Estimating measles transmission potential in Italy over the period 2010-2011

Background. Recent history of measles epidemiology in Italy is characterized by the recurrence of spatially localized epidemics. Aim. In this study we investigate the three major outbreaks occurred in Italy over the period 2010-2011 and estimate the measles transmission potential. The epidemics mainly involved individuals aged 10-28 years and the transmission potential, measured as effective reproduction number – i.e. the number of new infections generated by a primary infector – was estimated to be 1.9-5.9.Results. Despite such high values, we found that, in all investigated outbreaks, the reproduction number has remained above the epidemic threshold for no more than twelve weeks, suggesting that measles may hardly have the potential to give rise to new nationwide epidemics.Conclusion. In conclusion, the performed analysis highlights the need of planning additional vaccination programs targeting those age classes currently showing a higher susceptibility to infection, in order not to compromise the elimination goal by 201

Inferring high-resolution human mixing patterns for disease modeling

Mathematical and computational modeling approaches are increasingly used as quantitative tools in the analysis and forecasting of infectious disease epidemics. The growing need for realism in addressing complex public health questions is however calling for accurate models of the human contact patterns that govern the disease transmission processes. Here we present a data-driven approach to generate effective descriptions of population-level contact patterns by using highly detailed macro (census) and micro (survey) data on key socio-demographic features. We produce age-stratified contact matrices for 277 sub-national administrative regions of countries covering approximately 3.5 billion people and reflecting the high degree of cultural and societal diversity of the focus countries. We use the derived contact matrices to model the spread of airborne infectious diseases and show that sub-national heterogeneities in human mixing patterns have a marked impact on epidemic indicators such as the reproduction number and overall attack rate of epidemics of the same etiology. The contact patterns derived here are made publicly available as a modeling tool to study the impact of socio-economic differences and demographic heterogeneities across populations on the epidemiology of infectious diseases.Comment: 18 pages, 7 figure

The RAPIDD ebola forecasting challenge: Synthesis and lessons learnt.

Infectious disease forecasting is gaining traction in the public health community; however, limited systematic comparisons of model performance exist. Here we present the results of a synthetic forecasting challenge inspired by the West African Ebola crisis in 2014-2015 and involving 16 international academic teams and US government agencies, and compare the predictive performance of 8 independent modeling approaches. Challenge participants were invited to predict 140 epidemiological targets across 5 different time points of 4 synthetic Ebola outbreaks, each involving different levels of interventions and "fog of war" in outbreak data made available for predictions. Prediction targets included 1-4 week-ahead case incidences, outbreak size, peak timing, and several natural history parameters. With respect to weekly case incidence targets, ensemble predictions based on a Bayesian average of the 8 participating models outperformed any individual model and did substantially better than a null auto-regressive model. There was no relationship between model complexity and prediction accuracy; however, the top performing models for short-term weekly incidence were reactive models with few parameters, fitted to a short and recent part of the outbreak. Individual model outputs and ensemble predictions improved with data accuracy and availability; by the second time point, just before the peak of the epidemic, estimates of final size were within 20% of the target. The 4th challenge scenario - mirroring an uncontrolled Ebola outbreak with substantial data reporting noise - was poorly predicted by all modeling teams. Overall, this synthetic forecasting challenge provided a deep understanding of model performance under controlled data and epidemiological conditions. We recommend such "peace time" forecasting challenges as key elements to improve coordination and inspire collaboration between modeling groups ahead of the next pandemic threat, and to assess model forecasting accuracy for a variety of known and hypothetical pathogens

Containing the accidental laboratory escape of potential pandemic influenza viruses

BACKGROUND: The recent work on the modified H5N1 has stirred an intense debate on the risk associated with the accidental release from biosafety laboratory of potential pandemic pathogens. Here, we assess the risk that the accidental escape of a novel transmissible influenza strain would not be contained in the local community. METHODS: We develop here a detailed agent-based model that specifically considers laboratory workers and their contacts in microsimulations of the epidemic onset. We consider the following non-pharmaceutical interventions: isolation of the laboratory, laboratory workers’ household quarantine, contact tracing of cases and subsequent household quarantine of identified secondary cases, and school and workplace closure both preventive and reactive. RESULTS: Model simulations suggest that there is a non-negligible probability (5% to 15%), strongly dependent on reproduction number and probability of developing clinical symptoms, that the escape event is not detected at all. We find that the containment depends on the timely implementation of non-pharmaceutical interventions and contact tracing and it may be effective (>90% probability per event) only for pathogens with moderate transmissibility (reproductive number no larger than R(0) = 1.5). Containment depends on population density and structure as well, with a probability of giving rise to a global event that is three to five times lower in rural areas. CONCLUSIONS: Results suggest that controllability of escape events is not guaranteed and, given the rapid increase of biosafety laboratories worldwide, this poses a serious threat to human health. Our findings may be relevant to policy makers when designing adequate preparedness plans and may have important implications for determining the location of new biosafety laboratories worldwide

Mathematical modeling of amoeba-bacteria population dynamics

We present a mathematical model describing the dynamics occurring between two interacting populations, one of amoebae and one of virulent bacteria; it is meant to describe laboratory experiments with these two species in a mathematical framework and help understanding the role of the different mechanisms involved. In particular we aim to focus on how bacterial virulence may affect the dynamics of the system. The model is a modified reaction-diffusion-chemotaxis predator-prey one with a mechanism of redistribution of ingested biomass between amoeboid cells. The spatially homogeneous case is analyzed in detail; conditions for pattern formation are established; numerical simulations for the complete model are performed

Estimating the success probability of containment strategies in the case of accidental laboratory escape of potential pandemic pathogens

Epidemics4, Amsterdam, The Netherlands, 19-22 November 2013. [oral presentation

Additional file 2 of Estimating transmission probability in schools for the 2009 H1N1 influenza pandemic in Italy

Files of data on school A. Each row corresponds to a student for which we received the filled questionnaire. In the column ‘classe’ there is the name of the class (the 1st digit is the grade); in ’nstud’ the number of students in that class; in ‘infected’ whether that student showed ILI symptoms (1) or not (0); in ‘home’ the number of household members infected before that student (−1 if infected =0); ’day’ and ’month’ the date of ILI occurrence (−1 if infected =0). (TXT 4 kb

Additional file 2 of Estimating transmission probability in schools for the 2009 H1N1 influenza pandemic in Italy

Files of data on school A. Each row corresponds to a student for which we received the filled questionnaire. In the column ‘classe’ there is the name of the class (the 1st digit is the grade); in ’nstud’ the number of students in that class; in ‘infected’ whether that student showed ILI symptoms (1) or not (0); in ‘home’ the number of household members infected before that student (−1 if infected =0); ’day’ and ’month’ the date of ILI occurrence (−1 if infected =0). (TXT 4 kb