Uncovering Biosecurity Gaps: Risk Factors for PRRSV Seropositivity in Costa Rican Pig Farms Identified Through Machine Learning

Uncovering Biosecurity Gaps: Risk Factors for PRRSV Seropositivity in Costa Rican Pig Farms Identified Through Machine Learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Uncovering Biosecurity Gaps: Risk Factors for PRRSV Seropositivity in Costa Rican Pig Farms Identified Through Machine Learning Ronald Meléndez-Arce, Emily Jiménez-Loaiza, Berta Leiva-Bonilla, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7418920/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Feb, 2026 Read the published version in Porcine Health Management → Version 1 posted 9 You are reading this latest preprint version Abstract Background. Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) continues to impose significant economic losses on pig production globally. In Costa Rica, where the virus is endemic, there is limited knowledge of the farm-level risk factors influencing PRRSV spread. This study aimed to identify internal and external biosecurity factors associated with PRRSV seroprevalence in Costa Rican pig farms. Methods. A cross-sectional survey was conducted on 21 pig farms across Costa Rica. Data on farm management and biosecurity practices data were collected using a structured questionnaire and linked to PRRSV seroprevalence data from a companion study. Logistic regression, LASSO (Least Absolute Shrinkage and Selection Operator), and Random Forest models were used to identify significant risk factors associated with herd-level PRRSV positivity. Results. Three key risk factors were consistently identified by both LASSO and Random Forest models: historical controlled exposure to PRRSV, restrictions on employee access to the farm, and restrictions on employee visits to other pig farms. Additional risk factors identified included topography, disinfection practices for transport vehicles, sanitation measures for visitors, boot and clothing protocols, and feedback procedures. Farms with a history of controlled exposure had an odds ratio of 90 (95% CI: 7.6–3,55) for being PRRSV-positive. Conclusion. The findings underscore the importance of internal and external biosecurity measures, particularly in relation to personnel movement and intentional exposure practices. Modeling approaches such as LASSO and Random Forest provided complementary insights into PRRSV risk factors in a tropical production setting. These insights can guide tailored interventions to reduce PRRSV transmission in Costa Rica and similar regions. PRRSV biosecurity pig farms Costa Rica LASSO Random Forest seroprevalence controlled exposure risk factors Machine learning Background Porcine reproductive and respiratory syndrome (PRRS) was first reported in the United States (USA) in 1987 [ 1 ] and in Europe in 1990 [ 2 ]. Since then, the disease has spread widely across in many pig-producing countries, affecting both reproduction and respiratory health in pigs [ 3 ], [ 4 ]. Porcine Reproductive and Respiratory Syndrome Virus (PRRSV), the causative agent, is considered one of the most economically significant pathogens in pig farming globally. In the United States alone, annual losses to PRRS were estimated at $ 664 million in 2013, rising $ 1.2 billion by 2024 [ 5 ]. Given the high economic impact, preventing PRRSV-infection is of paramount importance. This can be achieved through a combination of vaccination and enhanced biosecurity measures [ 6 ]. While vaccination helps reduce viraemia and clinical signs, biosecurity measures are essential to lower the probability of infection by limiting both direct and indirect contact between pigs [ 7 ] In Costa Rica, PRRSV was first reported in 1996 [ 8 ] and has since had a major impact on the national pig industry, causing reproductive failures and respiratory illness [ 9 ]. A recent study reported a PRRSV seroprevalence of 44% in Costa Rican farms [ 10 ], consistent with findings from other regions in Latin America [ 11 ]. Controlling PRRSV remains one of the key challenges in Costa Rican pig production. Understanding and managing risk factors associated with virus introduction (external biosecurity), [ 12 ] and within-farm transmission (internal biosecurity) are crucial for effective control and prevention.[ 13 ]. Several studies have identified important risk factors for PRRSV infection [ 13 , 17 ], including importation of live pigs or semen, movement of people, contaminated clothing and boots, shared needles, insect vectors (mosquitoes/flies), transport vehicles and aerosol transmission. However, it remains unclear to what extent these risk factors are relevant for Costa Rican farms compared to those in the US or Europe. This study aimed to investigate the association between PRRSV herd status and internal and both external and internal biosecurity risk factors on pig farms in Costa Rica. The findings are expected to benefit not only Costa Rican producers, but also pig producers in other tropical and subtropical countries seeking to control PRRSV more effectively. Methods Study design and population. A cross-sectional study was conducted on 21 swine farms across the country. All participating farms included the three production phases: breeding, weaning, and growing/finishing and maintained reproductive and production records. Farms were stratified by herd size: Stratum 1 (large farms > 500 sows) and Stratum 2 (small farms, ≤ 500 sows). Farms were selected using the nationwide SIREA system (Integrated System of Registration of Agricultural Establishments) of the National Service of Animal Health. The geographical distribution of sampled farms across the country’s provinces is presented in Table 1 . Table 1 Distribution of sampled farms by stratum and province in Costa Rica. Province Stratum 1 Stratum 2 San José 0 3 Alajuela 2 5 Heredia 0 1 Cartago 1 1 Limón 4 1 Guanacaste 0 1 Puntarenas 0 2 Within-herd prevalence The within-herd seroprevalence data used for this study was derived from a previously published survey [ 15 ]. In summary, 52% of the farms (11/21) tested seropositive for PRRSV, with positive cases found in six of Costa Rica’s seven provinces. Questionnaire A structured questionnaire based on the PADRAP tool (Production Animal Disease Risk Assessment Program) [ 16 ] was adjusted to the Costa Rican production context. The modified version included 155 questions: 31 addressing internal risk factors (IR) and 124 addressing external risk factors (ER). Variables were grouped into risk categories according to their relevance to PRRSV introduction and spread (Supplementary Material, Table 1 ). All data collectors received standardized training in interviewing techniques, auditing and data quality assurance [ 17 ]. After completion, questionnaires were reviewed for completeness and clarity. In case of inconsistencies or missing information, data were verified and corrected via phone calls, e-mails, or follow-up fam visits. Statistical analysis The dependent variable was farm PRRSV serostatus, coded as binary: 1 = seropositive or 0 = seronegative. Numerical responses were categorized when exact values were unavailable (e.g., responses such as “greater than ...”) or dichotomized using the median (low versus high). Categories of categorical variables were merged when appropriate, particularly when some levels contained few or no positive or negative cases, which could compromise statistical power and model stability. The association between biosecurity variables and PRRSV status was assessed in two steps. Firstly, univariable binary logistic regression was used to estimate odds ratios (OR) with 95% confidence interval [ 18 ]. Firth's correction was applied when zero-cell counts were encountered. Secondly, based on the lowest AIC from the univariable models, 36 plausible variables were selected for multivariable models. These included a Random Forest model for classification [ 19 ] and a Least Absolute Shrinkage and Selection Operator (LASSO) model using the glmnet package [ 20 ]. For the LASSO model [ 21 ], 10-fold cross-validation identified an optimal lambda value of 0.026, corresponding to the model with the lowest deviance. Results Of the 226 variables analyzed, 36 were identified as promising based on their contribution to model performance. These variables either reduced the overall AIC or, if removed resulted in a higher AIC, indicating their significance in explaining variation in the dependent variable (Supplementary Material, Table 2 ). Combined modeling using Random Forest and LASSO selected three risk factors associated with PRRSV positivity status (Table 2 ), two associated with internal and one associated with external biosecurity. Farms with a history of controlled (intentional) exposure of PRRSV had a significantly higher risk of testing positive (OR = 91% CI: 7.6–3547). Conversely, farms requiring a change of clothing and boots between sites tended to have a lower risk (OR = 0.11, 95% CI: 0.00-1.04). Unexpectedly, farms without restrictions on employee access after specific hours had a lower risk (OR = 0.04, 95% CI: 0.00-0.41) compared to those imposing restrictions after specific hours. Random Forest identified three additional variables as important: sanitation measures for employees and visitors entering the site, the farm’s topography (physical landscape), and the type of disinfectant used on vehicles transporting breeding animals. LASSO, on the other hand, identified several other potentially relevant factors, including clothing and boot protocols, the sow-to-employee ratio, the practice of feedback (feeding tissues from deceased pigs to stimulate immunity) prior introduction, and restrictions on employees visiting other swine farms. Some variables, such as prior PRRSV status [ 22 ] and sow-to-employee ratio, remained in the LASSO model despite confidence intervals (CI) including 1, as these still contributed to the overall model performance. Table 2 Estimated odds ratios and 95% confidence intervals for positive PRRSV herd status, based on risk factors selected by Random Forest and LASSO models. Ref.= Reference level; OR = odds ratio; 95% CI = 95% confidence interval; LL = lower limit; UL = upper limit; (ER) = external risk; (IR) = internal risk. Firth’s correction was used for variables with quasi-complete separation. Variable Herd status for PRRSv OR 95% CI positive (n = 11) negative (n = 10) LL UL Selection by LASSO and Random Forest Historical controlled exposure (IR) No 1 9 Ref. - - Yes 10 1 90.02 7.59 3547.50 Employee restrictions on visits to other swine production facilities (ER) No restrictions 6 2 Ref. - - Not Applicable (farm has no employees) 1 0 1.15 0.03 35.86 Visits to other swine farms are restricted 4 8 0.17 0.02 1.10 Restrictions on employee access to the site (IR) Restricted after-hours only 10 2 Ref. - - Always restricted 0 3 0.03 0.00 6.05 Not restricted 1 5 0.04 0.00 0.41 Selection only by Random Forest Sanitation for employees-visitors entering the site (ER) Unrestricted entry 7 2 Ref. - - One or more restrictions 4 8 0.14 0.02 0.91 Topography at the site (ER) Flat 2 7 Ref. - - Hills or mountains 9 3 10.49 1.57 105.00 Disinfectant use on vehicles used to transport genetic animals (ER) Hypochlorite (Clorox, Halazone, Chloramine-T) or peroxygen (Virkon) used 0 2 Ref. - - Iodine (Wescodyne, Premise, Iofec, Iosdyn, Losan), or quaternary ammonium combinations (Synergize, Aseptol) used 0 2 1.01 0.00 73.24 No disinfectant used or unknown 2 3 3.63 0.12 111.68 Not Applicable (Select if vehicle used to transport animals is dedicated to this site) 3 0 35.04 0.48 2435.04 Phenol-based compound (BioPhene, Environ, Tek-Trol, Laro, Lysol) or aldehydes (DC&R, Cidex, Formaldegen) used 1 0 15.02 0.19 1236.03 Quaternary ammonium used (Roccal, Germex, Zephiran, Hi-Lethol, BioSentry) 5 3 7.90 0.27 217.08 Selection only by LASSO Boot and clothing restrictions between sites (IR) No requirements 7 4 Ref. - - Required to change boots but not clothing 3 1 1.71 0.15 41.39 Required to change clothing and boots 1 5 0.11 0.01 1.04 Feedback prior to entry (ER) All sites naive, negative, or unknown/not positive 6 9 Ref. - - Positive stable, or active 5 1 7.50 0.69 81.24 Breeding females per on-site employee (IR) 2.9–58.0 7 4 Ref. - - 58.1–167.0 3 7 4.08 0.70 29.08 Association of herd status with other PRRSV measurements PRRSV status (IR) Naive-entire herd never exposed 1 7 Ref. - - Negative but not naive (contains exposed animals) 1 2 3.50 0.11 123.60 Positive stable, or active 9 1 62.99 5.08 2525.01 Time since the most recent PRRSV clinical (IR) Never 2 9 Ref. - - Sometime ago 9 1 40.49 4.34 1037.95 N° of clinical outbreaks last 1 year (IR) 0 6 9 Ref. - - > 0 5 1 7.50 0.91 164.02 Discussion In this study, we identified three risk factors in Costa Rica that were significantly associated with the prevalence of PRRSV: historical controlled exposure, sanitation before human entries, and topography of the area. Both historical controlled exposure and feedback practices before entry demonstrate that intentional pathogen introduction - even under controlled conditions - increases the risk of PRRSV positivity. These practices involve exposing herds or individual animals to the pathogen in a controlled manner, often using feedback materials such as infected tissues or fluids [ 23 ], with the aim to stimulate the animal’s immune systems and induce immunity without causing severe disease [ 14 ]. However, this approach carries inherent risks. The virus could spread beyond the intended group of animals, potentially leading to larger outbreaks, as reported in Korea and Japan for PEDv [ 24 ] [ 21 ]). Breaches in biosecurity measures or unforeseen environmental factors can exacerbate this risk. Furthermore, feedback practices may promote the co-circulation of multiple viral strains on a farm, particularly given PRRSV’s high mutation rate and genetic variability [ 25 ]. This was an observational study, and certain biosecurity-related factors, such as restrictions on employee movement between sites, were paradoxically associated with PRRSV seropositivity. This may reflect a response to prior outbreaks, where stricter biosecurity protocols were implemented after infection occurred. Nonetheless, limiting the movement of personnel between different sites remains critical to reduce the risk of inadvertently carrying the virus from one location to another [ 26 ]. These findings underscore the importance of implementing robust biosecurity measures, such as changing clothing and footwear and showering, and disinfectant use of vehicles, to prevent the transmission of pathogens between different production sites and other external sources [ 27 ]. Strategies to mitigate these risk factors include enforcing stricter biosecurity protocols, optimizing animal-to-staff ratios, and strengthening communication channels for dissemination of information on disease prevention and control [ 28 ]. Although hills and mountains can act as natural barriers and are often assumed to reduce the risk of PRRSV infection in pig herds, our study found that the probability of infection was higher for farms located in mountainous terrain. A previous study also associated increased risk of infection with the premontane forest ecozone [ 29 ]. This may be due to the more humid environment found in such areas, which could favor viral transmission or persistence. A key limitation of this study is the relatively small number of farms included in the modelling. Even though these were 21 of all 87 commercial farms in Costa Rica, this limits the generalizability of the results to the broader pig farming population. To understand and predict PRRSV risk, we used two different models: the Random Forest model and the LASSO model. The strength of the Random Forest model lies in its ability to handle complex interactions and non-linear relationships [ 18 ] and is well-suited for classification tasks. However, it may highlight variables that, while influential, are not necessarily emphasized in more parsimonious models. In contrast, the LASSO model focuses on identifying the most statistically significant predictors by applying regularization, thereby narrowing the analysis to key risk factors such as historically controlled exposure, employee visit restrictions, and site access restrictions. While LASSO offers a more simplified and interpretable set of predictors, it may overlook other potentially influential factors captured by the Random Forest model. In summary, both models offer distinct advantages and limitations. The Random Forest model provides a comprehensive perspective by incorporating a broad range of variables, while the LASSO model narrows the focus to the most critical risk factors, enhancing interpretability. Integrating insights from both approaches can support the development of more targeted and effective strategies for PRRSV control. Abbreviations PRRS Porcine Reproductive Respiratory Syndrome PRRSV Porcine Reproductive Respiratory Syndrome Virus LASSO Least absolute shrinkage and selection operator PADRAP Production Animal Disease Risk Assessment Program Declarations Acknowledgments The authors acknowledgethe contribution of the technical staff of the Laboratorio de Virología, Escuela de Medicina Veterinaria, Universidad Nacional, and the staff of the Laboratorio de Bioseguridad del Servicio Nacional de Salud Animal, Costa Rica. We also appreciate Susana Ureña for helping us with sample collection and for providing data on the studied farms. We would also like to thank the pig producers involved in the research. Author information RM [email protected] , EJ [email protected] , BLB [email protected] , JV [email protected] , MR [email protected] , AVN [email protected] , AS [email protected] , JV [email protected] , JJR [email protected] *Corresponding author: [email protected] Author's contribution RM: study design, sample collection, questionnaire application, and paper writing; EJ, BLB, MR, JV: study design and questionnaire application; AVN, AS; JV: data analysis and paper writing; JJR: study design, data analysis, and paper writing. Funding RM received a scholarship from the Ministry of Science and Technology and Telecommunications (MICITT) PND-018-15-2 from PINN POSGRADOS of Costa Rica to conduct doctoral studies. This work was partially supported by the Department of Population Health Sciences, Utrecht University, The Netherlands. A financial contribution to this study also came from the Programa de Investigación en Medicina Poblacional (MedPob) of the Escuela de Medicina Veterinaria, Universidad Nacional, Costa Rica. Data availability A data set was generated for this study. It is available as a complementary material. Ethics declarations The authors declare no conflicts of interest. Ethics approval The Ethical Committee approved this protocol of the School of Veterinary Medicine, Universidad Nacional. Consent to participate. Consent from pork producers involved in the project was obtained. Consent of publications Not applicable Competing interests The authors declare no competing interests. References Keffaber KK. Reproductive failure of unknown etiology. Am Assoc Swine Pr Newsl. 1989; 1:1–10. Lindhaus W, Lindhaus. Ratselhafte Schweinekrankheit. Prakt Tieraerz. 1991; 72:423–5. Van Gucht S, Labarque G, Van Reeth K. The combination of PRRS virus and bacterial endotoxin as a model for multifactorial respiratory disease in pigs. Vet Immunol Immunopathol. 2004; 102:165–78. Arruda AG, Friendship R, Carpenter J, Hand K, Ojkic D, Poljak Z. Investigation of the Occurrence of Porcine Reproductive and Respiratory Virus in Swine Herds Participating in an Area Regional Control and Elimination Project in Ontario, Canada. Transbound Emerg Dis. 2017; 64:89–100. Roepke D, Growing Losses from PRRS Cost Pork Producers $1.2 Billion Per Year, New Study Shows - Iowa State Research [Internet]. 2024 [cited 2025 Jul 7]. Available from: https://research.iastate.edu/2024/07/30/growing-losses-from-prrs-cost-pork-producers-1-2-billion-per-year-new-study-shows/ Pileri E, Mateu E. Review on the transmission porcine reproductive and respiratory syndrome virus between pigs and farms and impact on vaccination. Vet Res. 2016; 47:108. Hancox L, Balasch M, Angulo J, Scott-Baird E, Mah CK. Comparison of viraemia and nasal shedding after PRRSV-1 challenge following vaccination with three commercially available PRRS modified live virus vaccines. Res Vet Sci. 2024; 180:105416. Bermúdez J. Estudio de Prevalencia de Anticuerpos a: Aujeszky, Peste Porcina Clásica, Gastroenteritis Transmisible, Coronavirus Respiratorio Porcino, Síndrome Reproductivo y Respiratorio Porcino en cerdos de Costa Rica. [Costa Rica]: Universidad Nacional; 1996. Nodelijk G, Nielen M, De Jong MCM, Verheijden JHM. A review of porcine reproductive and respiratory syndrome virus in Dutch breeding herds: population dynamics and clinical relevance. Prev Vet Med. 2003;60:37–52. Meléndez R, Guzmán M, Jiménez C, Piche M, Jiménez E, León B, et al. Seroprevalence of porcine reproductive and respiratory syndrome virus on swine farms in a tropical country of the Middle Americas: the case of Costa Rica. Trop Anim Health Prod. 2021; 53:441. Dewey C. PRRS in North America, Latin America, and Asia. Vet Res. 2000; 31:84–5. Rodriguez-Vega V, Figueras-Gourgues S, Hernández-Caravaca I, Sala-Echave R, Diaz E. Use of PADRAP – Production Animal Disease Risk Assessment Program – in 91 farms in Spain. :1. Velasova M, Alarcon P, Williamson S, Wieland B. Risk factors for porcine reproductive and respiratory syndrome virus infection and resulting challenges for effective disease surveillance. BMC Vet Res. 2012; 8:184. Martínez-Lobo FJ, Díez-Fuertes F, Simarro I, Castro JM, Prieto C. The Ability of Porcine Reproductive and Respiratory Syndrome Virus Isolates to Induce Broadly Reactive Neutralizing Antibodies Correlates With In Vivo Protection. Front Immunol [Internet]. 2021 [cited 2024 Aug 6];12. Available from: https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2021.691145/full Meléndez R, Guzmán M, Jiménez C, Piche M, Jiménez E, León B, et al. Seroprevalence of porcine reproductive and respiratory syndrome virus on swine farms in a tropical country of the Middle Americas: the case of Costa Rica. Trop Anim Health Prod. 2021; 53:441. Holtkamp DJ, Lin H, Wang C, O’Connor AM. Identifying questions in the American Association of Swine Veterinarian’s PRRS risk assessment survey that are important for retrospectively classifying swine herds according to whether they reported clinical PRRS outbreaks in the previous 3 years. Prev Vet Med. 2012; 106:42–52. Silva GS, Corbellini LG, Linhares DLC, Baker KL, Holtkamp DJ. Development and validation of a scoring system to assess the relative vulnerability of swine breeding herds to the introduction of PRRS virus. Prev Vet Med. 2018; 160:116–22. Puhr R, Heinze G, Nold M, Lusa L, Geroldinger A. Firth’s logistic regression with rare events: accurate effect estimates and predictions? Stat Med. 2017; 36:2302–17. Breiman L. Random Forests. Mach Learn. 2001; 45:5–32. Friedman J, Hastie T, Tibshirani R. Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw. 2010; 33:1–22. Lasso and Elastic-Net Regularized Generalized Linear Models [Internet]. [cited 2024 Feb 15]. Available from: https://glmnet.stanford.edu/ Holtkamp DJ, Torremorell M, Corzo CA, Linhares DCL, Almeida MN, Yeske P, et al. Proposed modifications to porcine reproductive and respiratory syndrome virus herd classification. J Swine Health Prod. 2021; 29:261–70. Kumar D, Anderson Reever AV, Pittman JS, Springer NL, Mallen K, Roman-Sosa G, et al. Role of Pre-Farrow Natural Planned Exposure of Gilts in Shaping the Passive Antibody Response to Rotavirus A in Piglets. Vaccines. 2023; 11:1866. Furutani A, Sekiguchi S, Sueyoshi M, Sasaki Y. Effect of intervention practices to control the porcine epidemic diarrhea (PED) outbreak during the first epidemic year (2013-2014) on time to absence of clinical signs and the number of dead piglets per sow in Japan. Prev Vet Med. 2019; 169:104710. Yamagami T, Miyama T, Toyomaki H, Sekiguchi S, Sasaki Y, Sueyoshi M, et al. Analysis of the effect of feedback feeding on the farm-level occurrence of porcine epidemic diarrhea in Kagoshima and Miyazaki Prefectures, Japan. J Vet Med Sci. 2021; 83:1772–81. Pitkin A, Deen J, Dee S. Further assessment of fomites and personnel as vehicles for the mechanical transport and transmission of porcine reproductive and respiratory syndrome virus. Can J Vet Res Rev Can Rech Veterinaire. 2009; 73:298–302. Amass SF, Pacheco JM, Mason PW, Schneider JL, Alvarez RM, Clark LK, et al. Procedures for preventing the transmission of foot-and-mouth disease virus to pigs and sheep by personnel in contact with infected pigs. Vet Rec. 2003; 153:137–40. Alarcón LV, Allepuz A, Mateu E. Biosecurity in pig farms: a review. Porc Health Manag. 2021; 7:5. Alkhamis MA, Arruda AG, Vilalta C, Morrison RB, Perez AM. Surveillance of porcine reproductive and respiratory syndrome virus in the United States using risk mapping and species distribution modeling. Prev Vet Med. 2018; 150:135–42. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.docx SupplementaryTable2.xlsx Cite Share Download PDF Status: Published Journal Publication published 21 Feb, 2026 Read the published version in Porcine Health Management → Version 1 posted Editorial decision: Revision requested 29 Sep, 2025 Reviews received at journal 22 Sep, 2025 Reviews received at journal 13 Sep, 2025 Reviewers agreed at journal 01 Sep, 2025 Reviewers agreed at journal 27 Aug, 2025 Reviewers invited by journal 22 Aug, 2025 Editor assigned by journal 21 Aug, 2025 Submission checks completed at journal 21 Aug, 2025 First submitted to journal 20 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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15:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7418920/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7418920/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40813-026-00495-4","type":"published","date":"2026-02-21T15:59:40+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":103251394,"identity":"0f9d88dc-78a5-4b41-9fed-8489e409b206","added_by":"auto","created_at":"2026-02-23 16:08:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":890296,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7418920/v1/26a6eca1-f91a-49af-887f-7ac1553758a2.pdf"},{"id":90297131,"identity":"0db8636b-dbcb-4e45-82dd-20ec95a3113e","added_by":"auto","created_at":"2025-09-01 08:27:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16930,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7418920/v1/9c2a61807291387ba36ad0c7.docx"},{"id":90297130,"identity":"9200975f-7a3e-46bf-9f8e-18beba835148","added_by":"auto","created_at":"2025-09-01 08:27:34","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":26969,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7418920/v1/469ed892fdb8da31c405cb8f.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Uncovering Biosecurity Gaps: Risk Factors for PRRSV Seropositivity in Costa Rican Pig Farms Identified Through Machine Learning","fulltext":[{"header":"Background","content":"\u003cp\u003ePorcine reproductive and respiratory syndrome (PRRS) was first reported in the United States (USA) in 1987 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and in Europe in 1990 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Since then, the disease has spread widely across in many pig-producing countries, affecting both reproduction and respiratory health in pigs [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Porcine Reproductive and Respiratory Syndrome Virus (PRRSV), the causative agent, is considered one of the most economically significant pathogens in pig farming globally. In the United States alone, annual losses to PRRS were estimated at \u003cspan\u003e$\u003c/span\u003e664\u0026nbsp;million in 2013, rising \u003cspan\u003e$\u003c/span\u003e1.2\u0026nbsp;billion by 2024 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGiven the high economic impact, preventing PRRSV-infection is of paramount importance. This can be achieved through a combination of vaccination and enhanced biosecurity measures [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. While vaccination helps reduce viraemia and clinical signs, biosecurity measures are essential to lower the probability of infection by limiting both direct and indirect contact between pigs [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eIn Costa Rica, PRRSV was first reported in 1996 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and has since had a major impact on the national pig industry, causing reproductive failures and respiratory illness [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A recent study reported a PRRSV seroprevalence of 44% in Costa Rican farms [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], consistent with findings from other regions in Latin America [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Controlling PRRSV remains one of the key challenges in Costa Rican pig production. Understanding and managing risk factors associated with virus introduction (external biosecurity), [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and within-farm transmission (internal biosecurity) are crucial for effective control and prevention.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Several studies have identified important risk factors for PRRSV infection [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], including importation of live pigs or semen, movement of people, contaminated clothing and boots, shared needles, insect vectors (mosquitoes/flies), transport vehicles and aerosol transmission. However, it remains unclear to what extent these risk factors are relevant for Costa Rican farms compared to those in the US or Europe.\u003c/p\u003e\u003cp\u003eThis study aimed to investigate the association between PRRSV herd status and internal and both external and internal biosecurity risk factors on pig farms in Costa Rica. The findings are expected to benefit not only Costa Rican producers, but also pig producers in other tropical and subtropical countries seeking to control PRRSV more effectively.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy design and population.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA cross-sectional study was conducted on 21 swine farms across the country. All participating farms included the three production phases: breeding, weaning, and growing/finishing and maintained reproductive and production records. Farms were stratified by herd size: Stratum 1 (large farms \u0026gt; 500 sows) and Stratum 2 (small farms, ≤ 500 sows).\u003c/p\u003e\u003cp\u003eFarms were selected using the nationwide SIREA system (Integrated System of Registration of Agricultural Establishments) of the National Service of Animal Health. The geographical distribution of sampled farms across the country’s provinces is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of sampled farms by stratum and province in Costa Rica.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProvince\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStratum 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStratum 2\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSan José\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlajuela\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeredia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCartago\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLimón\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuanacaste\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePuntarenas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eWithin-herd prevalence\u003c/h2\u003e\u003cp\u003eThe within-herd seroprevalence data used for this study was derived from a previously published survey [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In summary, 52% of the farms (11/21) tested seropositive for PRRSV, with positive cases found in six of Costa Rica’s seven provinces.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eQuestionnaire\u003c/h3\u003e\n\u003cp\u003eA structured questionnaire based on the PADRAP tool (Production Animal Disease Risk Assessment Program) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] was adjusted to the Costa Rican production context. The modified version included 155 questions: 31 addressing internal risk factors (IR) and 124 addressing external risk factors (ER). Variables were grouped into risk categories according to their relevance to PRRSV introduction and spread (Supplementary Material, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All data collectors received standardized training in interviewing techniques, auditing and data quality assurance [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. After completion, questionnaires were reviewed for completeness and clarity. In case of inconsistencies or missing information, data were verified and corrected via phone calls, e-mails, or follow-up fam visits.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe dependent variable was farm PRRSV serostatus, coded as binary: 1 = seropositive or 0 = seronegative. Numerical responses were categorized when exact values were unavailable (e.g., responses such as “greater than ...”) or dichotomized using the median (low versus high).\u003c/p\u003e\u003cp\u003eCategories of categorical variables were merged when appropriate, particularly when some levels contained few or no positive or negative cases, which could compromise statistical power and model stability.\u003c/p\u003e\u003cp\u003eThe association between biosecurity variables and PRRSV status was assessed in two steps. Firstly, univariable binary logistic regression was used to estimate odds ratios (OR) with 95% confidence interval [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Firth's correction was applied when zero-cell counts were encountered. Secondly, based on the lowest AIC from the univariable models, 36 plausible variables were selected for multivariable models. These included a Random Forest model for classification [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and a Least Absolute Shrinkage and Selection Operator (LASSO) model using the glmnet package [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. For the LASSO model [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], 10-fold cross-validation identified an optimal lambda value of 0.026, corresponding to the model with the lowest deviance. \u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eOf the 226 variables analyzed, 36 were identified as promising based on their contribution to model performance. These variables either reduced the overall AIC or, if removed resulted in a higher AIC, indicating their significance in explaining variation in the dependent variable (Supplementary Material, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCombined modeling using Random Forest and LASSO selected three risk factors associated with PRRSV positivity status (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), two associated with internal and one associated with external biosecurity. Farms with a history of controlled (intentional) exposure of PRRSV had a significantly higher risk of testing positive (OR = 91% CI: 7.6–3547). Conversely, farms requiring a change of clothing and boots between sites tended to have a lower risk (OR = 0.11, 95% CI: 0.00-1.04). Unexpectedly, farms without restrictions on employee access after specific hours had a lower risk (OR = 0.04, 95% CI: 0.00-0.41) compared to those imposing restrictions after specific hours.\u003c/p\u003e\u003cp\u003eRandom Forest identified three additional variables as important: sanitation measures for employees and visitors entering the site, the farm’s topography (physical landscape), and the type of disinfectant used on vehicles transporting breeding animals. LASSO, on the other hand, identified several other potentially relevant factors, including clothing and boot protocols, the sow-to-employee ratio, the practice of feedback (feeding tissues from deceased pigs to stimulate immunity) prior introduction, and restrictions on employees visiting other swine farms.\u003c/p\u003e\u003cp\u003eSome variables, such as prior PRRSV status [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and sow-to-employee ratio, remained in the LASSO model despite confidence intervals (CI) including 1, as these still contributed to the overall model performance.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEstimated odds ratios and 95% confidence intervals for positive PRRSV herd status, based on risk factors selected by Random Forest and LASSO models. Ref.= Reference level; OR = odds ratio; 95% CI = 95% confidence interval; LL = lower limit; UL = upper limit; \u003csup\u003e\u003cb\u003e(ER)\u003c/b\u003e\u003c/sup\u003e = external risk; \u003csup\u003e\u003cb\u003e(IR)\u003c/b\u003e\u003c/sup\u003e = internal risk. Firth’s correction was used for variables with quasi-complete separation.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eHerd status for PRRSv\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003epositive\u003c/p\u003e\u003cp\u003e(n = 11)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003enegative\u003c/p\u003e\u003cp\u003e(n = 10)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eUL\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eSelection by LASSO and Random Forest\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eHistorical controlled exposure \u003csup\u003e\u003cb\u003e(IR)\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e3547.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployee restrictions on visits to other swine production facilities \u003csup\u003e\u003cb\u003e(ER)\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNo restrictions\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNot Applicable (farm has no employees)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e35.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eVisits to other swine farms are restricted\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRestrictions on employee access to the site \u003csup\u003e\u003cb\u003e(IR)\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRestricted after-hours only\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAlways restricted\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e6.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNot restricted\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSelection only by Random Forest\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eSanitation for employees-visitors entering the site \u003csup\u003e\u003cb\u003e(ER)\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eUnrestricted entry\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOne or more restrictions\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eTopography at the site \u003csup\u003e\u003cb\u003e(ER)\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFlat\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHills or mountains\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e105.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisinfectant use on vehicles used to transport genetic animals \u003csup\u003e\u003cb\u003e(ER)\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHypochlorite (Clorox, Halazone, Chloramine-T) or peroxygen (Virkon) used\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIodine (Wescodyne, Premise, Iofec, Iosdyn, Losan), or quaternary ammonium combinations (Synergize, Aseptol) used\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e73.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNo disinfectant used or unknown\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e111.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNot Applicable (Select if vehicle used to transport animals is dedicated to this site)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e2435.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePhenol-based compound (BioPhene, Environ, Tek-Trol, Laro, Lysol) or aldehydes (DC\u0026amp;R, Cidex, Formaldegen) used\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e1236.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eQuaternary ammonium used (Roccal, Germex, Zephiran, Hi-Lethol, BioSentry)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e217.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSelection only by LASSO\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eBoot and clothing restrictions between sites \u003csup\u003e\u003cb\u003e(IR)\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNo requirements\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRequired to change boots but not clothing\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e41.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRequired to change clothing and boots\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eFeedback prior to entry \u003csup\u003e\u003cb\u003e(ER)\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAll sites naive, negative, or unknown/not positive\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePositive stable, or active\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e81.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBreeding females per on-site employee \u003csup\u003e\u003cb\u003e(IR)\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e2.9–58.0\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e58.1–167.0\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e29.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAssociation of herd status with other PRRSV measurements\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003ePRRSV status \u003csup\u003e\u003cb\u003e(IR)\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNaive-entire herd never exposed\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNegative but not naive (contains exposed animals)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e123.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePositive stable, or active\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e2525.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eTime since the most recent PRRSV clinical \u003csup\u003e\u003cb\u003e(IR)\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNever\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSometime ago\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e1037.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN° of clinical outbreaks last 1 year \u003csup\u003e\u003cb\u003e(IR)\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e\u0026gt; 0\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e164.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we identified three risk factors in Costa Rica that were significantly associated with the prevalence of PRRSV: historical controlled exposure, sanitation before human entries, and topography of the area. Both historical controlled exposure and feedback practices before entry demonstrate that intentional pathogen introduction - even under controlled conditions - increases the risk of PRRSV positivity. These practices involve exposing herds or individual animals to the pathogen in a controlled manner, often using feedback materials such as infected tissues or fluids [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], with the aim to stimulate the animal\u0026rsquo;s immune systems and induce immunity without causing severe disease [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, this approach carries inherent risks. The virus could spread beyond the intended group of animals, potentially leading to larger outbreaks, as reported in Korea and Japan for PEDv [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]). Breaches in biosecurity measures or unforeseen environmental factors can exacerbate this risk. Furthermore, feedback practices may promote the co-circulation of multiple viral strains on a farm, particularly given PRRSV\u0026rsquo;s high mutation rate and genetic variability [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis was an observational study, and certain biosecurity-related factors, such as restrictions on employee movement between sites, were paradoxically associated with PRRSV seropositivity. This may reflect a response to prior outbreaks, where stricter biosecurity protocols were implemented after infection occurred. Nonetheless, limiting the movement of personnel between different sites remains critical to reduce the risk of inadvertently carrying the virus from one location to another [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These findings underscore the importance of implementing robust biosecurity measures, such as changing clothing and footwear and showering, and disinfectant use of vehicles, to prevent the transmission of pathogens between different production sites and other external sources [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Strategies to mitigate these risk factors include enforcing stricter biosecurity protocols, optimizing animal-to-staff ratios, and strengthening communication channels for dissemination of information on disease prevention and control [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough hills and mountains can act as natural barriers and are often assumed to reduce the risk of PRRSV infection in pig herds, our study found that the probability of infection was higher for farms located in mountainous terrain. A previous study also associated increased risk of infection with the premontane forest ecozone [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This may be due to the more humid environment found in such areas, which could favor viral transmission or persistence. A key limitation of this study is the relatively small number of farms included in the modelling. Even though these were 21 of all 87 commercial farms in Costa Rica, this limits the generalizability of the results to the broader pig farming population.\u003c/p\u003e\u003cp\u003eTo understand and predict PRRSV risk, we used two different models: the Random Forest model and the LASSO model. The strength of the Random Forest model lies in its ability to handle complex interactions and non-linear relationships [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and is well-suited for classification tasks. However, it may highlight variables that, while influential, are not necessarily emphasized in more parsimonious models. In contrast, the LASSO model focuses on identifying the most statistically significant predictors by applying regularization, thereby narrowing the analysis to key risk factors such as historically controlled exposure, employee visit restrictions, and site access restrictions. While LASSO offers a more simplified and interpretable set of predictors, it may overlook other potentially influential factors captured by the Random Forest model.\u003c/p\u003e\u003cp\u003eIn summary, both models offer distinct advantages and limitations. The Random Forest model provides a comprehensive perspective by incorporating a broad range of variables, while the LASSO model narrows the focus to the most critical risk factors, enhancing interpretability. Integrating insights from both approaches can support the development of more targeted and effective strategies for PRRSV control.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePRRS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePorcine Reproductive Respiratory Syndrome\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePRRSV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePorcine Reproductive Respiratory Syndrome Virus\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLASSO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLeast absolute shrinkage and selection operator\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePADRAP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eProduction Animal Disease Risk Assessment Program\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledgethe contribution of the technical staff of the Laboratorio de Virología, Escuela de Medicina Veterinaria, Universidad Nacional, and the staff of the Laboratorio de Bioseguridad del Servicio Nacional de Salud Animal, Costa Rica. We also appreciate Susana Ureña for helping us with sample collection and for providing data on the studied farms. We would also like to thank the pig producers involved in the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRM [email protected], EJ [email protected], BLB [email protected], JV [email protected], MR [email protected], AVN [email protected], AS [email protected], JV [email protected], JJR [email protected]\u003c/p\u003e\n\u003cp\u003e*Corresponding author: [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor's contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRM: study design, sample collection, questionnaire application, and paper writing; EJ, BLB, MR, JV: study design and questionnaire application; AVN, AS; JV: data analysis and paper writing; JJR: study design, data analysis, and paper writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRM received a scholarship from the Ministry of Science and Technology and Telecommunications (MICITT) PND-018-15-2 from PINN POSGRADOS of Costa Rica to conduct doctoral studies. This work was partially supported by the Department of Population Health Sciences, Utrecht University, The Netherlands. A financial contribution to this study also came from the Programa de Investigación en Medicina Poblacional (MedPob) of the Escuela de Medicina Veterinaria, Universidad Nacional, Costa Rica.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA data set was generated for this study. It is available as a complementary material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Ethical Committee approved this protocol of the School of Veterinary Medicine, Universidad Nacional.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent from pork producers involved in the project was obtained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent of publications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKeffaber KK. Reproductive failure of unknown etiology. Am Assoc Swine Pr Newsl. 1989; 1:1\u0026ndash;10. \u003c/li\u003e\n\u003cli\u003eLindhaus W, Lindhaus. Ratselhafte Schweinekrankheit. Prakt Tieraerz. 1991; 72:423\u0026ndash;5. \u003c/li\u003e\n\u003cli\u003eVan Gucht S, Labarque G, Van Reeth K. The combination of PRRS virus and bacterial endotoxin as a model for multifactorial respiratory disease in pigs. Vet Immunol Immunopathol. 2004; 102:165\u0026ndash;78. \u003c/li\u003e\n\u003cli\u003eArruda AG, Friendship R, Carpenter J, Hand K, Ojkic D, Poljak Z. Investigation of the Occurrence of Porcine Reproductive and Respiratory Virus in Swine Herds Participating in an Area Regional Control and Elimination Project in Ontario, Canada. Transbound Emerg Dis. 2017; 64:89\u0026ndash;100. \u003c/li\u003e\n\u003cli\u003eRoepke D, Growing Losses from PRRS Cost Pork Producers $1.2 Billion Per Year, New Study Shows - Iowa State Research [Internet]. 2024 [cited 2025 Jul 7]. Available from: https://research.iastate.edu/2024/07/30/growing-losses-from-prrs-cost-pork-producers-1-2-billion-per-year-new-study-shows/\u003c/li\u003e\n\u003cli\u003ePileri E, Mateu E. Review on the transmission porcine reproductive and respiratory syndrome virus between pigs and farms and impact on vaccination. Vet Res. 2016; 47:108. \u003c/li\u003e\n\u003cli\u003eHancox L, Balasch M, Angulo J, Scott-Baird E, Mah CK. Comparison of viraemia and nasal shedding after PRRSV-1 challenge following vaccination with three commercially available PRRS modified live virus vaccines. Res Vet Sci. 2024; 180:105416. \u003c/li\u003e\n\u003cli\u003eBerm\u0026uacute;dez J. Estudio de Prevalencia de Anticuerpos a: Aujeszky, Peste Porcina Cl\u0026aacute;sica, Gastroenteritis Transmisible, Coronavirus Respiratorio Porcino, S\u0026iacute;ndrome Reproductivo y Respiratorio Porcino en cerdos de Costa Rica. [Costa Rica]: Universidad Nacional; 1996. \u003c/li\u003e\n\u003cli\u003eNodelijk G, Nielen M, De Jong MCM, Verheijden JHM. A review of porcine reproductive and respiratory syndrome virus in Dutch breeding herds: population dynamics and clinical relevance. Prev Vet Med. 2003;60:37\u0026ndash;52. \u003c/li\u003e\n\u003cli\u003eMel\u0026eacute;ndez R, Guzm\u0026aacute;n M, Jim\u0026eacute;nez C, Piche M, Jim\u0026eacute;nez E, Le\u0026oacute;n B, et al. Seroprevalence of porcine reproductive and respiratory syndrome virus on swine farms in a tropical country of the Middle Americas: the case of Costa Rica. Trop Anim Health Prod. 2021; 53:441. \u003c/li\u003e\n\u003cli\u003eDewey C. PRRS in North America, Latin America, and Asia. Vet Res. 2000; 31:84\u0026ndash;5. \u003c/li\u003e\n\u003cli\u003eRodriguez-Vega V, Figueras-Gourgues S, Hern\u0026aacute;ndez-Caravaca I, Sala-Echave R, Diaz E. Use of PADRAP \u0026ndash; Production Animal Disease Risk Assessment Program \u0026ndash; in 91 farms in Spain. :1. \u003c/li\u003e\n\u003cli\u003eVelasova M, Alarcon P, Williamson S, Wieland B. Risk factors for porcine reproductive and respiratory syndrome virus infection and resulting challenges for effective disease surveillance. BMC Vet Res. 2012; 8:184. \u003c/li\u003e\n\u003cli\u003eMart\u0026iacute;nez-Lobo FJ, D\u0026iacute;ez-Fuertes F, Simarro I, Castro JM, Prieto C. The Ability of Porcine Reproductive and Respiratory Syndrome Virus Isolates to Induce Broadly Reactive Neutralizing Antibodies Correlates With In Vivo Protection. Front Immunol [Internet]. 2021 [cited 2024 Aug 6];12. Available from: https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2021.691145/full\u003c/li\u003e\n\u003cli\u003eMel\u0026eacute;ndez R, Guzm\u0026aacute;n M, Jim\u0026eacute;nez C, Piche M, Jim\u0026eacute;nez E, Le\u0026oacute;n B, et al. Seroprevalence of porcine reproductive and respiratory syndrome virus on swine farms in a tropical country of the Middle Americas: the case of Costa Rica. Trop Anim Health Prod. 2021; 53:441. \u003c/li\u003e\n\u003cli\u003eHoltkamp DJ, Lin H, Wang C, O\u0026rsquo;Connor AM. Identifying questions in the American Association of Swine Veterinarian\u0026rsquo;s PRRS risk assessment survey that are important for retrospectively classifying swine herds according to whether they reported clinical PRRS outbreaks in the previous 3 years. Prev Vet Med. 2012; 106:42\u0026ndash;52. \u003c/li\u003e\n\u003cli\u003eSilva GS, Corbellini LG, Linhares DLC, Baker KL, Holtkamp DJ. Development and validation of a scoring system to assess the relative vulnerability of swine breeding herds to the introduction of PRRS virus. Prev Vet Med. 2018; 160:116\u0026ndash;22. \u003c/li\u003e\n\u003cli\u003ePuhr R, Heinze G, Nold M, Lusa L, Geroldinger A. Firth\u0026rsquo;s logistic regression with rare events: accurate effect estimates and predictions? Stat Med. 2017; 36:2302\u0026ndash;17. \u003c/li\u003e\n\u003cli\u003eBreiman L. Random Forests. Mach Learn. 2001; 45:5\u0026ndash;32. \u003c/li\u003e\n\u003cli\u003eFriedman J, Hastie T, Tibshirani R. Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw. 2010; 33:1\u0026ndash;22. \u003c/li\u003e\n\u003cli\u003eLasso and Elastic-Net Regularized Generalized Linear Models [Internet]. [cited 2024 Feb 15]. Available from: https://glmnet.stanford.edu/\u003c/li\u003e\n\u003cli\u003eHoltkamp DJ, Torremorell M, Corzo CA, Linhares DCL, Almeida MN, Yeske P, et al. Proposed modifications to porcine reproductive and respiratory syndrome virus herd classification. J Swine Health Prod. 2021; 29:261\u0026ndash;70. \u003c/li\u003e\n\u003cli\u003eKumar D, Anderson Reever AV, Pittman JS, Springer NL, Mallen K, Roman-Sosa G, et al. Role of Pre-Farrow Natural Planned Exposure of Gilts in Shaping the Passive Antibody Response to Rotavirus A in Piglets. Vaccines. 2023; 11:1866. \u003c/li\u003e\n\u003cli\u003eFurutani A, Sekiguchi S, Sueyoshi M, Sasaki Y. Effect of intervention practices to control the porcine epidemic diarrhea (PED) outbreak during the first epidemic year (2013-2014) on time to absence of clinical signs and the number of dead piglets per sow in Japan. Prev Vet Med. 2019; 169:104710. \u003c/li\u003e\n\u003cli\u003eYamagami T, Miyama T, Toyomaki H, Sekiguchi S, Sasaki Y, Sueyoshi M, et al. Analysis of the effect of feedback feeding on the farm-level occurrence of porcine epidemic diarrhea in Kagoshima and Miyazaki Prefectures, Japan. J Vet Med Sci. 2021; 83:1772\u0026ndash;81. \u003c/li\u003e\n\u003cli\u003ePitkin A, Deen J, Dee S. Further assessment of fomites and personnel as vehicles for the mechanical transport and transmission of porcine reproductive and respiratory syndrome virus. Can J Vet Res Rev Can Rech Veterinaire. 2009; 73:298\u0026ndash;302. \u003c/li\u003e\n\u003cli\u003eAmass SF, Pacheco JM, Mason PW, Schneider JL, Alvarez RM, Clark LK, et al. Procedures for preventing the transmission of foot-and-mouth disease virus to pigs and sheep by personnel in contact with infected pigs. Vet Rec. 2003; 153:137\u0026ndash;40. \u003c/li\u003e\n\u003cli\u003eAlarc\u0026oacute;n LV, Allepuz A, Mateu E. Biosecurity in pig farms: a review. Porc Health Manag. 2021; 7:5. \u003c/li\u003e\n\u003cli\u003eAlkhamis MA, Arruda AG, Vilalta C, Morrison RB, Perez AM. Surveillance of porcine reproductive and respiratory syndrome virus in the United States using risk mapping and species distribution modeling. Prev Vet Med. 2018; 150:135\u0026ndash;42. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"porcine-health-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"phmj","sideBox":"Learn more about [Porcine Health Management](http://porcinehealthmanagement.biomedcentral.com/)","snPcode":"40813","submissionUrl":"https://submission.nature.com/new-submission/40813/3","title":"Porcine Health Management","twitterHandle":"@animalplantsci","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"PRRSV, biosecurity, pig farms, Costa Rica, LASSO, Random Forest, seroprevalence, controlled exposure, risk factors, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-7418920/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7418920/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePorcine Reproductive and Respiratory Syndrome Virus (PRRSV) continues to impose significant economic losses on pig production globally. In Costa Rica, where the virus is endemic, there is limited knowledge of the farm-level risk factors influencing PRRSV spread. This study aimed to identify internal and external biosecurity factors associated with PRRSV seroprevalence in Costa Rican pig farms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA cross-sectional survey was conducted on 21 pig farms across Costa Rica. Data on farm management and biosecurity practices data were collected using a structured questionnaire and linked to PRRSV seroprevalence data from a companion study. Logistic regression, LASSO (Least Absolute Shrinkage and Selection Operator), and Random Forest models were used to identify significant risk factors associated with herd-level PRRSV positivity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThree key risk factors were consistently identified by both LASSO and Random Forest models: historical controlled exposure to PRRSV, restrictions on employee access to the farm, and restrictions on employee visits to other pig farms. Additional risk factors identified included topography, disinfection practices for transport vehicles, sanitation measures for visitors, boot and clothing protocols, and feedback procedures. Farms with a history of controlled exposure had an odds ratio of 90 (95% CI: 7.6–3,55) for being PRRSV-positive.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings underscore the importance of internal and external biosecurity measures, particularly in relation to personnel movement and intentional exposure practices. Modeling approaches such as LASSO and Random Forest provided complementary insights into PRRSV risk factors in a tropical production setting. These insights can guide tailored interventions to reduce PRRSV transmission in Costa Rica and similar regions.\u003c/p\u003e","manuscriptTitle":"Uncovering Biosecurity Gaps: Risk Factors for PRRSV Seropositivity in Costa Rican Pig Farms Identified Through Machine Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 08:27:30","doi":"10.21203/rs.3.rs-7418920/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-29T15:46:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-22T13:59:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-13T12:38:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"148918763412642729928973188827400551024","date":"2025-09-01T14:02:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"297416845620361181680651580086484847402","date":"2025-08-27T16:26:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-22T08:01:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-21T09:53:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-21T09:52:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Porcine Health Management","date":"2025-08-20T15:18:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"porcine-health-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"phmj","sideBox":"Learn more about [Porcine Health Management](http://porcinehealthmanagement.biomedcentral.com/)","snPcode":"40813","submissionUrl":"https://submission.nature.com/new-submission/40813/3","title":"Porcine Health Management","twitterHandle":"@animalplantsci","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5c11100c-dbac-45cc-aca1-25aac64b7e42","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T16:05:41+00:00","versionOfRecord":{"articleIdentity":"rs-7418920","link":"https://doi.org/10.1186/s40813-026-00495-4","journal":{"identity":"porcine-health-management","isVorOnly":false,"title":"Porcine Health Management"},"publishedOn":"2026-02-21 15:59:40","publishedOnDateReadable":"February 21st, 2026"},"versionCreatedAt":"2025-09-01 08:27:30","video":"","vorDoi":"10.1186/s40813-026-00495-4","vorDoiUrl":"https://doi.org/10.1186/s40813-026-00495-4","workflowStages":[]},"version":"v1","identity":"rs-7418920","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7418920","identity":"rs-7418920","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}