Meningitis

Cambridge meningitis model

Lead modeller: Caroline Trotter

Brief description of model:

This model began as an age-structured transmission dynamic model of group A Neisseria meningitidis (NmA) to investigate the impact of immunisation with MenAfriVac. Individuals may be susceptible, carriers, ill or recovered and in each of these states be vaccinated or unvaccinated, with vaccinated individuals having lower risks of infection (carriage acquisition) and disease. The model captures the key features of meningococcal epidemiology, including seasonality, age-specific carriage and periodic but irregular epidemics. Seasonality is implemented through seasonal forcing of the transmission rate, the extent of which varies stochastically every year. With the advent of new pentavalent vaccines in 2023, we extended the model to include groups CWYX.

Key publication(s):

Karachaliou A, Conlan AJ, Preziosi MP, Trotter CL. Modeling Long-term Vaccination Strategies With MenAfriVac in the African Meningitis Belt. Clin Infect Dis. 2015 Nov 15;61 Suppl 5(Suppl 5):S594-600. doi: 10.1093/cid/civ508. PMID: 26553693; PMCID: PMC4639487.

Karachaliou Prasinou A, Conlan AJK, Trotter CL. Understanding the Role of Duration of Vaccine Protection with MenAfriVac: Simulating Alternative Vaccination Strategies. Microorganisms. 2021 Feb 23;9(2):461. doi: 10.3390/microorganisms9020461. PMID: 33672209; PMCID: PMC7926406.

Model code (where available):

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CSRS meningitis model

Lead modeller: Kanny Diallo

Link to all modelling group members

Institution(s): Centre Suisse de Recherches Scientifiques (CSRS)

Brief description of model:

This model is currently in development. Models will incorporate key measurements of the disease dynamics, rather than using a proxy measure to represent seasonality. Climate change may affect both the distribution of the areas at risk within the meningitis belt - leading to either a contraction or expansion – and changes in the seasonal risk of meningitis. Epidemiological analyses will be conducted to better understand the patterns of meningococcal meningitis cases and meningococcal carriage by time and location and in the context of climactic risk factors. This will allow to better characterise mechanistic mathematical models of meningitis transmission.

Key publication(s):

Model code (where available):