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Background: Despite high vaccination coverage, many childhood infections pose a growing threat to human populations. Accurate disease forecasting would be of tremendous value to public health. Forecasting disease emergence using early warning signals (EWS) is possible in non-seasonal models of infectious diseases. Here, we assessed whether EWS also anticipate disease emergence in seasonal models. Methods: We simulated the dynamics of an immunizing infectious pathogen approaching the tipping point to disease endemicity. To explore the effect of seasonality on the reliability of early warning statistics, we varied the amplitude of fluctuations around the average transmission. We proposed and analyzed two new early warning signals based on the wavelet spectrum. We measured the reliability of the early warning signals depending on the strength of their trend preceding the tipping point and then calculated the Area Under the Curve (AUC) statistic. Results: Early warning signals were reliable when disease transmission was subject to seasonal forcing. Wavelet-based early warning signals were as reliable as other conventional early warning signals. We found that removing seasonal trends, prior to analysis, did not improve early warning statistics uniformly. Conclusions: Early warning signals anticipate the onset of critical transitions for infectious diseases which are subject to seasonal forcing. Wavelet-based early warning statistics can also be used to forecast infectious disease. [ABSTRACT FROM AUTHOR]

Background: Childhood obesity is one of the most serious public health issues of the twenty-first century affecting even low- and middle-income countries. Overweight and obese children are more likely to stay obese into adulthood. Due to the paucity of data on local practices, our study aimed to assess the knowledge and practices of physicians from the Middle East and North Africa region with respect to early-onset obesity.Methods: A specific questionnaire investigating the perception and knowledge on early-onset obesity was circulated to healthcare providers (general physicians, pediatricians, pediatric gastroenterologist, neonatologists) practicing in 17 Middle East and North African countries.Results: A total of 999/1051 completed forms (95% response) were evaluated. Of all respondents, 28.9% did not consistently use growth charts to monitor growth during every visit and only 25.2% and 46.6% of respondents were aware of the correct cut-off criterion for overweight and obesity, respectively. Of those surveyed, 22.3, 14.0, 36.1, 48.2, and 49.1% of respondents did not consider hypertension, type 2 diabetes, coronary heart disease, fatty liver disease, and decreased life span, respectively, to be a long-term complication of early childhood obesity. Furthermore, only 0.7% of respondents correctly answered all survey questions pertaining to knowledge of early childhood overweight and obesity.Conclusion: The survey highlights the low use of growth charts in the evaluation of early childhood growth in Middle East and North Africa region, and demonstrated poor knowledge of healthcare providers on the short- and long-term complications of early-onset obesity. This suggests a need for both continued professional education and development, and implementation of guidelines for the prevention and management of early childhood overweight and obesity. [ABSTRACT FROM AUTHOR]

Background: Influenza A viruses exhibit complex epidemiological patterns in a number of mammalian and avian hosts. Understanding transmission of these viruses necessitates taking into account their evolution, which represents a challenge for developing mathematical models. This is because the phrasing of multi-strain systems in terms of traditional compartmental ODE models either requires simplifying assumptions to be made that overlook important evolutionary processes, or leads to complex dynamical systems that are too cumbersome to analyse. Results: Here, we develop an Individual-Based Model (IBM) in order to address simultaneously the ecology, epidemiology and evolution of strain-polymorphic pathogens, using Influenza A viruses as an illustrative example. Conclusions: We carry out careful validation of our IBM against comparable mathematical models to demonstrate the robustness of our algorithm and the sound basis for this novel framework. We discuss how this new approach can give critical insights in the study of influenza evolution. [ABSTRACT FROM AUTHOR]