دورية أكاديمية
Modelling the field personnel resources to control foot-and-mouth disease outbreaks in New Zealand.
| العنوان: | Modelling the field personnel resources to control foot-and-mouth disease outbreaks in New Zealand. |
|---|---|
| المؤلفون: | Sanson RL; AsureQuality Limited, Palmerston North, New Zealand., Rawdon TG; Diagnostics and Surveillance Services Directorate, Ministry for Primary Industries, Upper Hutt, New Zealand., van Andel M; Chief Veterinary Officer, Ministry for Primary Industries, Wellington, New Zealand., Yu Z; Food Science and Risk Assessment, Ministry for Primary Industries, Wellington, New Zealand. |
| المصدر: | Transboundary and emerging diseases [Transbound Emerg Dis] 2022 Nov; Vol. 69 (6), pp. 3926-3939. Date of Electronic Publication: 2022 Dec 05. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Blackwell Verlag Country of Publication: Germany NLM ID: 101319538 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1865-1682 (Electronic) Linking ISSN: 18651674 NLM ISO Abbreviation: Transbound Emerg Dis Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Berlin : Blackwell Verlag |
| مواضيع طبية MeSH: | Foot-and-Mouth Disease*/epidemiology , Foot-and-Mouth Disease*/prevention & control , Foot-and-Mouth Disease Virus*/physiology , Epidemics*/veterinary , Cattle Diseases*/epidemiology , Cattle Diseases*/prevention & control, Animals ; Cattle ; New Zealand/epidemiology ; Disease Outbreaks/prevention & control ; Disease Outbreaks/veterinary ; Vaccination/veterinary |
| مستخلص: | The objective of the study was to simulate New Zealand's foot-and-mouth disease (FMD) operational plan to determine personnel requirements for an FMD response and understand how the numbers of front-line staff available could affect the size and duration of FMD outbreaks, when using stamping-out (SO) measures with or without vaccination. The model utilized a national dataset of all known livestock farms. Each simulation randomly seeded infection into a single farm. Transmission mechanisms included direct and indirect contacts, local and airborne spread. Prior to each simulation, the numbers of personnel available for front-line tasks (including contact tracing, surveillance of at-risk farms, depopulation and vaccination) were set randomly. In a random subset of simulations, vaccination was allowed to be deployed as an adjunct to SO. The effects of personnel numbers on the size and duration of epidemics were explored using machine learning methods. In the second stage of the study, using a subset of iterations where numbers of personnel were unconstrained, the number of personnel used each day were quantified. When personnel resources were unconstrained, the 95 th percentile and maximum number of infected places (IPs) were 78 and 462, respectively, and the 95 th percentile and maximum duration were 69 and 217 days, respectively. However, severe constraints on personnel resources allowed some outbreaks to exceed the size of the UK 2001 FMD epidemic which had 2026 IPs. The number of veterinarians available had a major influence on the size and duration of outbreaks, whereas the availability of other personnel types did not. A shortage of veterinarians was associated with an increase in time to detect and depopulate IPs, allowing for continued transmission. Emergency vaccination placed a short-term demand for additional staff at the start of the vaccination programme, but the overall number of person days used was similar to SO-only strategies. This study determined the optimal numbers of front-line personnel required to implement the current operational plans to support an FMD response in New Zealand. A shortage of veterinarians was identified as the most influential factor to impact disease control outcomes. Emergency vaccination led to earlier control of FMD outbreaks but at the cost of a short-term spike in demand for personnel. In conclusion, a successful response needs to have access to sufficient personnel, particularly veterinarians, trained in response roles and available at short notice. (© 2022 Wiley-VCH GmbH.) |
| References: | Anderson Inquiry (2002). Foot-and-mouth disease 2001: Lessons learned inquiry (p. 169). The Stationery Office. Accessed 26 November 2019, https://webarchive.nationalarchives.gov.uk/20100809105008/ http://archive.cabinetoffice.gov.uk/fmd/fmd_report/report/index.htm. Backer, J. A., Hagenaars, T. J., Nodelijk, G., & van Roermund, H. J. W. (2012a). Vaccination against foot-and-mouth disease I: Epidemiological consequences. Preventative Veterinary Medicine, 107, 27-40. Boklund, A., Mortensen, S., Johansen, M. H., & Halasa, T. (2017). Resource estimations in contingency planning for foot-and-mouth disease. Frontiers in Veterinary Science, 4, 64. https://doi.org/10.3389/fvets.2017.00064. Boulesteix, A. L., Janitza, S., Kruppa, J., & König, I. R. (2012). Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2, 493-507. https://doi.org/10.1002/widm.1072. Dubé, C., Stevenson, M. A., Garner, M. G., Sanson, R. L., Corso, B. A., Harvey, N., Griffin, J., Wilesmith, J. W., & Estrada, C. (2007). A comparison of predictions made by three simulation models of foot-and-mouth disease. New Zealand Veterinary Journal, 55(6), 280-288. Forbes, R., & van Halderen, A. (2014). MPI Technical Paper No: 2014/18. Foot-and-mouth disease economic impact assessment: What it means for New Zealand. Ministry for Primary Industries. Accessed 26 November 2019, https://www.mpi.govt.nz/document-vault/4406. Garner, M. G., Bombarderi, N., Cozens, M., Conway, M. L., Wright, T., Paskin, R., & East, I. J. (2016). Estimating resource requirements to staff a response to a medium to large outbreak of foot and mouth disease in australia. Transboundary and Emerging Diseases, 63(1), e109-e121. Portico. https://doi.org/10.1111/tbed.12239. Goldstein, B. A., Navar, A. M., & Carter, R. E. (2017). Moving beyond regression techniques in cardiovascular risk prediction: Applying machine learning to address analytic challenges. European Heart Journal, 38, 1805-1814. https://doi.org/10.1093/eurheartj/ehw302. Halasa, T., Willeberg, P., Christiansen, L. E., Boklund, A., Alkhamis, M., Perez, A., & Enøe, C. (2013). Decisions on control of foot-and-mouth disease informed using model predictions. Preventive Veterinary Medicine, 112, 194-202. https://doi.org/10.1016/j.prevetmed.2013.09.003. Halasa, T., & Boklund, A. (2014). The impact of resources for clinical surveillance on the control of a hypothetical foot-and-mouth disease epidemic in denmark. PLoS ONE, 9(7), e102480. https://doi.org/10.1371/journal.pone.0102480. Henderson, R. J. (1969). The outbreak of foot-and-mouth disease in Worcestershire. An epidemiological study: With special reference to spread of the disease by wind carriage of the virus. Journal of Hygiene, 67, 21-33. Kahn, S., Geale, D. W., Kitching, P. R., Bouffard, A., Allard, D. G., & Duncan, J. R. (2002). Vaccination against foot-and-mouth disease: The implications for Canada. Special Report. Canadian Veterinary Journal, 43, 349-54. Kuhle, S., Maguire, B., Zhang, H., Hamilton, D., Allen, A. C., Joseph, K. S., & Allen, V. M. (2018). Comparison of logistic regression with machine learning methods for the prediction of fetal growth abnormalities: A retrospective cohort study. BMC Pregnancy Childbirth, 18, 333. https://doi.org/10.1186/s12884-018-1971-2. Marschik, T., Kopacka, I., Stockreiter, S., Schmoll, F., Hiesel, J., Höflechner-Pöltl, A., Käsbohrer, A., & Pinior, B. (2021a). The epidemiological and economic impact of a potential foot-and-mouth disease outbreak in Austria. Frontiers in Veterinary Science, 7, 594753. https://doi.org/10.3389/fvets.2020.594753. Marschik, T., Kopacka, I., Stockreiter, S., Schmoll, F., Hiesel, J., Höflechner-Pöltl, A., Käsbohrer, A., & Conrady, B. (2021b). What are the human resources required to control a foot-and-mouth disease outbreak in Austria? Frontiers in Veterinary Science, 8, 727209. https://doi.org/10.3389/fvets.2021.727209. Marsot, M., Durand, B., Ben Hammouda, W., Hadj Ammar, H., Zrelli, M., & Khorchani, R. (2020). Evaluation of human resources needed and comparison with human resources available to implement emergency vaccination in case of foot and mouth disease outbreaks in tunisia. Epidemiology and Infection, 148. https://doi.org/10.1017/s0950268820001284. Owen, K., Stevenson, M. A., & Sanson, R. L. (2011). A sensitivity analysis of the New Zealand standard model of foot-and-mouth disease. Revue Scientifique et Technique de l'Office International des Epizooties, 30(2), 513-526. R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/. Rawdon, T. G., Garner, M. G., Sanson, R. L., Stevenson, M. A., Cook, C., Birch, C., Roche, S. E., Patyk, K. A., Forde-Folle, K. N., Dubé, C., Smylie, T., & Yu, Z. D. (2018). Evaluating vaccination strategies to control foot-and-mouth disease: A country comparison study. Epidemiology and Infection, 146, 1138-1150. https://doi.org/10.1017/S0950268818001243. Roche, S. E., Garner, M. G., Wicks, R. M., East, I. J., & de Witte, K. (2014). How do resources influence control measures during a simulated outbreak of foot and mouth disease in Australia? Preventive Veterinary Medicine, 113, 436-446. Roche, S. E., Garner, M. G., Sanson, R. L., Cook, C., Birch, C., Backer, J. A., Dubé, C., Patyk, K. A., Stevenson, M. A., Yu, Z., Rawdon, T. G., & Gauntlett, F. (2015). Evaluating vaccination strategies to control foot-and-mouth disease: A model comparison study. Epidemiology & Infection, 143(6), 1256-1275. https://doi.org/10.1017/S0950268814001927. Sanson, R. L. (1993). The development of a decision support system for an animal disease emergency (PhD thesis). Massey University. Sanson, R. L. (1994). The epidemiology of foot and mouth disease: Implications for New Zealand. New Zealand Veterinary Journal, 42, 41. Sanson, R. L. (2005). The AgriBaseTM farm location database. Proceedings of the New Zealand Society of Animal Production, Vol. 65 (pp. 93-96). Sanson, R. L., Harvey, N., Garner, M. G., Stevenson, M. A., Davies, T. M., Hazelton, M. L., O'Connor, J., Dubé, C., Forde-Folle, K. N., & Owen, K. (2011). Foot and mouth disease model verification and 'relative validation' through a formal model comparison. Revue Scientifique et Technique de l'Office International des Epizooties, 30(2), 527-540. Sanson, R. L., Dubé, C., Cork, S. C., Frederickson, R., & Morley, C. (2014). Simulation modelling of a hypothetical introduction of foot-and-mouth disease into Alberta. Preventive Veterinary Medicine, 114, 151-163. Sanson, R. L., Yu, Z. D., Rawdon, T. G., & van Andel, M. (2021a). Informing adaptive management strategies: Evaluating a mechanism to predict the likely qualitative size of foot-and-mouth disease outbreaks in New Zealand using data available in the early response phase of simulated outbreaks. Transboundary and Emerging Diseases, 68(3), 1504-1512. https://doi.org/10.1111/tbed.13820. Sanson, R. L., Yu, Z. D., Rawdon, T. G., & van Andel, M. (2021b). Investigations into a trigger-based approach for initiating emergency vaccination to augment stamping-out of foot-and-mouth disease in New Zealand: A simulation study. New Zealand Veterinary Journal, 69(6), 313-326. https://doi.org/10.1080/00480169.2021.1921069. Seitzinger, A., Garner, M., Bradhurst, R., Roche, S., Breed, A., Capon, T., Miller, C., & Tapsuwan, S. (2022). FMD vaccine allocation and surveillance resourcing options for a potential Australian incursion. Australian Veterinary Journal, 100(11), 550-561. Portico. https://doi.org/10.1111/avj.13195. Stevenson, M. A., Sanson, R. L., Stern, M. W., O'Leary, B. D., Sujau, M., Moles-Benfell, N., & Morris, R. S. (2013). InterSpread Plus: A spatial and stochastic simulation model of disease in animal populations. Preventive Veterinary Medicine, 109, 10-24. https://doi.org/10.1016/j.prevetmed.2012.08.015. |
| معلومات مُعتمدة: | New Zealand Ministry for Primary Industries (MPI) |
| فهرسة مساهمة: | Keywords: foot-and-mouth disease; personnel resources; resource requirements; simulation modelling |
| تواريخ الأحداث: | Date Created: 20221118 Date Completed: 20230206 Latest Revision: 20230206 |
| رمز التحديث: | 20231215 |
| DOI: | 10.1111/tbed.14764 |
| PMID: | 36397293 |
| قاعدة البيانات: | MEDLINE |
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