Integrated One Health Surveillance of Zoonotic Diseases at the Wildlife-Human Interface: How Pathogen Detection Informs Spillover Risks and Transmission Dynamics

Integrated One Health Surveillance of Zoonotic Diseases at the Wildlife-Human Interface: How Pathogen Detection Informs Spillover Risks and Transmission Dynamics | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Integrated One Health Surveillance of Zoonotic Diseases at the Wildlife-Human Interface: How Pathogen Detection Informs Spillover Risks and Transmission Dynamics View ORCID Profile Rajindra Napit , Binod Khadka , Bivisha Khadka , Roji Raut , View ORCID Profile Pei Yee Woh , Sheina Macy Manalo , View ORCID Profile Ajit Poudel , Shova Bhandari , Bindiya Bajracharya , Pranav Pandit , Terra Kelly , View ORCID Profile Jessica S. Schwind , Rajesh Rajbhandari , Bishwo Shrestha , View ORCID Profile Smriti Khadka , Kavya Dhital , Prajwol Manandhar , View ORCID Profile Saman Pradhan , View ORCID Profile Dibesh Karmacharya doi: https://doi.org/10.1101/2025.04.21.25326139 Rajindra Napit 1 School of Medicine, Deakin University , Victoria, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rajindra Napit Binod Khadka 2 One Health Division, Center for Molecular Dynamics Nepal , Kathmandu, Nepal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bivisha Khadka 2 One Health Division, Center for Molecular Dynamics Nepal , Kathmandu, Nepal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Roji Raut 2 One Health Division, Center for Molecular Dynamics Nepal , Kathmandu, Nepal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pei Yee Woh 3 Department of Food Science and Nutrition, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Pei Yee Woh Sheina Macy Manalo 4 Department of Veterinary Paraclinical Sciences, University of the Philippines Los Banos, The Philippines Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ajit Poudel 2 One Health Division, Center for Molecular Dynamics Nepal , Kathmandu, Nepal Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ajit Poudel Shova Bhandari 2 One Health Division, Center for Molecular Dynamics Nepal , Kathmandu, Nepal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bindiya Bajracharya 2 One Health Division, Center for Molecular Dynamics Nepal , Kathmandu, Nepal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pranav Pandit 5 EpiCenter for Disease Dynamics, University of California Davis , USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Terra Kelly 6 EpiEcos , USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jessica S. Schwind 7 Institute for Health Logistics & Analytics, Georgia Southern University , Statesboro, GA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jessica S. Schwind Rajesh Rajbhandari 2 One Health Division, Center for Molecular Dynamics Nepal , Kathmandu, Nepal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bishwo Shrestha 2 One Health Division, Center for Molecular Dynamics Nepal , Kathmandu, Nepal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Smriti Khadka 2 One Health Division, Center for Molecular Dynamics Nepal , Kathmandu, Nepal Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Smriti Khadka Kavya Dhital 2 One Health Division, Center for Molecular Dynamics Nepal , Kathmandu, Nepal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Prajwol Manandhar 2 One Health Division, Center for Molecular Dynamics Nepal , Kathmandu, Nepal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Saman Pradhan 2 One Health Division, Center for Molecular Dynamics Nepal , Kathmandu, Nepal Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Saman Pradhan Dibesh Karmacharya 2 One Health Division, Center for Molecular Dynamics Nepal , Kathmandu, Nepal 8 Health Program, Wildlife Conservation Society , New York, USA 9 School of the Environment, Faculty of Science, University of Queensland , Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Dibesh Karmacharya For correspondence: dibesh{at}cmdn.org dkarmacharya{at}wcs.org Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Emerging infectious diseases (EIDs) pose a significant global health challenge, with zoonotic pathogens— transmitted from animals to humans—accounting for about 75% of these threats. Traditionally, responses to EIDs have been reactive, focusing on post-outbreak containment, which often results in considerable health and economic losses. Recognizing the need for a proactive One Health approach, our study in the buffer zone (Thakurdwara) of Bardia National Park, Nepal aimed to establish a zoonotic disease surveillance program. We conducted disease risk assessments and collected biological samples from 100 households located in the wildlife-human interface of the park. This included one human sample (n=100) and at least two livestock samples per household (n=289). Fecal samples from the surrounding forest were non-invasively collected and identified using DNA barcoding. All samples were screened for ten target pathogens, including six viruses and four bacteria, using polymerase chain reaction (PCR) and DNA sequencing. By integrating laboratory findings with risk survey data using a One Health approach, we analyzed potential zoonotic pathogen spillover and transmission dynamics to better understand the interconnected factors influencing zoonotic disease risks. Campylobacter emerged as the most common pathogen, detected in 97 human households and 219 livestock samples. Mycobacteria were identified in two livestock and two wildlife samples, while Salmonella was confined to a single human household. Influenza A virus was observed in one livestock sample, and wildlife samples showed low pathogen prevalence overall. Campylobacter hotspots were identified through strain variation and network analysis, linking humans, livestock, and poultry, based on proximity, shared water sources, and contamination. Poultry likely served as a reservoir for human cases indicating zoonotic transmission pathways. Our findings highlight the critical need for integrated One Health measures and proactive, integrated surveillance systems that emphasize early detection, community education, and targeted interventions to mitigate emerging zoonotic threats. Background Emerging infectious diseases (EIDs) represent a significant challenge to global health, affecting not only human populations but also animals and economies. A substantial proportion of these EIDs are zoonotic pathogens, which are diseases that can be transmitted from animals to humans. Research indicates that approximately 75% of newly emerging infectious diseases are zoonotic in origin, highlighting the critical role that wildlife plays in the emergence of these pathogens [ 1 , 2 ]. Among the various types of pathogens, viruses account for a significant proportion of zoonotic diseases, underscoring the importance of understanding viral dynamics in the context of EIDs [ 3 ]. Historically, efforts to control zoonotic viruses such as SARS and MERS have been largely reactive, focusing on containment after outbreaks have occurred. This reactive approach often leads to significant loss of life and economic damage, as seen during the SARS outbreak in 2003 and the ongoing challenges posed by COVID-19 [ 3 ]. The reactive nature of these responses has prompted a shift towards proactive measures, largely influenced by initiatives such as USAID’s Emerging Pandemic Threats Program. Programs like PREDICT emphasized early identification of emerging diseases at their source, which can be crucial for developing intervention strategies to prevent spillover events before they escalate into widespread outbreaks [ 2 ]. Nepal, with its rich biodiversity and extensive wildlife-human interface, exemplifies a region at heightened risk for zoonotic disease outbreaks. This susceptibility is further compounded by cultural practices, ethnic diversity, and traditional beliefs that influence human-animal interactions [ 4 ]. Given the critical importance of identifying potential wildlife carriers of zoonotic diseases to mitigate their spread to humans, Nepal’s approach to biodiversity conservation and human-wildlife coexistence is of particular relevance. The country’s establishment of protected areas, including twelve National Parks and various conservation areas, embodies its commitment to preserving biodiversity. However, these conservation efforts also create buffer zones where wildlife and human communities intersect, potentially facilitating the transmission of zoonotic diseases [ 4 , 5 ]. The role of pathogens, including virus discovery, is paramount in preparing for future epidemics. By identifying pathogens in wildlife populations prior to their emergence in humans, researchers can gain insights into the ecology, evolution, and transmission risks associated with potentially pandemic strains[ 3 , 6 ]. For instance, ongoing studies on RNA viruses, particularly coronaviruses in regions like Nepal, aim to elucidate the dynamics of viral spillover and identify potential reservoirs [ 3 ]. This proactive surveillance is essential, as it allows for a better understanding of the factors that contribute to zoonotic spillover, including human behaviors and environmental changes that increase the likelihood of pathogen transmission [ 7 ]. In addition to identifying pathogens, establishing surveillance systems in areas where environmental and market changes occur is critical for mitigating zoonotic spillover risks. By raising awareness and building capacity among stakeholders, such initiatives can enhance the effectiveness of zoonotic disease surveillance and response strategies [ 8 ]. Increasing awareness of zoonotic diseases and the need for proactive surveillance systems are vital in addressing the challenges posed by emerging infectious diseases. Understanding the mechanisms of pathogen spillover and the dynamics of host-pathogen interactions will be crucial for developing effective strategies to mitigate the risks associated with zoonotic pathogens. Our study aimed to establish an active zoonotic disease surveillance and mitigation program, starting with a pilot study in Bardia National Park and its surrounding buffer zone. It sought to gather baseline data on zoonotic diseases in nearby communities, raise awareness of proactive surveillance systems, and collect biological samples for molecular analysis. By collecting and analyzing blood, oral, and stool samples from humans, livestock, and wildlife, the study identified pathogens present, assessed the risks of zoonotic spillover, and evaluated the potential for disease transmission between humans and wildlife. This comprehensive approach also focused on identifying risky local behaviors, building capacity through stakeholder training in sample collection and laboratory processing, and informing public health strategies and interventions in Nepal and similar contexts worldwide, where biodiversity and human populations closely interact. Methodology Site and Sampling Bardia National Park (28.3833° N and 81.3333° E), one of Nepal’s largest national parks with surrounding buffer zone, is home to over 130,000 people across several municipalities in Bardia, Banke, and Surkhet. These communities rely on wildlife interactions for their livelihoods, including eco-tourism. The park, rich in biodiversity with species like Bengal tigers, Indian elephants, gharials, and one-horned rhinoceroses, also attracts national and international tourists, further elevating transmission risks. Thakurdwara Municipality, adjacent to the park, has 8,772 residents, primarily Tharu, along with Brahmin, Chettri, and Dalit groups. Livestock farming dominates the local economy, while a growing tiger population has heightened human-wildlife conflicts. Buffer zone communities depend on park resources like grass, fodder, and medicinal herbs, intensifying human-wildlife interactions. These dynamics, combined with tourism, highlight the need for sustainable management practices to mitigate zoonotic risks and foster coexistence. Survey Procedures One Health Risk Assessment Survey One-Health risk assessment survey of all households was conducted using a standardized questionnaire, assessing factors that contribute to risks associated with transmission of zoonotic diseases between human, livestock and wildlife interfaces from 5th July 2023 to 26th July 2023. The survey questionnaire also captured other important disease risk factors such as water quality, sanitation, and animal handling practices. Collected data were digitized, cleaned, and statistical analysis was conducted using IBM SPSS 20. Enumerators were trained to administer survey questionnaires following the Center for Molecular Dynamics Nepal (CMDN) sampling strategies-ensuring representative and unbiased sample collection. Engagement with Local Stakeholders All pertinent stakeholders (local authorities, government and national park officials, and community leaders) were informed of the study activities in a workshop to ensure their cooperation. Field staff, including two local phlebotomists and veterinary technicians, were trained by the CMDN on biosafety and biosecurity practices, maintenance of cold chain and proper waste management. to build local capacity and support sampling efforts. Ward Number 9 of Thakurdwara was selected for the study given its proximity to the national park, and data was collected on human and livestock from 100 households (September-November 2023) ( Figure 2 ). Data was first gathered from the local Ward Offices and a screening survey was conducted to identify households with livestock, excluding those without. Simple random sampling (SRS) was then used to select households for inclusion in the study. Download figure Open in new tab Figure 1: Location of Thakurdwara Community in the buffer zone of Bardia National Park (source: https://bardianationalpark.gov.np/en/others/1/ ) Download figure Open in new tab Figure 2. Sample collection and pathogen screening for human, livestock, and wildlife samples from Thakudwara-9, Bardia National Park, conducted at the CMDN Lab. The figure was created with Biorender [ 9 ]. Ethical Consideration Written consent was obtained from all participants in the study to collect samples from them and their livestock. Trained phlebotomists and veterinary technicians were used to collect samples from humans and livestock respectively. Livestock were sampled in the presence of their primary caretaker. Ethical guidelines of the Department of Livestock Services (DLS) were followed, and ethical approval was obtained from the Nepal Health Research Council (Registration Number – 272/2023). Pathogen Selection and Screening We chose ten common pathogens for the study based on their endemicity and the availability of detection assays for screening ( Supplementary Table 2 ). These included six RNA viruses ( Influenza A , Rhabdovirus , Paramyxovirus , Filovirus , Flavivirus and Coronavirus ) and four bacteria ( Salmonella , Brucella , Campylobacter and Mycobacteria ). The justification for pathogen selection, along with their zoonotic potential and transmission pathways, is provided in the Supplementary Table 1 . View this table: View inline View popup Supplementary Table 1: Selected Pathogens and Justification for including them in the study Sample Collection and Processing The study involved the collection of various sample types, including blood in EDTA, blood in viral transport media (VTM), oral swabs, stool in PBS, and wildlife scat samples. A total of 100 humans, 289 livestock, and 14 wildlife fecal samples were collected. For each human and livestock subject, three types of samples—blood, oral swab, and stool—were taken. Trained personnel collected oral swabs from humans and livestock using labeled cryotubes containing 2 ml of VTM. Blood samples were drawn through venipuncture into EDTA tubes, and stool samples were placed in labeled, sterile, wide-mouthed containers and suspended in PBS. Wildlife fecal samples were collected in sterile falcon tubes. All samples were transported to the laboratory under cold chain conditions, maintaining a temperature of 2-8°C. Upon arrival at the lab, oral swabs in VTM, blood in VTM, and stool samples in PBS were stored at -20°C, while blood in EDTA were stored at 4°C ( Figure 2 ). Nucleic acid extraction for oral swabs in VTM and blood in EDTA was performed using the abGeneix Automated DNA and RNA extraction system (REF 800800) and Uni-medica nucleic acid extraction reagent, following the manufacturer’s instructions. Stool samples in PBS and wildlife fecal samples were extracted using the QIAamp Fast DNA Stool Mini kit using manufacturer’s instruction. cDNA was synthesized using the BIO-RAD iScript cDNA synthesis kit. The reaction was carried out in a 20 µl volume containing 4 µl of 5X iScript reaction mix, 1 µl iScript reverse transcriptase, 6 µl NFW, and 9 µl template RNA. Both RNA virus and bacterial pathogen detection were conducted using PCR protocols referenced in the Supplementary Table 2 . View this table: View inline View popup Supplementary Table 2: Pathogens with their target gene, PCR primer name and sequence, amplicon size, and references. To confirm DNA and RNA extraction, a quality control PCR was performed on all oral, blood, and stool samples, targeting the universal mammal CytB gene (457 bp amplicon) using the primers CytB_F (5′-GAGGMCAAATATCATTCTGAGG-3′) and CytB_R (5′-TAGGGCVAGGACTCCTCCTAGT-3′). The PCR was carried out in a 25 µl volume with 12.5 µl Qiagen Mastermix, 1.5 µl each of the primers (10 pmol), 7.5 µl NFW, and 2 µl template DNA. The amplification conditions were initial denaturation at 95°C for 15 minutes, followed by 50 cycles of 94°C for 30 seconds, 52°C for 50 seconds, and 72°C for 60 seconds, with a final extension at 72°C for 7 minutes. PCR products were visualized on a 1.5% gel using a 100 bp DNA ladder. Species Identification (DNA barcoding) of Wildlife Fecal Samples Opportunistic fecal samples (n=14) from the surrounding forest area were collected, and DNA was extracted using the QIAamp Fast DNA Stool Mini kit. PCR was performed in a 25 µl volume with 12.5 µl 2X Amplitaq, 1.5 µl each of CytB_F and CytB_R primers (10 pmol), 0.5 µl NFW, and 5 µl template DNA targeting Cytochrome b of mitochondrial DNA. Amplification was done in a ProFlex thermocycler with initial denaturation at 95°C for 10 minutes, followed by 40 cycles of 95°C for 30 seconds, 50°C for 30 seconds, and 72°C for 60 seconds, with a final extension at 72°C for 10 minutes. PCR products were visualized on a 1.5% gel using a 100 bp DNA ladder, and positive DNA amplicons were further sequenced and species identified using NCBI reference database. Characterization of Detected Pathogens Using Nanopore Sequencing Platform Following PCR analysis, a substantial number of samples were presumptively positive for pathogens including Campylobacter , Rhabdovirus, Paramyxovirus, Brucella , Salmonella , Influenza, and Filoviru s in PCR screening. To confirm these results, we performed molecular characterization and validation using next-generation DNA sequencing on PCR amplicons. The sequencing was carried out on an Oxford Nanopore MinION (MK1C) device. The sequencing was carried out with a dual barcoding approach with the SQK-NBD114.24 kit and EXP-PBC001 on an R10.4.1 flow cell. Sample quantification was carried out using the Qubit HS dsDNA assay kit, while size estimation was assessed through agarose gel electrophoresis. Characterization of Campylobacter with Phylogenetic Analysis The raw sequences from Nanopore sequencer were first assessed for quality with Nanoplot v1.42.0 [ 10 ] followed by adapter sequences trimming with Porechop v 0.2.4 [ 11 ]. The quality filtering was performed with Filtlong v0.2.1 [ 12 ] with minimum length and Q-score of 300bp and seven respectively. The cleaned sequence reads were then subjected to variant calling and consensus were generated with Medakka v2.1 [ 13 ]. Thus, generated consensus sequences were then used to develop a phylogenetic tree reconstructed from an ∼862 bp alignment of 417 16S rRNA gene sequences representing major species of Campylobacter . The tree was inferred using the Maximum Likelihood method in IQ-TREE v1.6.12 [ 14 ], applying the best-fit model parameters (GTR+F+I+G4) determined by the Akaike Information Criterion (AIC) in ModelFinder. Node values represent bootstrap support scores (1,000 replicates) from the Maximum Likelihood test. Reference sequences, denoted by black tips, were retrieved from GenBank, and their taxonomy was inferred from corresponding publications. Sequences generated in this study using MinION amplicon sequencing are indicated by red tips (human-derived) and blue tips (livestock-derived). Lineages or cluster groups corresponding to known Campylobacter species were annotated with abbreviations. The 16S rRNA gene sequence of Arcobacter butzleri (GenBank accession: JQ743025 ) was used as an outgroup to root the Campylobacter species tree. Network Analysis of the most Prevalent Pathogen- Campylobacter The presence of Campylobacter in almost all households and different livestock and humans were used to perform a network analysis. Incident data (human and animals) along with sub-species level classification data obtained from 16S Campylobacter phylogeny was utilized to create a matrix table. The table was then imported into Gephi v0.100 to create the network diagram using default settings. The stats were calculated with the built-in analysis plugin. The different communities were identified using the modularity function (using node weight, resolution of 1, and randomization ‘on’) within Gephi v0.10. Results Demographic and Socio-economic Profile of the Community Thakurdwara is a rural community with a median population of 36 years. Most respondents were married or widowed (86%), and a significant portion had a limited education, with 31% being illiterate and 26% having completed higher secondary school. Agriculture was the primary occupation, with 83% of individuals engaged in farming, while smaller groups worked in skilled (4%) and unskilled manual labor (2%). Households typically raised poultry (94%), goats (82%), and cattle (68%), with swine (46%) and dogs (22%) also commonly kept. Medical care was mainly sought at clinics or health centers (94%), with fewer people visiting hospitals (48%) or consulting community health workers (4%). Over the past year, common symptoms included fever with muscle aches (15%), while cases of encephalitis (7%) and hemorrhagic fever (1%) were less frequent. Most households depended on uncovered wells, ponds, or rivers for water (92%), with only half (49%) filtering it before use. Additionally, 72% of residents shared water for animal feeding ( Table 1 ). View this table: View inline View popup Table 1 Demographic, medical history, and drinking water condition of respondents (n=100) Living Conditions, Basic Amenities and Health Hygiene Practices Nearly half of the households surveyed (48%) had permanent housing structures, while the rest lived in more vulnerable, temporary dwellings. In terms of sanitation, 20% of households lacked any toilet facilities, with the majority (56%) using pit latrines with smaller portions using compost toilets (12%) or flush toilets (12%). Access to clean water was limited, with only 8% households having piped water, while the vast majority (92%) relied on uncovered wells, ponds, or rivers. Of the 100 people surveyed, 35% reported having been ill in the past year. Among them, 18% did not know the cause of their illness, 7% attributed it to contaminated food or water, and 5% believed they contracted the disease from other infected individuals. Animal Handling Practices Nearly all respondents handled and raised live animals with the majority (78%) sharing water sources with animals. High risk exposure activities were also reported with 26% of respondents mentioning slaughtering animals, 34% eating food touched by animals, and 20% consuming raw or undercooked meat ( Table 2 ). Meanwhile, 45% reported eating food contaminated with animal feces. View this table: View inline View popup Download powerpoint Table 2 Animal handling practices amongst the respondents (n=100). Wildlife-Livestock Interactions Wildlife-livestock interactions were reported by 31% of respondents, mainly involving tigers (35.5%), elephants (25.8%), and leopards (19.4%). Direct encounters with wildlife were also reported, including with rhinos (33%), tigers (29%), and elephants (29%). Pathogen Detected in Human, Livestock and Wildlife Samples Among human samples, Salmonella was detected in one case, while Campylobacter was found in 97 households, making it the most common human pathogen ( Table 3 ). In livestock, Influenza A virus was identified in one livestock sample, while Campylobacter was widespread, appearing in 219 samples. Mycobacteria were detected in two livestock samples. View this table: View inline View popup Download powerpoint Table 3: Pathogens detected in the study from n=403 samples. Using DNA barcoding, we identified fecal samples from two wild species, the Asiatic elephant ( Elephas maximus ) and hog deer ( Axis porcinus )—in the forest surrounding Thakurdwara. These samples showed low pathogen prevalence, with Campylobacter and Mycobacteria each found in two cases. Overall , Campylobacter was the most commonly found pathogen across livestock and humans. Mycobacteria appeared at much lower frequencies in both livestock and wildlife. Figure 3 illustrates various pathogens detected across hosts (human, livestock including poultry, and wildlife) in the community. Download figure Open in new tab Figure 3 Spatial map visualizing pathogen distribution across the study site, with pie charts representing various detected pathogens in humans and animals. Pathogens include Influenza A , Salmonella , Mycobacterium , and multiple Campylobacter species, with colors indicating specific hosts. The size of each chart reflects pathogen detection frequency, with the highest numbers of detections in the lower left and central regions, suggesting zoonotic disease hotspots near settlements and water bodies. Associations between Animal Handling Practices and Detected Pathogens in Humans Campylobacter was detected commonly detected pathogen in high-risk animal contact scenarios. Handling and raising live animals had the highest Campylobacter detections (97 and 96 cases, respectively), along with a few Salmonella and Mycobacteria cases. Sharing water with animals for washing was linked to 76 Campylobacter and one Salmonella detection. Other risky practices, such as eating near animal feces (44 Campylobacter ), consuming food touched by animals (33 Campylobacter ), and keeping animals inside homes (53 Campylobacter ), also were found. Cooking or handling recently killed animals resulted in 58 Campylobacter , one Salmonella , and one Mycobacteria detection. Distribution of Campylobacter Strains across Diverse Species within the Community We created a geo-spatial map to visualize all samples with detected Campylobacter ( Figure 4 ) and analyzed detected strains ( Supplementary Table 3 , Figure 5 ) using a phylogenetic tree ( Figure 5 ). Our analysis revealed distinct Campylobacter clusters that were genetically identical in both humans and animals. View this table: View inline View popup Supplementary Table 3: Detected Campylobacteria strains and their clinical significance Download figure Open in new tab Figure 4: Spatial distribution of Campylobacter in a community , with detection points in humans, livestock, and poultry. Clusters of overlapping cases indicate zoonotic hotspots where transmission is likely amplified by proximity, shared water sources, or environmental contamination, while outlier cases suggest sporadic exposure events. Poultry appears to be a potential reservoir, with zoonotic transmission risks. The map underscores the interconnected role of humans, livestock, and poultry in bacterial spread. Download figure Open in new tab Figure 5: Phylogenetic analysis of Campylobacter detected in fecal samples from humans and livestock in the Thakurdwara buffer zone of Bardia National Park, 2023. Phylogenetic tree reconstructed from an ∼862 bp alignment of 417 16S rRNA gene sequences representing major species of Campylobacter . Sequences generated in this study using MinION amplicon sequencing are indicated by red tips ( human-derived ) and blue tips (livestock-derived ). Lineages or cluster groups corresponding to known Campylobacter species are annotated with abbreviations, as follows: Ccol: C. coli , Cla: C. lari , Cjeje: C. jejuni subsp. jejuni , Cup: C. upsaliensis , Che: C. helveticus , Cin: C. insulaenigrae , Cjedo: C. jejuni subsp. doylei , Ccl: C. species clone, Chyla: C. hyointestinalis subsp. lawsonii , Cfefe: C. fetus subsp. fetus , Cfeve: C. fetus subsp. venerealis , Chyhy: C. hyointestinalis subsp. hyointestinalis , Cmu: C. mucosalis , Csp: C. sputorum , Cho: C. hominis , Cgr: C. gracilis , Cre: C. rectus , Csh: C. showae , Ccon: C. concisus , Ccu: C. curvus; Clan : C. lanienae. The 16S rRNA gene sequence of Arcobacter butzleri (GenBank accession: JQ743025 ) was used as an outgroup to root the Campylobacter species tree. In one of the Campylobacter clusters, the samples included a duck, two rabbits, and a human, all sharing the same Campylobacter strain and located in close geographic proximity. Similarly, another cluster of Campylobacter comprised samples from a human, buffalo, and chicken, which were located within a 1.92-kilometer radius. This phylogenetic tree ( Figure 5 ) illustrates the evolutionary relationships among Campylobacter strains, with distinct clusters marked by color-coded annotations. The branching structure highlights genetic variations, with closely related strains grouping together. Labeled clades, such as Ccol, Cla, and Cjeje , represent different Campylobacter species or subspecies, while some clusters contain multiple species, indicating possible genetic overlap. Sample labels, displayed in various colors, may correspond to different host origins, suggesting zoonotic transmission. The tree provides insights into strain diversity and potential cross-species transmission patterns, making it useful for understanding Campylobacter epidemiology. Network Analysis Underscoring the Interconnectedness of Livestock, Wildlife, and Humans in Sustaining and Spreading Campylobacter Network analysis based on ∼862 bp alignment of 417 16S rRNA gene sequences representing major species of Campylobacter identified a highly connected dense connectivity among livestock (these highly connected nodes representing-goat, chicken, pig, cow, duck, buffalo) ( Figure 6 ). Chickens and pigs appear to be central nodes, suggesting they play a major role in transmission dynamics. The human node is linked to multiple animal hosts, especially chicken and goat. Some nodes (e.g., rabbit, buffalo) connect to multiple species. However, modularity-based analysis separated the networks into five highly related distinct communities based on the interconnectedness of the nodes. The densely connected goat, chicken, pig and ox appears to be the largest community, probably pointing to intricate One Health interactions among these animals with three prominent subspecies of Campylobacter . Similarly, humans and rabbits appear to have formed a separate community with subspecies- Ccl/Ccon (C. species clone/ C. concisus ) and buffalo and cow-both formed separate unique communities with no other animals or humans. However, there was one unique community of households with subspecies of Cgr ( C. gracilis ) that did not form a community with any specific animals or humans, indicating the Campylobacter spillover and interaction might be more diverse. The network clearly shows the predominant species of Campylobacter among different communities of households along with potential One Health interface and interactions. Furthermore, these connections could represent Campylobacter presence in shared environments such as water, soil, or food supply chains. Download figure Open in new tab Figure 6: Network analysis underscoring the interconnectedness of livestock, wildlife, and humans in sustaining and spreading Campylobacter . Labeled nodes (e.g., human, goat, chicken, cow, pig, buffalo, rabbit, duck, etc.) represent host species or environmental reservoirs. TD codes (e.g., TD001, TD095, TD009, etc.) represent individual households, potentially linking to genetic sequences or sources of Campylobacter . Mycobacterium detection in wildlife and livestock samples We detected Mycobacterium spp. in both wildlife and livestock samples. 16S rRNA gene sequencing and subsequent phylogenetic analysis indicated the opportunistic samples from elephants and hog deer belonged to the Mycolibacterium group, while fecal samples from pigs (TD19) and chickens (TD20) clustered within the Mycobacteroide s group ( Figure 7 ). Download figure Open in new tab Figure 7: Phylogenetic analysis of Mycobacterium -positive samples, including the Mycobacteroides group, MTB complex, and Mycolibacterium group . The Mycobacterium (MTB complex) includes pathogenic strains like M. tuberculosis and M. bovis, with host-specific isolates from pigs and chickens. The Mycolicibacterium group contains environmental and host-associated strains, including those from elephants and hog deer. Discussion Public Health Risks and Pathogen Transmission Dynamics in Thakurdwara This study highlights public health risks from human-animal interactions in Thakurdwara, where zoonotic disease transmission is influenced by socio-economic, environmental, and behavioral factors. With a low literacy rate (32%), effective disease prevention remains a challenge. Despite 52% awareness of zoonotic risks, high-risk behaviors persist, including handling live animals (100%), sharing water sources with livestock (78%), and consuming food near animal feces (45%). Livestock ownership is common, with chickens (30.3%), goats (27.4%), and cattle (23.2%) being the most prevalent. Wildlife-livestock interactions were reported by 31% of participants, mainly involving tigers (35.5%), elephants (25.8%), and leopards (19.4%). Direct encounters with wildlife during open grazing in the buffer zones heighten the risk of cross-species transmission. These risks are further amplified by poor sanitation and limited access to safe water. About half (48%) of households live in non-permanent structures, 20% lack toilets, and majority (92%) rely on uncovered wells for their water supply. Only 8% have piped water, and few households treat drinking water, increasing their risk of water-borne infections. Similar practices were observed in other studies in Nepal, which were correlated with high prevalence of the waterborne diseases [ 15 ].These conditions and interconnected risks continue to be a public health concern, particularly in vulnerable households in Thakurdwara. Pathogen Ecology, Reservoirs, Spillover, and Transmission Dynamics Network analysis revealed that livestock, particularly poultry, act as primary reservoirs for Campylobacter spillover, highlighting the need for targeted interventions in poultry farming, slaughterhouse operations, and food hygiene practices to mitigate transmission risks. The identification of distinct household communities based on Campylobacter interconnectedness suggests complex transmission dynamics driven by One Health interactions. Humans formed a unique cluster with rabbits, supporting the likelihood of bidirectional disease spillover, as rabbits are commonly used as model organisms for human diseases [ 16 , 17 ]. Based on the network analysis, the largest host community included chickens, goats, pigs, and oxen, while buffalo and cows formed distinct, isolated communities, indicating minimal One Health interactions with these animals. Surprisingly, the second-largest community lacked specific host species but connected with multiple other communities, suggesting that a particular Campylobacter subspecies ( C. ccol ) has spilled over to multiple hosts, reflecting a highly dynamic One Health interface. Shared nodes among different Campylobacter strains further indicate indirect transmission pathways through contaminated water, feed, or environmental surfaces. Geospatial analysis of these communities revealed distinct geographical separations with overlapping regions where One Health interactions occur. Community which lacks clear links to specific animals or humans and is dispersed across the map and often found near or between other communities, potentially representing hotspots for zoonotic spillover. Additionally, different Campylobacter subspecies clustered with specific host species, suggest host preferences, a phenomenon observed in C. jejuni , which exhibits distinct host specificity [ 18 , 19 ]. However, given the high zoonotic potential of Campylobacter spp. [ 20 – 22 ], cross-species transmission remains a significant concern. Past studies have confirmed the high prevalence of Campylobacter in humans and livestock. A meta-analysis in Iran found Campylobacter in 43.9% of white meat samples, with chicken at 48.6%, red meat at 7.9%, buffalo at 13.5%, and cattle at 8.4%. Contamination was also found in 19.4% of vegetable samples. [ 23 ]. A review in Ethiopia found that Campylobacter prevalence was higher in animals (14.6%) than humans (9%) and noted significant antimicrobial resistance [ 24 ] In our study human exposure was closely linked to multiple animal hosts which emphasizes the need for integrated disease surveillance and control strategies across veterinary, environmental, and public health sectors. In addition to Campylobacter , Salmonella was detected in one human sample, while Influenza A appeared in one livestock sample. Mycobacteria were identified in two livestock and two wildlife samples, with phylogenetic analysis revealing distinct bacterial clusters between pigs, chickens ( Mycobacteroides group), and elephants, hog deer ( Mycolibacterium group), indicating diverse transmission sources. Key Drivers of Zoonotic Disease Spread Geo-spatial mapping and network analysis visually demonstrated the interconnections between humans, livestock, and wildlife in pathogen maintenance and transmission. The identification of Campylobacter hotspots in areas with high human-livestock cohabitation, with chickens and goats acting as central transmission nodes, underscores the urgent need for targeted biosecurity measures, hygiene education, improved animal husbandry, and enhanced access to clean water and sanitation. Understanding One Health Disease Dynamics and Predicting Future Outbreaks Identifying key variables that influence pathogen reservoirs and spillover events at high-risk interfaces and closely monitoring them (variables), researchers can detect early warning signals that indicate the potential emergence of zoonotic diseases, thereby improving our ability to predict future outbreaks and their transmission patterns. The integration of advanced machine learning techniques has revolutionized the field of disease surveillance. Both supervised and unsupervised models are increasingly being used to analyze vast datasets, revealing patterns and trends associated with emerging and re-emerging zoonotic diseases, including those linked to antimicrobial resistance (AMR). These models can process complex ecological, genetic, and epidemiological data to identify hidden correlations and predict disease emergence with greater accuracy. By leveraging these computational tools, researchers and public health authorities can develop more effective early warning systems, enhance preparedness strategies, and implement targeted interventions. This represents a significant advancement in global health security, ultimately strengthening efforts to prevent future outbreaks and mitigate their impact on human and animal populations. Policy and Practical Implications This study highlighted key policy and practical implications for zoonotic disease prevention in Nepal, particularly in Bardia National Park’s buffer zones. A One Health approach is essential, integrating public health, veterinary, and conservation efforts into a national surveillance program. Water safety improvements, livestock management, and public awareness campaigns should address risk factors like water contamination and unsafe food practices. Conservation policies must consider zoonotic risks, ensuring wildlife protection does not increase human exposure. Strengthening laboratory capacity, early warning systems, and proactive health strategies will enhance disease prevention and public health resilience in Nepal. As we prepare this manuscript, efforts are underway to develop Nepal’s first Wildlife Health Surveillance Standard Operating Protocol (SOP). Study strengths and limitations This study was the first of its kind to investigate the wildlife-human interface in Nepal, integrating Knowledge, Attitudes, and Practices (KAP) assessments with laboratory analyses. It establishes a foundational understanding of pathogen and disease ecology in a high-risk zoonotic interface. Proactive surveillance of zoonotic pathogens demands significant resources, including technical expertise. While targeted pathogen detection provides valuable insights, it may not fully capture the complexity of disease ecology. Effective proactive surveillance must integrate diverse datasets, such as clinical findings and large-scale behavioral surveys, to provide a more comprehensive picture. As a pilot study, this research does not incorporate these additional datasets, including a greater representation of wildlife taxa with high risk for zoonoses such as birds, bats and rodents. However, implementing a robust surveillance program will require a holistic approach that combines and analyzes critical variables influencing pathogen reservoirs, spillover events, and transmission dynamics. Conclusion Proactive surveillance is essential for preventing zoonotic spillover by enabling early detection, targeted intervention, and risk mitigation. Given the critical role of wildlife in disease ecology, a One Health approach is crucial in vulnerable communities where human-livestock-wildlife interactions facilitate pathogen transmission. Routine pathogen screening in livestock, wildlife, and environmental samples allows for early identification of emerging threats like Campylobacter , Salmonella , and Influenza A , enabling timely interventions. Network analysis and spatial mapping help pinpoint high-risk areas, allowing for targeted biosecurity measures such as improved waste management, vaccination programs, and farm hygiene practices. Community-led disease monitoring enhances early warning systems, while environmental surveillance of contaminated water sources and shared grazing areas helps track indirect transmission pathways. Strengthening multisectoral collaboration between veterinarians, epidemiologists, ecologists, and public health officials improves data sharing and outbreak response. Investing in proactive surveillance also reduces economic burdens by preventing livestock losses, medical costs, and productivity declines. By integrating surveillance within a One Health framework, zoonotic spillover risks can be mitigated effectively, safeguarding both public health and economic resilience in at-risk communities. Data Availability All data produced in the present work are contained in the manuscript https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1225206 Data Availability All the data are included in the manuscript and supplementary information. All the sequences obtained in this study have been uploaded to the NCBI GenBank under Bioproject-PRJNA1225206 with individual accession numbers within the Bioproject and are publicly available. Acknowledgement This study was partially funded by the USAID Nepal Biodiversity Project, and we extend our gratitude to USAID for their generous support. We also wish to thank the Nepal Health Research Council (NHRC) for their guidance, support, and permission to conduct this study. The successful completion of this work would not have been possible without the support of the people of Thakurdwara Municipality and Bardia National Park. We appreciate the dedicated efforts of the field and laboratory teams from the CMDN and Intrepid Nepal Pvt. Ltd. We utilized AI tools to improve language in this manuscript. References 1. ↵ Borsky S , Hennighausen H , Leiter A , Williges K . CITES and the Zoonotic Disease Content in International Wildlife Trade . Environ Resour Econ (Dordr ). 2020 ; 76 : 1001 – 1017 . doi: 10.1007/S10640-020-00456-7/FIGURES/3 OpenUrl CrossRef PubMed 2. ↵ Firth C , Bhat M , Firth MA , Williams SH , Frye MJ , Simmonds P , et al. 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