Coronavirus testing indicates transmission risk increases along wildlife supply chains for human consumption in Viet Nam, 2013-2014

Nguyen Quynh Huong; Nguyen Thi Thanh Nga; Nguyen Van Long; Bach Duc Luu; Alice Latinne; Mathieu Pruvot; Nguyen Thanh Phuong; Le Tin Vinh Quang; Vo Van Hung; Nguyen Thi Lan; Nguyen Thi Hoa; Phan Quang Minh; Nguyen Thi Diep; Nguyen Tung; Van Dang Ky; Scott I Roberton; Hoang Bich Thuy; Nguyen Van Long; Martin Gilbert; Leanne Wicker; Jonna A K Mazet; Christine Kreuder Johnson; Tracey Goldstein; Alex Tremeau-Bravard; Victoria Ontiveros; Damien O Joly; Chris Walzer; Amanda E Fine; Sarah H Olson

doi:10.1371/journal.pone.0237129

. 2020 Aug 10;15(8):e0237129. doi: 10.1371/journal.pone.0237129

Coronavirus testing indicates transmission risk increases along wildlife supply chains for human consumption in Viet Nam, 2013-2014

Nguyen Quynh Huong ^1,^#, Nguyen Thi Thanh Nga ^1,^#, Nguyen Van Long ², Bach Duc Luu ², Alice Latinne ^1,^3,⁴, Mathieu Pruvot ³, Nguyen Thanh Phuong ⁵, Le Tin Vinh Quang ⁵, Vo Van Hung ⁵, Nguyen Thi Lan ⁶, Nguyen Thi Hoa ⁶, Phan Quang Minh ², Nguyen Thi Diep ², Nguyen Tung ², Van Dang Ky ^2,^¤a, Scott I Roberton ¹, Hoang Bich Thuy ¹, Nguyen Van Long ¹, Martin Gilbert ^3,^¤b, Leanne Wicker ^1,^¤c, Jonna A K Mazet ⁷, Christine Kreuder Johnson ⁷, Tracey Goldstein ⁷, Alex Tremeau-Bravard ⁷, Victoria Ontiveros ⁷, Damien O Joly ^3,^¤d, Chris Walzer ^3,⁸, Amanda E Fine ^1,^3,^‡,^*, Sarah H Olson ^3,^‡

Editor: Dong-Yan Jin⁹

¹Wildlife Conservation Society, Viet Nam Country Program, Ha Noi, Viet Nam

²Department of Animal Health, Ministry of Agricultural and Rural Development of Viet Nam, Ha Noi, Viet Nam

³Wildlife Conservation Society, Health Program, Bronx, New York, United States of America

⁴EcoHealth Alliance, New York, New York, United States of America

⁵Regional Animal Health Office No. 6, Ho Chi Minh City, Viet Nam

⁶Faculty of Veterinary Medicine, Viet Nam National University of Agriculture, Ha Noi, Viet Nam

⁷One Health Institute, School of Veterinary Medicine, University of California, Davis, California, United States of America

⁸Research Institute of Wildlife Ecology, University of Veterinary Medicine, Vienna, Austria

⁹University of Hong Kong, HONG KONG

Competing Interests: The authors have declared that no competing interests exist.

^¤a

Current address: The Animal Asia Foundation Viet Nam, Ha Noi, Viet Nam

^¤b

Current address: Cornell Wildlife Health Center, College of Veterinary Medicine, Cornell University, Ithaca, New York, United States of America

^¤c

Current address: Australian Wildlife Health Centre, Healesville Sanctuary, Zoos Victoria, Healesville, Victoria, Australia

^¤d

Current address: British Columbia Ministry of Environment and Climate Change Strategy, Victoria, British Columbia, Canada

‡ These authors also contributed equally to this work.

^✉

* E-mail: afine@wcs.org

Contributed equally.

Roles

Nguyen Quynh Huong: Conceptualization, Formal analysis, Writing – original draft, Writing – review & editing

Nguyen Thi Thanh Nga: Conceptualization, Formal analysis, Investigation, Project administration, Writing – original draft, Writing – review & editing

Nguyen Van Long: Project administration, Supervision, Writing – review & editing

Bach Duc Luu: Supervision, Writing – review & editing

Alice Latinne: Formal analysis, Writing – original draft, Writing – review & editing

Mathieu Pruvot: Formal analysis, Writing – original draft, Writing – review & editing

Nguyen Thanh Phuong: Supervision, Writing – review & editing

Le Tin Vinh Quang: Investigation, Writing – review & editing

Vo Van Hung: Supervision, Writing – review & editing

Nguyen Thi Lan: Supervision, Writing – review & editing

Nguyen Thi Hoa: Investigation, Writing – review & editing

Phan Quang Minh: Investigation, Writing – review & editing

Nguyen Thi Diep: Investigation, Writing – review & editing

Nguyen Tung: Investigation, Writing – review & editing

Van Dang Ky: Investigation, Writing – review & editing

Scott I Roberton: Conceptualization, Supervision, Writing – review & editing

Hoang Bich Thuy: Supervision, Writing – review & editing

Nguyen Van Long: Conceptualization, Investigation, Project administration, Writing – review & editing

Martin Gilbert: Project administration, Writing – review & editing

Leanne Wicker: Project administration, Writing – review & editing

Jonna A K Mazet: Conceptualization, Funding acquisition, Project administration, Writing – review & editing

Christine Kreuder Johnson: Conceptualization, Project administration, Writing – review & editing

Tracey Goldstein: Conceptualization, Investigation, Project administration, Writing – review & editing

Alex Tremeau-Bravard: Investigation, Writing – review & editing

Victoria Ontiveros: Investigation, Writing – review & editing

Damien O Joly: Conceptualization, Funding acquisition, Project administration, Supervision, Writing – review & editing

Chris Walzer: Supervision, Writing – review & editing

Amanda E Fine: Conceptualization, Investigation, Project administration, Supervision, Writing – original draft, Writing – review & editing

Sarah H Olson: Conceptualization, Formal analysis, Project administration, Writing – original draft, Writing – review & editing

Dong-Yan Jin: Editor

PMCID: PMC7416947 PMID: 32776964

Abstract

Outbreaks of emerging coronaviruses in the past two decades and the current pandemic of a novel coronavirus (SARS-CoV-2) that emerged in China highlight the importance of this viral family as a zoonotic public health threat. To gain a better understanding of coronavirus presence and diversity in wildlife at wildlife-human interfaces in three southern provinces in Viet Nam 2013–2014, we used consensus Polymerase Chain Reactions to detect coronavirus sequences. In comparison to previous studies, we observed high proportions of positive samples among field rats (34.0%, 239/702) destined for human consumption and insectivorous bats in guano farms (74.8%, 234/313) adjacent to human dwellings. Most notably among field rats, the odds of coronavirus RNA detection significantly increased along the supply chain from field rats sold by traders (reference group; 20.7% positivity, 39/188) by a factor of 2.2 for field rats sold in large markets (32.0%, 116/363) and 10.0 for field rats sold and served in restaurants (55.6%, 84/151). Coronaviruses were also detected in rodents on the majority of wildlife farms sampled (60.7%, 17/28). These coronaviruses were found in the Malayan porcupines (6.0%, 20/331) and bamboo rats (6.3%, 6/96) that are raised on wildlife farms for human consumption as food. We identified six known coronaviruses in bats and rodents, clustered in three Coronaviridae genera, including the Alpha-, Beta-, and Gammacoronaviruses. Our analysis also suggested either mixing of animal excreta in the environment or interspecies transmission of coronaviruses, as both bat and avian coronaviruses were detected in rodent feces on wildlife farms. The mixing of multiple coronaviruses, and their apparent amplification along the wildlife supply chain into restaurants, suggests maximal risk for end consumers and likely underpins the mechanisms of zoonotic spillover to people.

Introduction

Human-wildlife contact with a bat or an intermediate host species in China likely triggered a coronavirus spillover event that may have involved wildlife markets and led to the pandemic spread of SARS-CoV-2 [1,2]. The pandemic risk of commercial trade in live wildlife was first recognized during the 2002–2003 Severe Acute Respiratory Syndrome (SARS) outbreak due to SARS-CoV [3]. This virus spread to countries in Asia, Europe, and the Americas with 8,096 people infected and 774 deaths, costing the global economy about $US 40 billion in response and control measures [4,5]. Unfortunately, the impact of COVID-19, the disease caused by SARS-CoV-2, has reached nearly every country and greatly surpassed those numbers by many orders of magnitude [6]. While bats are thought to be the ancestral hosts for all groups of coronaviruses [7], for both SARS-CoV and SARS-CoV-2 wildlife trade supply chains are suspected to have contributed the additional conditions necessary for the emergence, spillover, and amplification of these viruses in humans [8,9]. In Viet Nam, between 2013 to 2014, we conducted coronavirus surveillance to understand the presence and diversity of coronaviruses in wildlife at sites identified as high-risk interfaces for viral spillover from wildlife to humans [10]. We sampled at three sub-interfaces along the live field rat trade (Rattus sp. and Bandicota sp.) including field rats sold by rat traders, by vendors in large markets, and rats butchered and sold in restaurants as prepared dishes. We also sampled rodents raised on wildlife farms to assess risk from different wildlife supply chains destined for human consumption. We sampled bat guano, primarily on bat guano farms to assess the potential occupational risk of this practice given that bat guano farm artificial roost structures are often erected near human dwellings.

In the early 2000s, the Vietnamese field rat trade was estimated to process 3,300–3,600 tons of live rats annually for consumption, a market valued at US$2 million [11]. Although rats are still commonly traded in wet markets and sold live for food consumption along the Mekong Delta in southern Viet Nam, no recent published data on the scale and scope of the trade is available [12]. This human-wildlife interface involves the capture of wild free-ranging field rats, subsequent trade, and consumption along a supply chain involving the entire Mekong Delta region, particularly Cambodia and Viet Nam [13]. Driving this trade are consumers in Viet Nam and Cambodia, some of whom report eating rats at least once per week because of their good flavor, low cost, and perception of rats as ‘healthy, nutritious, natural, or disease free’ [13]. Rat parts (heads, tails, and internal organs discarded at slaughter) are also often fed to domestic livestock or herptiles raised in captivity including frogs, snakes, and crocodiles [12].

Over the past three decades, commercial wildlife farming has developed in many countries in Southeast Asia, including Viet Nam. Although there are historic references to the occurrence of wildlife farms in Viet Nam dating back to the late 1800s, the rapid expansion in terms of farm numbers, species diversity, and scale of operations has occurred in recent decades in response to growing domestic and international demand for wildlife [14]. A 2014 survey across 12 provinces in southern Viet Nam identified 6,006 registered wildlife farms of which 4,099 had active operations. The surveyed farms were stocked with approximately one million wild animals including, rodents, primates, civets, wild boar, Oriental rat-snakes, deer, crocodiles, and softshell turtles. Ninety-five percent of the farms held 1–2 species of wildlife, and 70% of the farms also raised domestic animals on the same premises [15]. A key component of the wildlife farm industry in Viet Nam is the raising of wild species for meat for human consumption [15]. These farms sell to urban wild meat restaurants serving increasingly affluent populations throughout the country and also supply international markets with wild meat [16]. Commercial wildlife farming in Viet Nam is part of the expanded international trade of wildlife that has been hypothesized to contribute to the cause of global epidemics, such as SARS [17] and now COVID-19.

Emerging evidence suggests zoonotic virus spillover risk is a concern at bat-human interfaces in Asia. Guano harvested from a cave in Thailand were positive for a group C betacoronavirus, which includes MERS-CoV, and 2.7% of 218 people living in close proximity to bats known to carry viruses related to SARS-CoV tested positive for SARS-related antibodies in China [18,19]. The traditional practice of guano farming in parts of Cambodia and Viet Nam involves the construction of artificial bat roosts in gardens or backyard farms, under which domestic animals and crops are raised, and children often play [20,21]. Cambodian development programs promoted the practice in 2004 to enhance soil fertility, reduce reliance on chemical fertilizers, generate income ($US 0.50/kg), control insect pests, and protect the lesser Asiatic yellow bats (Scotophilus kuhlii) that were being hunted [20–22]. No personal protection measures are taken when harvesting the guano, which is reported to improve the growth rate in five economically important plant species [23].

In this study we investigated the presence and diversity of coronavirus sequences in the field rat trade distribution chain, wildlife farms specializing in raising rodents for human consumption, and bat guano “farms” and roosts near human dwellings to better understand the natural hosts of coronaviruses and the risk for these interfaces to facilitate spillover into humans.

Materials and methods

Sampling locations

Sampling was performed at multiple sites representing several high-risk interfaces for contacts among people, rodents, and bats. Rodent sampling focused on the live field rat trade supply chain and wildlife farms specializing in raising rodents [Malayan porcupines (Hystrix brachyura) and bamboo rats (Rhizomys sp.)] for meat. Along the field rat supply chain, we targeted eight sites involved in the private sale and butchering of rats for consumption, defined as ‘traders’ for the purpose of this study in Dong Thap and Soc Trang provinces, 14 large market sites where rats were butchered and sold in Dong Thap and Soc Trang provinces (>20 vendors), and two restaurant sites in Soc Trang province where live rats were kept on the premises and butchered and served as food (Fig 1). The 28 rodent farm sites targeted in Dong Nai province produced Malayan porcupines and bamboo rats for human consumption (Fig 2). Other species observed or raised at the wildlife farm sites included dogs, cattle, pigs, chickens, ducks, pigeons, geese, common pheasant, monitor lizards, wild boar, fish, python, crocodiles, deer, civets, non-human primates as pets or part of private collections, free-flying wild birds, and free-ranging peri-domestic rats.

Fig 1 — Slaughtering rats at a large market (left) and a rat vendor stall displaying live rats in cages in a large market (right) in Dong Thap province, October 2013.

Bat sampling occurred at bat guano “farms” and a natural bat roost located at a religious site. Bat guano farms consisted of artificial roosts constructed with a concrete base and pillars topped with fronds of coconut palm or Asian Palmyra Palm (Borassus flabellifer) (Fig 3). Seventeen bat guano farms were sampled in the two provinces of Dong Thap and Soc Trang. The natural bat roost was located at a religious site in Soc Trang province known as the “bat pagoda”, where Pteropus sp. have historically roosted in trees protected from hunting, and light and noise pollution [24].

All study sampling occurred from January 2013 to March 2014 at 41 sites in the wet (south Viet Nam: May 1^st—November 30^th) and 30 in the dry (south Viet Nam: December 1^st—April 30^th) seasons. Given the distances between sites, 69 sites were sampled once except the bat pagoda natural roost in Soc Trang province, which was visited three times and sampled in both seasons.

Animal sampling

Samples from animals were humanely collected using standard and previously published protocols and no animals were purchased for this study (S1 Table) [25]. Samples from rats at all three sub-interfaces in the field rat trade were collected from individual carcasses after the rats were slaughtered by a trader, market vendor, or restaurant kitchen staff as part of the rat meat preparation process during normal sales to customers. Oral swabs were collected from the severed heads of all the rats, with at least one additional tissue sample collected from the discarded internal organs of each individual. The small intestine was the additional tissue most frequently collected with brain, kidney, lung, rectal swab, and urine also collected from some individuals. Rats were usually butchered at a common site for each observed time period that was only cleaned intermittently following the trader’s, vendor’s, or restaurant’s regular practices.

Feces, urine, and swabs of the pen floors (environmental samples), were collected non-invasively (without handling the animals) from rodents on wildlife farms. Samples were classified as ‘fecal sample’ or ‘urine sample’ when voided feces or urine was collected from an animal housed individually in its own cage, and as ‘environmental sample’ when collected as a swab from cages housing multiple individuals.

Fecal samples and a small number of urine samples excreted by bats in guano farms and the natural roost site were collected on clean plastic cover sheets within 1–2 hours after placement under bat roosts, and thus each sample may represent one or multiple bats. Oral and rectal swabs were also collected from live-captured bats during one sampling visit at the natural pagoda roost site.

All animals were identified in the field to the lowest taxonomic level possible based on morphological characteristics, and species was identified in a subset of animals through genetic barcoding [15]. Due to difficulty of morphologic identification in the field, unless barcoded, rodents (Rattus argentiventer, R. tanezumi, R. norvegicus, R. exulans, R. losea, and Bandicota indica; [12,26]) were categorized as “field rats”. Bats were classified as “Microchiroptera” following the traditional taxonomic classification (new classification of two new suborders Yangochiroptera and Yinpterochiroptera, was only published near the end of the study, so for consistency we used the historical classification [27]).

All samples were collected in cryotubes containing RNAlater (RNA stabilization reagent, Qiagen), and stored in liquid nitrogen in the field before being transported to the laboratory for storage at -80 ˚C. Samples were tested by the Regional Animal Health Office No. 6 (RAHO6) laboratory in Ho Chi Minh City. The study and sampling activities for specified dates and locations were approved by the Department of Animal Health of the Ministry of Agriculture and Rural Development and animal sampling protocols were reviewed by the Institutional Animal Care and Use Committee at the University of California at Davis (protocol number 16048).

Sample testing

RNA was extracted (RNA MiniPrep Kit, Sigma-Aldrich) and cDNA transcribed (SuperScript III First Strand cDNA Synthesis System, Invitrogen). Coronavirus RNA was detected using two broadly reactive consensus nested-PCR assays targeting the RNA dependent RNA polymerase (RdRp) gene [28,29]. The positive control was a synthetic plasmid containing the primer-binding sites for both assays. Distilled water was used as a negative control and included in each test batch. PCR products were visualized using 1.5% agarose gels, and bands of the correct size were excised, cloned, and sequenced by Sanger dideoxy sequencing using the same primers as for amplification.

Phylogenetic analysis

For sequence analysis and classification operating taxonomic units were defined with a cut off of 90% identity, i.e. virus sequences that shared less than 90% identity to a known sequence were labelled sequentially as PREDICT_CoV-1, -2, -3, etc. and groups sharing ≥ 90% identity to a sequence already in GenBank were given the same name as the matching sequence [7]. A phylogenetic tree was constructed for sequences amplified using the Watanabe protocol, as this PCR protocol yielded longer sequences and more positive results than the Quan protocol. Several representative sequences for each viral species found in our study were included for analysis and are available in GenBank (S3 Table). Alignments were performed using MUSCLE, and trees were constructed using Maximum likelihood and the Tamura 3-parameter model in MEGA7 [30]. The best-fit model of DNA substitution was selected in MEGA7 using BIC scores (Bayesian Information Criterion) and Maximum Likelihood values (lnL). Bootstrap values were calculated after 1,000 replicates. In addition, a median-joining network was constructed using Network 5.0.0.3 [31] to explore phylogenetic relationships among bat coronavirus 512/2005 sequences at the intraspecies level, as haplotype networks may better represent the relationships among viral sequences with low sequence diversity compared with phylogenetic trees [32].

Statistical analyses

Visualization of sampling locations in provinces in Viet Nam, along with the distribution by species and interface was constructed with the ggplot2 and sf packages [33]. The source of the Viet Nam provincial map is geoBoundaries v. 3.0.0 (https://www.geoboundaries.org; [34]) and Open Development Mekong (https://vietnam.opendevelopmentmekong.net). All analyses were done using R version 3.5.0 or higher (R Development Core Team, Vienna, Austria). Data (S1 Data) and code (S1 R Code) are available in the supplementary materials. The effect of risk factors (season, sub-interface type) was examined and limited to interfaces for which the distribution of samples across factors could support the analysis. These included season for Pteropus bat samples collected in the bat pagoda natural roost and the effect of season and sub-interface for samples collected in the rodent trade in southern Viet Nam. Given the low sample size, the effect of season for Pteropus bats samples positive for coronaviruses was assessed using a Fisher exact test. The effect of season (dry, wet, with dry season as reference category) and sub-interface type (trader, large markets, restaurants, with trader as reference category) in traded rodent samples positive for coronaviruses was assessed with a mixed effect multivariable logistic regression, with sites as random effect (i.e. grouping variable) using the lme4 R package [35]. A p-value of less than 0.05 was considered statistically significant. The 95% binomial confidence intervals for proportions were calculated using binom.test in R.

The comparison of the proportion of coronavirus positives in different sample types was performed on positive individuals sampled in the live field rat trade with multiple sample types collected per individual. We then calculated the proportion of individuals positive for each sample type, as a proxy for the probability of detection by each sample type.

Results

Detection of coronavirus by animal taxa and interface

A total of 2,164 samples collected between January 2013 and March 2014 from rodents and bats were tested for coronaviruses (Table 1, S1 Table). Assuming that non-invasive samples from bats and farmed rodents represented unique distinct individuals, these samples came from 1,506 individuals, including 1,131 rodents (702 field rats and 429 wildlife farm rodents) and 375 bats from 70 sites sampled in Dong Thap, Soc Trang, and Dong Nai provinces in the southern region near the Mekong River Delta (Fig 4).

Table 1. Summary of coronavirus positives by taxa and interface.

Co-infection is defined as the detection of two different coronavirus taxonomic units in an individual animal.

Taxa group	Interface	Sub-interface	Taxa group	% site positive	% individual positive	Viral species	# of co-infected animals
Rodents	Rat trade	Rat trader (selling live rats and slaughtering live rats for sale as meat)	Field rat^ᵅ	100% (8/8)	20.7% (39/188)	Murine coronavirus (n = 36), Longquan aa coronavirus (n = 5)	2
		Large market (selling live rats and slaughtering live rats for sale as meat)	Field rat^ᵅ	100% (14/14)	32.0% (116/363)	Murine coronavirus (n = 103), Longquan aa coronavirus (n = 31)	18
		Restaurant (slaughtering live rats held on the premises and preparing as food)	Field rat^ᵅ	100% (2/2)	55.6% (84/151)	Murine coronavirus (n = 70), Longquan aa coronavirus (n = 20)	6
	Wildlife farm		Hystrix sp.	47.8% (11/23)	6.0% (20/331)	Bat coronavirus 512/2005 (n = 19), Infectious bronchitis virus (IBV) (n = 1)	0
			Rhizomys sp.	45.5% (5/11)	6.3% (6/96)	Bat coronavirus 512/2005 (n = 5), Infectious bronchitis virus (IBV) (n = 1)	0
			Rattus sp.^b	100% (1/1)	100% (1/1)	Bat coronavirus 512/2005 (n = 1)	0
			Sciuridae sp.	0% (0/1)	0% (0/1)
Bats	Human dwelling	Natural bat roost
			Pteropus sp.	100% (1/1)	6.7% (4/60)	PREDICT_CoV-17 (n = 3), PREDICT_CoV-35 (n = 1)	0
			Cynopterus horsfieldii	0% (0/1)	0% (0/2)
		Bat guano farm (artificial bat roost)	Microchiroptera^c	94.1% (16/17)	74.8% (234/313)	PREDICT_CoV-17 (n = 1), PREDICT_CoV-35 (n = 38), Bat coronavirus 512/2005 (n = 216)	21^d
				82.9% (58/70)	33.5% (504/1506)		47

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ᵅ Field rat here refers to a mix of Rattus sp. and Bandicota sp.

ᵇ This environmental sample collected from a porcupine cage on a porcupine farm was barcoded as Rattus sp., suggesting this species was free-ranging at the site (Fig 2). The detection of a bat virus from this sample is suggestive of either environmental mixing or viral sharing.

^c Suborder

^d Co-infections included PREDICT_CoV-17 with Bat coronavirus 512/2005 (n = 1) and PREDICT_CoV-35 with Bat coronavirus 512/2005 (n = 20).

Fig 4 — The color of each bar represents the animal taxonomic group sampled in Dong Nai, Dong Thap, and Soc Trang provinces. *Sciuridae* and *Rattus argentiventer* were only sampled one time apiece from wildlife farms. Map was made using geoBoundaries v. 3.0.0 (https://www.geoboundaries.org; [34]) and Open Development Mekong (https://vietnam.opendevelopmentmekong.net) data under a CC BY 4.0 license.

Out of 70 sites, coronavirus positives were detected at 58 including 100% (24/24) of live rat trade sites, 60.7% (17/28) of rodent wildlife farm sites, 94.1% (16/17) of bat guano farm sites, and at the one natural pteropid bat roost. Wildlife farms were only sampled in Dong Nai province and the live rat trade and bat interfaces were sampled in Dong Thap and Soc Trang provinces (Fig 4).

Coronaviruses were detected in the field rat trade (a mix of Rattus and Bandicota genera) at all sites in Dong Thap (n = 16) and Soc Trang (n = 8) provinces, with 34.6% (95% CI 29.8–39.7%, 129/373) and 33.4% (95% CI 28.4–38.9%, 110/329) positives respectively. The overall proportion of positives in field rats was 34.0% (95% CI 30.6–37.7%, 239/702), ranging from 3.2% to 74.4% across sites. Field rats sampled at sub-interfaces in the live rat trade had an increasing proportion of positives along the distribution chain. Starting with traders, the proportion positive was 20.7% (95% CI 15.3–27.4%, 39/188), 32.0% (95% CI 27.2–37.1%, 116/363) in large markets, and 55.6% (95% CI 47.3–63.6%, 84/151) at restaurants (Fig 5). The proportion of positives was higher in the wet season (36.7%, 95% CI 32.8–40.8%, 210/572) than the dry season (22.3%, 95% CI 15.7–30.6%, 29/130). In a multivariate model with site as random effect, both season and sub-interface type were significantly associated with the risk of rat coronavirus infection, with higher risk of infection in the wet season (OR = 4.9, 95% CI 1.4–18.0), and increasing risk along the supply chain from traders (baseline) to large markets (OR = 2.2, 95% CI 1.05–4.7), to restaurants (OR = 10.0, 95% CI 2.7–39.5) (S2 Table). It should be noted, however, that since sites were only visited during one season, both independent variables were defined at the site level and confounding effects with other site-level characteristics cannot be excluded.

Fig 5 — Bars show 95% confidence intervals.

Among the positive field rats with more than one sample tested (n = 220), the proportion positive by sample type was 79.9% (95% CI 73.9–84.9%, 175/219) in oral swabs, 52.9% (95% CI 38.6–66.8%, 27/51) in lung, 51.6% (95% CI 43.5–59.7%, 80/155) in small intestine, 31.2% (95% CI 12.1–58.5%, 5/16) in brain, 23.1% (95% CI 6.2–54.0%, 3/13) in kidney, 50.0% in feces (1/2), 100% in spleen (1/1), and 0% in urine/urogenital swabs (0/1).

At the rodent wildlife farm interface, 6.0% (95% CI 3.8–9.3%, 20/331) of Hystrix brachyura and 6.3% (95% CI 2.6–13.6%, 6/96) of Rhizomys sp. were positive. The overall proportion of positives was 6.3% (95% CI 4.3–9.1%, 27/429) (Table 1 and Fig 4). There was no difference among species or season and proportion positive in rodent farms, and low sample size and unequal sampling limited analysis.

The proportion of coronavirus positives at the two bat interfaces differed by an order of magnitude as 74.8% (95% CI 69.5–79.4%) of the non-invasive samples collected from Microchiroptera bats at bat guano farms were positive, and 6.7% (95% CI 2.2–17.0%) of the Pteropus genus samples at the natural roost in Soc Trang province (Fig 4) were positive (Table 1). Pteropid bats sampled at the natural roost had higher proportions of positives in the wet season (27.3%, 95% CI 7.3–60.7%, 3/11) compared with the dry season (2.0%, 95% CI 0.1–12.2%, 1/50; Fisher exact test p = 0.02, OR = 16.6 [1.2–956.8]), although low sample size and single sampling per season warrants cautious interpretation.

Phylogenetic analysis

Six distinct taxonomic units of coronaviruses corresponding to bat coronavirus 512/2005, Longquan aa coronavirus, avian infectious bronchitis virus (IBV), murine coronavirus, PREDICT_CoV-17, and PREDICT_CoV-35 were detected. All these viruses were detected using both the Watanabe and Quan assays, except IBV sequences that were detected only using the Quan protocol. Of the 504 positive animals, 433 were positive by the Watanabe assay, 410 were positive by the Quan assay, and 339 were positive by both. Phylogenetic analysis showed that among the six coronaviruses detected, PREDICT_CoV-35 and bat CoV 512/2005 clustered within the Alphacoronaviruses, while PREDICT_CoV-17, Longquan aa CoV and murine CoV clustered within the Betacoronaviruses. The virus identified within the Gammacoronavirus genus was avian IBV.

PREDICT_CoV-17 and PREDICT_CoV-35 were first reported by Anthony et al. [17]. We found PREDICT_CoV-17 in Pteropus bats and in Microchiroptera (Table 1). The PREDICT_CoV-17 sequences from Pteropus detected in this study clustered closely with PREDICT_CoV-17 sequences from Pteropus giganteus bats in Nepal and Pteropus lylei bats in Thailand [36] (Fig 6, S3 Table). PREDICT_CoV-35 was found in Microchiroptera in bat guano farms and in a pteropid bat (Table 1). PREDICT_CoV-35 sequences from Viet Nam clustered with other PREDICT_CoV-35 sequences found previously in samples from hunted Scotophilus kuhlii bats in Cambodia (S3 Table; Dr. Lucy Keatts personal communication), and with sequences found in bats from an earlier study in the Mekong Delta region in Viet Nam (Fig 6).

Fig 6 — The analysis is based on 387 bp fragment of the *RdRp* gene using maximum likelihood with the Tamura 3-parameter model, Gamma distributed (G), and 1000 bootstrap replicates via MEGA7. The analysis included 17 sequences from this study (red from bat hosts, blue from rodent hosts), six sequences (in gray) from a previous study in Viet Nam [26], and 26 reference sequences (in black) available in the GenBank database (S3 Table). The tree was rooted by a strain of Night-heron coronavirus HKU19 (GenBank accession No. NC_016994).

Bat coronavirus 512/2005 was detected in Microchiroptera bat guano; and in H. brachyura (feces and environmental samples), R. pruinosus (feces barcoded), and R. argentiventer (barcoded environmental sample) in wildlife farms (Table 1 and S1 Table). In Microchiroptera, Bat coronavirus 512/2005 was frequently found in co-infection with PREDICT_CoV-35 (Table 1, S1 Table). Network analysis showed the relationships among the bat coronavirus 512/2005 sequences from the three provinces in south Viet Nam (Fig 7). We observed two main clusters and a shallow geographic structure of genetic diversity, perhaps illustrative of sampling effort but also of localized transmission and circulation of bat coronavirus 512/2005 strains in these provinces. One cluster was exclusively detected in Microchiroptera and mostly restricted to Dong Thap province and another cluster included sequences shared among all hosts and distributed in the three provinces (Fig 7). Parts of the network showed a star-like topology (Fig 7), typical of populations in expansion that have recently increased size. There were two sequence types that were shared among Microchiroptera and farmed rodents. The remaining 11 sequence types isolated from rodents on wildlife farms were not identical to those isolated from bats and were characterized by several nucleotide differences (Fig 7).

Fig 7 — Median-joining networks of bat coronavirus 512/2005 *RdRp* sequences color-coded according to (A) host and (B) sampling location. Each circle represents a sequence type, and circle size is proportional to the number of animals sharing a sequence type. Numbers on branches indicate the number of mutations between sequence types if it was higher than one. Branches without a specified number of mutations correspond to a single mutation. Circles are colored-coded by animal host: bat (*Microchiroptera*), rodent (*Rattus* & *Bandicota*, *Rhizomys*, and *Hystrix*) and sampling location (Dong Thap (blue), Dong Nai (yellow), and Soc Trang (green)). Small black circles represent median vectors (ancestral or unsampled intermediate sequence types).

Murine coronavirus and Longquan aa coronavirus were detected in 209 and 56 field rat samples, respectively, and 26 were coinfected with both (Table 1). Two sequences of IBV were detected in rodent feces collected on two wildlife farms, one in a bamboo rat and another in a Malayan porcupine. The rodent farm interface where bat and avian coronaviruses were detected in feces were not full containment facilities and possibly had bats and birds flying and roosting overhead (Fig 2). The IBV positives were detected in fecal samples from wildlife farms that had chickens, pigs, and dogs on site.

Discussion

High prevalence and amplification along the supply chain for human consumption

Significant findings of this study are the high proportion of coronavirus positive wildlife (bats and rodents) and the increasing proportion of positives found along the rat trade supply chain from sub-interfaces close to the capture site (rat traders) to restaurants. The transit of multiple rat species through the supply chain, and admixing with other species and taxa at sub-interfaces along the supply chain, offers opportunities for inter- and intra-species viral exchange and recombination. Capture and transport of wildlife combined with overcrowding and close confinement of live animals in cages results in increased animal contact, likely leading to stress. While methodologically similar to rodent surveys in Zhejiang province, China (2%), Dong Thap province, Viet Nam (4.4%), and globally (0.32%), our overall proportion of coronavirus positives was much higher among field rats (34.5%) and somewhat higher among farmed rodents (6.3%) [7,26,37]. Stress, dehydration, and poor nutrition reduce animal condition and alter immune function and likely contribute to both increased shedding of viruses by infected animals, and increased susceptibility to infection of animals in the wildlife trade chain for human consumption [38].

The amplification of coronavirus along the supply chain may be seasonal as field rats were significantly more positive in the wet season. Rattus argentiventer generally reproduce year-round in Viet Nam, but are particularly abundant in the wet season (May through October) following the rice harvest when an abundance of food supports the population increase [39]. If these seasonal population increases affect density dependent contact, there could be increased coronavirus prevalence and shedding in wild field rats during certain times of the year, which could then be further amplified along the trade.

Our survey was not a comprehensive multi-year evaluation of the field rat supply chain and was restricted to two provinces with this human-wildlife interface. These limitations mean we are not able to make inferences about larger spatial patterns or the inter-annual variability of coronavirus prevalence in wildlife populations found in this interface, which spans into neighboring Cambodia. Field rat carcasses were sampled immediately after they were slaughtered by traders, market vendors, or restaurant kitchen staff to optimize viral detection. Some viral cross contamination of carcasses during the butchering process may have increased the proportion of coronaviruses detected in individual animals. The degree to which cross-contamination may have elevated the proportion of coronaviruses detected in individual animals is unknown, however, this proportion accurately reflects the risk of human exposure from handling and consumption of field rats at sub-interfaces along this wild meat food chain.

From a mechanistic perspective as animals progress along the wildlife supply chain, opportunity for human contact increases, including close direct contact with traders, butchers, cooks, and consumers [40]. The combination of increased coronavirus prevalence in traded wildlife and greater opportunity for human-wildlife contact as well as intra- and inter-species contact in trade systems is likely to increase the risk of zoonotic transmission of coronaviruses in wildlife markets, restaurants, and other trade interfaces.

Viral sharing or environmental mixing

We detected avian and bat coronaviruses in rodents raised on wildlife farms, including Malayan porcupines and bamboo rats, but we did not detect rodent-associated coronaviruses. The only previously published coronavirus testing of Malayan porcupine samples carried out in China were negative [41]. It is unclear if the Malayan porcupine samples from animals screened in this study were infected with the avian or bat viruses or if environmental contamination or mixing occurred with avian and bat guano. Chickens were present at the two sites where the IBV-positive rodents were detected, and bats fly and potentially roost overhead at most farms. ‘Artificial market’ studies of influenza A viruses have found cage-stacking of species on top of other species and shared water sources facilitate viral transmission [42,43]. Nevertheless, viral sharing between species and environmental contamination or mixing (i.e. bat/bird guano landing on rat feces) are two equally likely explanations for the presence of bat and avian coronaviruses detected in rodent fecal and environmental samples.

The field rats were co-infected with the Longquan aa coronavirus and the murine coronaviruses, both of which are from the Lineage A (Embecovirus) Betacoronavirus genus. Co-infections with multiple coronaviruses deserve particular attention as this co-occurrence may facilitate viral recombination leading to the emergence of new viruses [44,45].

At the very least, we conclude that rodents in the field rat and farmed rodent supply chains are being exposed to coronaviruses from rodents, bats, and birds and perhaps creating opportunities for coronavirus recombination events, which may lead to viruses that could spill over into humans [46,47]. Our findings indicate a high risk of consumer exposure even if cross-contamination due to shared butchering materials influenced the proportion of positive individuals. Repeated and more direct individual sampling of these species at these interfaces would be advantageous to determine if viral sharing was occurring versus environmental contamination of samples.

Bat guano farms

The high proportion of positive bat feces at bat guano farms indicates the potential risk of bat guano farmers, their families, and their animals being exposed to bat coronaviruses. The overall proportion of positives (74.8%) was higher than previous studies using similar testing methods targeting bats in Viet Nam (22%), Thailand (7.6%), Lao PDR (6.5%), and Cambodia (4.85%) [26,48,49]. In this region of Viet Nam, artificial roosts are typically erected in backyard family owned plots that incorporate a mosaic of duck, goat, or pig production and crops such as guava tress or other fruit trees and large scale kitchen gardens.

Bats have been shown to be an important evolutionary hosts of coronaviruses, including those infecting humans [7,50–53]. Both PREDICT_CoV-17 and PREDICT_CoV-35 have been detected previously in the Pteropus and Microchiroptera bats in Viet Nam, Cambodia, and Nepal, which confirms that coronaviruses are capable of infecting distantly related hosts [7]. The finding of the same virus in different bat species raises the question of whether they co-roost and/or share viruses through contact during other activities. Utilizing shared resources such as water or feeding on and around crops and fruit could lead to contact and facilitate a host jump. The presence of the same virus in bat species in multiple neighboring countries supports the suggestion by others that virus distribution coincides with their bat host distribution [7,54,55]. While there has been no testing of the pathogenicity of these bat coronaviruses in humans or animals, they are found at close contact bat-human interfaces and further characterization is needed to understand their host range and potential for spillover. Any general persecution of bats because of zoonotic viruses they may carry can actually increase the number of susceptible bats and increase transmission risk to people [56], and would interfere with the important ecosystem services that bats provide, such as controlling insect pests of rice fields [57], plant pollination, and seed dispersal.

Capacity building and outreach

Beyond the viral findings, this work represented an important opportunity for capacity development in field, laboratory, and scientific disciplines, as well as opportunities for social engagement and education of high-risk communities on zoonotic disease threats. The consensus PCR approach for viral detection provides a cost-effective tool to detect emerging viruses in low-resource settings. Our work adds to the growing body of research demonstrating the utility of this approach to detect both known and novel viruses and co-infections in a variety of taxa, sample types, and interfaces. In Viet Nam, the direct result is an enhanced One Health surveillance capacity to detect important emerging or unknown viruses in humans, wildlife, and livestock. In the communities with which we partnered, strong engagement enabled teams to sample a wide diversity of wild animals at high-risk interfaces. Importantly, we have returned to these same communities to share the viral findings and to educate participants with an outreach program on how to live safely with bats [58].

Conclusions

Large percentages of coronaviruses were detected in bats and rodents at sites where people have close contact and interact with wildlife including sub-interfaces along wildlife trade chains, wildlife farms, and artificial bat roosts where bat guano is collected for use as fertilizer. The high proportion of coronavirus positive samples at these human-wildlife interfaces highlights the potential for human exposure to wildlife origin coronaviruses. The observed viral amplification along the wildlife trade supply chain for human consumption, illustrated by the field rat trade in this study, likely resulted from the admixing of different species or sub-populations, and the close confinement of stressed live animals. This highlights the potential for coronavirus (and other virus) shedding and amplification along other wildlife supply chains (e.g., civets, pangolins) where similarly large numbers of animals are collected from a wide range of locations, transported, and confined. The detections of rodent, bat, and avian coronaviruses confirm concerns about productions systems and supply chains that increase contact between wildlife and domestic species. Livestock and people living in close contact with rodents, bats, and birds shedding coronaviruses provides opportunities for intra- and inter-species transmission and potential recombination of coronaviruses.

Human behavior is facilitating the spillover of viruses, such as coronavirus, from animals to people. The wildlife trade supply chain from the field to restaurant and end consumer provides multiple opportunities for such spillover events to occur [1]. Since the SARS outbreak, broad scientific consensus exists that long term, structural changes, and wildlife trade and market closures will be required to prevent future epidemics. To minimize the public health risks of viral disease emergence from the consumption of wildlife and to safeguard livestock-based production systems, we recommend precautionary measures that restrict the killing, commercial breeding, transport, buying, selling, storage, processing and consuming of wild animals. The time has come for the global community to collectively assume responsibility through targeted wildlife trade reform. The world must also increase vigilance through building and improving detection capacity; actively conducting surveillance to detect and characterize coronaviruses in humans, wildlife, and livestock; and to inform human behaviors in order to reduce zoonotic viral transmission to humans. The more opportunities we provide for humans to come into direct contact with a multitude of wildlife species, the higher the likelihood of another spillover event. The costs of inaction are astronomically high and we must ensure that future food production and security is sustainable, just, and supports global health.

Supporting information

S1 Table. Summary of all testing results by genus, interface, sub-interface, sample types, sites, percentage of samples testing positive, and viral species.

(PDF)

Click here for additional data file.^{(90.7KB, pdf)}

S2 Table. Multivariate mixed effect logistic regression showing the association between season and sub-interface with coronavirus positives in field rats.

(PDF)

Click here for additional data file.^{(52.5KB, pdf)}

S3 Table. GenBank accession numbers for coronavirus sequences detected in this study and for reference sequences.

(PDF)

Click here for additional data file.^{(77.7KB, pdf)}

S1 Data

(TXT)

Click here for additional data file.^{(119B, txt)}

S1 R Code. Code used to conduct the analysis described.

(HTML)

Click here for additional data file.^{(1.3MB, html)}

Acknowledgments

We are thankful to the government of Viet Nam, the Wildlife Conservation Society Health team for conducting field sampling, partnering laboratories for running diagnostic tests, and many other agencies for collaborations on this project. Specifically, we would like to acknowledge Le Viet Dung, Ton Ha Quoc Dung and Nguyen Van Dung (Dong Nai Province Forest Protection Department); Vo Be Hien (Dong Thap Sub-Department of Animal Health); Quach Van Tay (Soc Trang Sub-Department of Animal Health); and the late Ngo Thanh Long (Regional Animal Health Office No. 6) for his visionary leadership and commitment to this initiative in Viet Nam. The authors are indebted to the cheerful and resourceful help of Tammie O’Rourke and Dan O’Rourke and others who developed and curated the database used to maintain PREDICT data through the Emerging Infectious Disease Information Technology Hub (EIDITH).

Data Availability

All relevant data are available at https://doi.org/10.5061/dryad.7h44j0zrj.

Funding Statement

This study was made possible by the generous support of the American people through the United States Agency for International Development (USAID) Emerging Pandemic Threats PREDICT project (cooperative agreement numbers GHN-A-OO-09-00010-00 [J.A.K.M., C.K.J., T.G., D.O.J.] and AID-OAA-A-14-00102 [J.A.K.M, C.K.J., T.G., A.E.F, S.H.O.]). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The URL to the USAID Emerging Pandemic Threats Program (EPT-1 and 2) is https://www.usaid.gov/ept2.

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PLoS One. doi: 10.1371/journal.pone.0237129.r001

Decision Letter 0

Dong-Yan Jin

19 Jun 2020

PONE-D-20-17798

Coronavirus testing indicates transmission risk increases along wildlife supply chains for human consumption in Viet Nam, 2013-2014

PLOS ONE

Dear Dr. Olson,

Thank you for submitting your manuscript to PLOS ONE.

I have read your paper and found it of great interest. I therefore put it on the fast track and invited three experts reviewers who are leaders in the field to give you comments. All three are supportive and one provided detailed technical suggestions and comments that help you improve your work. You are therefore given the chance to revise your paper and address concerns raised by the three reviewers. Please send back your paper at an earliest possible time.

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Dong-Yan Jin

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PLOS ONE

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Reviewers' comments:

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Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Huong et al. present a coronavirus detection study in the rats and bats in Vietnam. They used PCR method for detection and amplicon sequencing for verification and characterization of the evolutionary origin of the detected coronaviruses. They found a significant prevalence of coronavirus in these animals. Interestingly, the positive rates in rat increase from farm, to markets and to restaurants. This result supports that the virus spillover risk increases along the wildlife consumption chain by human. The study is nice, and the manuscript was well written. The only things I hope could be improved are below:

In the phylogenetic tree (Figure 6) I did not see the benefit, but indeed confusing, of putting the clade name in front of each of the sequences produced by this study. E.g. Coronavirus PREDICT CoV-35/VN23F0226 and Scotophilus bat coronavirus 512 2005/PREDICT-VN13F0333. “Coronavirus PREDICT CoV-35” and “Scotophilus bat coronavirus 512 2005” are the virus that is representative of the clades where the virus strain VN23F0226 and VN13F0333 are falling into (This is what I guessed). I could not find “Coronavirus PREDICT CoV-35” nor “Scotophilus bat coronavirus 512 2005” representative strains in the tree, and I must say these are not widely-used clade names but some clades defined by one or two previous paper. I am not trying to say these clade designation are invalid, but their representative strains should be included in the tree and provided with GenBank accession number so that other researchers could follow. Using “CoV-512 Clade” etc in the vertical bar designation of the lineage are great, but then you don’t have to repeat “Scotophilus bat coronavirus 512 2005” for every strain such as VN13F0333, VN13F0161, etc. Please use some more widely used standard format for virus naming E.g. Rat/VN13F0333/Vietnam/2014. This more understandable. Putting “Scotophilus bat coronavirus 512 2005” in front of “VN13F0333” just confuse reader that it was sampled from bats, but in fact this was sampled from rats.

In the median-joining network (Figure 7), some branches did not show with the number of the mutations as specified in the legend. It would be useful if the branch are drawn in scale with the number of mutations.

Reviewer #2: This is an interesting study examining the prevalence of coronaviruses at various points in the animal/human interface in Vietnam. Although timely in the context of COVID-19, I have some issues with the sampling techniques and data presentations as below:

Major comments

• A number of terms are used to describe rodent sampling sites. ‘Trader’, ‘rodent farms’, ‘live rodent trade supply chain’, ‘Large Market’, ‘Restaurant’, ‘Live rodent trade sites’, ‘rodent wildlife farm sites’, ‘wildlife farms’, ‘rodents in the trade’ and ‘field rat trade’. On the other hand, the analysis in figure 5 clearly classifies the sites into only three types: traders, large markets and restaurants. The sampling site terminology should be clarified with straightforward definitions and the authors should stick with these definitions throughout the manuscript. I understand that the authors are trying to analyse the data according to interface or sub-interface, but it is very confusing as currently presented.

• Figure 6 shows a tree based on a 387 bp RdRp fragment. Several of the bat-derived 512 2005 and rodent-derived 512 2005 appear to have an identical sequence (see CoV-512 clade). This is rather astonishing if they are really infecting coronavirus strains from different animals. I would interpret this as a signal of cross-contamination due to amplicon carry-over in the laboratory. If the cross-contamination occurred in the field, I would expect some nucleotide differences in this fragment as I gather that the sites of rodent sampling and bat sampling are geographically distinct?? I don't think it is reasonable to conclude that bat/ avian coronaviruses can infect rodents based on the evidence presented.

• Another concern I have is cross-contamination during sampling. Faeces collected from the ground of cages (even of individually housed animals) should ideally be called environmental samples as there is no way to tell if they really originate from the animal in the cage at the time. I believe this is confirmed by the presence of IBV and bat coronaviruses in rodent farms.

• Also, if a butcher/ vendor/ chef is using the same knife to handle several animals, there would be extensive cross-contamination if one of them is positive. Would this explain the high PCR positive rates?

• The sampling is comprehensive, but also very heterogeneous in terms of type of sample collected. Why wasn’t a standard sample type applied across all sites? I am concerned whether the type of sample collected at different settings might have contributed to the apparent variation in coronavirus detection rate between settings.

Minor comments:

• The introduction could be a bit more concise.

• Line 151: Are these 28 rodent farm sites classified as ‘traders’?

• Line 174 – 175 mentions 41 + 30 = 71 sites sampled. However, line 262 and 270 mention 70 sites. Why is this?

• Figure 4: Bars representing Sciuridae and R. argentiventer are not presented in this figure. Why are they in the legend?

• Figure 5: Could add between-group comparisons by chi-square to this figure.

Reviewer #3: In this manuscript, the authors have performed a surveillance study on the presence of the coronaviruses in the wildlife and the wildlife-human interfaces in Viet Nam. They detected the coronaviruses by consensus PCR and discovered the infected cases were increased along the supply chain. Although this study is performed in 2013-2014, its findings have a very good insight into the current COVID-19 outbreak, especially showing the potential risk of spreading the coronaviruses along the wild animal trade supply chain. Their findings can help to dissect how coronaviruses can spread from wild animals to humans by social activities. In view of this, I support to publish this manuscript in PLoS-One.

**********

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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PLoS One. 2020 Aug 10;15(8):e0237129. doi: 10.1371/journal.pone.0237129.r002

Author response to Decision Letter 0

17 Jul 2020

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

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https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Response: Thank you for these reminders. We reviewed and applied these requirements where there were discrepancies, in particular removing the list of key words and the funding information from the acknowledgment section. In addition we re-assigned the header styles, which had somehow disappeared from the formatting, and revised the order of affiliation for one of the co-authors.

2. In your Methods section, please provide additional location information of the study sites, including geographic coordinates for the data set if available.

Response: Our analysis presented location data at the province level and a spatial map of those provinces are provided in Figure 4. In response to this request we included the district name as well as the latitude and longitude rounded to two significant digits for each test result. The revised S1 Data table available at (pending DOI processing): https://doi.org/10.5061/dryad.7h44j0zrj OR https://datadryad.org/stash/share/pk3wVUxFNzTuCYZ9t8haKRPmx7V8YhTDBuHpG8JJ9kU.

Response: Thank you for this comment. The ‘permitting’ process in Viet Nam is perhaps a bit unusual to those not conducting wildlife research in the country. We have added some additional context to the relevant statement in the manuscript’s method section, which now reads, ‘The study and sampling activities for specified dates and locations were approved by the Department of Animal Health of the Ministry of Agriculture and Rural Development and animal sampling protocols were reviewed by the Institutional Animal Care and Use Committee at the University of California at Davis (protocol number 16048)’. These constitute the full names of the Vietnamese authorities that approved access to the study sites. The study approval with the Department of Animal Health (DAH) was confirmed in an Memorandum of Understanding dated September 2011, and a Work Plan Agreement dated August 16, 2012. The Department of Animal Health used internal communication channels to confirm their approval for the activities and directed the provincial level Department of Animal Health (Soc Trang Province subDAH, Dong Thap Province subDAH, and Dong Nai Province subDAH) to support the sampling at the study sites. No permit numbers were provided by the Vietnamese authorities.

4. We note that Figure 4 in your submission contain map images which may be copyrighted.

Response: We switched our vector layer to a source that is CC BY 4.0 compliant. The source of the Viet Nam province level vector layer is now geoBoundaries v. 3.0.0 (https://www.geoboundaries.org; Runfola et al. 2020) and Open Development Mekong (ODM; https://vietnam.opendevelopmentmekong.net). Note the geoBoundaries record of the latest ODM source license is not yet updated to reflect a recent change by ODM to CC BY 4.0 as indicated by the following ticket: https://github.com/wmgeolab/gbRelease/issues/53. The attributions to these sources have been updated in the methods section and in the Figure 4 legend.

Reviewers' comments:

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: Yes

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

3. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

Response: In our supporting information we provide the following statement on S1 Data: ‘Data required for all analysis and metadata for each parameter, now including site location information for each test, is available at (pending DOI processing): https://doi.org/10.5061/dryad.7h44j0zrj OR https://datadryad.org/stash/share/pk3wVUxFNzTuCYZ9t8haKRPmx7V8YhTDBuHpG8JJ9kU

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copy edit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

5. Review Comments to the Author

Response: Thank you for this support.

The only things I hope could be improved are below:

Response: We renamed all sequences included in our phylogenetic tree following the Reviewer’s suggestion (e.g. Rat/VN13F0333/Vietnam/2013). PREDICT_CoV-35 and PREDICT_CoV-17 were first described in Anthony et al. 2017 and we used the same clade names in this study. Representative sequences for PREDICT_CoV-35 and PREDICT_CoV-17 were included in the tree (PREDICT_CoV-17: KX284941, PREDICT_CoV-35 = KX284991 and KX285074). We added another sequence (DQ648858) to our phylogenetic tree as a representative sequence of the Scotophilus bat coronavirus 512 clade.

Response: Number of mutations were indicated above branches only when this number was higher than one. All branches without a specified number of mutations correspond to a single mutation as it is now explained in the figure legend. When possible, we re-drew the length of some branches to scale to make it proportional to the number of mutations. However this was not always possible due to the complexity of the network.

Major comments

Response: We strived to consistently use a minimal and descriptive set of terms but as Reviewer 2 points out, there was still considerable room for improvement. Throughout we have revised the phrasing to create more consistency and to further clarify the distinction between the field rat trade associated sub-interfaces and the wildlife farm rodents. Figure 4 & 5 are now redesigned to better align with the interface listed in Table 1, which was intentionally configured to help readers understand the main interfaces and sub-interfaces. Figure 5 focuses on the field rat trade sub-interfaces where sampling was standardized and consistent across sites, and therefore does not include data from other interfaces.

Response: We identified 13 CoV 512 sequence types in rodents. Among these 13 sequence types, only two of them were identical to sequence types isolated from bats. The remaining 11 sequence types isolated from rodents were not identical to those isolated from bats and were characterized by several nucleotide differences; this is clearly visible in the phylogenetic network in Fig 7. We can therefore conclude that rodents were infected by slightly divergent but closely related CoV 512 strains and that this was not the result of a lab contamination as most of the rodent sequence types (11/13) were never detected in our bat samples. All rodent sequence types were included in the phylogenetic network (Fig 7) and colored in red, blue, and yellow (Fig 7) but only 3 of them were included in the phylogenetic tree (Fig 6) as representative sequences of the whole dataset. To address this well-stated concern for other readers, in our revised submission we have re-run our phylogenetic tree using more divergent rodent CoV 512 sequence representatives so it is easier to see in the phylogenetic tree that sequence types from rodents and bats are not identical.

Response: Additional details were included to clarify the criteria used to distinguish between what were called “environmental samples” and feces or urine collected non-invasively from animals at sites. Feces and urine samples voided from animals observed in cages (individual animals in cages) or in the case of the bat sampling, visible above in bat roots, these were identified as feces and urine. Swabs of feces piles in cages or surfaces of the animal cages on wildlife farms were identified as environmental samples.

Response: Yes, it is possible that as the field rats are slaughtered there is cross contamination of individual animals through knives used or contaminated surfaces. Sample collection from individual animals, however, involved using individual sterile swabs and sterile sample collection tools, to prevent any cross contamination during sample collection. The proportion of viruses we identified was the proportion identified in individual animals destined for human consumption so the proportion of positives reflects the proportion of exposure of the trader, market vendor, restaurant butcher, or end consumer to coronaviruses at those points of contact or interfaces between wildlife and humans. The high rates of positives and increase in positive rate along the field rat trade chain was across all sites and periods of sample collection. To make this situation clearer to readers we’ve added the following text to the discussion: ‘Our findings indicate a high risk of consumer exposure even if cross-contamination due to shared butchering materials influenced the proportion of positive individuals (see Methods).’

Response: We agree that this is a caveat in the study, and we have transparently highlighted this in the description of the Material and Methods and the results (including in the shared code). We agree that ideally, the same sample types would have been collected consistently from all settings/interfaces, however, the different settings/interfaces did not allow identical access to all sample types. These inconsistencies are the reason why we did not conduct analysis across the entire dataset, but instead focused analysis where it could be supported by the data (i.e. we only compared what was comparable).

Minor comments:

• The introduction could be a bit more concise.

Response: We tightened up the introduction by removing some lengthier text while maintaining the relevant background and content as these interfaces are relatively unique to this region and we believe the readership will benefit from more detailed descriptions.

• Line 151: Are these 28 rodent farm sites classified as ‘traders’?

Response: No the rodent farms are not classified as traders. We have updated the text throughout to clarify what is the field rat trade chain with vendors as the first sub-interface in the trade chain and indicated that the rodents raised in captivity on wildlife farms (porcupines and bamboo rats) are a separate interface.

• Line 174 – 175 mentions 41 + 30 = 71 sites sampled. However, line 262 and 270 mention 70 sites. Why is this?

Response: The bat pagoda site was the only site that was sampled in both the dry and the wet season. The lines in question clarify this with the following now revised statement ‘Given the distances between sites, 69 sites were sampled once except the bat pagoda natural roost in Soc Trang province, which was visited three times and sampled in both seasons’. Also of note, in the process of reviewing this comment we discovered and fixed a basic calculation error in the last row of Table 1.

• Figure 4: Bars representing Sciuridae and R. argentiventer are not presented in this figure. Why are they in the legend?

Response: We struggled with a way to represent Sciuridae and Rattus argentiventer in this figure because they were only sampled one time apiece from wildlife farms (see Table 1). They are present in the figure (if one zooms in they are visible as a red/purple line at the bottom of Dong Nai barplot) and we added a statement to the figure legend because of this concern.

• Figure 5: Could add between-group comparisons by chi-square to this figure.

Response: We vacillated between reporting univariate (i.e. chi-square statistics) versus multivariate model statistics for the amplification finding. In lieu of adding chi-square statistics to this figure we point the reviewer to the multivariate model (S2 Table) that allowed us to adjust for sites as random effects, and which also show the between group findings are significantly different.

Response: Thank you for this support.

Attachment

Submitted filename: Response_to_Reviewers.docx

Click here for additional data file.^{(23.5KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0237129.r003

Decision Letter 1

Dong-Yan Jin

22 Jul 2020

Coronavirus testing indicates transmission risk increases along wildlife supply chains for human consumption in Viet Nam, 2013-2014

PONE-D-20-17798R1

Dear Dr. Olson,

I have put your manuscript in the fast track in both rounds of review. Your revised paper has now been reviewed by one original reviewer and he promptly recommended acceptance of your work for publication, We’re therefore pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Congratulations!

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Dong-Yan Jin

Academic Editor

PLOS ONE

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #2: Thanks for addressing the comments. The manuscript has been improved considerably with the clarifications of the terminology.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: Yes: Siddharth Sridhar

PLoS One. doi: 10.1371/journal.pone.0237129.r004

Acceptance letter

Dong-Yan Jin

28 Jul 2020

PONE-D-20-17798R1

Coronavirus testing indicates transmission risk increases along wildlife supply chains for human consumption in Viet Nam, 2013-2014

Dear Dr. Olson:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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on behalf of

Professor Dong-Yan Jin

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PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. Summary of all testing results by genus, interface, sub-interface, sample types, sites, percentage of samples testing positive, and viral species.

(PDF)

Click here for additional data file.^{(90.7KB, pdf)}

S2 Table. Multivariate mixed effect logistic regression showing the association between season and sub-interface with coronavirus positives in field rats.

(PDF)

Click here for additional data file.^{(52.5KB, pdf)}

S3 Table. GenBank accession numbers for coronavirus sequences detected in this study and for reference sequences.

(PDF)

Click here for additional data file.^{(77.7KB, pdf)}

S1 Data

(TXT)

Click here for additional data file.^{(119B, txt)}

S1 R Code. Code used to conduct the analysis described.

(HTML)

Click here for additional data file.^{(1.3MB, html)}

Attachment

Submitted filename: Response_to_Reviewers.docx

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Data Availability Statement

All relevant data are available at https://doi.org/10.5061/dryad.7h44j0zrj.

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PERMALINK

Coronavirus testing indicates transmission risk increases along wildlife supply chains for human consumption in Viet Nam, 2013-2014

Nguyen Quynh Huong

Nguyen Thi Thanh Nga

Nguyen Van Long

Bach Duc Luu

Alice Latinne

Mathieu Pruvot

Nguyen Thanh Phuong

Le Tin Vinh Quang

Vo Van Hung

Nguyen Thi Lan

Nguyen Thi Hoa

Phan Quang Minh

Nguyen Thi Diep

Nguyen Tung

Van Dang Ky

Scott I Roberton

Hoang Bich Thuy

Nguyen Van Long

Martin Gilbert

Leanne Wicker

Jonna A K Mazet

Christine Kreuder Johnson

Tracey Goldstein

Alex Tremeau-Bravard

Victoria Ontiveros

Damien O Joly

Chris Walzer

Amanda E Fine

Sarah H Olson

Roles

Abstract

Introduction

Materials and methods

Sampling locations

Fig 1.

Fig 2. Malayan porcupine (Hystrix brachyura) farm in Dong Nai province, November 2013.

Fig 3. Bat guano farms in Soc Trang Province, October 2013.

Animal sampling

Sample testing

Phylogenetic analysis

Statistical analyses

Results

Detection of coronavirus by animal taxa and interface

Table 1. Summary of coronavirus positives by taxa and interface.

Fig 4. Map of sampling sites by province and multi-panel plots showing individual counts of animals sampled by province, taxa, and sub-interface (rat trade) or interface.

Fig 5. Plot of the proportion of coronavirus positives in field rats by sub-interface in the live field rat trade chain.

Phylogenetic analysis

Fig 6. Phylogenetic tree of bat and rodent coronavirus sequences detected in Viet Nam.

Fig 7.

Discussion

High prevalence and amplification along the supply chain for human consumption

Viral sharing or environmental mixing

Bat guano farms

Capacity building and outreach

Conclusions

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Dong-Yan Jin

Roles

Author response to Decision Letter 0

Decision Letter 1

Dong-Yan Jin

Roles

Acceptance letter

Dong-Yan Jin

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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