Excess Invasive Meningococcal Disease Associated With Seasonal Influenza, South Africa, 2003-2018.

BACKGROUND: Invasive meningococcal disease (IMD) is a devastating illness with high mortality rates. Like influenza, endemic IMD is seasonal, peaking in winter. Studies suggest that circulation of influenza virus may influence the timing and magnitude of IMD winter peaks. METHODS: This ecological study used weekly data from 2 nationwide surveillance programs: Viral Watch (proportion of outpatient influenza-positive cases from throat or nasal swab samples) and GERMS-SA (laboratory-confirmed cases of IMD), occurring across South Africa from 2003 through 2018 in all age bands. A bivariate time series analysis using wavelet transform was conducted to determine cocirculation of the diseases and the time lag between the peak seasons. We modeled excess meningococcal disease cases attributable to influenza cocirculation, using univariate regression spline models. Stata and R statistical software packages were used for the analysis. RESULTS: A total of 5256 laboratory-confirmed IMD cases were reported, with an average annual incidence of 0.23 episodes per 100 000 population and a mean seasonal peak during week 32 (±3 weeks). Forty-two percent of swab samples (10 421 of 24 741) were positive for influenza during the study period. The mean peak for all influenza occurred at week 26 (±4 weeks). There was an average lag time of 5 weeks between annual influenza and IMD seasons. Overall, 5% (1%-9%) of IMD cases can be attributable to influenza cocirculation, with, on average, 17 excess IMD cases per year attributable to influenza. CONCLUSIONS: A quantifiable proportion of IMD in South Africa is associated with influenza cocirculation; therefore, seasonal influenza vaccination may have an effect on preventing a small portion of IMD in addition to preventing influenza.

African based researchers' output on models for the transmission dynamics of infectious diseases and public health interventions: A scoping review.

BACKGROUND: Applied epidemiological models are used in predicting future trends of diseases, for the basic understanding of disease and health dynamics, and to improve the measurement of health indicators. Mapping the research outputs of epidemiological modelling studies concerned with transmission dynamics of infectious diseases and public health interventions in Africa will help to identify the areas with substantial levels of research activities, areas with gaps, and research output trends. METHODS: A scoping review of applied epidemiological models of infectious disease studies that involved first or last authors affiliated to African institutions was conducted. Eligible studies were those concerned with the transmission dynamics of infectious diseases and public health interventions. The review was consistent with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) extension for scoping reviews. Four electronic databases were searched for peer-reviewed publications up to the end of April 2020. RESULTS: Of the 5927 publications identified, 181 met the inclusion criteria. The review identified 143 publications with first authors having an African institutional affiliation (AIA), while 81 had both first and last authors with an AIA. The publication authors were found to be predominantly affiliated with institutions based in South Africa and Kenya. Furthermore, human immunodeficiency virus, malaria, tuberculosis, and Ebola virus disease were found to be the most researched infectious diseases. There has been a gradual increase in research productivity across Africa especially in the last ten years, with several collaborative efforts spread both within and beyond Africa. CONCLUSIONS: Research productivity in applied epidemiological modelling studies of infectious diseases may have increased, but there remains an under-representation of African researchers as leading authors. The study findings indicate a need for the development of research capacity through supporting existing institutions in Africa and promoting research funding that will address local health priorities.

Calibration of individual-based models to epidemiological data: A systematic review.

Individual-based models (IBMs) informing public health policy should be calibrated to data and provide estimates of uncertainty. Two main components of model-calibration methods are the parameter-search strategy and the goodness-of-fit (GOF) measure; many options exist for each of these. This review provides an overview of calibration methods used in IBMs modelling infectious disease spread. We identified articles on PubMed employing simulation-based methods to calibrate IBMs informing public health policy in HIV, tuberculosis, and malaria epidemiology published between 1 January 2013 and 31 December 2018. Articles were included if models stored individual-specific information, and calibration involved comparing model output to population-level targets. We extracted information on parameter-search strategies, GOF measures, and model validation. The PubMed search identified 653 candidate articles, of which 84 met the review criteria. Of the included articles, 40 (48%) combined a quantitative GOF measure with an algorithmic parameter-search strategy-either an optimisation algorithm (14/40) or a sampling algorithm (26/40). These 40 articles varied widely in their choices of parameter-search strategies and GOF measures. For the remaining 44 (52%) articles, the parameter-search strategy could either not be identified (32/44) or was described as an informal, non-reproducible method (12/44). Of these 44 articles, the majority (25/44) were unclear about the GOF measure used; of the rest, only five quantitatively evaluated GOF. Only a minority of the included articles, 14 (17%) provided a rationale for their choice of model-calibration method. Model validation was reported in 31 (37%) articles. Reporting on calibration methods is far from optimal in epidemiological modelling studies of HIV, malaria and TB transmission dynamics. The adoption of better documented, algorithmic calibration methods could improve both reproducibility and the quality of inference in model-based epidemiology. There is a need for research comparing the performance of calibration methods to inform decisions about the parameter-search strategies and GOF measures.