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One Health proof of concept: Bringing a transdisciplinary approach to surveillance for zoonotic viruses at the human-wild animal interface
research-article
Author(s):
Terra R. Kelly
a ,
William B. Karesh
b ,
Christine Kreuder Johnson
a ,
Kirsten V.K. Gilardi
a ,
Simon J. Anthony
b
,
c ,
Tracey Goldstein
a ,
Sarah H. Olson
d ,
Catherine Machalaba
b ,
PREDICT Consortium
1 ,
Jonna A.K. Mazet
a
,
*
Publication date (Electronic):
14 December 2016
Journal:
Preventive Veterinary Medicine
Publisher:
The Author(s). Published by Elsevier B.V.
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Abstract
As the world continues to react and respond inefficiently to emerging infectious diseases, such as Middle Eastern Respiratory Syndrome and the Ebola and Zika viruses, a growing transdisciplinary community has called for a more proactive and holistic approach to prevention and preparedness – One Health. Such an approach presents important opportunities to reduce the impact of disease emergence events and also to mitigate future emergence through improved cross-sectoral coordination. In an attempt to provide proof of concept of the utility of the One Health approach, the US Agency for International Development’s PREDICT project consortium designed and implemented a targeted, risk-based surveillance strategy based not on humans as sentinels of disease but on detecting viruses early, at their source, where intervention strategies can be implemented before there is opportunity for spillover and spread in people or food animals. Here, we share One Health approaches used by consortium members to illustrate the potential for successful One Health outcomes that can be achieved through collaborative, transdisciplinary partnerships. PREDICT’s collaboration with partners around the world on strengthening local capacity to detect hundreds of viruses in wild animals, coupled with a series of cutting-edge virological and analytical activities, have significantly improved our baseline knowledge on the zoonotic pool of viruses and the risk of exposure to people. Further testament to the success of the project’s One Health approach and the work of its team of dedicated One Health professionals are the resulting 90 peer-reviewed, scientific publications in under 5 years that improve our understanding of zoonoses and the factors influencing their emergence. The findings are assisting in global health improvements, including surveillance science, diagnostic technologies, understanding of viral evolution, and ecological driver identification. Through its One Health leadership and multi-disciplinary partnerships, PREDICT has forged new networks of professionals from the human, animal, and environmental health sectors to promote global health, improving our understanding of viral disease spillover from wildlife and implementing strategies for preventing and controlling emerging disease threats.
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Most cited references31
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Middle East Respiratory Syndrome Coronavirus in Bats, Saudi Arabia
Ziad Memish, Nischay Mishra, Kevin J. Olival … (2013)
The source of human infection with Middle East respiratory syndrome coronavirus remains unknown. Molecular investigation indicated that bats in Saudi Arabia are infected with several alphacoronaviruses and betacoronaviruses. Virus from 1 bat showed 100% nucleotide identity to virus from the human index case-patient. Bats might play a role in human infection.
- Record: found
- Abstract: found
- Article: found
A Strategy To Estimate Unknown Viral Diversity in Mammals
Introduction The majority of emerging infectious diseases (EIDs) of humans are zoonoses, and the majority of these originate in wildlife (1–3). These diseases are largely viral (e.g., severe acute respiratory syndrome [SARS] and Nipah virus) and represent a significant global health threat. Analyses of trends in EIDs suggest that the rate of infectious disease emergence is increasing (3) and that the emergence of new viruses is not yet constrained by the richness (number of viruses) or diversity (genetic variability) of unknown viruses in wildlife, which is thought to be high. Systematically measuring viral richness, abundance, and diversity (here termed “virodiversity”) in wildlife is hindered by the large number of host species (e.g., around 5,500 mammals), their global distribution and often remote habitats (4), and the expense of collection, sampling, and viral identification or discovery (5), and it has not yet been achieved for even a single host species. In this study, we repeatedly sampled a mammalian host known to harbor emerging zoonotic pathogens (the Indian Flying Fox, Pteropus giganteus) and used PCR with degenerate primers targeting nine viral families to discover a large number and diversity of viruses. We then adapted the techniques normally used to estimate biodiversity in vertebrates and plants to estimate the total viral richness within these nine families in P. giganteus. Our analyses demonstrate proof-of-concept and provide the first statistically supported estimates of the unknown viral richness of a mammalian host and the sampling effort required to achieve it. RESULTS Viral discovery. A total of 12,793 consensus PCR assays were performed for the detection of viruses from nine different families/genera, including coronaviruses (CoVs; n = 1,631), paramyxoviruses (PMVs; n = 1,108), hantaviruses (HTVs; n = 1,108), astroviruses (AstVs; n = 1,348), influenza A viruses (IFAVs; n = 1,108), bocaviruses (BoVs; n = 1,739), adenoviruses (AdVs; n = 1,902), herpesviruses (HVs; n = 1,741), and polyomaviruses (PyVs; n = 1,108) (Table 1). None of the samples were positive for IFAVs or HTVs, despite previous studies documenting their presence in other bat species (6–8); however, a total of 985 viral sequences representing the other seven viral families were detected in these bats. These sequences were segregated into 55 discrete viruses based on distinct monophyletic clustering (see Materials and Methods) (Table 1), and a virus was considered novel if the sequence identity to its closest relative was less than or equal to the identity between the two closest species for a given viral family (9). TABLE 1 Summary of viral discovery performed on P. giganteus Virus No. of samples PCR positive/no. tested a Urine Throat Feces Roost urine Total Herpesvirus PgHV-1 9/926 29/711 0/78 0/26 38/1,741 PgHV-2 4/926 9/711 0/78 0/26 13/1,741 PgHV-3 1/926 0/711 0/78 0/26 1/1,741 PgHV-4 9/926 21/711 0/78 0/26 30/1,741 PgHV-5 1/926 0/711 0/78 1/26 2/1,741 PgHV-6 1/926 0/711 0/78 0/26 1/1,741 PgHV-7 2/926 8/711 0/78 0/26 10/1,741 PgHV-8 23/926 157/711 0/78 0/26 180/1,741 PgHV-9 0/926 3/711 0/78 0/26 3/1,741 PgHV-10 15/926 68/711 0/78 0/26 83/1,741 PgHV-11 0/926 4/711 0/78 0/26 4/1,741 PgHV-12 10/926 99/711 0/78 0/26 109/1,741 PgHV-13 6/926 159/711 0/78 0/26 165/1,741 Total 81/926 557/711 0/78 1/26 Paramyxovirus PgPMV-1 1/598 0/510 NT b NT 1/1,108 PgPMV-2 2/598 0/510 NT NT 2/1,108 PgPMV-3 0/598 2/510 NT NT 2/1,108 PgPMV-4 0/598 1/510 NT NT 1/1,108 PgPMV-5 0/598 3/510 NT NT 3/1,108 PgPMV-6 1/598 7/510 NT NT 8/1,108 PgPMV-7 0/598 2/510 NT NT 2/1,108 PgPMV-8 1/598 0/510 NT NT 1/1,108 PgPMV-9 2/598 0/510 NT NT 2/1,108 PgPMV-10 1/598 1/510 NT NT 2/1,108 PgPMV-11 (NiV) 1/598 2/510 NT NT 3/1,108 Total 9/598 18/510 Polyomavirus PgPyV-1 1/598 0/510 NT NT 1/1,108 PgPyV-2 0/598 3/510 NT NT 3/1,108 PgPyV-3 3/598 1/510 NT NT 4/1,108 Total 4/598 4/510 Coronavirus PgCoV-1 8/816 1/745 NT 5/70 14/1,631 PgCoV-2 33/816 10/745 NT 17/70 60/1,631 PgCoV-3 (bovine/human-like) 1/816 0/745 NT 0/70 1/1,631 PgCoV-4 (avian IBV-like) 0/816 1/745 NT 0/70 1/1,631 Total 42/816 12/745 22/70 Adenovirus PgAdV-1 1/931 0/806 0/78 0/87 1/1,902 PgAdV-2 (avian AdV) 1/931 0/806 0/78 0/87 1/1,902 PgAdV-3 4/931 4/806 0/78 0/87 8/1,902 PgAdV-4 0/931 1/806 0/78 0/87 1/1,902 PgAdV-5 34/931 16/806 0/78 3/87 53/1,902 PgAdV-6 0/931 2/806 0/78 0/87 2/1,902 PgAdV-7 11/931 1/806 0/78 1/87 13/1,902 PgAdV-8 17/931 2/806 0/78 0/87 19/1,902 PgAdV-9 5/931 1/806 0/78 2/87 8/1,902 PgAdV-10 1/931 0/806 0/78 0/87 1/1,902 PgAdV-11 4/931 0/806 0/78 0/87 4/1,902 PgAdV-12 1/931 0/806 0/78 0/87 1/1,902 PgAdV-13 22/931 3/806 0/78 6/87 31/1,902 PgAdV-14 38/931 11/806 0/78 5/87 54/1,902 Total 139/931 41/806 0/78 17/87 Astrovirus PgAstV-1 0/696 1/585 NT 1/67 2/1,348 PgAstV-2 1/696 0/585 NT 0/67 1/1,348 PgAstV-3 3/696 0/585 NT 8/67 11/1,348 PgAstV-4 0/696 0/585 NT 2/67 2/1,348 PgAstV-5 0/696 0/585 NT 3/67 3/1,348 PgAstV-6 0/696 0/585 NT 1/67 1/1,348 PgAstV-7 1/696 0/585 NT 0/67 1/1,348 PgAstV-8 0/696 0/585 NT 15/67 15/1,348 Total 5/696 1/585 30/67 Bocavirus PgBoV-1 (human BoV) 1/925 0/710 0/78 0/26 1/1,739 PgBoV-2 (human BoV) 0/925 1/710 0/78 0/26 1/1,739 Total 1/925 1/710 0/78 0/26 a A total of 55 viruses from seven viral families were discovered. The discovery effort (number of samples tested) and prevalence of each virus is presented. b NT, not tested. Eleven PMVs were detected, including 10 novel viruses (PMV-1 from P. giganteus [PgPMV-1] to PgPMV-10) and Nipah virus (PgPMV-11). These PMVs exhibited high sequence variation and clustered phylogenetically with either the rubulaviruses or an unassigned group related to the henipaviruses (Fig. 1). Within the AdV family, 14 viruses were discovered (PgAdV-1 to -14). Thirteen were novel mastadenoviruses, while one virus (PgAdV-2) had 98% nucleotide identity to the aviadenovirus Fowl adenovirus E (Fig. 2). Eight different AstVs were found (PgAstV-1 to -8), all of which were novel and clustered within the genus Mamastrovirus (Fig. 3). Within the CoV family, four distinct viruses were discovered. The first two were closely related betacoronaviruses (PgCoV-1 and -2). The third was also a betacoronavirus (PgCoV-3) but was more distantly related and showed 97% nucleotide identity to bovine and human coronaviruses (human strains 4408 and OC43). The fourth CoV was a gammacoronavirus (PgCoV-4) with 91% nucleotide identity (97% at the amino acid level) to the avian Infectious bronchitis virus (Fig. 4). Three novel PyVs were identified (PgPyV-1 to -3), all of which clustered with viruses in the genus Orthopolyomavirus (Fig. 5). A total of 639 HV sequences were detected, which segregated into 13 distinct clades (PgHV 1 to 13) using hierarchical clustering (see Materials and Methods). None could be reliably classified within any existing genus, and they likely represent new groups within the Betaherpesvirinae and Gammaherpesvirinae subfamilies (Fig. 6). One virus, PgHV-11, appears to be a recombinant between PgHV-10 and PgHV-13, with a breakpoint evident at approximately nucleotide 90. Upstream from this breakpoint, the sequences for PgHV-11 are related to PgHV-10, while downstream from the breakpoint, they are related to PgHV-13. Finally, two different BoVs were discovered (PgBoV-1 and -2), both of which showed >98% nucleotide identity to known human BoVs (Fig. 7). FIG 1 Phylogenetic tree (ML) of PMV large gene (RdRp). Alignment length, 534 bp of nucleotide sequence. PgPMV-1 to -10 were discovered in this study. PgPMV-11 is Nipah virus. The number of samples that tested positive for each respective virus in urine (U) and throat (T) is indicated in parentheses. *, published bat PMV sequences. Novel viruses detected in this study are identified with the prefix Pg (Pteropus giganteus) and were assigned accession numbers KC692403 to KC692412 FIG 2 Phylogenetic tree (ML) of AdV polymerase. Alignment length, 301 bp of nucleotide sequence. PgAdV-1 to -14 were discovered in this study. The number of samples that tested positive for each respective virus in urine (U), throat (T), feces (F), and roost urine (RU) is indicated in parentheses. *, published bat AdV sequences. Viruses detected in this study are identified with the prefix Pg (Pteropus giganteus) and were assigned accession numbers KC692417 to KC692430 FIG 3 Phylogenetic tree (ML) of AstV RdRp. Alignment length, 320 bp of nucleotide sequence. PgAstV-1 to -8 were discovered in this study. The number of samples that tested positive for each respective virus in urine (U), throat (T), and roost urine (RU) is indicated in parentheses. *, published bat AstV sequences. Viruses detected in this study are identified with the prefix Pg (Pteropus giganteus) and were assigned accession numbers KC692431 to KC692437 FIG 4 Phylogenetic tree (ML) of CoV RdRp. Alignment length, 310 bp of nucleotide sequence. PgCoV-1 to -4 were discovered in this study. The number of samples that tested positive for each respective virus in urine (U), throat (T), and roost urine (RU) is indicated in parentheses. *, published bat CoV sequences. Bat coronaviruses cluster based on the host family (indicated). ~, HKU2 seems anomalously positioned as it was detected in Rhinolophus sinicus, which is unrelated to bats from the families Vespertilionidae or Molossidae. The reason for this is unknown. Viruses detected in this study are identified with the prefix Pg (Pteropus giganteus), and were assigned accession numbers KC692413 to KC692416. IBV, infectious bronchitis virus; MHV, mouse hepatitis virus; PHEV, porcine hemagglutinating encephalomyelitis virus; HCoV, human CoV; BtCoV, bat CoV; FIPV, feline infectious peritonitis virus; TGEV, transmissible gastroenteritis coronavirus; PEDV, porcine epidemic diarrhea virus. FIG 5 Phylogenetic tree (ML) of PyV VP1 (major capsid protein). Alignment length, 320 bp of nucleotide sequence. PgPyV-1 to -3 were discovered in this study. The number of samples that tested positive for each respective virus in urine (U) and throat (T) is indicated in parentheses. *, published bat PyV sequences. Viruses detected in this study are identified with the prefix Pg (Pteropus giganteus) and were assigned accession numbers KC692400 to KC692402. FIG 6 Phylogenetic tree (ML) of HV polymerase. Alignment length, 211 bp of nucleotide sequence. PgHV-1 to -13 were discovered in this study. The number of samples that tested positive for each respective virus in urine (U), throat (T), feces (F), and roost urine (RU) is indicated in parentheses. *, published bat HV sequences. Viruses detected in this study are identified with the prefix Pg (Pteropus giganteus) and were assigned accession numbers KC692438 to KC692450. FIG 7 Phylogenetic tree (ML) of BoV NS1. Alignment length, 287 bp of nucleotide sequence. PgBoV-1 and -2 were detected in this study. The number of samples that tested positive for each respective virus in urine (U), throat (T), feces (F), and roost urine (RU) is indicated in parentheses. Viruses detected in this study are identified with the prefix Pg (Pteropus giganteus) and were assigned accession numbers KC692451 to KC692452. Viral discovery curves and estimates of viral richness. Asymptotic viral richness was estimated from observed detections using three statistical models, Chao2, ICE, and Jackknife (10). To ensure internal consistency, only those samples screened for the full complement of nine viral families/genera were included (n = 1,092), which accounted for 44/55 viruses identified in this study. The relative frequency of these viruses is presented in Fig. S1 in the supplemental material. Of the 1,092 samples included, 766 were negative for all viruses. There were 595 viral detections from 326 positive samples, with 167 samples containing >1 virus. When all 44 viruses were considered, the accumulative discovery curve began to show signs of saturation (Fig. 8). The Chao2 estimator demonstrated asymptotic behavior as early as 500 samples ( 1 virus, including urine (n = 56), throat swabs (n = 199), and roost urine (n = 56). Between 2 and 5 viruses were found to coexist, and both intrafamilial (n = 223/276) and interfamilial (n = 93/276) viral family cooccurrences were observed (Fig. 9). Intraspecific codetections were limited to the families Herpesviridae and Adenoviridae (Fig. 9 and see Table S1 in the supplemental material). The patterns of HV cooccurrence were significantly nonrandom (P = 7% nucleotide difference (Hamming distance) was used to define HV clusters. PgHV sequences were then segregated using hierarchical clustering, as implemented in the SciPy package (59) using average linkage clustering. Virus richness and sample estimation. We implemented models from the biodiversity literature that utilize incidence distributions to estimate virus richness (number of unique viruses) and, hence, to estimate the number of undetected viruses in the assemblage (60, 61). Incidence data result where each virus detected in the assemblage is noted in each sample as either present (verified detection) or absent (not detected, which could result due to the virus being absent or being present but not detected by the test, i.e., false absence). From our samples, we first constructed virus accumulation and rarefaction curves for visualization. The asymptote of the rarefaction curve provides the estimate of the number of viruses that characterizes the assemblage. However, sampling to reach this asymptote is impractical, as the number of samples required may be prohibitively large (61). We thus used statistical methods to estimate the asymptote from the data at hand. We used the nonparametric asymptotic estimator, Chao2 (15, 10), and also calculated ICE and Jackknife statistics for comparison. Unlike conventional curve-fitting procedures, the nonparametric estimators make no assumptions of an underlying abundance distribution, do not require ad hoc or a priori model fitting, are relatively robust to spatial autocorrelation and scale, and frequently outperform other methods of richness estimation (61). They rely on the principle that the frequencies of the rarest species in a set of samples can be used to estimate the frequencies of undetected species and provide a minimum richness estimate. All analyses were conducted with the fossil package (62) implemented in R (63). We followed Chao et al. (10) to calculate how many additional samples would be required to detect any proportion (including 100%) of the asymptotic virus richness. All statistics were incorporated into a single plot. Cooccurrence. Patterns of association/disassociation were explored with the Fortran software program PAIRS (11), utilizing the C score statistic as our measure of species cooccurrence. PAIRS implements a Bayesian approach (Bayes M criterion) to detect nonrandom associations between pairs of species (12). Assumptions and caveats We considered the detection and discovery of viruses akin to the problem of detection and discovery of biodiversity, as is frequently the goal of ecological studies. The basic mechanism of species detection occurs from drawing samples by collection from some larger assemblage (61). In this context, our samples are as described above, urine, throat, fecal, or roost urine taken from an individual bat or bat roost, which represent the biomes for our assemblage of interest. These methods require the assemblage of viruses under sampling to be closed for valid inference, that is, that the assemblage size and composition remained stable throughout the course of the study, an assumption we felt was justified. Although each of these sample types targets a unique biome of potential viral habitat from the host species, each with potentially differing efficacy for detecting any given virus, for the purposes of our analyses, we considered each sample a random and equivalent draw from the assemblage of viruses associated with this host species. We also assumed sample independence, even though multiple samples (e.g., urine and throat) were often drawn from the same individual host and sampled bat populations are likely to be geographically nonrandom. The consequence of this sampling strategy is that our analysis is blind to this additional source of geographical variation and occasional pseudoreplication, which means our virus accumulation results are specific to our sampling methodology and our extrapolations assume ongoing sampling with a similar average composition of samples. The results of additional analyses in which we isolated sample types and individuals and considered geographic variation are not presented herein. Nucleotide sequence accession numbers. The GenBank accession numbers for viruses discovered in this study are KC692400 to KC692452. SUPPLEMENTAL MATERIAL Text S1 Supplemental discussion. Download Text S1, DOCX file, 0.1 MB Figure S1 Relative distribution of viruses included in discovery curve analyses (see also Fig. 8). A subset of samples (n = 1,092) was used for discovery curve analysis. Only those samples that were screened for all nine viral families were included, ensuring internal consistency. Eleven of the 55 identified viruses had zero abundance in this subset and were therefore not considered in the analysis. The 11 omitted viruses were PgHV-2, -5, -6, and -9, PgAdV-1 and -10, PgAstV-4, -5, -6, and -8, and PgBoV-1. Forty-four viruses were therefore retained in our estimates, and the relative frequency of each is presented here. Download Figure S1, PDF file, 0.1 MB Figure S2 Viral discovery curves are presented for (i) all viruses (see also Fig. 8), (ii) PMV, (iii) AstV, (iv) HV, and (v) AdV. Discovery effort (number of samples tested) is indicated by the horizontal dotted line. Red line, collector curve showing accumulation of novel viruses over samples tested; blue line, Chao2 estimator at every sample point, with arrow indicating 95% confidence intervals; gray lines, ICE and Jackknife estimators at every sample point; Φ, estimated total diversity; dashed horizontal lines, required sampling effort to discover an arbitrary proportion of the total diversity; Ω, effort required to discover 100% of the estimated diversity. Download Figure S2, PDF file, 0.3 MB Table S1 Summary of intraspecific coinfections. The total number of samples testing positive for each family is presented, together with the number of the samples containing >1 virus of the same family. Table S1, PDF file, 0.6 MB.
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Bushmeat Hunting, Deforestation, and Prediction of Zoonotic Disease
Nathan Wolfe, Peter Daszak, A. Kilpatrick … (2005)
Approximately three fourths of human emerging infectious diseases are caused by zoonotic pathogens ( 1 ). These include agents responsible for global mortality (e.g., HIV-1 and -2, influenza virus) and others that cause limited deaths but result in high case-fatality rates and for which no effective therapies or vaccines exist (e.g., Ebola virus, hantaviruses, Nipah virus, severe acute respiratory syndrome [SARS]-associated coronavirus) ( 2 ). Despite the growing threat of zoonotic emerging infectious diseases, our understanding of the process of disease emergence remains poor. Public health measures for such diseases often depend on vaccine and drug development to combat diseases once pathogens have emerged. Indeed, many believe that predicting emergence of new zoonoses is an unattainable goal ( 3 ). Despite this, a growing trend in emerging disease research attempts to empirically analyze the process of emergence and move towards predictive capacity for new zoonoses. These studies track broad trends in the emergence of infectious diseases, analyze the risk factors for their emergence, or examine the environmental changes that drive them ( 4 – 6 ). Many new zoonoses are viruses that emerge as human and domestic animal populations come into increasing contact with wildlife hosts of potentially zoonotic pathogens ( 1 ). The risk for emergence of new zoonotic agents from wildlife depends largely on 3 factors: 1) the diversity of wildlife microbes in a region (the "zoonotic pool" [5]); 2) the effects of environmental change on the prevalence of pathogens in wild populations; and 3) the frequency of human and domestic animal contact with wildlife reservoirs of potential zoonoses. The first factor is largely the domain of virologists, particularly those analyzing evolutionary trends in emerging viruses ( 7 ) (Figure). The last 2 factors are studied by wildlife veterinarians, disease ecologists, wildlife population biologists, anthropologists, economists, and geographers ( 4 , 8 ). Understanding the process of emergence requires analyzing the dynamics of microbes within wildlife reservoir populations, the population biology of these reservoirs, and recent changes in human demography and behavior (e.g., hunting, livestock production) against a background of environmental changes such as deforestation and agricultural encroachment. To fully examine zoonotic emergence, a multidisciplinary approach is needed that combines all of these disciplines and measures the background biodiversity of wildlife microbes. We use hunting and deforestation in Cameroon as an example to discuss the complex interactions between human behavior, demography, deforestation, and viral dynamics that underpin the emergence of diseases. Figure Location of the International Institute of Tropical Agriculture Humid Forest Benchmark Region, Cameroon. ha, hectares. Logging, Hunting, and Viral Traffic Hunting of wildlife by humans is an ancient practice that carries a substantial risk for cross-species transmission. Despite the discovery of cooking ≈1.9 million years ago ( 9 ), the risk of zoonotic diseases emerging from hunting and eating wildlife is still of global importance because of increases in human population density, globalized trade, and consequent increased contact between humans and animals. Deforestation of tropical forests is 1 cause of increasing contact between wildlife and hunters. However, the mechanics of disease emergence are complex. For example, clear-cut logging may be less likely to result in zoonotic emergence than selective extraction because of the relatively low contact rate between people and wildlife during clear-cutting. Because of the high costs of extraction and transportation, logging in central Africa generally involves selective extraction of high-value timber species. Selective extraction is also more likely to sustain natural diversity of wildlife than clear-cutting ( 10 ) and therefore to sustain the diversity of potentially zoonotic pathogens available to hunters. Selective logging generally involves constructing roads and transporting workers into relatively pristine forest regions. Although roads can bring health care to rural communities, they also provide increased contact between low-density, remote human populations and urban populations with access to international travel, which allows localized emergence events the potential for rapid global spread ( 11 , 12 ). Building logging roads also leads to habitat fragmentation as forest edges along roads are degraded, which lowers the movement of wildlife between forest patches. This process may have 3 counteractive effects. First, as patch size decreases, smaller, more discrete, less dense populations of reservoirs result, some of which may be lowered below the threshold density of some potentially zoonotic microbes ( 13 ). In these cases, mathematical models of infectious diseases predict that the microbes will become extinct, lowering the risk for transmission to humans. Second, in some cases, the loss of vertebrate reservoir host species richness may result in increased abundance of highly competent reservoirs of some zoonotic agents, increasing the risk for transmission to humans. Although this phenomenon has only been demonstrated for 1 pathogen, Borrelia burgdorferi, the causative agent of Lyme disease ( 14 ), it may be more widespread. In this case, fragmentation increases the relative abundance of the highly competent reservoir, the white-footed mouse (Peromyscus leucopus) and results in a higher risk for infection to humans ( 14 ). Third, fragmentation due to road building may increase the functional interface between human populations and reservoir hosts. Historically, hunting activities radiated in a circular fashion from isolated villages, with decreasing impact at the periphery of the hunting range. Roads provide an increased number of points at which hunting activities can commence. Road-side transport means that hunters can lay traps and hunt at the same distance from roads. This changes the pattern of human contact from a circular pattern to a banded pattern surrounding developed roads, increasing the area in which hunting can be conducted with economic returns. Anthropology of Bushmeat Hunting, Trade, and Consumption Different activities associated with bushmeat trade will involve different levels of risk for microbial emergence. Hunting (tracking, capturing, handling, sometimes basic field butchering, and transporting of the carcass) involves contact with potentially infected vectors, whereas distant consumption may not. Particularly high risks may be associated with hunting nonhuman primates, and even greater risks in hunting species such as chimpanzee, which are phylogenetically closest to humans. Butchering (opening, cutting, dressing, and preparing the carcass) is obviously more high risk for bloodborne pathogens than the transportation, sale, purchase, and eating of the butchered meat. Research in medical anthropology has begun to examine indigenous theories of infectious disease ( 15 ) and the cultural contexts within which diseases emerge ( 16 ), but little data exist on local perceptions of health or other risks associated with hunting and eating bushmeat. Humans as well as other animals employ behavioral adaptations to avoid exposure to infections, yet the type of protective strategies that hunters might use and the effectiveness of such strategies remain unknown. For this reason, anthropologic studies of bushmeat should include not only the details of hunting, but also the transportation of meat to the village, the market, the kitchen, and onto the table. These practices are often articulated along lines of gender and ethnicity and within cultural contexts. The demand for bushmeat in West and central Africa is as much as 4 times greater than that in the Amazon Basin ( 10 ). Estimates of the extraction rate in the Congo Basin suggest that >282.3 g of bushmeat per person per day may be eaten there, with a total of 4.5 million tons of bushmeat extracted annually ( 17 ). Expanded demand for bushmeat will likely lead to changes in the exposure of humans to potentially zoonotic microbes. Therefore, assessing the risk that bushmeat extraction and consumption poses to public health will include an assessment of the economy and geography of bushmeat demand and supply. Case Study: Bushmeat Hunting in Cameroon A collaboration between Johns Hopkins University and the Cameroon Ministry of Health and Ministry of Defense is exploring emergence of infectious diseases in Cameroon (Figure). The ecologic diversity in Cameroon and the range of new and changing land-use patterns make it an ideal setting to examine the impact of environmental changes on novel disease transmission. Deforestation rates in Cameroon are high, with a loss of 800–1,000 km2 forest cover per year and corresponding increase in road-building and expansion of settlements ( 18 ). Finally, Cameroon is representative of the region from which a range of notable emerging infectious diseases, including HIV/AIDS, Ebola and Marburg viruses, and monkeypox, have emerged (Table). Table Some zoonotic pathogens that have emerged in the Cameroon–Congo Basin region, 1970–2005* Pathogen or disease Reservoir species Outcome of transmission Risk behavior Confirmed or probable transmission routes Ref. Body fluids Bites/
saliva Organs/tissues Feces/urine Vectors (indirect) Arboviruses (dengue, yellow fever) Various Localized outbreaks Human presence in region for habitation, work or leisure X (5,19,
20) Ebola Unknown Localized epidemics, short timescale Hunting or wildlife necropsy X X X X ( 21 ) Monkeypox Squirrels and others Localized epidemics (at least four transmission cycles recorded) X X ( 22 ) HIV-1 and -2 Chimpanzee, sooty mangabe Repeated single infections or localized outbreaks, followed by national then global emergence Hunting & butchering nonhuman primates X X X ( 23 ) Anthrax Ungulates Single infections or localized epidemics Butchering or eating carcasses X X X Salmonellosis Range of nonhuman primates Single infections Keeping pets X ( 24 ) Herpes B virus (did not emerge locally) Range of non-human primates Single infections Keeping pets X X X ( 25 ) Cutaneous leishmaniasis, Loa loa Localized outbreaks Logging/road-building, ecotourism, research X X X Simian foamy viruses Gorilla, mandarin, De Brazza’s guenon, other unknown spp. Exposure without replication, or replication in a single human Hunting nonhuman primates X X X X ( 26 ) Chromomycosis Wood collection X X X *Note that herpes B virus did not infect humans locally in the Cameroon-Congo basin. A key factor driving the bushmeat trade in Cameroon is the large and growing urban demand for bushmeat in conjunction with the opening up of logging concessions in the East Province. The construction of the World Bank–funded Yaoundé–Douala truck road in the mid-1980s and the European Union–funded extension of this road to the border of the timber-rich East Province in 1992 dramatically reduced the cost of extracting timber and increased access to these areas for bushmeat hunters. One of the most important non-timber forest product activities within this region is the poaching of bushmeat by market hunters. The bushmeat market among households for sauce preparation in Yaoundé alone is estimated at ≈$4 million annually (International Institute of Tropical Agriculture [IITA], unpub. data). A recently conducted consumption study showed that bushmeat plays an important dietary role among poor households and is not a luxury product eaten mainly by the rich. Across income classes, the poorest 2 quantiles spent 16% and 17%, respectively, of their meat budgets on bushmeat versus 7% for the richest quantile and 9% overall (IITA, unpub. data). Finally, our work in Cameroon has shown that not only bushmeat hunters but also persons who keep various species of vertebrate pets or butcher and handle meat are at risk for zoonotic transmission due to bites, cuts, and other exposures to fluids or tissue ( 27 ). Viral Chatter and Globalized Emergence The global emergence of a zoonotic pathogen such as SARS or HIV-1 and -2 requires 3 steps. First, the pathogen must be successfully transmitted between a wild reservoir and humans or their domestic animals. Several recently emerging zoonoses have achieved this stage without further transmission, e.g., Hendra virus. Second, the pathogen must be directly transmitted between humans. Finally, the pathogen must move from a local epidemic into the global population. Understanding and predicting the global emergence of pathogens require knowledge of the drivers of each of these steps or processes. These are, in fact, stages of emergence that have been described previously as invasion, establishment, and persistence of infectious diseases introduced into new host populations ( 8 ). Evidence suggests that many pathogens are transmitted between their animal reservoirs and humans but fail to be transmitted from human to human or do so at rates that do not allow pathogen establishment within the human population. For example, sequence data from HIV-1 and HIV-2 suggest that as many as 10 prior transmission events into human populations occurred over the last century before this virus emerged globally ( 23 ). Recent data from our own field sites suggest that simian foamy viruses infect bushmeat hunters regularly, so far without evidence of human-to-human transmission ( 26 ). Other pathogens, such as avian influenza and Hendra viruses, which do not appear to be transmitted through bushmeat consumption, have also led to several small epidemics with little or no evidence of human-to-human transmission. We have termed this "viral chatter," a seemingly common phenomenon of repeated transmission of nonhuman viruses to humans, most of which results in no human-to-human transmission ( 28 ). We hypothesize that this mechanism is common in viral emergence. High rates of viral chatter will increase the diversity of viruses and sequence variants moving into humans, increase the probability of transmission of a pathogen that can successfully replicate, and ultimately increase the ability of a human-adapted virus to emerge in a more widespread manner. In some cases this process may result in the evolution of a new viral strain ( 29 ) and may be a very common mechanism for viral emergence into the human population ( 23 , 28 ). Monkeypox and Nipah viruses are examples of the second stage towards global emergence. These viruses have shown limited human-to-human transmission in a number of relatively small epidemics before fading out ( 22 , 30 ). This phenomenon can be understood by using what mathematical modelers of disease dynamics refer to as the reproductive ratio (R0 ), which measures a pathogen's ability to cause an outbreak. R0 is the number of secondary cases in a population caused by a single case, assuming that all other members are susceptible ( 8 ). When R0 is >1, the pathogen will amplify within a population and cause an outbreak. In the environmental conditions in which monkeypox and Nipah viruses emerged, R0 was <1, and ultimately the epidemics faded out ( 22 ). One of the crucial questions in disease emergence is: What environmental or evolutionary changes cause the R0 of wildlife viruses to rise above 1 in human populations? In mathematical models for density-dependent transmission, R0 is proportional to host density, so that there is a critical threshold of human population density (known as the threshold density, NT), below which a pathogen will fade to extinction. Increasing densities of human populations in urban centers close to bushmeat hunting areas and the increasing rates of movement of people between village, town, and city, will increase R0 and the risk for new epidemic zoonoses. Alternatively, changes to human behavior that increase the transmission of viruses between people (e.g., sexual contact, injected drug use, or fluid contact by means of medical procedures) will increase R0 and may also assist in driving their emergence. In the final stage of emergence, increased travel or migration facilitate the global spread of new zoonoses. For example, increased movements between villages or cities and higher between-person contact rates through increased numbers of sexual partners appear to have facilitated the early emergence of HIV/AIDS in Africa ( 12 ). This disease became a global pandemic following the expansion of road networks, changes in workforce demography, and increases in international air travel to central Africa and globally ( 12 , 23 ). Our review suggests that predicting the emergence of new zoonoses will be a difficult but important task for future medical research. This goal has been described as challenging or impossible by some researchers ( 3 ). However, we propose that it is now becoming possible to conduct the science of predicting emerging zoonoses and that far more attention should be paid to this approach than is currently given ( 31 ). We have previously proposed 3 criteria that can be used to predict which microbes are most likely to emerge ( 6 ). These include microbes that have a proven ability to 1) lead to human pandemics, 2) lead to panzootics in (nonhuman) animal populations, and 3) mutate at high rates and recombine with other similar or dissimilar microbes. The high mutation rates of RNA viruses and their predominance within zoonotic emerging infectious diseases that are transmitted from human to human suggest that this group is a key candidate for future emergence ( 7 ). Simian foamy viruses are members of this group, and the high rates of viral chatter observed in Cameroon suggest a strong potential for their emergence as a human-to-human transmitted pathogen. Little is known about the complexity of this process, but with ≈75% of human emerging infectious diseases classified as zoonoses ( 1 ), understanding the process is critical to global health. We propose that more attention be given to multidisciplinary studies at all stages of the process. For example, understanding how the rates of viral chatter respond to anthropogenic land-use changes (e.g., deforestation, mining) that affect the density of wildlife species and the prevalence of viruses that affect them will be critical for predicting hotspots of disease emergence. Second, understanding which viruses are likely to rapidly evolve in humans, rather than become dead-end hosts, will involve a combination of host immunologic and viral evolutionary traits ( 7 , 32 ). Studies of the characteristics of the zoonotic pool (i.e., the biodiversity of yet-to-emerge wildlife viruses [5]) may explain these events. Some strains within viral quasispecies may be able to infect and be transmitted between humans far more readily than others. Such complexity requires the collaboration of medical scientists with many other disciplines, including geography, ecologic and evolutionary biology, conservation biology, medical anthropology, and veterinary medicine. Recent advances in a number of fields include some of direct relevance to predicting unknown zoonoses, among them modeling multihost disease dynamics in wildlife and humans ( 33 ), modeling the evolutionary dynamics of pathogens ( 34 ), insights into the phylogenetic characteristics of emerging pathogens ( 7 , 32 ), greater understanding of the environmental changes that drive emergence (4), risk assessments for pathogen transmission ( 35 , 36 ) and introduction ( 37 ), and major advances in the technology for microbial discovery (e.g., microarrays) and characterization (e.g., noninvasive sequencing) ( 38 ). A number of collaborative initiatives between veterinary medicine, human medicine, and ecology have already begun ( 39 , 40 ), and our analysis suggests these should be strengthened by even wider collaboration. The fusion of these diverse, rapidly evolving fields will allow the first steps to be taken towards emerging disease research's ultimate challenge of predicting new zoonotic disease emergence.
Author and article information
Contributors
Terra R. Kelly
William B. Karesh
Christine Kreuder Johnson
Kirsten V.K. Gilardi
Simon J. Anthony
Tracey Goldstein
Sarah H. Olson
Catherine Machalaba
Jonna A.K. Mazet
Journal
Journal ID (nlm-ta): Prev Vet Med
Journal ID (iso-abbrev): Prev. Vet. Med
Title:
Preventive Veterinary Medicine
Publisher:
The Author(s). Published by Elsevier B.V.
ISSN
(Print):
0167-5877
ISSN
(Electronic):
1873-1716
Publication date PMC-release: 14
December
2016
Publication date
(Print):
1
February
2017
Publication date
(Electronic):
14
December
2016
Volume: 137
Pages: 112-118
Affiliations
[a
]One Health Institute & Karen C. Drayer Wildlife Health Center, School of Veterinary
Medicine, 1089 Veterinary Medicine Drive, University of California, Davis, CA, 95616,
USA
Author notes
Article
Publisher ID: S0167-5877(16)30641-9DOI: 10.1016/j.prevetmed.2016.11.023
PMC ID: 7132382
PubMed ID: 28034593
SO-VID: 18e8bb37-a6a1-4298-8624-fde9ce793281
Copyright © © 2016 The Author(s)
License:
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
History
Date
received
: 6
June
2016
Date
accepted
: 30
November
2016
Categories
Subject:
Article
ScienceOpen disciplines: Veterinary medicine
Data availability:
ScienceOpen disciplines: Veterinary medicine