Compartmental models for seasonal hyperendemic bacterial meningitis in the African meningitis belt.

The pathophysiological mechanisms underlying the seasonal dynamic and epidemic occurrence of bacterial meningitis in the African meningitis belt remain unknown. Regular seasonality (seasonal hyperendemicity) is observed for both meningococcal and pneumococcal meningitis and understanding this is critical for better prevention and modelling. The two principal hypotheses for hyperendemicity during the dry season imply (1) an increased risk of invasive disease given asymptomatic carriage of meningococci and pneumococci; or (2) an increased transmission of these bacteria from carriers and ill individuals. In this study, we formulated three compartmental deterministic models of seasonal hyperendemicity, featuring one (model1-'inv' or model2-'transm'), or a combination (model3-'inv-transm') of the two hypotheses. We parameterised the models based on current knowledge on meningococcal and pneumococcal biology and pathophysiology. We compared the three models' performance in reproducing weekly incidences of suspected cases of acute bacterial meningitis reported by health centres in Burkina Faso during 2004-2010, through the meningitis surveillance system. The three models performed well (coefficient of determination R2, 0.72, 0.86 and 0.87, respectively). Model2-'transm' and model3-'inv-transm' better captured the amplitude of the seasonal incidence. However, model2-'transm' required a higher constant invasion rate for a similar average baseline transmission rate. The results suggest that a combination of seasonal changes of the risk of invasive disease and carriage transmission is involved in the hyperendemic seasonality of bacterial meningitis in the African meningitis belt. Consequently, both interventions reducing the risk of nasopharyngeal invasion and the bacteria transmission, especially during the dry season are believed to be needed to limit the recurrent seasonality of bacterial meningitis in the meningitis belt.

Cost-effectiveness and public health impact of using high dose quadrivalent influenza vaccine in the French older adults population.

BACKGROUND: Seasonal influenza outbreaks in France cause a surge in patients, exacerbating the overburdened healthcare system each winter. Older adults are particularly vulnerable to serious events related to influenza. Quadrivalent influenza high dose (QIV HD) vaccines have been developed to offer better clinical protection in older adults, who often exhibit suboptimal immune response to quadrivalent influenza standard dose vaccines (QIV SD). This study aims to evaluate the public health impact and cost-effectiveness of administering HD versus SD vaccines to individuals aged 65+ in France. METHODOLOGY: Using a static model and decision-tree approach, the study analyzed health outcomes such as influenza cases, GP (general practitioner) visits, hospitalizations, and mortality; relative vaccine efficacy (rVE) estimates were derived from a pivotal randomized-controlled trial and a meta-analysis comparing HD to SD vaccines. Two approaches were implemented to model hospitalizations (conditional on influenza or not), and analyses on bed occupancy were performed. RESULTS: Results showed that using QIV HD instead of QIV SD during an average influenza season in France led to the prevention of 57,209 additional cases of influenza, 13,704 GP visits, and 764 influenza-related deaths. Moreover, switching to QIV HD resulted in an additional 1,728-15,970 hospitalizations avoided and 15,124-138,367 reduced days of hospitalization depending on the hospitalization approach used. The cost-utility analysis showed a cost per quality-adjusted life year (QALY) gained ranging from 24,020 €/QALY to 5,036 €/QALY. CONCLUSIONS: Switching to QIV HD in older adults was shown to be cost-effective, with even greater public health benefits at a higher coverage rate, regardless of the season severity.

sispread: A software to simulate infectious diseases spreading on contact networks.

OBJECTIVES: We present a simulation software which allows studying the dynamics of a hypothetic infectious disease within a network of connected people. The software is aimed to facilitate the discrimination of stochastic factors governing the evolution of an infection in a network. In order to do this it provides simple tools to create networks of individuals and to set the epidemiological parameters of the outbreaks. METHODS: Three popular models of infectious disease can be used (SI, SIS, SIR). The simulated networks are either the algorithm-based included ones (scale free, small-world, and random homogeneous networks), or provided by third party software. RESULTS: It allows the simulation of a single or many outbreaks over a network, or outbreaks over multiple networks (with identical properties). Standard outputs are the evolution of the prevalence of the disease, on a single outbreak basis or by averaging many outbreaks. The user can also obtain customized outputs which address in detail different possible epidemiological questions about the spread of an infectious agent in a community. CONCLUSIONS: The presented software introduces sources of stochasticity present in real epidemics by simulating outbreaks on contact networks of individuals. This approach may help to understand the paths followed by outbreaks in a given community and to design new strategies for preventing and controlling them.

Increased incidence of acute parvovirus B19 infections in Marseille, France, in 2012 compared with the 2002-2011 period.

Human parvovirus B19 occurs worldwide and causes mild or asymptomatic disease in the form of cyclic local epidemics usually occurring in late winter and early summer. In 2012, a dramatic increase in cases was observed in the Public hospitals system of Marseille, with a total of 53 cases reported. Here, we describe the characteristics of this outbreak and compare it with the local epidemiology of B19V infections observed during the 2002-2011 period.