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One Health 13 (2021) 100323
Available online 3 September 2021
2352-7714/© 2021 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Emerging infectious zoonotic diseases: The neglected role of food animals
Joachim Otte , Ugo Pica-Ciamarra
*
Food and Agriculture Organization of the United Nations, Italy
ARTICLE INFO
Keywords:
Emerging zoonoses
Food animals
Wildlife
Drivers
ABSTRACT
This paper compares the relative frequency of zoonotic disease emergence associated with food animals versus
emergence from other animal sources and explores differences in disease characteristics and drivers of emer-
gence between the two sources. It draws on a published compilation of 202 Emerging Infectious Zoonotic Disease
(EIZD) events for the period 1940–2004. Of the 202 zoonotic EID events in the dataset, 74 (36.6%) were
associated with animals kept for food production, which acted as reservoir for the zoonotic pathogen in 64 events
and as intermediate / amplifying host in 8 events. Signicant differences exist both in the characteristics of the
causal agents and the drivers of emergence of zoonotic diseases from food animals and non-food animals.
However, the prevailing policy debate on prevention, detection and control of EIZDs largely focuses on diseases
of non-food animal origin (wildlife), neglecting the role of food animals. Policies and investments that ensure
appropriate veterinary public health measures along and within food animal value chains are essential to
mitigate the global risk of EIZDs, particularly in developing regions where the livestock sector is experiencing
rapid growth and structural transformation.
1. Introduction
Globally, the number of infectious disease outbreaks affecting
humans has increased signicantly since 1980 [26] and new virus spe-
cies affecting humans are being discovered at an average rate of over 3
per year [37]. At least 60% of human emerging infectious diseases
(EIDs) are zoonotic, i.e. stem from non-human hosts, and zoonotic
pathogens are twice as likely to be associated with emerging diseases
than non-zoonotic pathogens [15,27]. Zoonotic pathogens emerge
either from wildlife or from domesticated animals. In a seminal paper on
“Global trends in emerging infectious diseases” covering the period from
1940 to 2004, Jones et al. [15] estimated that 72% of zoonotic EIDs
(EIZDs) originated in wildlife. Woolhouse & Gowtage-Sequeria [36]
identied changes in land use and agricultural practices and changes in
human demographics and society as the two categories of drivers most
frequently associated with the (re)emergence of human infectious dis-
eases. The ranking of drivers across different categories of pathogen
showed poor concordance, with one of the most notable differences
being the greater importance of land use change and agricultural prac-
tices for zoonotic than for non-zoonotic diseases. Indeed, the trans-
formation of the natural landscape promotes encroachment into wildlife
habitats, thereby creating opportunities for closer and more frequent
interactions between humans, livestock, wildlife and vectors, while the
intensication of livestock farming, associated with increased animal
numbers and density, facilitates disease transmission when effective
management and biosecurity measures are not in place [14].
In response to major outbreaks of EIZDs linked to wildlife, such as
SARS and Ebola, a substantial amount of resources is being devoted to
the identication of wildlife reservoirs and associated emergence hot-
spots. The US Agency for International Development, for instance, has
spent around USD170 million over 8 years to conduct viral discovery in
wildlife hosts [2]. This trend is likely to be reinforced by the recent
emergence of COVID-19 [41]. The current narrative on preventing the
next pandemic ([3,6,16,29,34]; US [30]) stresses the role of wildlife in
the emergence of human infectious diseases, while it appears to
underappreciate the role food animals may play, despite the recognition
that a considerable share of human diseases of evolutionary and his-
torical signicance originated in livestock [35]. The pathogen pool of
food animals is itself not static but also constantly undergoing evolu-
tionary changes. In swine, for example, a systematic review of publi-
cations between 1985 and 2010, found 173 new pathogen variants from
91 species, of which 73 species had not been previously described in
pigs. One third of these new species was zoonotic and discovery of
zoonotic species was more likely to occur in low- and middle-income
than in high-income countries [12].
EIZDs are best prevented by policies and investments targeting the
* Corresponding author at: FAO, Viale delle Terme di Caracalla, 00153 Rome, Italy.
E-mail address: ugo.picaciamarra@fao.org (U. Pica-Ciamarra).
Contents lists available at ScienceDirect
One Health
journal homepage: www.elsevier.com/locate/onehlt
https://doi.org/10.1016/j.onehlt.2021.100323
Received 1 July 2021; Received in revised form 30 August 2021; Accepted 30 August 2021
One Health 13 (2021) 100323
2
main source(s) and driver(s) of emergence. To provide decision-makers
with information for policy and investment design that minimize the risk
of EIZDs, this note compares the relative frequency of zoonotic disease
emergence associated with food animals versus emergence from other
animal sources and explores pathogen characteristics and drivers of
emergence between the two sources of EIZDs.
2. Methods
The compilation of 335 EID events over the period 1940 to 2004 by
Jones et al. [15] provides the basis for the analysis. This dataset was
chosen, despite reports of more EIZDs since 2004, because it provides
the most comprehensive supplementary information for each EI(Z)D
event and because other authors have used it for subsequent analyses (e.
g. [1,22,24]).
Zoonotic EID events in the dataset are classied as potentially
associated with food animals (large and small ruminants, pigs, poultry,
camels) if the pathogen has been found in the latter and comprise those
where (i) food animals are known reservoirs (e.g. M. bovis, B. melitensis)
or (ii) where food animals acted as temporarily amplifying host (e.g.
RVF in Egypt, Nipah in Malaysia). Horses, dogs and wild exotic species
consumed as food, such as pangolins or nonhuman primates, are not
considered as food animals for the purpose of this analysis. The dataset
also includes information on pathogen characteristics (e.g. taxonomy,
mode of transmission) and surmised driver of emergence (e.g. land use
change, change in human susceptibility).
3. Results
Of the total of 335 EID events identied by Jones et al., 202 (60.3%)
were regarded as zoonotic by the authors. Of these 202 zoonotic EID
events, 128 (63.4%) were not associated with food animals (they
involved wildlife, pets/recreational animals, environmental sources),
while in 74 (36.6%) events the pathogen could be associated with ani-
mals kept for food production. In 85.5% (64/74) of food animal asso-
ciated zoonotic EID events, food animals were a known reservoir for the
associated pathogen, while in 10.8% (8 events) (Alkhurma, Banna,
CCHF, HPAI H5N1, JE, Menangle, Nipah, and RVF virus) food animals
acted as ampliers and ‘bridge’ hosts (the role of food animals was not
clear in two events, 2.7%). Pathogen type, mode of transmission, drug
resistance and surmised driver of emergence for the non-food animal
and food animal associated EIZDs are displayed in Table 1.
Of the non-food animal associated EIZDs, 40.6% were caused by
bacteria/rickettsia, 37.5% by viruses, and 21.9% by protozoa, fungi or
helminths. For the food animal associated EIZD pathogens, the respec-
tive gures were 70.3% bacteria/rickettsia, 13.5% viruses and 16.2%
protozoa, fungi or helminths. The differences in frequency of pathogen
type between non-food animal and food animal associated EIZDs is
statistically highly signicant (Chi square: 18.2, p <0.001).
A large share (39.8%) of the non-food animal associated EIZD
pathogens were transmitted by arthropods while only 13.5% of the food
animal associated pathogens were vector-borne (Chi square: 15.4, p <
0.001). Transmission by arthropods was far more prominent in events
where food animals acted as ‘bridge’ (62.5%, 5/8) than in events where
food animals acted as reservoir host (7.8%, 5/64).
Drug resistance was signicantly more prevalent in EIZD pathogens
associated with food animals than in those from non-food animals
(14.9% vs 5.5%, Chi square: 5.1, p =0.023). For comparison, 39.1%
(52/133) of non-zoonotic EID pathogens in Jones et al.’s dataset were
drug resistant.
For non-food animal associated EIZDs, changes in human suscepti-
bility (e.g. HIV-AIDS, immunosuppressive therapy) were the most
frequently identied driver for emergence (24.2%) followed by land use
change (LUC) (23.4%). LUC was the most frequent surmised driver for
vector-borne EIZDs (39.2%%) while changes in human susceptibility
was the surmised principal driver (33.7%) for non-vector-borne EIZDs
associated with non-food animal sources. For food animal associated
EIZDs, the main drivers associated with emergence by Jones et al. [15]
were food industry changes, 35.1% (26/74), and agricultural industry
changes, 24.3% (18/74), while LUC was only linked to 5.4% (4/74) of
food animal associated emergence events (three of these four were
vector-borne).
4. Discussion and conclusion
Even though the dataset used is not up-to-date and new diseases have
emerged since 2004, the data provides a sufciently large sample from
which to draw conclusions that are not likely to substantially change by
including diseases that have emerged over the past 15 years. However,
replicating this analysis on a dataset that also includes most recent
emerging infectious zoonozes, such as H1N1 and MERS, would provide
additional insights into the role of food and non-food animals in the
emerge of EIZDs.
A high proportion, over 36%, of EIZD events (identied by [15])
were associated with food animals and food animals were a known
reservoir for the respective pathogen in 31.7% (64/202) of EIZD events.
This is not surprising as, historically, about half of humanity’s estab-
lished temperate diseases have been acquired from domestic livestock,
because of their high local abundance and frequent contact with humans
[35]. A recent analysis of virus-mammal interactions concludes that
domesticated species were the most central species (after humans) in the
entire mammal–virus association network [31]. The 5 most central po-
sitions in the network of all virus species were occupied by H. sapiens,
B. taurus, S. scrofa, O. aries, and C. lupus (in order of descending cen-
trality), i.e. included 3 mammal species kept for food production.
Overall, the proportion of zoonotic viruses carried by domestic species
was 1.8 times higher than in wildlife (idem).
Even though past trends do not necessarily predict the future with
accuracy, population growth, increasing disposable incomes and pro-
gressive urbanization are anticipated to lead to major changes in global
food animal industries in the coming decades, with a possible increase in
the number of zoonotic viruses emerging from livestock, particularly in
the developing world. Projected growth in demand for meat and milk to
2050 (from 2015) is approximately ve times higher in low/middle-
income countries (LMICs) than in high-income countries (HICs), with
sub-Saharan Africa (SSA) accounting for around one third of LMIC de-
mand growth [7]. Concomitant to uneven global growth in demand for
meat and milk, growth of livestock industries will be substantially
Table 1
Pathogen characteristics and surmised driver of non-food animal associated (n
=128) and food animal associated (n =74) EIZDs as reported by [15]
Pathogen characteristics and surmised driver Non-food animal
associated EIZD
Food animal
associated
EIZD
N % N %
Pathogen type
Bacteria/rickettsia 52 40.6 52 70.3
Virus 48 37.5 10 13.5
Other 28 21.9 12 16.2
Transmission
Vector 51 39.8 10 13.5
No vector 77 60.2 64 86.5
Drug resistance
Yes 7 5.5 11 14.9
No 121 94.6 63 85.1
Driver
Human susceptibility 31 24.2 7 9.5
Land use change 30 23.4 4 5.4
Ag industry change 13 10.2 18 24.3
Food industry change 1 0.8 26 35.1
International travel & commerce 13 10.2 7 9.5
Climate & weather 9 7.0 0 0.0
Other 31 24.2 12 16.2
J. Otte and U. Pica-Ciamarra
One Health 13 (2021) 100323
3
higher in the ‘global South’ with livestock numbers predicted to more
than double in sub-Saharan Africa (SSA) (Table 2).
The ongoing rapid expansion and intensication of livestock in-
dustries in LMICs without incorporation of the stringent biosecurity
measures and animal health / veterinary oversight that have helped
maintain the health and productivity of large herds in industrialized
countries signicantly enhances the likelihood of zoonotic disease
emergence from food animals. Even HICs with high levels of veterinary
oversight of animal industries have experienced important outbreaks of
food animal associated EIZDs such as the BSE/vCJD crisis in the UK in
the 1980s or the 2007 to 2010 Q fever epidemic in Holland, both linked
to industry changes [18,33].
While the current attention on EIZDs associated with wildlife is
warranted, policy makers cannot afford to ignore the role of food ani-
mals in EIZD dynamics, particularly as pathogen characteristics and the
relative importance of surmised drivers of emergence differ signicantly
between food and non-food animal associated EIZDs. The main drivers
of food animal associated EIZDs are changes in agricultural practices at
farm level and transformations of the food industries along the livestock
value chain, from transporting through processing to retailing [15].
These two drivers play a minor role in the emergence of EIZDs from non-
food animals, which are primarily associated with land use changes and
changes in human susceptibility. By promoting land use change, food
animal production may indirectly contribute to the emergence of non-
food animal associated EIZDs.
Policies and investments to address EIZDs have long relied on
responsive measures that aim to reduce the impact of a disease after its
emergence through improved capacity and speed of outbreak detection
and emergency control measures [22]. In the last decade, proactive
measures have gained prominence, including multisectoral collabora-
tion (‘one health’), pathogen discovery, behavioral change and
improved biosecurity along the food animal value chain [2,25,32]. Pike
et al. [22] nd that proactive policies and investments need to be only
minimally effective in reducing EID risk to be worth implementing.
Given the agricultural and food animal industries are (to a large
extent regulated) human activities, designing and implementing policies
to mitigate the risk of food animal associated EIZD should be ‘simpler’
and probably more cost-effective than mitigation of EIZD risks stemming
from wildlife. The World Bank [38] estimates that improving farm
biosecurity in 139 LMICs would require an annual expenditure of be-
tween USD76 and 136 million (7.7% of all animal health expenditures),
which is dwarfed by the historical costs of EIZDs of about USD6.9
billion/year. LMICs should thus prioritize the implementation of a
minimum set of veterinary public health (VPH) measures in food animal
production – such as animal vaccination, cleaning and disinfection and
farm and market inspection – to reduce global pandemic risk. This holds
particularly true for SSA, which is not only expected to undergo the most
extensive changes in its livestock industries but is also the region with
the lowest economic and institutional capacity to deal with EIZDs. SSA
has the lowest per capita income among all world regions (PPP $ 3500
per year); the lowest per-capita health expenditure (PPP $ 200 per year);
and the second lowest Country Policy and Institutional Assessment
(CPIA) quality of public administration rating, an index of the extent to
which governments are able to implement policies [40]. Growing trade
volumes, increased national and international travel and migration, and
high rates of urbanization, often associated with large informal urban
settlements, vastly increase the potential spread and consequences of
EIZDs.
A positive note is that existing policies and legislations on veterinary
public health in LMICs, including in SSA, often recommend the adoption
of ‘basic’ standards along the animal food value chains [8,9]. Their
implementation, however, remains scattered and piecemeal [17,19]. In
most circumstances, lack of nancial and human resources makes it
challenging to ensure compliance with the existing veterinary public
health legislation. For example, in two of the wealthier counties of
Kenya, Kiambu and Nairobi City County, each public animal health of-
cer is supposed to provide services to 1635 and 570 livestock farms,
respectively, with an average annual budget of USD 2.1 and 3.1 per
livestock farm [10]. Given such resource scarcity, LMICs governments
should adopt a market-based approach to facilitate compliance with
veterinary public health legislation and minimize the risk of EIZD events
along the food animal value chain. Such an approach should primarily
target mid- to large scale operators and include a research and an
institutional pillar.
While in many cases there are positive private returns to investments
in veterinary public health measures, small scale food animal operators
usually have few incentives to make such investment because livestock
is only one of their many income generating activities and rarely
contribute the largest share to their livelihoods [20,21]. Conversely, mid
to large scale livestock operators have established a business around
animals and are often willing to take any investment that improves the
protability of their enterprise [17,39]. In addition, mid and large-scale
food animal enterprises are those that are growing and transforming
more rapidly in LMICs, which could create novel and emerging public
health threats [4,5,13,14].
In order to effectively target mid to large-scale animal food opera-
tors, it is necessary to generate in-country evidence that the adoption of
basic veterinary public health practices is likely to improve the prot-
ability and long-term sustainability of businesses along the animal food
value chain [11,23,28]. Undeniably, in many circumstances the adop-
tion of simple practices – such as using disinfectants and separating sick
from healthy animals – is low-cost and, by signicantly reducing the risk
of pathogen introduction and spread, improves protability. This evi-
dence would allow animal health staff on the ground to utilize a business
approach when providing services to mid and large-size livestock op-
erators. In particular, animal health ofcers should not only assist
farmers and other value chain actors in preventing, detecting and con-
trolling animal diseases from a technical perspective, but also in
improving the protability and sustainability of their business, which
involves the adoption of a core set of veterinary public health measures.
In other words, investments in veterinary public health measures should
not be presented as risk-reduction practices but as business practices
that can reduce the cost / improve the revenue of the enterprise.
Overall, unless existing policies and legislations on veterinary public
health along the animal food industry are properly enforced, the current
global, regional and national investments to minimize the risk of EIZDs
from non-food animal, may generate little returns as over one third of
EIZDs events are associated with animals kept for food production.
Authors’ statement
MJO: conceptualization and preparing the rst draft manuscript.
MJO and UPC: writing reviews and editing. UPC: addressing the com-
ments of reviewers and editors. Both authors approved the submitted
version.
Table 2
Projected 2015–2050 growth in demand for meat and milk (million metric tons,
MMT) and in livestock numbers (million head, MH) for high-income countries
(HICs), low/middle income countries (LMICs) and sub-Saharan Africa (SSA) [7].
Growth 2015–2050 HICs LMICs SSA
Demand MMT % MMT % MMT %
Meat 21.5 21.1 108.2 50.3 41.1 233.5
Milk 30.5 12.6 174.8 39.3 56.7 141.4
Livestock populations MH % MH % MH %
Cattle −4.7 −1.9 586.2 56.2 364.7 113.3
Pigs 31.2 12.1 158.1 20.7 69.1 181.5
Sheep & goats 7.7 9.1 681.4 55.7 517.1 125.0
Poultry 1029.1 19.9 9109.0 47.9 5057.2 301.5
J. Otte and U. Pica-Ciamarra
One Health 13 (2021) 100323
4
Funding statement
This work was implemented under the FAO Africa Sustainable
Livestock 2050 Project (OSRO/GLO/602/USA), supported by the United
States Agency for International Development (USAID) under the FAO
Emerging Pandemic Threats 2 programme.
Declaration of interests
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
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J. Otte and U. Pica-Ciamarra
