Epidemiology and Control of Meningitis in Ghana: Application of Mathematical Modelling

Meningitis remains a major global health challenge, with over 2.5 million cases reported annually. The African meningitis belt–spanning 26 countries, including Ghana—has experienced recurrent, often large-scale epidemics for more than a century, primarily caused by Neisseria meningitidis. Vaccination has been the main tool for meningitis control in the belt. Since 2010, many countries, including Ghana, have successfully introduced MenAfriVac—a protein-conjugate polysaccharide vaccine targeting N. meningitidis serogroup A (MenA), the predominant cause of epidemics. The vaccine was administered to the high-risk age group (1–29 years), resulting in no confirmed reported cases of MenA in the region since 2018. As part of enhanced global efforts to control meningitis, the World Health Assembly endorsed a global roadmap in November 2020 to Defeat Meningitis by 2030. In support of this initiative, my thesis seeks to contribute to national control efforts by assessing Ghana’s progress, identifying core gaps, and proposing data-driven strategies aligned with the objectives of the roadmap. The central aim of my research is to understand the epidemiology and control of meningococcal meningitis in Ghana, with a particular focus on MenA. While I employed a range of analytical approaches, mathematical modelling formed the core of the study. I began by synthesizing existing evidence from published literature and WHO surveillance bulletins to contextualize the burden and dynamics of meningitis across the region. To assess Ghana’s alignment with the global strategy, I conducted a review of relevant literature, including national policy documents, and consulted key stakeholders. This analysis revealed considerable progress, particularly in prevention and surveillance. However, persistent gaps remain in the provision of care and support for individuals affected by meningitis. To better understand the geographic distribution of meningitis risk within the country, I also examined climatic data in relation to district-level case distributions. This analysis reaffirmed that the northern regions bear the highest burden—accounting for about two-thirds of recent suspected cases in the country—though the middle belt also contributes substantially, with approximately 27% [95% CI: 25.9, 28.1] of the suspected cases. Furthermore, I assessed Ghana’s meningitis research capacity and found that while significant progress has been made, particularly through international collaborations, there remains a need to strengthen local research infrastructure and expertise. To better understand population immunity dynamics against MenA and to inform potential risk of resurgence, I applied an age-specific exponential decay model to evaluate population immunity induced by MenAfriVac against MenA in Ghana. The results indicated that, by 2030, only about 33% of the target population in the north would remain protected, emphasizing the need for continued and enhanced intervention. As part of the global roadmap to defeat meningitis, multivalent meningococcal conjugate vaccines (MMCVs); which protect against multiple serogroups, have been developed and licensed for use in the meningitis belt in 2025. Compared to MenAfriVac which only protects against serogroup A, MMCVs offer additional protection against C, W, Y and X. With this development and building on insights from the immunity profile analysis, I used an age-structured deterministic transmission model to evaluate efficient vaccination strategies for upcoming MMCVs. My analysis identified the 1–19-year age group, under high coverage, as the most efficient target population, based on its lowest number needed to vaccinate to prevent a case and the proven success of MenAfriVac. All strategies tested showed a considerably long "honeymoon period" of at least 15 years, characterized by sustained low levels of cases and carriage, although never reaching zero. This 1-19 years age target is consistent with the recommendations of the World Health Organization’s (WHO) Strategic Advisory Group of Experts on Immunization (SAGE) on the use of MMCVs in the meningitis belt. Finally, I developed and applied an age-structured stochastic transmission model to address key limitations of the deterministic approach, particularly its inability to allow for extinction due to the retention of fractional carriers following a very successful vaccination program. This approach enabled a more realistic assessment of the full transmission dynamics of MenA, including the feasibility of its elimination. In addition, I accounted for the impact of external infection pressure, a necessary complexity, on the dynamics of MenA in this stochastic framework. My results suggest that, in the absence of external infection, MenAfriVac alone could drive local elimination of MenA in Ghana. When external infection pressure is included, the probability of eliminating the epidemics of MenA remains high, approximately 72% [95% CI: 67, 76], if countries follow WHO SAGE recommendations on the use of MMCVs. However, without further vaccine intervention, epidemic elimination is not achievable. These findings are sensitive to several factors including assumptions about external infection pressure and the duration of vaccine-induced protection. Overall, this work underscores the need for sustained national commitment and strong regional cooperation. I conclude by recommending that Ghana and other countries in themeningitis belt implement MMCVs as advised by WHO SAGE to achieve the goal of eliminating the epidemics of meningococcal serogroup A.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2025

Author dc:creator

• Owusu, Mark

Advisor dc:contributor.advisor

• Trotter, Caroline

• Neisseria meningitidis serogroup A (MenA)
• African meningitis belt
• Vaccination
• Methematical modelling
• Elimination

Licence

• https://www.repository.cam.ac.uk/bitstreams/406f8591-f0a9-43c0-9702-fe0b99dbb813/download
• https://creativecommons.org/licenses/by/4.0/

Language dc:language

eng

DOI dc:identifier.doi

https://doi.org/10.17863/CAM.124839

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/395288