PMID- 28606148
OWN - NLM
STAT- MEDLINE
DCOM- 20171031
LR  - 20181113
IS  - 1746-6148 (Electronic)
IS  - 1746-6148 (Linking)
VI  - 13
IP  - 1
DP  - 2017 Jun 12
TI  - Development of a stochastic agent-based model to evaluate surveillance strategies 
      for detection of emergent porcine reproductive and respiratory syndrome strains.
PG  - 171
LID - 10.1186/s12917-017-1091-7 [doi]
LID - 171
AB  - BACKGROUND: The objective of the current study was to develop a stochastic 
      agent-based model using empirical data from Ontario (Canada) swine sites in order 
      to evaluate different surveillance strategies for detection of emerging porcine 
      reproductive and respiratory syndrome virus (PRRSV) strains at the regional 
      level. Four strategies were evaluated, including (i) random sampling of fixed 
      numbers of swine sites monthly; (ii) risk-based sampling of fixed numbers, 
      specifically of breeding sites (high-consequence sites); (iii) risk-based 
      sampling of fixed numbers of low biosecurity sites (high-risk); and (iv) 
      risk-based sampling of breeding sites that are characterized as low biosecurity 
      sites (high-risk/high-consequence). The model simulated transmission of a 
      hypothetical emerging PRRSV strain between swine sites through three important 
      industry networks (production system, truck and feed networks) while considering 
      sites' underlying immunity due to past or recent exposure to heterologous PRRSV 
      strains, as well as demographic, geographic and biosecurity-related PRRS risk 
      factors. Outcomes of interest included surveillance system sensitivity and time 
      to detection of the three first cases over a period of approximately three years. 
      RESULTS: Surveillance system sensitivities were low and time to detection of 
      three first cases was long across all examined scenarios. CONCLUSION: Traditional 
      modes of implementing high-risk and high-consequence risk-based surveillance 
      based on site's static characteristics do not appear to substantially improve 
      surveillance system sensitivity. Novel strategies need to be developed and 
      considered for rapid detection of this and other emerging swine infectious 
      diseases. None of the four strategies compared herein appeared optimal for early 
      detection of an emerging PPRSV strain at the regional level considering model 
      assumptions, the underlying population of interest, and absence of other forms of 
      surveillance.
FAU - Arruda, A G
AU  - Arruda AG
AD  - Department of Population Medicine, Ontario Veterinary College, University of 
      Guelph, 50 Stone Rd East, Guelph, ON, N1G 2W1, Canada. goncalvesarruda.1@osu.edu.
FAU - Poljak, Z
AU  - Poljak Z
AD  - Department of Population Medicine, Ontario Veterinary College, University of 
      Guelph, 50 Stone Rd East, Guelph, ON, N1G 2W1, Canada.
FAU - Knowles, D
AU  - Knowles D
AD  - Department of Computer Science, Computational Epidemiology and Public Health 
      Informatics Lab, University of Saskatchewan, 176 Thorvaldson Bldg, 110 Science 
      Place, Saskatoon, SK, S7N 5C9, Canada.
FAU - McLean, A
AU  - McLean A
AD  - Department of Computer Science, Computational Epidemiology and Public Health 
      Informatics Lab, University of Saskatchewan, 176 Thorvaldson Bldg, 110 Science 
      Place, Saskatoon, SK, S7N 5C9, Canada.
LA  - eng
PT  - Journal Article
DEP - 20170612
PL  - England
TA  - BMC Vet Res
JT  - BMC veterinary research
JID - 101249759
SB  - IM
MH  - Animals
MH  - Canada/epidemiology
MH  - Communicable Diseases, Emerging/*veterinary/virology
MH  - Computer Simulation
MH  - Epidemiological Monitoring/*veterinary
MH  - *Models, Biological
MH  - Porcine Reproductive and Respiratory Syndrome/transmission/*virology
MH  - *Porcine respiratory and reproductive syndrome virus
MH  - Stochastic Processes
MH  - Swine
PMC - PMC5468968
OTO - NOTNLM
OT  - Porcine reproductive and respiratory syndrome control
OT  - Porcine reproductive and respiratory syndrome surveillance
OT  - Risk-based surveillance
OT  - Stochastic agent-based model
OT  - Surveillance system sensitivity
EDAT- 2017/06/14 06:00
MHDA- 2017/11/01 06:00
PMCR- 2017/06/12
CRDT- 2017/06/14 06:00
PHST- 2015/12/03 00:00 [received]
PHST- 2017/06/05 00:00 [accepted]
PHST- 2017/06/14 06:00 [entrez]
PHST- 2017/06/14 06:00 [pubmed]
PHST- 2017/11/01 06:00 [medline]
PHST- 2017/06/12 00:00 [pmc-release]
AID - 10.1186/s12917-017-1091-7 [pii]
AID - 1091 [pii]
AID - 10.1186/s12917-017-1091-7 [doi]
PST - epublish
SO  - BMC Vet Res. 2017 Jun 12;13(1):171. doi: 10.1186/s12917-017-1091-7.