Utilize the power of communicable disease models to inform your analyses and help you better understand the potential impact of your novel intervention.
You rely on realistic disease models to understand and communicate the value of your products to stakeholders. Static models are a useful tool, but they can only assess the direct impact of an intervention. When developing medicines, vaccines, or diagnostics for infectious diseases, you need to consider additional factors such as human behavior and herd immunity to effectively demonstrate the full impact of your product. Communicable disease models can capture indirect effects and population dynamics to help you better understand transmission of a contagious disease and enable you to present your product to its full potential.
Understand how your intervention affects disease transmission, health outcomes, resource use, and costs.
We have an expert team of modelers that includes researchers who have advanced degrees in operations research, industrial engineering, and mathematics with experience developing both simple and complex infectious disease models. We follow best practices established by ISPOR and WHO and have developed various types of communicable disease models to study the impact of new vaccines and treatments including:
- Compartmental dynamic transmission models
- Agent-based models
- Simple epidemic forecasting models
- Bernoulli models and other R0 estimators
- Resource allocation/optimization models
- Static cohort models
Our communicable disease models have been used for publication and to support health technology assessment (HTA) submissions and discussions with vaccine advisory committees such as the Advisory Committee on Immunization Practices (ACIP) and the Joint Committee on Vaccination and Immunisation (JCVI). Our therapeutic area experience spans a wide range of infectious disease research, including:
- Ebola virus
- Seasonal influenza
- Pneumococcal disease
- Respiratory syncytial virus
- Varicella zoster virus
Contact us to discuss your objectives and we will collaborate with you to design and build the most appropriate model to demonstrate the full value of your product.
Gould I, Herring W, Benninghoff B, Standaert B. Explaining the rotavirus serotype-distribution shift in children after vaccination using a simple transmission model. Poster presented at the ISPOR 24th Annual International Meeting; May 21, 2019. New Orleans, LA.
Wasserman M, Lucas A, Jones D, Wilson M, Hilton B, Vyse A, Madhava H, Brogan A. Dynamic transmission modelling to address infant pneumococcal conjugate vaccine schedule modifications in the UK. Epidemiol Infect. 2018 Oct;146(14):1797-806.
Khurana N, Yaylali E, Farnham PG, Hicks KA, Allaire BT, Jacobson E, Sansom SL. Impact of improved HIV care and treatment on PrEP effectiveness in the United States, 2016–2020. J Acquir Immune Defic Syndr. 2018 Aug 1;78(4):399-405.
Graham J, Talbird SE, Monsanto H, Perez Bolde-Villareal C, Daniels V, Pillsbury M, Wolfson LJ. Budget impact of a one-dose varicella vaccination program using outputs of a dynamic transmission model: Mexican national perspective. Presented at the 17th Annual Congress of the Sociedad Latinoamericana de Infectología Pediátrica (SLIPE); November 8-11, 2017. Cancun, Mexico.
Brogan AJ, Talbird SE, Davis AE, Thommes EW, Meier G. Cost-effectiveness of seasonal quadrivalent versus trivalent influenza vaccination in the United States: a dynamic transmission modeling approach. Hum Vaccin Immunother. 2017 Mar 4;13(3):533-542.
Mauskopf J, Talbird S, Standaert B. Categorization of methods used in cost-effectiveness analyses of vaccination programs based on outcomes from dynamic transmission models. Expert Rev Pharmacoecon Outcomes Res. 2012 Jun;12(3):357-71.