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In 2022, the Kingston, Frontenac, Lennox & Addington Public Health (KFL&A PH) region saw a sharp increase in syphilis cases. Local infection rates had more than doubled the provincial average, and reports of congenital syphilis, where the infection is passed from parent to baby, were on the rise.

Syphilis is a sexually transmitted infection caused by the bacterium Treponema pallidum. It can cause serious health complications if left untreated, but it is easily cured with antibiotics, usually a single dose of penicillin. Many people who are infected do not experience obvious symptoms, and without testing, infections can go undetected and continue to spread.

In response to the outbreak, a regional steering committee was formed, bringing together clinicians, academics, public health practitioners, and front-line health care workers. To support this effort, Queen’s University researcher Dr. Sahar Saeed (Public Health Sciences) and Dr. Megan Carter (KFL&A PH) co-led a multidisciplinary Canadian Institutes of Health Research funded study called (Syphilis Point-of-care Rapid testing and Immediate Treatment Evaluation). The team also included Dr. Felicia Magpantay (Mathematics and Statistics) and former PhD student Sicheng Zhao (Mathematics and Statistics). Their work, alongside that of other team members, contributed to the study’s findings, which were published in .

A new approach to modeling syphilis spread 

One of the first tasks for the team was to understand how the outbreak was spreading and how extensively it might affect the region. However, traditional mathematical models could not accurately capture the local epidemic, leading the team to explore new methods.

Dr. Magpantay and Zhao developed a mathematical framework aimed at better reflecting how syphilis spreads through local networks. Traditional models, based on the Susceptible-Infectious-Recovered (SIR) framework, assume everyone has the same chance of infecting each other, but that doesn't reflect the reality of how syphilis is typically transmitted.

Instead, the team used an approach called edge-based network modeling which can be used to study how diseases spread through networks where the number of connections per individual or 'degree distribution' is known. By basing the model on the at-risk population’s reported number of sexual contacts, the team was able to generate a more accurate estimate of the outbreak's potential scale without needing to estimate the exact structure of the contact network of the community.

“Rather than assuming everyone interacts randomly, we built a mathematical model that incorporates characteristics of the underlying transmission network,” says Dr. Magpantay. “This gives public health teams a more accurate picture of how an outbreak is likely to unfold and where interventions can have the biggest impact.”

The team worked to calibrate the model with local case data, accounting for underreporting and risk factors. The model also accounted for the wide variation in the number of sexual partners across the population, capturing key drivers of transmission, such as the sex trade, anonymous encounters, and substance use, which are often missing from more generic frameworks. 

 A traditional SIR model fitted to the same data estimated that more than 15,000 people in the target population could become infected. The network model, in contrast, projected closer to 1,700 cases. This major difference has important consequences for how public health teams plan testing, outreach, and treatment strategies.

Informing public health strategies

In addition to forecasting the size of the outbreak, the model was also used to assess the potential impact of scaling up point-of-care testing. The team found that even a modest increase in testing of five per cent could reduce the final epidemic size by over 13 per cent, providing strong evidence for expanding rapid test and treatment programs.

While accurate modeling can improve health outcomes, it also has economic benefits. By narrowing the projected size of the outbreak, the network model helps avoid over-allocation of resources and supports more cost-effective testing and intervention strategies. The method developed in this study could also have broader applications for understanding the spread of other sexually transmitted infections. 

“This work shows what’s possible when researchers, public health, and community organizations come together with a shared goal,” says Dr. Saeed. “By combining data, local insights, and modelling expertise, we were able to respond more effectively to an urgent public health challenge.”

The team’s work is shaping public health strategies both locally and in other regions across Ontario involved in the SPRITE Study. The modeling framework has also been prepared as a statistical package that can be used by other public health regions to inform decision-making and resource allocation in response to syphilis outbreaks.

Read the full study in the .