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Meningococcal carriage dynamics drive patterns of invasive disease. The distribution of carriage by age has been well described in Europe, but not in the African meningitis belt, a region characterised by frequent epidemics of meningitis. We aimed to estimate the age-specific prevalence of meningococcal carriage by season in the African meningitis belt. We searched PubMed, Web of Science, the Cochrane Library and grey literature for papers reporting carriage of Neisseria meningitidis in defined age groups in the African meningitis belt. We used a mixed-effects logistic regression to model meningococcal carriage prevalence as a function of age, adjusting for season, location and year. Carriage prevalence increased from low prevalence in infants (0.595% in the rainy season, 95% CI 0.482-0.852%) to a broad peak at age 10 (1.94%, 95% CI 1.87-2.47%), then decreased in adolescence. The odds of carriage were significantly increased during the dry season (OR 1.5 95% CI 1.4-1.7) and during outbreaks (OR 6.7 95% CI 1.6-29). Meningococcal carriage in the African meningitis belt peaks at a younger age compared to Europe. This is consistent with contact studies in Africa, which show that children 10-14 years have the highest frequency of contacts. Targeting older children in Africa for conjugate vaccination may be effective in reducing meningococcal transmission.

Norovirus is one of the leading causes of viral gastroenteritis worldwide and responsible for substantial morbidity, mortality and healthcare costs. To further understanding of the epidemiology and control of norovirus, there has been much recent interest in describing the transmission dynamics of norovirus through mathematical models. In this study, we review the current modelling approaches for norovirus transmission. We examine the data and methods used to estimate these models that vary structurally and parametrically between different epidemiological contexts. Many of the existing studies at population level have focused on the same case notification dataset, whereas models from outbreak settings are highly specific and difficult to generalise. In this review, we explore the consistency in the description of norovirus transmission dynamics and the robustness of parameter estimates between studies. In particular, we find that there is considerable variability in estimates of key parameters such as the basic reproduction number, which may mean that the effort required to control norovirus at the population level may currently be underestimated.

SUMMARYMeningococcal meningitis is a major public health problem in a large area of sub-Saharan Africa known as the meningitis belt. Disease incidence increases every dry season, before dying out with the first rains of the year. Large epidemics, which can kill tens of thousands of people, occur frequently but unpredictably every 6-14 years. It has been suggested that these patterns may be attributable to complex interactions between the bacteria, human hosts and the environment. We used deterministic compartmental models to investigate how well simple model structures with seasonal forcing were able to qualitatively capture these patterns of disease. We showed that the complex and irregular timing of epidemics could be caused by the interaction of temporary immunity conferred by carriage of the bacteria together with seasonal changes in the transmissibility of infection. This suggests that population immunity is an important factor to include in models attempting to predict meningitis epidemics.

Longitudinal carriage studies of colonizing bacteria such as Neisseria meningitidis can provide important insights into the transmission dynamics of these organisms. Carriage is detected by culturing from a nasopharyngeal swab, but the sensitivity of this technique is low and varies between studies. This paper applies a statistical method for estimating the sensitivity of swabbing, infection rate, recovery rate and initial prevalence of carriage to three longitudinal carriage studies of N. meningitidis. These parameters and 95% confidence intervals were estimated using maximum likelihood techniques. The sensitivity of swabbing was estimated to be 60-83% and this should be taken into account when interpreting carriage studies. The estimates of force of infection and recovery rates seem to be consistent with estimates from more traditional methods. Differences in the parameter estimates between datasets may be due to differences in study design. This method could be used to assist in the design of future carriage studies.