Comparability of Results from Pair and Classical Model Formulations for Different Sexually Transmitted Infections

Abstract

The “classical model” for sexually transmitted infections treats partnerships as instantaneous events summarized by partner change rates, while individual-based and pair models explicitly account for time within partnerships and gaps between partnerships. We compared predictions from the classical and pair models over a range of partnership and gap combinations. While the former predicted similar or marginally higher prevalence at the shortest partnership lengths, the latter predicted self-sustaining transmission for gonorrhoea (GC) and Chlamydia (CT) over much broader partnership and gap combinations. Predictions on the critical level of condom use ( C c ) required to prevent transmission also differed substantially when using the same parameters. When calibrated to give the same disease prevalence as the pair model by adjusting the infectious duration for GC and CT, and by adjusting transmission probabilities for HIV, the classical model then predicted much higher C c values for GC and CT, while C c predictions for HIV were fairly close. In conclusion, the two approaches give different predictions over potentially important combinations of partnership and gap lengths. Assuming that it is more correct to explicitly model partnerships and gaps, then pair or individual-based models may be needed for GC and CT since model calibration does not resolve the differences.

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“…Several studies have compared the estimated impact of interventions between pair formation models and instantaneous contact model ( Althaus et al., 2012a , Heijne et al., 2011 , Lloyd-Smith et al., 2004 , Ong et al., 2012 , Van de Velde et al., 2010 ). Lloyd-Smith et al.…”

A dynamic power-law sexual network model of gonorrhoea outbreaks

“…In 2017 the WHO added Neisseria gonorrhoeae to its priority list of bacterial pathogens in response to 21 the global spread of antibiotic resistance [1]. The bacteria have developed resistance to every therapy 22 used against them, from penicillin through to third-generation cephalosporins [2].…”

“…Compartmental models that do not explicitly represent partnerships 58 have been shown to underestimate the importance of core groups of highly sexually active individuals 59 in sustaining STI transmission, while overestimating the contribution of long-term partnerships and 60 low-activity individuals [19]. Furthermore, several studies have shown that, in order to explain observed 61 patterns of infection, it is important to take into account not only the network structure but also the 62 duration of partnerships, and the gaps between them [20,21]. It may therefore be necessary to use a 63 dynamically evolving network to correctly simulate the spread of STI outbreaks.…”

“…We assessed 192 the one-year impact of these four measures on the probability of a outbreak stemming from a single 193 introduction of gonorrhoea into a dynamic sexual network (Fig 6A), the total number of gonorrhoea 194 diagnoses ( Fig 6B), and the number of sexual health clinic visits from both screening and symptomatic 195 treatment-seeking ( Fig 6C). [25,269] 83 [21,205] 115 [26,291] 68 [19,161] 60 [17,140] Increasing condom usage by 20% reduced the probability of an outbreak by around 10% to 28% (95% 203 range: [13%, 47%]); A 20% increase in the rate of screening for asymptomatic cases had a similar 204 effect, reducing the probability of an outbreak to 29% (95% range: [11%, 49%]). 205 The expected size of the visible outbreak in a population of 10,000, as measured by the total number 206 of infected individuals diagnosed and receiving treatment, was reduced by a fifth from 98 (95% range: 207 [25,269]) to 79 (95% range: [19,217]) with a 20% increase in condom-use, and could be halved using 208 vaccination: down to 47 cases (95% range: [14,114]) using the randomised strategy and 45 cases 209 (95% range: [14,105]) by targeting the most sexually-active individuals.…”