Results from the centers for disease control and prevention’s predict the 2013–2014 Influenza Season Challenge

Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination-one of the most important preventive measures of modern times-is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research.

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“…A nice summary the results of such approaches in providing actual predictions for the 2013-2014 flu season in the USA is detailed in Ref. [737].…”

Section: Social Media Based Methodsmentioning

confidence: 99%

Statistical physics of vaccination

et al. 2016

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“…Early identification of the timing and intensity of the annual seasonal epidemic of influenza is useful to inform public health planning and response. To determine if reasonably accurate forecasts can be made during flu season, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-2014 United States influenza season (8). Findings indicated that flu forecasting can yield reasonably accurate estimates of the start, peak, and intensity of the influenza season.…”

Section: The Expanding Use Of Surveillance Systemsmentioning

confidence: 99%

Public Health Surveillance Systems: Recent Advances in Their Use and Evaluation

Groseclose

Buckeridge

2017

Annu. Rev. Public Health

236

192

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Surveillance is critical for improving population health. Public health surveillance systems generate information that drives action, and the data must be of sufficient quality and with a resolution and timeliness that matches objectives. In the context of scientific advances in public health surveillance, changing health care and public health environments, and rapidly evolving technologies, the aim of this article is to review public health surveillance systems. We consider their current use to increase the efficiency and effectiveness of the public health system, the role of system stakeholders, the analysis and interpretation of surveillance data, approaches to system monitoring and evaluation, and opportunities for future advances in terms of increased scientific rigor, outcomes-focused research, and health informatics.

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“…Influenza can be fatal to those in patients with poor weak immunity (mostly infants, and young children, and the elderly people). The difference between the two is that common cold is caused by rhinovirus, whereas influenza is caused by influenza is caused by influenza virus the influenza virus causes influenza [14][15][16][17][18], as a comparison of these illnesses is listed in Table I. Comparison of the differences of the common cold and influenza.…”

Section: Influenza Investigationmentioning

confidence: 99%

A Rapid Screening Test for the Diagnosis of Influenza Infection Incubation Period Using Coincidence Analysis of Pulse Waves

Chen¹,

Tu²,

Yu³

et al. 2017

Preprint

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Viral infections have long been the biggest threat to human survival, and from a medical perspective, the development of noninvasive high-throughput screening methods that target the incubation period to either treat diseases or limit viral spread would be strikingly effective. Using this technology to target viral incubation periods would also be inexpensive to perform. The current study proposes to transform pulse signals into a rapid diagnostic test using "coincidence analysis" in the hope of preventing or reducing the symptoms of viral infections.The heart plays a critical role in calculating and supplying the needs of all tissues of the body. Pulse waves are pressurized signals in response to heart's calculations and include all phases of the cardiac cycle, which maintains life and provides energy needed to perform tasks. Any small movement gives a corresponding signal to pulse waves. The current study investigated conclusive data on self-limiting infections, such as common cold. We used pulse wave, coincidence analysis technology to capture signals from individuals with common cold during the incubation period and investigated if particular characteristic signals could be applied to influenza during the incubation period. Preliminary work demonstrated that pulse waves could generate signals using this technology that would be worthwhile for future research.A small amount of analytical data from common cold existed previously. The data structure is based on the idea that a single pulse wave at differing physiological conditions would have slight modifications that would be amplified and presented by various geometrical shapes after extensive data are accumulated. These geometric shapes can then be sliced vertically or horizontally to extract data during different illness stages.The significance of these findings are impressive; from a personal or a public hygiene perspective, this analytical technology provides many benefits, such as rapid and precise decision-making that can be directly visualized or can be analyzed using software programs. Also, this technology also uses futuristic wearable technology that brings practical problem solving to physiology.

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Results from the centers for disease control and prevention’s predict the 2013–2014 Influenza Season Challenge

Cited by 169 publications

References 26 publications

Statistical physics of vaccination

Statistical physics of vaccination

Public Health Surveillance Systems: Recent Advances in Their Use and Evaluation

A Rapid Screening Test for the Diagnosis of Influenza Infection Incubation Period Using Coincidence Analysis of Pulse Waves

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