Quantification of the impact of climate change on animal health: effects on

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Animal health

Outline Vector-borne infections Climate change

Direct effects Indirect effects

Quantification Climate changes Health

Perspectives

Recolad workshop, Paris, 11-12 Feb. 2015

International Action RECOLAD - Collaboration network on livestock adaptation to climate change

Quantification of the impact of climate change on animal health: effects on

pathogens, vectors, and hosts

R. Lancelot

renaud.lancelot@cirad.fr

UMR CIRAD-INRA “Contrôle des maladies animales exotiques et émergentes”

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Animal health

Perspectives

Outline

1 Vectors and vector-borne infections as models of the effect of climate change on health

2 How climate change can affect animal (and human) health?

3 How can we quantify changes?

Climate changes Health

4 Requirements and perspectives for assessing impacts of climate change on health

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Outline

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Animal health

Perspectives

Why bothering?

Jones et al. (2008)

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Animal health

Perspectives

Vectors & vector-borne infections

Arthropods: insects, ticks

Hematophagous Get infected through blood meal on an infected host

Extrinsic cycle: pathogen multiplication within the vector

Pathogen transmission during the next blood meal

Tiger mosquitoAedes albopictus

Dengue, chikungunya. . .

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Animal health

Perspectives

Vectors & vector-borne infections

Arthropods: insects, ticks Hematophagous

Get infected through blood meal on an infected host

TickHyalomma marginatum

CCHF. . .

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Animal health

Perspectives

Vectors & vector-borne infections

Sandfly

Leishmaniasis, Phlebovirus. . .

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Animal health

Perspectives

Vectors & vector-borne infections

Culicoides imicola

Bluetongue, Schmallenberg. . .

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Animal health

Perspectives

Vectors & vector-borne infections

TickIxodes ricinus

Lyme borreliosis, tick-borne encephalitis. . .

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Outline

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Outline

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Animal health

Perspectives

R

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: basic reproduction number

R0: number of secondary cases after the introduction of a single infectious individual in a homogeneous, fully susceptible population of hosts

Epidemic risk: ifR0>1, epidemics may occur

R0for malaria (Ross, 1915):Plasmodiumparasite transmitted to humans byAnophelesmosquitoes

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R0=C×b×1 r C=m×a×a×pⁿ

−log(p) where

C: vectorial capacity of the vector population m: vectors / hosts ratio

a: number of hosts bitten mosquito⁻¹day⁻¹(aggressivity) p: daily survival rate for mosquitoes

n: length of extrinsic cycle (days)

b: vector competence, i.e. proportion of infecting bites r: host recovery rate (day⁻¹)

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Animal health

Perspectives

Possible effects of climate changes on R

₀

Minor effects

Vector competenceb:

genetic or symbiotic factors, possibly influenced by temperature

Recovery rater: immunity, physiological status Large expected effects on vectorial capacityC

Vectors / hosts ratio: abundance of vectors (and hosts)

Length of extrinsic cycle: temperature

Aggressivitya, survivalp: temperature and relative humidity (RH)

Hypothetic interactions between genetic factors and temperature (Tabachnick, 2003)

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Animal health

Perspectives

Possible effects of climate changes on R

₀

Minor effects

Vector competenceb:

Recovery rater: immunity, physiological status

Large expected effects on vectorial capacityC

Vectors / hosts ratio: abundance of vectors (and hosts)

Hypothetic interactions between genetic factors and temperature (Tabachnick, 2003)

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Animal health

Perspectives

Possible effects of climate changes on R

₀

Minor effects

Vector competenceb:

Vectors / hosts ratio:

abundance of vectors (and hosts)

Modelled distribution ofCulicoides imicolain Calabria, Italy (Van Doninck et al., 2014)

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Animal health

Perspectives

Possible effects of climate changes on R

₀

Minor effects

Vector competenceb:

Length of extrinsic cycle:

temperature

Modelled distribution ofCulicoides imicolain Calabria, Italy (Van Doninck et al., 2014)

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Animal health

Perspectives

Possible effects of climate changes on R

₀

Minor effects

Vector competenceb:

Length of extrinsic cycle:

temperature

Aggressivitya, survivalp:

temperature and relative humidity (RH)

Abortion rate in a tsetse population, Senegal, according to environmental features (Bouyeret

al., unpublished)

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Outline

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Animal health

Direct effects Indirect effects Quantification

Perspectives

Indirect effect of climate changes on health

Changes in livestock production systems: less cattle, more small ruminants and camels (Seo and

Mendelsohn, 2008)

Increasing demand on red meat in large cities⇒ intensification of livestock trade

Increased risk of transboundary diseases

Observed changes in camel populations (Faye et al., 2012)

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Animal health

Perspectives

Indirect effect of climate changes on health

Mendelsohn, 2008) Increasing demand on red meat in large cities⇒ intensification of livestock trade

Sheep and goats trade from Somaliland to Saudi Arabia

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Animal health

Perspectives

Indirect effect of climate changes on health

Mendelsohn, 2008) Increasing demand on red meat in large cities⇒ intensification of livestock trade

Known emergence of PPR 1942-2009

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Outline

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Outline

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Animal health

Perspectives

Environmental data

Remotely-sensed data

We need data with:

a wide (global) coverage, a moderate spatial resolution and a high temporal resolution (day at the finer level) to model seasonal & annual changes

long time series to detect & model pluri-annual changes

Popular datasets are (Kalluri et al., 2007):

AVHRRsensor (NOAA satellites, NASA), 1-km resolution, as from early 1980’s. Mostly for NDVI (Pinzon and Tucker, 2014) MODISsensor (Aqua / Terra satellites, NASA), 250-m to 1-km resolution and broader spectral range, as from late 1990’s Spot 4 Vegetation(Spot satellites, ESA): 1-km resolution, from late 1990’s to 2013

They are publicly available, either raw or with variable amount of pre-processing e.g., temporal Fourier analysis (Scharlemann et al., 2008).

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Animal health

Perspectives

Environmental data

Remotely-sensed data

We need data with:

a wide (global) coverage, a moderate spatial resolution and a high temporal resolution (day at the finer level) to model seasonal & annual changes

long time series to detect & model pluri-annual changes Popular datasets are (Kalluri et al., 2007):

AVHRRsensor (NOAA satellites, NASA), 1-km resolution, as from early 1980’s. Mostly for NDVI (Pinzon and Tucker, 2014) MODISsensor (Aqua / Terra satellites, NASA), 250-m to 1-km resolution and broader spectral range, as from late 1990’s Spot 4 Vegetation(Spot satellites, ESA): 1-km resolution, from late 1990’s to 2013

They are publicly available, either raw or with variable amount of pre-processing e.g., temporal Fourier analysis (Scharlemann et al., 2008).

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Animal health

Perspectives

Environmental data

Ground & composite data

Several datasets are available, providing “present” data, e.g. CRU or WorldClim (Hijmans et al., 2005)

set of raster layers of interpolated climatic variables at 30” (ca. 1 km) resolution

monthly values of Tmin, Tmax and Tmean, and total precipitation for 1950-2000, data accuracy restricted by the density of the

meteorological stations

Several historical and near-real time precipitation datasets, including TAMSAT

Distribution of meteorological stations according to meteorological items (top to bottom: precipitation, Tmean, and Tmin/Tmax)

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Animal health

Perspectives

Environmental data

Ground & composite data

Several datasets are available, providing “present” data, e.g. CRU or WorldClim (Hijmans et al., 2005)

set of raster layers of interpolated climatic variables at 30” (ca. 1 km) resolution

monthly values of Tmin, Tmax and Tmean, and total precipitation for 1950-2000, data accuracy restricted by the density of the

meteorological stations Several historical and near-real time precipitation datasets, including TAMSAT

Tamsat data for the Rift Valley fever in northern Mauritania, 2010 (El Mamy et al., 2014)

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Animal health

Perspectives

Datasets for possible future climates

IPCC (data distribution center): coarse data provided by the GCM: 300 km

resolution

European ENSEMBLES datasets: finer data provided by RCMs: 25 km resolution

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Animal health

Perspectives

A nice resource: http://www.edenextdata.com

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Outline

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Animal health

Quantification Climate changes Health Perspectives

Vectors

Africa: a few good datasets:

tsetse flies, mosquitoes (malaria, RVF), some tick species

Caribbean region: tick Amblyomma variegatum Europe: mosquitoes (Aedes albpictus), ticks (Ixodes ricinus), biting midges (Culicoides imicola), sandflies: coordinated field studies spanning over 15 years: EDEN / EDENext / Vbornet / Vectornet

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Animal health

Vectors

Caribbean region: tick Amblyomma variegatum

Europe: mosquitoes (Aedes albpictus), ticks (Ixodes ricinus), biting midges (Culicoides imicola), sandflies: coordinated field studies spanning over 15 years: EDEN / EDENext / Vbornet / Vectornet

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Animal health

Vectors

Caribbean region: tick Amblyomma variegatum Europe: mosquitoes (Aedes albpictus), ticks (Ixodes ricinus), biting midges (Culicoides imicola), sandflies:

coordinated field studies spanning over 15 years:

EDEN / EDENext / Vbornet / Vectornet

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Animal health

Back to Culicoides and bluetongue

R0for BTV transmission byCulicoidesbiting midges Obsoletus group) with 2 different host species - cattle and sheep - playing different epidemiological roles (Guis et al., 2012):

R0∝bβa² µ

ν µ+ν

mφ²

r_c+d_c+m(1−φ)² r_s+d_s

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Animal health

Past R

₀

for bluetongue in Europe

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Animal health

Future R

₀

for bluetongue in Europe

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Outline

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Animal health

Perspectives

Requirement and perspective

We need:

Long time series of high-quality health datacollected with the same protocols in diverse areas with respect to environmental drivers⇒close collaborations with veterinary services in the frame of regional health networks

Quantitative, predictivemodels as tools for animal health decision making⇒accurate definitions of relevant scenarios to be assessed (e.g., vaccination strategy)

Involve stakeholders(farmers, vets, labs. . . ) for a better appropriation of results: important for sustainability

Adopt amulti-disciplinary approach with social sciences and economics, as from the start of the projects, for at least

cost-benefits assessment, and perception studies with respect to diseases and control strategies.

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Animal health

Perspectives

Requirement and perspective

We need:

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Animal health

Perspectives

Requirement and perspective

We need:

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Animal health

Perspectives

Requirement and perspective

We need:

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Welcome to GERI-2015

Heraklion, 21-23 April 2015

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Animal health

References

References. . . I

El Mamy, A. B., Lo, M. M., Thiongane, Y., Diop, M., Isselmou, K., Doumbia, B., Baba, M. O., El Arbi, A. S., Lancelot, R., Kane, Y., Albina, E., and Cêtre-Sossah, C. (2014). Comprehensive phylogenetic reconstructions of Rift Valley fever virus: the 2010 northern Mauritania outbreak in theCamelus dromedariusspecies.Vector Borne Zoonotic Dis., 14(12):856–861.

Faye, B., Chaibou, M., , and Vias, G. (2012). Integrated impact of climate change and socioeconomic development on the evolution of camel farming systems.British Journal of Environment and Climate Change, 2:227–244.

Guis, H., Caminade, C., Calvete, C., Morse, A. P., Tran, A., and Baylis, M. (2012). Modelling the effects of past and future climate on the risk of bluetongue emergence in Europe.J. R. Soc.

Interface, 9(67):339–350.

Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., and Jarvis, A. (2005). Very high resolution interpolated climate surfaces for global land areas.International Journal of Climatology, 25(15):1965–1978.

Jones, K. E., Patel, N. G., Levy, M. A., Storeygard, A., Balk, D., Gittleman, J. L., and Daszak, P.

(2008). Global trends in emerging infectious diseases.Nature, 451(7181):990–993.

Kalluri, S., Gilruth, P., Rogers, D., Szczur, M., and Finlay, B. (2007). Surveillance of arthropod vector-borne infectious diseases using remote sensing techniques: a review.PLoS Pathog., 3(10):e116.

Pinzon, J. E. and Tucker, C. J. (2014). A non-stationary 1981–2012 AVHRR NDVI3g time series.

Remote Sensing, 6(8):6929–6960.

Ross, R. (1915). Some a priori pathometric equations.Br. Med. J., 1(2830):546–547.

Scharlemann, J. P. W., Benz, D., Hay, S. I., Purse, B. V., Tatem, A. J., Wint, G. R. W., and Rogers, D. J. (2008). Global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS Data.PLoS ONE, 3(1):e1408.

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Animal health

References

References. . . II

Seo, S. N. and Mendelsohn, R. (2008). Measuring impacts and adaptations to climate change: a structural ricardian model of african livestock management1.Agricultural Economics, 38(2):151–165.

Tabachnick, W. J. (2003). Reflections on theAnopheles gambiaegenome sequence, transgenic mosquitoes and the prospect for controlling malaria and other vector borne diseases.J. Med.

Entomol., 40(5):597–606.

Van Doninck, J., De Baets, B., Peters, J., Hendrickx, G., Ducheyne, E., and Verhoest, N. E. (2014).

Modelling the spatial distribution ofCulicoides imicola: climatic versus remote sensing data.

Remote Sensing, 6(7):6604–6619.