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1 Leading the way in Agriculture and Rural Research, Education and Consulting Modelling macro- parasite risk in a changing climate [email protected] Disease Systems Team, Animal and Veterinary Sciences Group, SRUC,Edinburgh, UK
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Page 1: Modelling macro-parasite risk in a changing climate_Naomi Fox

1 Leading the way in Agriculture and Rural Research, Education and Consulting

Modelling macro-

parasite risk in a changing

climate

[email protected] Disease Systems Team, Animal and

Veterinary Sciences Group, SRUC,Edinburgh, UK

Page 2: Modelling macro-parasite risk in a changing climate_Naomi Fox

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Background

General helminth lifecycle

Non-infectious stage Infectious stage

Parasitic worms (helminths) offer one of the most pervasive challenges to livestock

They cause weight loss, decreased production efficiency and even death of the host

The free-living stages of the lifecycle are heavily influenced by climatic conditions

Climate change is already affecting the prevalence and distribution of

macro-parasites

Adult parasite

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Need to understand the potential impacts of future climate change to prepare for changing risk, implement control measures and set up long term management strategies.

Aim:

To determine how changes in climate sensitive parameters affect parasite dynamics

Background

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Adults(Ak) Immunity(r)

Infective

larvae (L3) Non infective

Larvae(L1) qL1

ωL1

ρL3

Ar

r Ar)(

Non-spatial, population level model

3L

Mechanistic transmission model of gastrointestinal helminth in grazing livestock, including survival and development of the parasite’s free-living stages

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Results: General infection pattern

The model successfully reproduces known epidemiological patterns:

Adults parasites in the host

Infectious larvae ingested per day

Hosts resistance to infection

Increase in adult parasites & host

resistance as infectious larvae are ingested

Host resistance then impedes parasite establishment and

fecundity

Parasite burden decreases as adult

parasites die off and are not replaced

(Fox et al., 2013)

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Results: Impacts of climate

Higher temperatures increased development rate higher parasite burden

Climate change can lead to non-linear increases in parasite risk, as small

temperature changes around critical thresholds can lead to sudden increases

in parasite intensity.

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Transmission is a complex process

• Macro-parasite transmission is influenced by:

– Host behaviour

– Spatial effects

– Larvae survival

– Larvae development

– The hosts immune response

Climate change will impact on many stages of the transmission process.

Aim

To incorporate wider elements of transmission to test the robustness of results to farm-level processes

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Grass height

Faecal contamination

Infective Larvae

Distance from cow

Parasite burden

Immunity

Grazing decisions

The spatial, individual level model

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i(k)

Stomach contents (sk)

Faeces(fi)

J(k)

Adults(Ak) Immunity (rk)

Infectious

larvae (L3i) Larvae

(L1i)

Eggs(Ek)

qL1i

ωL1i

patc

h

cow

φfi

ρL3i

if

i ehh

)( 0

kr kk Ar )( εEk

Grass

height(hi)

The spatial, individual level model

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Results: Spatial heterogeneity and grazing behaviour

Day

Faecal contamination

L3 larvae

Sward height

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Influence of other factors

• Our model outputs are robust to the inclusion of more complex,

farm level processes.

• These farm-level factors (e.g. spatial heterogeneity in infection

risk and resource distributions, host grazing behaviour, host

immunity) influence outbreak intensity under different

temperature change scenarios.

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Conclusions

• Climate influences parasite risk e.g. by changing development rates of the free-living stages

• Small changes in climate around critical thresholds can lead to sudden increases in parasite intensity

• Climate change will affect broader elements of the system, and there will be farm level variation in the response to climate change

• This model can be used to predict the impacts of climate change on parasite risk, and asses the efficiency of different control strategies

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Thanks

Mike Hutchings (SRUC)

Ross Davidson (SRUC)

Glenn Marion (Biomathematics & Statistics Scotland)

Piran White (University of York)

This work was funded by the Scottish Government

Questions?

[email protected]