Insights into the epidemiology of foot-and-mouth disease in East Africa

provides opportunities for targeted control

T. Lembo, M. Casey, R. Reeve, H. Auty, K. Bachanek-Bankowska, V. Fowler, P. Hamblin, D. Haydon, R. Kazwala, T. Kibona, D. King, A. Ludi, A. Lugelo, T. Marsh, V. Mioulet, D. Mshanga, S. Parida,

D. Paton, K. Parekh, S. Cleaveland

The Boyd Orr Centre for Population and Ecosystem Health, University of Glasgow, UK

• No poverty

• No hunger

• Double agricultural productivity and incomes of small-scale food producers

• Correct and prevent trade restrictions and distortions in world agricultural markets

• Reduce inequality within and among countries

• Sustainable management and efficient use of natural resources

By 2030

Livestock health, production and poverty alleviation

• Progress towards sustainable development has been uneven and the most vulnerable countries remain in Africa

• International trade essential for inclusive economic growth and poverty reduction

• Majority of poor live in rural areas and > 85% of livestock keepers live in poverty

• ~ 150 million of the rural poor dependent on livestock for sustainability

• 200 million cattle supplying a rapidly-increasing demand for meat

• Livestock production and equitable market opportunities important role in poverty reduction

Endemic FMD restricts Africa’s economic growth

• Among top 10 diseases constraining poverty alleviation (Perry et al., 2002)

• Consistently ranked in top five livestock diseases most important to people in African studies (Jost et al. 2010; Ohaga et al. 2007; Bedelian et al., 2007; Cleaveland et al. 2001 ; Onono et al. 2013)

• Associated with calf deaths, reduced milk supply, poor reproductive performance and heat intolerance syndrome (Cleaveland et al. 2001; Catley et al. 2004; Barasa et al. 2008; Rufael et al. 2008)

• Major constraint to local and international trade of livestock and livestock products

Most African countries still struggling to get on the PCP-FMD

The impact of endemic FMD on household food security and economic growth in

Africa has not been fully quantified

AND THEREFORE

There are no incentives for its control in more traditional settings, where

interventions would have the greatest impacts on livelihoods

Poverty impacts in Africa

Relative importance of livestock- and wildlife-related factors in maintenance and transmission?

How much cattle infection is associated with spill-over from wildlife?

FMD epidemiology in Africa

Sian Brown

?

Methods• Household-level

questionnaire surveys on:1. Outbreak impacts on herd

production and performance

2. Morbidity and mortality due to outbreaks

• Serology• Risk factor analyses

– For seropositivity– For outbreaks (case-control

study design)

• Outbreak investigations and virus isolation

Study area

Preliminary Risk Factor Analysis for exposure to FMDV•Cattle > sheep + goats•Pastoralist + Agropastoralist > Smallholder•No effect of measures of wildlife contact•No effect of distance walked to grazing/pasture

• 58% seroprevalence in all livestock, 67% cattle, 83% buffalo

• Serial infections with different antigenic types

Outbreak impacts on production

Outbreak impacts on milk production, consumption and

sale

Outbreak impacts on grazing and traction

Significant risk factorsLRT Chi squared

Probability < Chi squared

Coefficient (95% CI)

Odds Ratio (95% CI)

Age (per extra year)

219.6 <10^-6 0.4 (0.3-0.4) 1.4 (1.4-1.5)

Species 144.9 <10^-16

Cattle compared to small ruminants 1.2 (1-1.4) 3.3 (2.7-4)

Livestock practice

17.1 0.0002

Agropastoral compared to smallholder 2.1 (1-3.2) 8.1 (2.8-23.6)

Pastoral compared to smallholder 2 (1.1-2.9) 7.1 (2.9-17.6)

LRT Chi squared

Probability < Chi squared

Coefficient (95% CI)

Odds Ratio (95% CI)

Cattle in herd (per extra bovine)

12.9 <10^-3 0.02 (0-0.03) 1.02 (1-1.03)

New animals acquired in risk period (yes versus no)

4.6 0.03 1.72 (0.01-3.431)

5.57 (1.01-30.91)

Non-significant variablesLRT Chi squared Probability < Chi

squareCoefficient (95% CI)

Odds Ratio (95% CI)

Log (total cattle) 2.76 0.1 0.3 (0-0.6) 1.3 (1-1.8)

Log (maximum minutes walked to reach grazing and water)

2.37 0.12 0.1 (0-0.3) 1.1 (1-1.3)

Buffalo sighting weekly or more often

1.32 0.3 -0.4 (-1-0.3) 0.7 (0.4-1.4)

Log (distance to buffalo area) 0.09 0.75 0 (-0.3-0.2) 1(0.7-1.3)

Acquired livestock in the past four months (Y or N)

0.6 0.44 0.2 (-0.3-0.8) 1.2 (0.7-2.1)

LRT Chi squared Probability < Chi square

Coefficient (95% CI) Odds Ratio (95% CI)

Buffalo sighting weekly or more often1.26 0.26 0.8 (-0.635-2.227) 2.22 (0.53-9.27)

Grazing or watering area different to usual

1.03 0.31 -0.62 (-1.833-0.582) 0.54 (0.16-1.79)

Measure of livestock contacts during grazing and watering

1.3 0.26 0.04 (-0.03-0.122) 1.05 (0.97-1.13)

Measure of livestock contacts during dipping

0.19 0.66 -0.08 (-0.431-0.278) 0.92 (0.65-1.32)

Visitors in past month0.03 0.87 0.11 (-1.204-1.418) 1.12 (0.3-4.13)

Inference of infection history in 2011 from cross-sectional

serology

Virus isolation Serengeti2012-2015

Northern Tanzania2011-2015

Virus isolation 2008-2015Tanzania & Kenya

Conclusions

• Frequent FMD outbreaks (up to three/year) reduce milk production and sales, and traction power

• FMD epidemiology in northern Tanzania is driven by livestock-related factors

• Serotype-specific cattle outbreaks sweep across the region in a sequential and therefore predictable fashion

• FMD in Africa is amenable to control through vaccination…

… which would be culturally and politically acceptable

http://www.sadc.int/fanr/naturalresources/transfrontier/index.php

SADC TRANSFRONTIER CONSERVATION AREAS

(TFCAs)“Nodes of rural development and environmental conservation”

Fencing not compatible with the TFCA vision

http://www.wcs-ahead.org/gltfca_grants/pdfs/ferguson_final_2010.pdf

http://www.wcs-ahead.org/documents/asthefencescomedown.pdf

University of Glasgow, UKThe Pirbright Institute, UKUniversity of Edinburgh, UKOnderstepoort Veterinary InstituteDirectorate Veterinary Services, TanzaniaTanzania Veterinary Laboratory Agency,

TanzaniaZonal Veterinary Investigation Centres

(Arusha & Mwanza), TanzaniaTanzania Wildlife Research InstituteTanzania National ParksNgorongoro Conservation Area AuthoritySokoine University of Agriculture, TanzaniaWashington State University, USA

Boyd Orr Centre forPopulation and Ecosystem Health

THANK YOU