Seasonal dynamics of bacterial meningitis: a time-series analysis

Abstract

Background: Bacterial meningitis, which is caused mainly by Neisseria meningitidis, Haemophilus influenzae, and Streptococcus pneumoniae, inflicts a substantial burden of disease worldwide. Yet, the temporal dynamics of this disease are poorly characterised and many questions remain about the ecology of the disease. We aimed to comprehensively assess seasonal trends in bacterial meningitis on a global scale.

Methods: We developed the first bacterial meningitis global database by compiling monthly incidence data as reported by country-level surveillance systems. Using country-level wavelet analysis, we identified whether a 12 month periodic component (annual seasonality) was detected in time-series that had at least 5 years of data with at least 40 cases reported per year. We estimated the mean timing of disease activity by computing the centre of gravity of the distribution of cases and investigated whether synchrony exists between the three pathogens responsible for most cases of bacterial meningitis.

Findings: We used country-level data from 66 countries, including from 47 countries outside the meningitis belt in sub-Saharan Africa. A persistent seasonality was detected in 49 (96%) of the 51 time-series from 38 countries eligible for inclusion in the wavelet analyses. The mean timing of disease activity had a latitudinal trend, with bacterial meningitis seasons peaking during the winter months in countries in both the northern and southern hemispheres. The three pathogens shared similar seasonality, but time-shifts differed slightly by country.

Interpretation: Our findings provide key insight into the seasonal dynamics of bacterial meningitis and add to knowledge about the global epidemiology of meningitis and the host, environment, and pathogen characteristics driving these patterns. Comprehensive understanding of global seasonal trends in meningitis could be used to design more effective prevention and control strategies.

Funding: Princeton University Health Grand Challenge, US National Institutes of Health (NIH), NIH Fogarty International Center Research and Policy for Infectious Disease Dynamics programme (RAPIDD), Bill & Melinda Gates Foundation.

Figure 1. Map of countries for which time-series data were available for inclusion in the bacterial meningitis database

Monthly incidence data were obtained for the 66 countries highlighted in pink or red. The 38 countries in red met our inclusion criteria for the wavelet analyses for at least one pathogen.

Figure 2. Wavelet analysis for Neisseria meningitidis in South Africa, 2000–14

(A) Raw time-series of reported cases. (B) Log-transformed, detrended, and standardised time-series used for wavelet analysis. (C) Wavelet power spectrum: wavelet power values increase from blue to red, and white contour lines indicate the 5% significance level. In this example, the time-series shows a significant 12 month periodicity over the entire time period. Shaded regions on either end delimit the cone of influence, where edge effects become important and spectral information is less robust. (D) Average wavelet power over time, with red dots indicating significant periods at the 5% level. Here the significant peak of power occurred at the 12 month period. Additional details and analyses for all other countries are available in the appendix.

Figure 3. Mean timing of bacterial meningitis season by country and aetiology

Countries listed in order of mean latitude of their most populous metropolitan area. Dots represent the centre of gravity of the monthly distribution of cases. Horizontal segments show 95% CI. The dashed line shows a regression spline (weighted natural cubic spline with two degrees of freedom) with 95% CIs.