Development of a biosecurity assessment tool and the assessment of biosecurity levels by this tool on Japanese commercial swine farms

Sasaki, Yosuke; Furutani, Aina; Furuichi, Tomohiro; Hayakawa, Yuiko; Ishizeki, Sayoko; Kano, Rika; Koike, Fumihiko; Miyashita, Mali; Mizukami, Yoshihiro; Watanabe, Yugo; Otake, Satoshi

doi:10.1016/j.prevetmed.2019.104848

Cited by 17 publications

(18 citation statements)

References 24 publications

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“…To objectively assess biosecurity, it is important to quantify the practices on each farm and provide concrete management suggestions based on the evaluation. BioAsseT, a quantitative risk assessment tool for measuring biosecurity on a pig farm level developed by Sasaki et al (2020), could be useful to assess biosecurity on commercial Japanese pig farms. A high biosecurity level can prevent the entry of infectious diseases into the herd and also prevent the spread of diseases within the farm.…”

Section: Ta B L Ementioning

confidence: 99%

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Assessment of reproductive and growth performance of pigs on commercial swine farms in southern Kyushu, Japan

Sasaki

Kawabata

Nakatake

et al. 2020

Animal Science Journal

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The objective of the present study was to assess the productivity of pigs to investigate the time‐dependent change in productivity and compare productivity according to breeding company group and porcine reproductive and respiratory syndrome (PRRS) status on commercial swine farms in southern Kyushu, Japan. Data used in the present study were 245 annual productivity records from 2014 to 2018 obtained from 72 farms. Average sow inventory was 481.2 sows. Mean numbers of pigs weaned per sow per year and market pigs sold per sow per year were 23.7 and 21.6, respectively. Pigs born alive per litter increased from 11.1 to 11.9 pigs from 2014 to 2018 (p < .05). Farms using domestic breeding companies had similar numbers in 2016 to those using international breeding companies, but fewer pigs in 2014, 2015, 2017, and 2018 (p < .05). Farms with an unknown or unstable PRRS status had fewer pigs born alive per litter and pigs weaned per sow per year than those with stable or negative PRRS statuses (11.2 ± 0.06 vs. 11.8 ± 0.08 pigs and 22.6 ± 0.38 vs. 25.0 ± 0.22 pigs, respectively; p < .05). These results can be used to establish feasible targets and standards of performance to identify problem areas and improve production.

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Section: Ta B L Ementioning

confidence: 99%

“…PRRS status was divided into two groups based on their PRRS status as from I to V; unknown or unstable (status unknown, I, and II) and stable or negative (status III-V). Definition of PRRS status from I to V was described previously (Sasaki et al, 2020)…”

Section: Definitions and Categorymentioning

confidence: 99%

Assessment of reproductive and growth performance of pigs on commercial swine farms in southern Kyushu, Japan

Sasaki

Kawabata

Nakatake

et al. 2020

Animal Science Journal

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“…Benchmarking of farms according to their biosecurity practices is not a novel application; in 2011, the American Association of Swine Veterinarians released their Production Animal Disease Risk Assessment Programme which assessed a farm's risk of a clinical outbreak by their biosecurity strategy (Holtkamp et al., 2012). Similar tools have been developed by several institutions and organisations including Biocheck.UGent at Ghent University (Gelaude et al., 2014); the ASF focussed tool ‘ASF Combat’ at Boehringer Ingelheim (Boehringer Ingelheim, 2018) and BioAsset created by the PRRS‐Japan Elimination Team (Sasaki et al., 2020). However, these tools are built upon qualitative methodologies such as multi‐criteria decision analysis and the assignation of weights or values to biosecurity practices based on expert opinion (Alarcón et al., 2021; Gelaude et al., 2014; Sasaki et al., 2020; Silva et al., 2018), which could potentially introduce bias into the risk estimates (Alarcón et al., 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Fortunately, the selection and implementation of biosecurity practices can be facilitated by the use of biosecurity assessments, which are often able to highlight biosecurity practices with the potential for improvement (Alarcón et al., 2021; Gelaude et al., 2014; Holtkamp et al., 2012; Rodrigues Da Costa et al., 2019; Sasaki et al., 2020; Silva et al., 2018, 2019). While traditionally this is performed through rigorous but time‐consuming surveys (Alarcón et al., 2021; Rodrigues Da Costa et al., 2019), recent developments in swine health management software, such as Biocheck.UGent (Gelaude et al., 2014; Ghent University, 2021), BioAsseT (Sasaki et al., 2020) and ASF combat (Boehringer Ingelheim, 2018), have allowed the incorporation of biosecurity information to guide the implementation of biosecurity practices through weight‐based analyses. Alas, these analyses are prone to subjectivity and often precluded by the lack of quantitative measures, which could provide insight into the impact of on‐farm biosecurity practices on infection risk (Alarcón et al., 2021; Galvis et al., 2021; Jara et al., 2020; Silva et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Interpretable machine learning applied to on‐farm biosecurity and porcine reproductive and respiratory syndrome virus

Sykes

Silva

Holtkamp

et al. 2021

Transbounding Emerging Dis

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Effective biosecurity practices in swine production are key in preventing the introduction and dissemination of infectious pathogens. Ideally, on-farm biosecurity practices should be chosen by their impact on bio-containment and bio-exclusion, however quantitative supporting evidence is often unavailable. Therefore, the development of methodologies capable of quantifying and ranking biosecurity practices according to their efficacy in reducing disease risk have the potential to facilitate better informed choices of biosecurity practices.Using survey data on biosecurity practices, farm demographics, and previous outbreaks from 139 herds, a set of machine learning algorithms were trained to classify farms by porcine reproductive and respiratory syndrome virus status, depending on their biosecurity practices and farm demographics, to produce a predicted outbreak risk. A novel interpretable machine learning toolkit, MrIML-biosecurity, was developed to benchmark farms and production systems by predicted risk, and quantify the impact of biosecurity practices on disease risk at individual farms.Quantifying the variable impact on predicted risk 50% of 42 variables were associated with fomite spread while 31% were associated with local transmission. Results from machine learning interpretations identified similar results, finding substantial contribution to predicted outbreak risk from biosecurity practices relating to: the turnover and number of employees; the surrounding density of swine premises and pigs; the sharing of haul trailers; distance from the public road; and farm production type. In addition, the development of individualized biosecurity assessments provides the opportunity to better guide biosecurity implementation on a case-by-case basis. Finally, the flexibility of the MrIML-biosecurity toolkit gives it the potential to be applied to wider areas of biosecurity benchmarking, to address biosecurity weaknesses in other livestock systems and industry relevant diseases.

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“…The three components of biosecurity measures are isolation, traffic control, and sanitation [ 8 ]. Van Limbergen et al [ 4 ] and Sasaki et al [ 9 ] disclosed that biosecurity is classified into internal and external. Biosecurity consists of conceptual, structural, and operational frameworks [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Biosecurity Status in Commercial Chicken Farms Found in Bishoftu Town, Oromia Regional State, Ethiopia

Ismael

Abdella

Shimelis

et al. 2021

Veterinary Medicine International

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A survey was undertaken from December 2017 to April 2018 to assess the biosecurity status of 44 commercial chicken farms established in Bishoftu town, Ethiopia, by interviewing farm owners using a structured questionnaire. The obtained data were summarized using frequency tables and analyzed with Pearson’s chi-square test and Fischer’s exact value using Stata 14 statistical software. From the assessed chicken farms, 31 (70.45%) were located within 0–50 m from the main road, 39 (88.64%) situated 500 m from the nearest farms, and 27 (61.36%) placed within 0–20 m from the residential areas. Forty-one (93.18%) participants disclosed that their employees did not receive training on biosecurity. From the assessed chicken farms, 30 (68.18%) had fences, 40 (90.91%) had footbaths at their gates, 31 (70.45%) prohibited visitors entrance, and 39 (88.64%) did not exchange equipment with other farms. In addition, 26 (59.09%) farms were easily accessed by wild birds, each of 42 (95.45%) farms purchased day-old chicks and feed, and 40 (90.91%) shared trucks with other farms as well. Among the assessed farms, only 2 (4.55%) had signages to restrict people’s access, 9 (20.45%) had isolation rooms for diseased chickens, 14 (31.82%) disposed of dead birds properly, and 10 (22.73%) kept various types of records. Occupation (Fischer’s exact value = 8.40; P = 0.019 ), previous training (Fischer’s exact value = 4.40; P = 0.044 ), source of the premises (χ2 = 5.50; P = 0.019 ), and farm capacity (Fischer’s exact value = 13.50; P = 0.002 ) were found statistically significantly associated with the farm biosecurity status. The farm biosecurity status was found to be good in farms that were run by civil servants, had trained employees, are owned premises, and were of large and medium scales. In conclusion, the higher poor biosecurity status on chicken farms calls for the implementation of good biosecurity practices in each farm as well as the provision of training to the farm owners and their employees.

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Development of a biosecurity assessment tool and the assessment of biosecurity levels by this tool on Japanese commercial swine farms

Cited by 17 publications

References 24 publications

Assessment of reproductive and growth performance of pigs on commercial swine farms in southern Kyushu, Japan

Assessment of reproductive and growth performance of pigs on commercial swine farms in southern Kyushu, Japan

Interpretable machine learning applied to on‐farm biosecurity and porcine reproductive and respiratory syndrome virus

Assessment of Biosecurity Status in Commercial Chicken Farms Found in Bishoftu Town, Oromia Regional State, Ethiopia

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