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Revised simulation model does not predict rebound in gonorrhoea prevalence where core groups are treated in the presence of antimicrobial resistance
  1. Molly A Trecker1,2,
  2. Daniel J Hogan3,
  3. Cheryl L Waldner2,4,
  4. Jo-Anne R Dillon1,5,
  5. Nathaniel D Osgood3
  1. 1Vaccine and Infectious Disease Organization—International Vaccine Centre, Saskatoon, Saskatchewan, Canada
  2. 2School of Public Health, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
  3. 3Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
  4. 4Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
  5. 5Department of Microbiology and Immunology, College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
  1. Correspondence to Professor Nathaniel D Osgood, Department of Computer Science, University of Saskatchewan, 110 Science Place, Saskatoon, Saskatchewan, Canada SK S7N 5C9; osgood{at}


Objectives To determine the effects of using discrete versus continuous quantities of people in a compartmental model examining the contribution of antimicrobial resistance (AMR) to rebound in the prevalence of gonorrhoea.

Methods A previously published transmission model was reconfigured to represent the occurrence of gonorrhoea in discrete persons, rather than allowing fractions of infected individuals during simulations.

Results In the revised model, prevalence only rebounded under scenarios reproduced from the original paper when AMR occurrence was increased by 105 times. In such situations, treatment of high-risk individuals yielded outcomes very similar to those resulting from treatment of low-risk and intermediate-risk individuals. Otherwise, in contrast with the original model, prevalence was the lowest when the high-risk group was treated, supporting the current policy of targeting treatment to high-risk groups.

Conclusions Simulation models can be highly sensitive to structural features. Small differences in structure and parameters can substantially influence predicted outcomes and policy prescriptions, and must be carefully considered.


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