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65
ONE HEALTH | SEÇÃO 3
INTRODUCTION
In recent decades, explosive human popula on growth
and environmental changes have resulted in increased num-
bers of people living in close contact with animals. The re-
sul ng intensifi ed contact, together with extensive land use
change, have altered the inherent ecological balance betwe-
en pathogens and their human and animal hosts.
Historical reviews of emerging infec ous disease (EID)
events have shown that over 60 percent of all new patho-
gens aff ec ng humans have originated in animals, and 75%
of them originated in wildlife (Jones et al. 2008). Of the
1,399 species of known human pathogens, 87 have been
discovered in the last three decades, they are dispropor-
onately viruses (75%), have a global distribu on, and are
mostly associated with wildlife reservoirs (Woolhouse and
Gaunt 2007). Remarkable examples of EIDs of wildlife origin
are Ebola hemorrhagic fever, Nipah viral encephali s, severe
acute respiratory syndrome (SARS), hantavirus pulmonary
syndrome, H5N1 highly pathogenic avian infl uenza, and
the pandemic 2009 H1N1 infl uenza virus (Karesh and Cook
2005, Flanagan et al. 2012). The speed with which these
diseases can emerge and spread presents serious public
health, economic, and development concerns. These facts
underscore the need for the development of comprehen-
sive disease detec on and response capaci es, par cularly
in those geographic areas where disease threats are likely to
emerge. The risks of emergence are greater in developing
countries, where people and animals live in close proximity
and livelihoods are highly dependent on natural resources.
A ‘One Health’ Approach to Predict
Emerging Zoonoses in the Amazon
Marcela Uhart1,2, Alberto A. Pérez2,
Melinda Rostal3, Erika Alandia Robles2,
Ana Patricia Mendoza2, Alessandra Nava3,
Catia Dejuste de Paula2, Flavia Miranda2,
Volga Iñiguez4, Carlos Zambrana3,
Edison Durigon5, Padu Franco2,
Damien Joly2, Tracey Goldstein1,
William Karesh3, and Jonna Mazet1.
1 One Health Ins tute, University of California, Davis. 1089 Veterinary
Medicine Drive. VM3B, Ground fl oor. Davis, CA 95616. muhart@ucdavis.edu
2 Wildlife Conserva on Society, 2300 Southern Boulevard, Bronx, New York,
NY 10460.
3 EcoHealth Alliance, 460 West 34th Street – 17th fl oor, New York, NY 10001
4 Ins tuto de Biología Molecular y Biotecnología – UMSA, La Paz, Bolivia
5 Ins tuto de Ciências Biomédicas, Universidade de São Paulo, Sao Paulo, Brasil.
Abstract
Explosive human popula on growth and environmental changes have resulted in increased numbers of people living in close contact with
animals. The risks of disease emergence are greater in developing countries, where people and animals live in close proximity, livelihoods
are highly dependent on natural resources, and capacity for detec ng pathogens in wildlife is limited. The USAID’s Emerging Pandemic
Threats PREDICT program is applying a One Health approach to detect zoono c pathogens in the highest risk areas of the globe for dise-
ase emergence “hotspots” before they emerge. Since 2010, PREDICT has worked jointly with local governments and key stakeholders to
conduct wildlife disease surveillance in priority animal taxa across four Amazon countries (Bolivia, Brazil, Colombia and Peru). Addi onal
targets have been training fi eld and laboratory personnel, conduc ng family level screening for priority viral pathogens, communica ng
risks, and collabora ng in outbreak response. In a rela vely short me, capacity was signifi cantly enhanced in these countries for de-
tec ng wildlife pathogens and conduc ng outbreak inves ga ons. Eff orts should be maximized to secure sustainability for the capacity
building process, with the aim to prevent human and animal emerging disease threats.
Key words: Emerging zoonoses, wildlife, disease risk, Amazon, One Health, predict, surveillance
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66
Saúde Silvestre e Humana: Experiências e Perspectivas
A ‘One Health’ Approach to Predict Emerging Zoonoses in the Amazon.
Developing countries are also commonly characterized by
limited capacity for detec ng disease emergence in wild-
life prior to spread to humans, and by limited or no public
health repor ng infrastructure.
In order to predict, respond to, and prevent the emergen-
ce of novel infec ous diseases in humans, pathogens must
be iden fi ed at their source. The most sensible strategy to
pursue such an aim is by applying a One Health approach,
which, as defi ned by the United Na ons Food and Agricultu-
re Organiza on (FAO 2010), is a ‘collabora ve, interna onal,
cross-sectorial, and mul disciplinary mechanism to address
threats and reduce risks of detrimental infec ous diseases
at the animal-human-ecosystem interface’. The One Health
approach combats health threats around the world by buil-
ding bridges between diff erent scien fi c disciplines regar-
ding the movement of diseases among humans, domes c
animals, and wildlife (Karesh and Cook 2005).
INTERNATIONAL CONSENSUS ON ONE
HEALTH
The concept of One Health (commonly known also as
‘One World, One Health’) was launched by the Wildlife Con-
serva on Society during a symposium held at the Rockefel-
ler University in September 2004 (One World, One Health
2004), in which health experts from around the world con-
vened to discuss the movements of diseases, and to set prio-
ri es (the Manha an Principles) and discuss strategies for
comba ng threats to the health of life on Earth. The main
goal of the One Health approach is to urge world leaders,
civil society, the global health community, and ins tu ons of
science to holis cally approach the preven on of epidemic/
epizoo c disease and the maintenance of ecosystem inte-
grity. Interna onal organiza ons such as the World Health
Organiza on (WHO), the World Organiza on for Animal He-
alth (OIE), and the Food and Agriculture Organiza on (FAO)
encourage governments and non-government ins tu ons
to jointly pursue the One Health approach at a regional and
global scale. Recommenda ons by OIE (2011) stress that ‘it
is necessary to develop science-based standards on disease
detec on, preven on, and control, and to harmonise the
policies related to disease risks at the interfaces between
wildlife, domes c animals, and humans’.
Five principles have been proposed to reverse the global
trend on EIDs and pandemic threats:
Assess impact of human and animal diseases.
Clarify drivers infl uencing disease emergence and
pandemic risks.
Confront and redress the emergence of wildlife patho-
gens as hazards and threats.
Enhance hygiene and biosecurity rou nes and prac -
ces in food value chains and agro-ecological landscape
levels.
Pursue partnerships and alliances between medical,
veterinary and environmental agencies with the con-
cept of ‘One Health’.
THE EMERGING PANDEMIC THREATS
PROGRAM: AN EXAMPLE OF ONE HEALTH
IN PRACTICE.
The Emerging Pandemic Threats (EPT) program is an
interna onal and mul disciplinary eff ort supported by
the United States Agency for Interna onal Development
(USAID), which seeks to aggressively pre-empt or combat di-
seases that could spark future pandemics. The EPT program
emphasizes early iden fi ca on of, and response to, dange-
rous pathogens in animals before they can become signifi -
cant threats to human health. Using a risk-based approach,
EPT focuses eff orts on geographic areas where these threats
are most likely to emerge. These eff orts are cri cal to the
sustainability of long-term pandemic preven on and pre-
paredness. They help develop be er predic ve models for
iden fi ca on of future viral and other biological threats.
The EPT program draws on exper se from across the ani-
mal and human health sectors to build regional, na onal,
and local capaci es for early disease detec on, laboratory-
-based disease diagnosis, rapid disease response and con-
tainment, and risk reduc on. These eff orts target a limited
number of geographic areas, known as “hotspots”, where
new disease threats have emerged in the past. Five key are-
as of emphasis comprise the EPT program: 1. wildlife patho-
gen detec on, 2. risk determina on, 3. ins tu onaliza on
of a ‘one health’ approach, 4. outbreak response capacity,
and 5. risk reduc on. The EPT program consists of six pro-
jects known as PREDICT, RESPOND, IDENTIFY, DELIVER, PRE-
VENT, and PREPARE.
PREDICT PROJECT: BUILDING GLOBAL
CAPACITY FOR WILDLIFE DISEASE
SURVEILLANCE
The PREDICT project aims to create a global early war-
ning system for disease emergence that detects, tracks, and
predicts new infec ous diseases in high-risk wildlife (e.g.
bats, rodents and non-human primates) that could pose a
major threat to human health (University of California, Davis
2013). Par cular focus is placed on establishing enhanced
wildlife monitoring capacity in those geographic “hotspots”
that pose greater risk for the emergence of new pathogens
(Jones et al. 2008), where host species are likely to have
signifi cant interac on with domes c animals and with high-
-density human popula ons. Priority regions are:
Africa (the Congo Basin)
Asia (Gange c Plain and Southeast Asia)
La n America (the Amazon region and Mexico)
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67
ONE HEALTH | SEÇÃO 3
Focused on 22 key countries, the PREDICT project imple-
ments a strategy called SMART Surveillance (Strategic, Me-
asurable, Adap ve, Responsive, and Targeted Surveillance)
which is designed for the early detec on of novel diseases
with pandemic poten al. SMART surveillance eff orts are
concentrated on strategic interfaces (e.g. sites and situa-
ons where people and animals have contact and there is
an increased poten al for transmission of zoono c diseases)
and target important sen nel species to improve the effi -
ciency of surveillance.
PREDICT follows the principles of the One Health approa-
ch, and jointly with local governments and key stakeholders,
trains personnel, collects wildlife samples, performs family
level screening for priority viral pathogens, communicates
risk, and collaborates in outbreak response. Specifi c priority
pathogens are monitored, par cularly RNA viruses associa-
ted with cross-species transmission from wildlife and lives-
tock to humans with signifi cant public health impact, such
as: alphaviruses, arenaviruses, bunyaviruses, coronaviruses,
fi loviruses, fl aviviruses, henipaviruses, orthomyxoviruses,
paramyxoviruses, reoviruses, retroviruses, rhabdoviruses,
and other emerging viral pathogens.
PREDICT is a One Health partnership centrally managed
by four ins tu ons (University of California, Davis, Metabio-
ta, Wildlife Conserva on Society and EcoHealth Alliance),
with contribu ons from pres gious partners from around the
world (e.g., the Smithsonian Ins tu on, Columbia University,
HealthMap, ProMED, OIE, FAO, WHO, and many others).
PREDICT SURVEILLANCE: TARGETING
HIGH-RISK PATHOGENS, HOSTS AND
INTERFACES
In order to detect known, as well as novel pathogens,
it’s necessary to develop a surveillance strategy at the in-
terfaces where zoono c diseases are most likely to emer-
ge, based on the best available knowledge. As previously
men oned, almost 60% of EIDs events in the 20th century
were zoonoses, and 75% of them originated in wildlife (Jo-
nes et al. 2008). Wildlife host species richness seems to be
a signifi cant predictor for the emergence of zoono c EIDs
with a wildlife origin. Therefore, the areas at greatest risk for
zoono c pathogen emergence “hotspots” seem to be the
equatorial tropics where biodiversity is higher and human
density is high. Regions with greater host richness may have
a higher total richness of pathogens, such that the species
pool of pathogens capable of jumping to humans may be
higher (Flanagan et al. 2012). Mathema cal models show
that pathogen richness and prevalence are strongly correla-
ted with richness of mammal and bird species (Dunn et al.
2010). However, changes in biodiversity have the poten al
to aff ect the risk of infec ous disease exposure in animals
and humans. Studies revealed that for certain EIDs (e.g.,
hantavirus pulmonary syndrome), biodiversity loss tends to
increase pathogen transmission and disease incidence, by
aff ec ng factors such as species abundance, behaviour, and
condi on of hosts or vectors (Keesing 2010).
The poten al of certain viral pathogens to “jump” spe-
cies and sustain human-human transmission is an issue of
major concern. Commonly, RNA viruses show higher muta-
on rates than DNA viruses (with the excep on of certain
strains in the la er group). To “jump” species, animal viruses
undergo gene c changes that render them newly able to
spread effi ciently among humans. Examples of cross-species
viral transmission and pandemic spread are SARS coronavi-
rus (originated from carnivores and bats), HIV-1 (origina ng
in chimpanzees), Infl uenza A subtype pdmH1N1 (originated
from domes c pigs), and Dengue fever (originated from
non-human primates via repeated exposure to mosquito
vectors) (Karesh and Cook 2005, Vasilakis et al. 2011, Flana-
gan et al. 2012).
Of the more than 4,600 recognized species of mammals,
50% are rodents and 20% are bats (Calisher et al. 2006,
Dunn et al. 2010). This rich species diversity, plus other eco-
logical and biological traits (e.g., great popula on densi es,
high reproduc ve rates, ability to develop persistent infec-
ons), suggests that surveillance eff orts focused on rodents
and bats can result in high viral yields (Calisher et al. 2006).
Furthermore, bats and rodents are evolu onarily ancient,
diverse and include many species with peridomes c habits.
More than 61 zoono c viruses have been isolated from bats,
and 68 from rodent species (Luis et al. 2013). For these rea-
sons bats and rodents remain serious concerns as reservoirs
for future zoono c disease emergence. Evolu onary rela-
onships among primates, rodents and bats are close, whi-
ch par ally explains the taxonomic suscep bility to cross-
-species viral transmission (Wildman et al. 2007, Campbell
and Lapointe 2010).
Analysis of mammal-virus associa on databases showed
that species in the orders Chiroptera and Roden a are less li-
kely than species in other orders to have visible disease, and
therefore healthy animal surveillance is a sensible strategy
to iden fy poten
ally zoono c pathogens, in combina on
with syndromic surveillance in other wildlife and domes c
animal hosts (Levinson et al. 2013).
As suggested by many authors (Jones et al. 2008, Dunn
et al. 2010), disease control eff orts would be best focused in
those regions where prevalence remains high, popula ons
are large, and resources for EID surveillance and inves ga-
on are poorly allocated. Early detec on of poten ally high-
risk pathogens in animals could enable mi ga on strategies
to prevent human disease (for example, by avoiding high-
risk areas, distribu ng prophylac c drugs, or mobilizing sur-
veillance and medical resources) (Flanagan et al. 2012).
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68
Saúde Silvestre e Humana: Experiências e Perspectivas
A ‘One Health’ Approach to Predict Emerging Zoonoses in the Amazon.
WHY CONCENTRATE EFFORTS IN THE
AMAZON?
The Amazon rainforest is an extremely interes ng envi-
ronment in which to study emerging pathogens of wildlife
origin. Widely recognized as the most biodiverse ecosystem
on Earth, it encompasses an unimaginable variety of fl ora
and fauna and associated microorganisms. With a few ex-
cep ons, wildlife disease events have been poorly docu-
mented in the Amazon, and no consistent long-term wildlife
health surveillance eff orts have been sustainably maintai-
ned in any country.
As a result of human ac vity in recent decades, the
Amazon rainforest has been drama cally modifi ed in cer-
tain areas. Vast extensions have been transformed by de-
foresta on for agriculture (crops, livestock and biofuels),
by extrac ve industries (logging, mining, produc on of oil,
gas and hydroelectric energy), for biomedical and industrial
uses of biodiversity, and for the construc on of roads. Other
anthropogenic ac vi es, such as wildlife trade and subsist-
ence hun ng, together with the nega ve eff ects caused by
certain natural phenomena (e.g., heavy fl oods associated to
El Niño-Southern Oscilla on), are all factors that increase
the pressure on natural habitats, and the risks of zoono c
disease emergence. Simula on models forecast that, by
2050, current trends in agricultural expansion will eliminate
a total of 40% of Amazon forests (Soares-Filho et al. 2006).
In Brazil, the massive deforesta on for the produc on of
livestock and monocultures (par cularly soybean and sug-
arcane) results in intense fragmenta on and modifi ca on
of natural landscapes, and has been suggested to play an
important role as a driver for disease emergence in recent
years (i.e., yellow fever, malaria, Mayaro and Oropouche fe-
vers) (Vasconcelos et al. 2001). Of note, the main vectors of
malaria are par cularly present and more abundant in al-
tered landscapes (Olson et al 2010). In Colombia, the annual
deforesta on rate is es mated at 0.18%, and is also linked to
livestock farming, expanding ci es and oil palm planta ons
(an increasing problem also in eastern Peru). Peru is current-
ly undergoing major land use changes as three transoceanic
highways are being built to traverse Brazilian and Peruvian
Amazon territories. A large extension of natural forest and
rural se lements are currently being disturbed and modi-
fi ed, and it is expected that in the near future normal dis-
ease pa erns will also be altered by ecological disturbance
and changes in vectors and reservoir popula ons. In Bolivia,
human migra
on from urban to forest areas is increasing,
o en following infrastructure development projects such as
the construc on of highways and dams, expansion of gas, oil
and mber extrac on and mining, thereby bringing people
into close contact with forest wildlife and pathogens.
A variety of viruses and parasites are normally main-
tained in enzoo c cycles in the rainforest, and can infect
humans and domes c animals a er encroachment into the
wild. The Oropouche virus is the best documented example
in this category (Vasconcelos et al. 2001). Numerous zoonot-
ic disease events have been linked to mining ac vi es in
Peru and Brazil in the past thirty years, with vampire-borne
rabies and cutaneous leishmaniasis at the top of the list
(Chagas et al. 2006, da Rosa et al. 2006, López 2007, Gomez-
Benavides et al. 2007, Salmón-Mulanovich et al. 2009, Sch-
neider et al. 2009). Inves ga ons conducted in the Amazon
in the 1970s and 80s showed increased incidence of known
vector-borne viruses and emergence of new viruses of pub-
lic health importance (e.g., fl avivirus, bunyavirus, alphavi-
rus, reovirus) during the construc on of a hydroelectric dam
in Tucuruí (State of Para, Brazil), and highways across vast
areas of virgin tropical forest (Vasconcelos et al 2001). At
least 187 diff erent arboviruses have been isolated in Brazil
over the second half of the twen eth century. Thirty-two of
these are known to cause disease in humans (fever, exan-
thema c fever, hemorrhagic disease, and encephali s), and
some of them (especially dengue, yellow fever, mayaro, and
oropouche fever viruses) are highly relevant to public health
as they may be involved in epidemics causing severe illness
or even death (Vasconcelos et al 2001).
BUSHMEAT HUNTING AND WILDLIFE
TRADE: TWO HIGH-RISK INTERFACES IN
THE AMAZON
Contact with wild animals, including hun ng, butchering
and keeping wildlife pets, can lead to the transmission of
poten ally severe diseases for the health of both individuals
and communi es. Tradi onally, bushmeat played an impor-
tant dietary role among indigenous people and poor hou-
seholds, but there is a recent growing demand by people
moving into the forest for logging concessions and other ex-
trac ve projects, as well as by forest people moving into ur-
ban areas. The handling and consump on of infected meat
is considered a signifi cant route of pathogen transmission.
Cross-species transmission of microbes during hun ng and
butchering has been linked to human outbreaks of monkey
pox and Ebola hemorrhagic fever, and to infec ons with re-
troviruses, such as simian foamy virus, primate T-cell lym-
photropic viruses, and simian immunodefi ciency virus (Wol-
fe et al., 2005, Smith et al. 2012). Individuals who undertake
butchering are in contact with the animal’s blood and body
fl uids during skinning, opening of the body cavity, removal
of organs and cu ng of meat. They risk infec on through
open wounds or through injuries from knives and bone frag-
ments (Le Breton et al. 2006). The es mated consump on
of wild animal meat in the Amazon Basin ranges from 67
to 164 million kilograms annually; for mammals alone, this
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69
ONE HEALTH | SEÇÃO 3
consump on reaches about 6.4 million to 15.8 million indi-
vidual animals (Karesh et al. 2005).
The wildlife trade, much of which is conducted illegally
or through informal networks, poses signifi cant health thre-
ats to humans, domes c animals, na ve wildlife species,
and ecosystems (Karesh et al. 2005, Rosen et al. 2010). The
transport of illegal live animals or their parts can facilitate
the movement of pathogens to new regions. Wildlife trade
may result in devasta ng economic losses, and it also re-
presents an animal welfare concern as live specimens are
o en handled and transported under inhumane condi ons.
According to some es mates, almost 40,000 live primates,
4 million live birds, 640,000 live rep les, and 350 million
live tropical fi sh are traded globally each year (Karesh et al.
2005). Analysis of confi sca on records for a period of 12 ye-
ars, revealed that mammals and mammal parts dominate
the global trade of animal products (51% of all seizures),
though rep les and amphibians are usually traded as live
specimens (Rosen and Smith 2010).
Transloca on of wild animals for the pet and wildlife tra-
de poses risks of disease introduc on from one part of the
world to another. There are many examples of how diseases
origina ng in the wildlife trade can impact human, animal
and environmental health (e.g., SARS, HPAI H5N1, monkey-
pox, avian paramyxovirus, amphibian chytridiomycosis)
(Kock et al. 2010, Travis et al. 2011). Recently, zoono c
retroviruses (simian foamy virus) and herpesviruses were
iden fi ed in smuggled bushmeat from non-human primates
at the JFK Interna onal Airport (New York), origina ng from
West Africa (Smith et al. 2012).
Wildlife trade represents a route of disease exposure in
humans yet to be documented adequately in Amazon coun-
tries, where wetmarkets are signifi cant and expanding. In
Bolivia, wild animals are sold legally and illegally for both
internal and external markets. The trade of wildlife consists
mainly of live animals that are sold as pets (psi acine birds,
primates and rep les), and animal parts or by-products that
are sold for tradi onal medicine, rituals and as tourist sou-
venirs. A study conducted in Cochabamba (La Pampa mar-
ket) showed that 27 diff erent mammal species are object of
trade (including carnivores, bats, ungulates and xenarthra)
(Suarez and Alandia 2011). Wildlife trade is also a major
concern for the conserva on of endangered species; it is
es mated that 4 out of 5 primates taken from their natural
habitat die before reaching the market (Suarez and Alandia
2011). More than three thousand bats (of frugivorous, insec-
vorous and hematophagous species) are hunted in Bolivia
every two months for the trade. These bats are commonly
sold in urban markets (La Paz, El Alto, Oruro, Cochabamba
and Santa Cruz) by the so-called ‘chifl eras’ (a Spanish term
for witches), for use in tradi onal medicine either in des-
iccated forms or as live individuals. People suff ering from
epilepsy are off ered raw blood from live bats, which are be-
headed on-site at market se ngs; blood is preferably drunk
while the person is fas ng (Jemio 2007).
In Peru, non-human primates are commonly illegally
traded for the pet market, and as a result, thousands of
monkeys are confi scated or abandoned every year in rescue
centers. Confi scated animals are mostly of unknown origin
and found in very poor health condi on, represen ng po-
ten al health risks to humans and animals. A recent survey
of 7 out of 12 rescue centers in Peru used samples from 165
confi scated non-human primates to test for zoono c patho-
gens. It iden fi ed infec ons with Trypanosoma sp., Herpes
sp., Foamy virus, enteroparasites, and pathogenic entero-
bacteria including Aeromonas sp., Campylobacter sp., Sal-
monella sp., and Shigella sp. (Murillo et al. 2013).
PREDICT IN AMAZON COUNTRIES:
OVERVIEW AND OUTCOMES
PREDICT is conduc ng the fi rst scien fi c characteriza on
of disease risks posed by human-wildlife interfaces, and pro-
viding informa on necessary to detect and respond to dise-
ase emergence in the Amazon Basin. From project start in
2010, PREDICT has focused eff orts on crea ng awareness on
the importance of wildlife disease surveillance as a valuable
strategy to prevent infec ons in humans. Wildlife pathogens
are monitored in priority taxonomic groups (bats, primates
and rodents) at seven animal-human interfaces: subsisten-
ce hun ng (indigenous territories), wildlife trade (wetma-
rkets), cap ve se ngs (sanctuaries, rehabilita on centers,
zoos), disease events (outbreaks), peri-domes c se ngs
(near villages or urban areas), extrac ve industries (logging,
mining), agriculture (livestock), and remote areas without
human disturbance (for baseline comparison) (Fig. 1).
Informa on is managed through a specialized database
(the Global Animal Informa on System, GAINS), which in-
cludes data from both fi eld and laboratory work. Following
government permission to release the PREDICT data, we will
conduct analyses that should reveal some socio-cultural and
economic drivers of hun ng, trade and consump on of wild
animals, and characterizing disease risks through predic ve
models and EID-risk maps, to prepare and respond to future
disease events.
A er three years of project implementa on in Amazon
countries (as of May 2013), PREDICT has trained 842 peo-
ple in wildlife sampling and surveillance methods, includ-
ing fi eld staff , biologists, veterinarians, indigenous hunters,
laboratory technicians, and public health, veterinary service
and other government personnel. Diagnos c capacity for
screening fourteen viral families of pandemic poten al was
established at university and government laboratories in
Bolivia, Peru and Brazil. Over 34,082 animal samples were
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70
Saúde Silvestre e Humana: Experiências e Perspectivas
A ‘One Health’ Approach to Predict Emerging Zoonoses in the Amazon.
collected from 835 rodents, 1058 primates, 1978 bats, 558
birds, 218 rep les and 242 ungulates for pathogen screen-
ing, and tes ng is currently in progress.
In order to ensure sustainability, PREDICT facilitated
inter-ministerial forums to design na onal strategies for
wildlife disease surveillance in Colombia, Bolivia and Peru.
Partnerships were formalized with more than 60 ins tu ons
in Bolivia, Peru, Brazil and Colombia, by engaging ministries,
laboratories, academia, NGOs, and civil organiza ons.
Par cipatory community-based surveillance is one of the
most notable eff orts conducted in the region. The Wildlife
Conserva on Society (PREDICT leader in Bolivia) and Tacana
indigenous communi es developed a strategy for commu-
nity-based surveillance to prevent and control transmissible
diseases of backyard domes c animals in the Great Madidi-
-Tambopata landscape (northern Bolivian Amazonia). Being
successfully implemented over fi ve years, this collabora -
ve model (Fig. 2) is characterized by an agile network for
prompt disease communica on and data recording, and was
adapted to target zoono c diseases. Bolivian government
agencies such as the Veterinary Service (SENASAG) and the
Public Health Ministry have used this framework to enhance
surveillance of pathogens of economic importance, but also
of pathogens of public health concern.
PREDICT is also helping improve the current infrastruc-
ture for outbreak response in the Amazon, by working colla-
bora vely with government agencies for fi eld inves ga ons
and training. PREDICT joined the na onal task forces in Boli-
via and Peru to respond to ten diff erent zoonoses outbreaks
associated with wildlife (i.e., yellow fever, Oropouche fever,
rabies, plague, leptospirosis, hantavirus pulmonary syndro-
me and arenavirus hemorrhagic fever). Response eff orts
were focused on ac ve sampling of animal reservoirs (ur-
ban, peri-urban and wild caught) and training of fi eld staff
(from public agencies, universi es, wildlife sanctuaries, and
NGOs) in wildlife sampling and disease repor ng methods.
FIGURE 1. Map showing PREDICT study areas and interfaces in Amazon countries. A) Bolivia: Tacana,Tsimane-Mosetén and Uchupiamonas indigenous territories
and uninhabi ed areas in northern Amazonia (Madidi Na onal Park and Pilón Lajas Biosphere Reserve, northern Bolivia); peri-urban areas in Carmen Pampa
(Coroico District, La Paz), Cochabamba and Santa Cruz; rescue centers and zoos in La Paz, , Coroico District (Department of La Paz) and Villa Tunari District
(Department of Cochabamba); wetmarkets in La Paz and Cochabamba; agriculture areas in Beni (Elvira, Perotó); logging areas in Santa Cruz. B) Brazil: Unaltered
and uninhabited areas of the Amazon rainforest along Jatapu River, State Road BR-319, BR-174 highway, Santa Izabel do Rio Negro, and Roraima; intermediate
site of Rio Preto da Eva and the disturbed forest sites within the city of Manaus (States of Amazonas and Roraima, north-western Brazil). C) Colombia: wildlife
rescue centers in the Departments of Amazonas, Le cia and Caquetá. D) Peru: wetmarkets and rescue centers in Iquitos, Yurimaguas, Tarapoto, Tumbes, Piura,
Chiclayo, Lima, Cuzco, Madre de Dios and Pucallpa; riverside communi es in Yavari-Mirim Private Conserva on Area, and Pacaya-Samiria Na onal Reserve; peri-
urban areas in Pucallpa.
Red rectangles and dots
show areas surveyed by
WCS (Peru, Bolivia and
Brazil). Purple rectangle
and dots show areas
covered by EHA (Bolivia,
Brazil and Colombia)
Book_Fiocruz.indb 70Book_Fiocruz.indb 70 26/08/2013 17:25:2326/08/2013 17:25:23
71
ONE HEALTH | SEÇÃO 3
An outstanding example of how ‘One Health’ work can
contribute to improve outbreak response, is the one im-
plemented in Bolivia to deal with the fi rst yellow fever (YF)
outbreak aff ec ng monkeys, in which prompt inves ga ons
and response ac vi es of government offi cials and PREDICT
staff allowed to prevent infec ons in humans. No YF-mor-
tality had been previously reported in monkeys un l March
2012, when six free-ranging red howler monkeys (Alouat-
ta sara) were found dead near a wildlife sanctuary in Santa
Cruz (eastern Bolivia). Necropsies conducted on two howler
monkey carcasses demonstrated pathological lesions com-
pa ble with YF (e.g., jaundiced mucous membranes, hemor-
rhages in gingiva, liver and kidneys, swollen lymph nodes,
and splenomegaly) (Figure 3). RT-PCR for detec on of viral
families was conducted on liver samples at the Ins tute of
Molecular Biology (University of San Andres). Results sho-
wed infec ons by a fl avivirus, and immediate no fi ca on
was sent to the Ministry of Public Health. Later RNA sequen-
cing confi rmed that infec ons had been caused by two YF
viral strains (TVP11767 and TN-96 NS5), both related to hu-
man cases in Trinidad and Tobago and Brazil, respec vely.
Only eight days elapsed between the onset of the outbre-
ak and no fi ca on of the Bolivian government. Preven ve
measures were promptly implemented in the aff ected area,
including vaccina on campaigns, public outreach, and mos-
quito control. As a result, no human cases occurred during
this outbreak (Alandia et al. 2013).
DEEP FOREST METAGENOMICS:
PREDICTING DISEASE RISK ALONG A
LANDSCAPE DISTURBANCE GRADIENT IN
BRAZIL
PREDICT’s Deep Forest Metagenomics project is aimed at
determining the eff ect of anthropogenic landscape disturban-
ce on biodiversity and the infec ous diseases of zoono c in-
terest harbored by wild species. As land-use change is an im-
portant driver of emerging infec ous disease, a pla orm was
developed to understand how landscape disturbance may al-
ter pa erns of biodiversity, corresponding pa erns of viral di-
versity and pa erns of human-animal contact in landscapes.
This project is currently being conducted in Brazil and Malay-
sian Borneo by EcoHealth Alliance, and in Uganda by Univer-
sity of California, Davis. This project emphasizes rigorous sam-
pling design that targets non-lethal sampling of rodents, bats
and primates across a gradient of human disturbance.
In Brazil we collaborate closely with partners at the Uni-
versidade de São Paulo, Universidade Federal do Amazonas,
and Ins tuto Nacional de Pesquisas da Amazônia. The pro-
ject is based in Manaus and the three study sites selected
are: the City of Manaus (disturbed with high human den-
sity), Rio Preto da Eva (intermediate) and a large protected
area to the west of the BR-174 highway (pris ne forest). A
landscape development intensity index (LDI) will be calcula-
ted at each site using a local site-based analysis and satellite
images at numerous spa al scales. Rodents, marsupials and
bats are being trapped, sampled, iden fi ed and morphome-
FIGURE 2. Animal disease repor ng network developed in collabora on with
the Tacana indigenous people of Bolivia (from Alandia et al. 2012).
FIGURE 3. Necropsy of a howler monkey infected with yellow fever virus,
performed by PREDICT staff at the Municipal Zoo ‘Vesty Pakos’ (La Paz) (Photo
(c) E. Alandia, WCS 2012).
CAHPs: Community Animal Health Promoters; CIPTA: Tacana People’s
Indigenous Council; CIPTA NNRR Coordinator: CITA Natural Resources
Coordinator; SENASAG: Na onal Veterinary Service
Book_Fiocruz.indb 71Book_Fiocruz.indb 71 26/08/2013 17:25:2426/08/2013 17:25:24
72
Saúde Silvestre e Humana: Experiências e Perspectivas
A ‘One Health’ Approach to Predict Emerging Zoonoses in the Amazon.
tric measurements are taken, then the animals are released.
The family-level PCR analyses (described previously) will be
performed at Biosciences Ins tute (ICB II) at the Universi-
dade de São Paulo (USP) and a subset of samples will be
submi ed for high-throughput deep sequencing. Studies
such as these are important for understanding viral diversity
in high-risk wildlife taxa. The gradient system used by the
Deep Forest Project will be the fi rst step in answering im-
portant ques ons about the eff ect of landscape disturbance
on viral diversity, biodiversity and human-animal contact as
well as assessing the poten al risk of spillover into human
popula ons.
PERSPECTIVES
With the implementa on of the strategies described
above, and in a rela vely short me, capacity for iden fying
pathogens in wildlife and responding to infec ous disease
of wildlife origin was signifi cantly enhanced in the targeted
Amazon countries by PREDICT partners. Eff orts to secure
con nuity are of upmost priority, and should ideally evolve
from newly established inter-ministerial collabora ons at a
na onal and regional scale.
As suggested by FAO (2010), ‘it is well known that pre-
ven on is be er than cure, both in the fi ght against exis ng
and new emerging diseases’. The world is experiencing an
unprecedented genera on of science-based knowledge on
the movement of pathogens in the human-animal-ecosys-
tem interface. This informa on becomes invaluable input
for new predic ve models to help target, with the highest
probability of emerging disease detec on, the best areas in
which to adap vely focus eff orts and deploy resources. The
One Health approach, on the basis of enhanced coopera on
among governments and scien sts around the world, is the
most effi cient, cost-eff ec ve and sustainable strategy to an-
cipate and prevent the next pandemic threats.
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