Revised simulation model does not predict rebound in gonorrhoea prevalence where core groups are treated in the presence of antimicrobial resistance

• Published in: Sexually transmitted infections Vol. 91; no. 4; pp. 300 - 302
• Main Authors: Trecker, Molly A, Hogan, Daniel J, Waldner, Cheryl L, Dillon, Jo-Anne R, Osgood, Nathaniel D
• Format: Journal Article
• Language: English
• Published: England BMJ Publishing Group LTD 01.06.2015
• Subjects: Anti-Bacterial Agents - administration & dosage; Antimicrobial agents; Communicable Disease Control; Drug resistance; Drug Resistance, Bacterial - drug effects; Equilibrium; Gonorrhea; Gonorrhea - drug therapy; Gonorrhea - epidemiology; Humans; Infections; Microbial Sensitivity Tests; Models, Statistical; Neisseria gonorrhoeae - isolation & purification; Ordinary differential equations; Population; Predictive Value of Tests; Prevalence; Sensitivity analysis; Canada; ANTIBIOTIC RESISTANCE; MATHEMATICAL MODEL; NEISSERIA GONORRHOEA; GONORRHOEA; ANTIMICROBIAL RESISTANCE
• ISSN: 1368-4973; 1472-3263
• DOI: 10.1136/sextrans-2014-051792

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.