Real-time numerical forecast of global epidemic spreading: Case study of 2009 A/H1N1pdm

Background Mathematical and computational models for infectious diseases are increasingly used to support public-health decisions; however, their reliability is currently under debate. Real-time forecasts of epidemic spread using data-driven models have been hindered by the technical challenges posed by parameter estimation and validation. Data gathered for the 2009 H1N1 influenza crisis represent an unprecedented opportunity to validate real-time model predictions and define the main success criteria for different approaches. Methods We used the Global Epidemic and Mobility Model to generate stochastic simulations of epidemic spread worldwide, yielding (among other measures) the incidence and seeding events at a daily resolution for 3,362 subpopulations in 220 countries. Using a Monte Carlo Maximum Likelihood analysis, the model provided an estimate of the seasonal transmission potential during the early phase of the H1N1 pandemic and generated ensemble forecasts for the activity peaks in the northern hemisphere in the fall/winter wave. These results were validated against the real-life surveillance data collected in 48 countries, and their robustness assessed by focusing on 1) the peak timing of the pandemic; 2) the level of spatial resolution allowed by the model; and 3) the clinical attack rate and the effectiveness of the vaccine. In addition, we studied the effect of data incompleteness on the prediction reliability. Results Real-time predictions of the peak timing are found to be in good agreement with the empirical data, showing strong robustness to data that may not be accessible in real time (such as pre-exposure immunity and adherence to vaccination campaigns), but that affect the predictions for the attack rates. The timing and spatial unfolding of the pandemic are critically sensitive to the level of mobility data integrated into the model. Conclusions Our results show that large-scale models can be used to provide valuable real-time forecasts of influenza spreading, but they require high-performance computing. The quality of the forecast depends on the level of data integration, thus stressing the need for high-quality data in population-based models, and of progressive updates of validated available empirical knowledge to inform these models

On the Treatment of Airline Travelers in Mathematical Models

The global spread of infectious diseases is facilitated by the ability of infected humans to travel thousands of miles in short time spans, rapidly transporting pathogens to distant locations. Mathematical models of the actual and potential spread of specific pathogens can assist public health planning in the case of such an event. Models should generally be parsimonious, but must consider all potentially important components of the system to the greatest extent possible. We demonstrate and discuss important assumptions relative to the parameterization and structural treatment of airline travel in mathematical models. Among other findings, we show that the most common structural treatment of travelers leads to underestimation of the speed of spread and that connecting travel is critical to a realistic spread pattern. Models involving travelers can be improved significantly by relatively simple structural changes but also may require further attention to details of parameterization

Measles vaccination in humanitarian emergencies: a review of recent practice

<p>Abstract</p> <p>Background</p> <p>The health needs of children and adolescents in humanitarian emergencies are critical to the success of relief efforts and reduction in mortality. Measles has been one of the major causes of child deaths in humanitarian emergencies and further contributes to mortality by exacerbating malnutrition and vitamin A deficiency. Here, we review measles vaccination activities in humanitarian emergencies as documented in published literature. Our main interest was to review the available evidence focusing on the target age range for mass vaccination campaigns either in response to a humanitarian emergency or in response to an outbreak of measles in a humanitarian context to determine whether the current guidance required revision based on recent experience.</p> <p>Methods</p> <p>We searched the published literature for articles published from January 1, 1998 to January 1, 2010 reporting on measles in emergencies. As definitions and concepts of emergencies vary and have changed over time, we chose to consider any context where an application for either a Consolidated Appeals Process or a Flash Appeal to the UN Central Emergency Revolving Fund (CERF) occurred during the period examined. We included publications from countries irrespective of their progress in measles control as humanitarian emergencies may occur in any of these contexts and as such, guidance applies irrespective of measles control goals.</p> <p>Results</p> <p>Of the few well-documented epidemic descriptions in humanitarian emergencies, the age range of cases is not limited to under 5 year olds. Combining all data, both from preventive and outbreak response interventions, about 59% of cases in reports with sufficient data reviewed here remain in children under 5, 18% in 5-15 and 2% above 15 years. In instances where interventions targeted a reduced age range, several reports concluded that the age range should have been extended to 15 years, given that a significant proportion of cases occurred beyond 5 years of age.</p> <p>Conclusions</p> <p>Measles outbreaks continue to occur in humanitarian emergencies due to low levels of pre-existing population immunity. According to available published information, cases continue to occur in children over age 5. Preventing cases in older age groups may prevent younger children from becoming infected and reduce mortality in both younger and older age groups.</p

Towards a characterization of behavior-disease models

The last decade saw the advent of increasingly realistic epidemic models that leverage on the availability of highly detailed census and human mobility data. Data-driven models aim at a granularity down to the level of households or single individuals. However, relatively little systematic work has been done to provide coupled behavior-disease models able to close the feedback loop between behavioral changes triggered in the population by an individual's perception of the disease spread and the actual disease spread itself. While models lacking this coupling can be extremely successful in mild epidemics, they obviously will be of limited use in situations where social disruption or behavioral alterations are induced in the population by knowledge of the disease. Here we propose a characterization of a set of prototypical mechanisms for self-initiated social distancing induced by local and non-local prevalence-based information available to individuals in the population. We characterize the effects of these mechanisms in the framework of a compartmental scheme that enlarges the basic SIR model by considering separate behavioral classes within the population. The transition of individuals in/out of behavioral classes is coupled with the spreading of the disease and provides a rich phase space with multiple epidemic peaks and tipping points. The class of models presented here can be used in the case of data-driven computational approaches to analyze scenarios of social adaptation and behavioral change.Comment: 24 pages, 15 figure

Challenges in measuring measles case fatality ratios in settings without vital registration

Measles, a highly infectious vaccine-preventable viral disease, is potentially fatal. Historically, measles case-fatality ratios (CFRs) have been reported to vary from 0.1% in the developed world to as high as 30% in emergency settings. Estimates of the global burden of mortality from measles, critical to prioritizing measles vaccination among other health interventions, are highly sensitive to the CFR estimates used in modeling; however, due to the lack of reliable, up-to-date data, considerable debate exists as to what CFR estimates are appropriate to use. To determine current measles CFRs in high-burden settings without vital registration we have conducted six retrospective measles mortality studies in such settings. This paper examines the methodological challenges of this work and our solutions to these challenges, including the integration of lessons from retrospective all-cause mortality studies into CFR studies, approaches to laboratory confirmation of outbreaks, and means of obtaining a representative sample of case-patients. Our experiences are relevant to those conducting retrospective CFR studies for measles or other diseases, and to those interested in all-cause mortality studies

The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale

<p>Abstract</p> <p>Background</p> <p>Computational models play an increasingly important role in the assessment and control of public health crises, as demonstrated during the 2009 H1N1 influenza pandemic. Much research has been done in recent years in the development of sophisticated data-driven models for realistic computer-based simulations of infectious disease spreading. However, only a few computational tools are presently available for assessing scenarios, predicting epidemic evolutions, and managing health emergencies that can benefit a broad audience of users including policy makers and health institutions.</p> <p>Results</p> <p>We present "GLEaMviz", a publicly available software system that simulates the spread of emerging human-to-human infectious diseases across the world. The GLEaMviz tool comprises three components: the client application, the proxy middleware, and the simulation engine. The latter two components constitute the GLEaMviz server. The simulation engine leverages on the Global Epidemic and Mobility (GLEaM) framework, a stochastic computational scheme that integrates worldwide high-resolution demographic and mobility data to simulate disease spread on the global scale. The GLEaMviz design aims at maximizing flexibility in defining the disease compartmental model and configuring the simulation scenario; it allows the user to set a variety of parameters including: compartment-specific features, transition values, and environmental effects. The output is a dynamic map and a corresponding set of charts that quantitatively describe the geo-temporal evolution of the disease. The software is designed as a client-server system. The multi-platform client, which can be installed on the user's local machine, is used to set up simulations that will be executed on the server, thus avoiding specific requirements for large computational capabilities on the user side.</p> <p>Conclusions</p> <p>The user-friendly graphical interface of the GLEaMviz tool, along with its high level of detail and the realism of its embedded modeling approach, opens up the platform to simulate realistic epidemic scenarios. These features make the GLEaMviz computational tool a convenient teaching/training tool as well as a first step toward the development of a computational tool aimed at facilitating the use and exploitation of computational models for the policy making and scenario analysis of infectious disease outbreaks.</p

Health care seeking behavior for diarrhea in children under 5 in rural Niger: results of a cross-sectional survey

Diarrhea remains the second leading cause of death in children under 5 years of age in sub-Saharan Africa. Health care seeking behavior for diarrhea varies by context and has important implications for developing appropriate care strategies and estimating burden of disease. The objective of this study was to determine the proportion of children under five with diarrhea who consulted at a health structure in order to identify the appropriate health care levels to set up surveillance of severe diarrheal diseases

Comparing large-scale computational approaches to epidemic modeling: Agent-based versus structured metapopulation models

<p>Abstract</p> <p>Background</p> <p>In recent years large-scale computational models for the realistic simulation of epidemic outbreaks have been used with increased frequency. Methodologies adapt to the scale of interest and range from very detailed agent-based models to spatially-structured metapopulation models. One major issue thus concerns to what extent the geotemporal spreading pattern found by different modeling approaches may differ and depend on the different approximations and assumptions used.</p> <p>Methods</p> <p>We provide for the first time a side-by-side comparison of the results obtained with a stochastic agent-based model and a structured metapopulation stochastic model for the progression of a baseline pandemic event in Italy, a large and geographically heterogeneous European country. The agent-based model is based on the explicit representation of the Italian population through highly detailed data on the socio-demographic structure. The metapopulation simulations use the GLobal Epidemic and Mobility (GLEaM) model, based on high-resolution census data worldwide, and integrating airline travel flow data with short-range human mobility patterns at the global scale. The model also considers age structure data for Italy. GLEaM and the agent-based models are synchronized in their initial conditions by using the same disease parameterization, and by defining the same importation of infected cases from international travels.</p> <p>Results</p> <p>The results obtained show that both models provide epidemic patterns that are in very good agreement at the granularity levels accessible by both approaches, with differences in peak timing on the order of a few days. The relative difference of the epidemic size depends on the basic reproductive ratio, <it>R</it>₀, and on the fact that the metapopulation model consistently yields a larger incidence than the agent-based model, as expected due to the differences in the structure in the intra-population contact pattern of the approaches. The age breakdown analysis shows that similar attack rates are obtained for the younger age classes.</p> <p>Conclusions</p> <p>The good agreement between the two modeling approaches is very important for defining the tradeoff between data availability and the information provided by the models. The results we present define the possibility of hybrid models combining the agent-based and the metapopulation approaches according to the available data and computational resources.</p

Paternal education status significantly influences infants’ measles vaccination uptake, independent of maternal education status

<p><b>Abstract</b></p> <p><b>Background</b></p> <p>Despite increased funding of measles vaccination programs by national governments and international aid agencies, structural factors encumber attainment of childhood measles immunisation to levels which may guarantee herd immunity. One of such factors is parental education status. Research on the links between parental education and vaccination has typically focused on the influence of maternal education status. This study aims to demonstrate the independent influence of paternal education status on measles immunisation.</p> <p><b>Methods</b></p> <p>Comparable nationally representative survey data were obtained from six countries with the highest numbers of children missing the measles vaccine in 2008. Logistic regression analysis was applied to examine the influence of paternal education on uptake of the first dose of measles vaccination, independent of maternal education, whilst controlling for confounding factors such as respondent’s age, urban/rural residence, province/state of residence, religion, wealth and occupation.</p> <p><b>Results</b></p> <p>The results of the analysis show that even if a mother is illiterate, having a father with an education of Secondary (high school) schooling and above is statistically significant and positively correlated with the likelihood of a child being vaccinated for measles, in the six countries analysed. Paternal education of secondary or higher level was significantly and independently correlated with measles immunisation uptake after controlling for all potential confounders.</p> <p><b>Conclusions</b></p> <p>The influence of paternal education status on measles immunisation uptake was investigated and found to be statistically significant in six nations with the biggest gaps in measles immunisation coverage in 2008. This study underscores the imperative of utilising both maternal and paternal education as screening variables to identify children at risk of missing measles vaccination prospectively.</p