correlates with IE

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses (and other pathogens) o data analysis o horses

Linking MST data with the presence of pathogens Bear with me while I ramble

Pathogenic E coli Diversity of mechanisms causing illness Specific phage carry toxin genes Plasmid DNA carries adherence genes Toxin genes VT1 and VT2 Adherence gene(s) eae Typically E coli O157H7 in the UK are VT2 positive eae positive

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses (and other pathogens) o data analysis o horses

Linking MST data with the presence of pathogens Bear with me while I ramble

Pathogenic E coli Diversity of mechanisms causing illness Specific phage carry toxin genes Plasmid DNA carries adherence genes Toxin genes VT1 and VT2 Adherence gene(s) eae Typically E coli O157H7 in the UK are VT2 positive eae positive

Linking MST data with the presence of pathogens Bear with me while I ramble

Pathogenic E coli Diversity of mechanisms causing illness Specific phage carry toxin genes Plasmid DNA carries adherence genes Toxin genes VT1 and VT2 Adherence gene(s) eae Typically E coli O157H7 in the UK are VT2 positive eae positive

Pathogenic E coli Diversity of mechanisms causing illness Specific phage carry toxin genes Plasmid DNA carries adherence genes Toxin genes VT1 and VT2 Adherence gene(s) eae Typically E coli O157H7 in the UK are VT2 positive eae positive

Pathogenic bacteria Pathogenic bacteria - enrichment cultures (presenceabsence)

E coli pathogenicity genes VT1 amp VT2 (toxins) eae (attachment) Campylobacter jejuni hipO Campylobacter coli cadF C

DC

D

r P

atr

icia

Fie

lds D

r C

olle

tte

Fitzg

era

ld P

ho

to C

red

it

Ja

nic

e C

arr

Linking MST data with the presence of pathogens When does bear become bore

Bringing this bit together Combining MST data and pathogen data

MST marker Pathogen a b c d

Low Present 1 0 0 0

High Present 0 1 0 0

Low Absent 0 0 1 0

High Absent 0 0 0 1

Associations of pathogens with different sources

HuBacgtRuBac eae High HuBac Low HuBac Total

Present 8 12 20 Absent 28 19 47 Total 36 31 67

ab 067 cd 147

Odds Ratio 045

Associations of pathogens with different sources Shellfish waters south Devon _________________________________________________________ Pathogenicity marker Odds ratio (High ruminant pollution) _________________________________________________________ VT1 088 VT2 221 eae 156 VT1VT2eae 163 VT2eae 272 _________________________________________________________

Associations of pathogens with different sources Some ldquojumpsrdquo in the chain but an increased likelihood (times27) of finding VT2 and eae when ruminant pollution exceeded human pollution Many clinical cases of E coli O157 are VT2eae positive Not absolute ndash but interesting

____________________________________________________________ Bacterial pathogen Odds ratio Viral pathogen Odds ratio ____________________________________________________________ VT1 108 Enterovirus na VT2 6 NV GI 04 eae 38 NV GII 10 VT1VT2eae 92 Adenovirus 07 VT2eae 6 hipO 51 cadF 15 cadFhipO 39 ____________________________________________________________ Odds ratios for the presence of the microbial pathogens when ruminant-specific pollution exceeds human-specific pollution

Thus emboldened we marched bravely on

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses and pathogens o data analysis o horses

Ima

ge

co

urt

esy o

f B

ria

n S

ne

lso

n H

ockle

y E

sse

x

En

gla

nd

lice

nse

d u

nd

er

the

Cre

ative

Com

mo

ns A

ttri

bu

tio

n

20

Ge

ne

ric lic

en

se

MST for horses

bull Mitochondrial

bull Bacterial

MST ndash a work in progress

o seabird marker ndash Catmar o E coli isolates ndash a step closer o viruses and pathogens o data analysis ndash slowly gaining confidence o horses ndash mitochondrial and bacterial

icrew

Linking MST data with the presence of pathogens When does bear become bore

Bringing this bit together Combining MST data and pathogen data

MST marker Pathogen a b c d

Low Present 1 0 0 0

High Present 0 1 0 0

Low Absent 0 0 1 0

High Absent 0 0 0 1

Associations of pathogens with different sources

HuBacgtRuBac eae High HuBac Low HuBac Total

Present 8 12 20 Absent 28 19 47 Total 36 31 67

ab 067 cd 147

Odds Ratio 045

Associations of pathogens with different sources Shellfish waters south Devon _________________________________________________________ Pathogenicity marker Odds ratio (High ruminant pollution) _________________________________________________________ VT1 088 VT2 221 eae 156 VT1VT2eae 163 VT2eae 272 _________________________________________________________

Associations of pathogens with different sources Some ldquojumpsrdquo in the chain but an increased likelihood (times27) of finding VT2 and eae when ruminant pollution exceeded human pollution Many clinical cases of E coli O157 are VT2eae positive Not absolute ndash but interesting

____________________________________________________________ Bacterial pathogen Odds ratio Viral pathogen Odds ratio ____________________________________________________________ VT1 108 Enterovirus na VT2 6 NV GI 04 eae 38 NV GII 10 VT1VT2eae 92 Adenovirus 07 VT2eae 6 hipO 51 cadF 15 cadFhipO 39 ____________________________________________________________ Odds ratios for the presence of the microbial pathogens when ruminant-specific pollution exceeds human-specific pollution

Thus emboldened we marched bravely on

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses and pathogens o data analysis o horses

Ima

ge

co

urt

esy o

f B

ria

n S

ne

lso

n H

ockle

y E

sse

x

En

gla

nd

lice

nse

d u

nd

er

the

Cre

ative

Com

mo

ns A

ttri

bu

tio

n

20

Ge

ne

ric lic

en

se

MST for horses

bull Mitochondrial

bull Bacterial

MST ndash a work in progress

o seabird marker ndash Catmar o E coli isolates ndash a step closer o viruses and pathogens o data analysis ndash slowly gaining confidence o horses ndash mitochondrial and bacterial

icrew

Bringing this bit together Combining MST data and pathogen data

MST marker Pathogen a b c d

Low Present 1 0 0 0

High Present 0 1 0 0

Low Absent 0 0 1 0

High Absent 0 0 0 1

Associations of pathogens with different sources

HuBacgtRuBac eae High HuBac Low HuBac Total

Present 8 12 20 Absent 28 19 47 Total 36 31 67

ab 067 cd 147

Odds Ratio 045

Associations of pathogens with different sources Shellfish waters south Devon _________________________________________________________ Pathogenicity marker Odds ratio (High ruminant pollution) _________________________________________________________ VT1 088 VT2 221 eae 156 VT1VT2eae 163 VT2eae 272 _________________________________________________________

Associations of pathogens with different sources Some ldquojumpsrdquo in the chain but an increased likelihood (times27) of finding VT2 and eae when ruminant pollution exceeded human pollution Many clinical cases of E coli O157 are VT2eae positive Not absolute ndash but interesting

____________________________________________________________ Bacterial pathogen Odds ratio Viral pathogen Odds ratio ____________________________________________________________ VT1 108 Enterovirus na VT2 6 NV GI 04 eae 38 NV GII 10 VT1VT2eae 92 Adenovirus 07 VT2eae 6 hipO 51 cadF 15 cadFhipO 39 ____________________________________________________________ Odds ratios for the presence of the microbial pathogens when ruminant-specific pollution exceeds human-specific pollution

Thus emboldened we marched bravely on

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses and pathogens o data analysis o horses

Ima

ge

co

urt

esy o

f B

ria

n S

ne

lso

n H

ockle

y E

sse

x

En

gla

nd

lice

nse

d u

nd

er

the

Cre

ative

Com

mo

ns A

ttri

bu

tio

n

20

Ge

ne

ric lic

en

se

MST for horses

bull Mitochondrial

bull Bacterial

MST ndash a work in progress

o seabird marker ndash Catmar o E coli isolates ndash a step closer o viruses and pathogens o data analysis ndash slowly gaining confidence o horses ndash mitochondrial and bacterial

icrew

Associations of pathogens with different sources

HuBacgtRuBac eae High HuBac Low HuBac Total

Present 8 12 20 Absent 28 19 47 Total 36 31 67

ab 067 cd 147

Odds Ratio 045

Associations of pathogens with different sources Shellfish waters south Devon _________________________________________________________ Pathogenicity marker Odds ratio (High ruminant pollution) _________________________________________________________ VT1 088 VT2 221 eae 156 VT1VT2eae 163 VT2eae 272 _________________________________________________________

Associations of pathogens with different sources Some ldquojumpsrdquo in the chain but an increased likelihood (times27) of finding VT2 and eae when ruminant pollution exceeded human pollution Many clinical cases of E coli O157 are VT2eae positive Not absolute ndash but interesting

____________________________________________________________ Bacterial pathogen Odds ratio Viral pathogen Odds ratio ____________________________________________________________ VT1 108 Enterovirus na VT2 6 NV GI 04 eae 38 NV GII 10 VT1VT2eae 92 Adenovirus 07 VT2eae 6 hipO 51 cadF 15 cadFhipO 39 ____________________________________________________________ Odds ratios for the presence of the microbial pathogens when ruminant-specific pollution exceeds human-specific pollution

Thus emboldened we marched bravely on

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses and pathogens o data analysis o horses

Ima

ge

co

urt

esy o

f B

ria

n S

ne

lso

n H

ockle

y E

sse

x

En

gla

nd

lice

nse

d u

nd

er

the

Cre

ative

Com

mo

ns A

ttri

bu

tio

n

20

Ge

ne

ric lic

en

se

MST for horses

bull Mitochondrial

bull Bacterial

MST ndash a work in progress

o seabird marker ndash Catmar o E coli isolates ndash a step closer o viruses and pathogens o data analysis ndash slowly gaining confidence o horses ndash mitochondrial and bacterial

icrew

Associations of pathogens with different sources Shellfish waters south Devon _________________________________________________________ Pathogenicity marker Odds ratio (High ruminant pollution) _________________________________________________________ VT1 088 VT2 221 eae 156 VT1VT2eae 163 VT2eae 272 _________________________________________________________

Associations of pathogens with different sources Some ldquojumpsrdquo in the chain but an increased likelihood (times27) of finding VT2 and eae when ruminant pollution exceeded human pollution Many clinical cases of E coli O157 are VT2eae positive Not absolute ndash but interesting

____________________________________________________________ Bacterial pathogen Odds ratio Viral pathogen Odds ratio ____________________________________________________________ VT1 108 Enterovirus na VT2 6 NV GI 04 eae 38 NV GII 10 VT1VT2eae 92 Adenovirus 07 VT2eae 6 hipO 51 cadF 15 cadFhipO 39 ____________________________________________________________ Odds ratios for the presence of the microbial pathogens when ruminant-specific pollution exceeds human-specific pollution

Thus emboldened we marched bravely on

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses and pathogens o data analysis o horses

Ima

ge

co

urt

esy o

f B

ria

n S

ne

lso

n H

ockle

y E

sse

x

En

gla

nd

lice

nse

d u

nd

er

the

Cre

ative

Com

mo

ns A

ttri

bu

tio

n

20

Ge

ne

ric lic

en

se

MST for horses

bull Mitochondrial

bull Bacterial

MST ndash a work in progress

o seabird marker ndash Catmar o E coli isolates ndash a step closer o viruses and pathogens o data analysis ndash slowly gaining confidence o horses ndash mitochondrial and bacterial

icrew

Associations of pathogens with different sources Some ldquojumpsrdquo in the chain but an increased likelihood (times27) of finding VT2 and eae when ruminant pollution exceeded human pollution Many clinical cases of E coli O157 are VT2eae positive Not absolute ndash but interesting

____________________________________________________________ Bacterial pathogen Odds ratio Viral pathogen Odds ratio ____________________________________________________________ VT1 108 Enterovirus na VT2 6 NV GI 04 eae 38 NV GII 10 VT1VT2eae 92 Adenovirus 07 VT2eae 6 hipO 51 cadF 15 cadFhipO 39 ____________________________________________________________ Odds ratios for the presence of the microbial pathogens when ruminant-specific pollution exceeds human-specific pollution

Thus emboldened we marched bravely on

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses and pathogens o data analysis o horses

Ima

ge

co

urt

esy o

f B

ria

n S

ne

lso

n H

ockle

y E

sse

x

En

gla

nd

lice

nse

d u

nd

er

the

Cre

ative

Com

mo

ns A

ttri

bu

tio

n

20

Ge

ne

ric lic

en

se

MST for horses

bull Mitochondrial

bull Bacterial

MST ndash a work in progress

o seabird marker ndash Catmar o E coli isolates ndash a step closer o viruses and pathogens o data analysis ndash slowly gaining confidence o horses ndash mitochondrial and bacterial

icrew

____________________________________________________________ Bacterial pathogen Odds ratio Viral pathogen Odds ratio ____________________________________________________________ VT1 108 Enterovirus na VT2 6 NV GI 04 eae 38 NV GII 10 VT1VT2eae 92 Adenovirus 07 VT2eae 6 hipO 51 cadF 15 cadFhipO 39 ____________________________________________________________ Odds ratios for the presence of the microbial pathogens when ruminant-specific pollution exceeds human-specific pollution

Thus emboldened we marched bravely on

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses and pathogens o data analysis o horses

Ima

ge

co

urt

esy o

f B

ria

n S

ne

lso

n H

ockle

y E

sse

x

En

gla

nd

lice

nse

d u

nd

er

the

Cre

ative

Com

mo

ns A

ttri

bu

tio

n

20

Ge

ne

ric lic

en

se

MST for horses

bull Mitochondrial

bull Bacterial

MST ndash a work in progress

o seabird marker ndash Catmar o E coli isolates ndash a step closer o viruses and pathogens o data analysis ndash slowly gaining confidence o horses ndash mitochondrial and bacterial

icrew

MST ndash a work in progress

o bird markers unsatisfactory o bacterial isolates (E coli) o viruses and pathogens o data analysis o horses

Ima

ge

co

urt

esy o

f B

ria

n S

ne

lso

n H

ockle

y E

sse

x

En

gla

nd

lice

nse

d u

nd

er

the

Cre

ative

Com

mo

ns A

ttri

bu

tio

n

20

Ge

ne

ric lic

en

se

MST for horses

bull Mitochondrial

bull Bacterial

MST ndash a work in progress

o seabird marker ndash Catmar o E coli isolates ndash a step closer o viruses and pathogens o data analysis ndash slowly gaining confidence o horses ndash mitochondrial and bacterial

icrew

Ima

ge

co

urt

esy o

f B

ria

n S

ne

lso

n H

ockle

y E

sse

x

En

gla

nd

lice

nse

d u

nd

er

the

Cre

ative

Com

mo

ns A

ttri

bu

tio

n

20

Ge

ne

ric lic

en

se

MST for horses

bull Mitochondrial

bull Bacterial

MST ndash a work in progress

o seabird marker ndash Catmar o E coli isolates ndash a step closer o viruses and pathogens o data analysis ndash slowly gaining confidence o horses ndash mitochondrial and bacterial

icrew

MST ndash a work in progress

o seabird marker ndash Catmar o E coli isolates ndash a step closer o viruses and pathogens o data analysis ndash slowly gaining confidence o horses ndash mitochondrial and bacterial

icrew

icrew

Thank you