Symptoms of influenza-like illness (ILI) were reported daily by health workers via the integrated syndromic surveillance system in rural China (ISSC)(1).
Objective
To analyze the growth and variation of ILI reports using individual growth curve model.
Emerging event detection is the process of automatically identifying novel and emerging ideas from text with minimal human intervention. With the rise of social networks like Twitter, topic detection has begun leveraging measures of user influence to identify emerging events. Twitter's highly skewed follower/followee structure lends itself to an intuitive model of influence, yet in a context like the Emerging Infections Network (EIN), a sentinel surveillance listserv of over 1400 infectious disease experts, developing a useful model of authority becomes less clear. Who should we listen to on the EIN? To explore this, we annotated a body of important EIN discussions and tested how well 3 models of user authority performed in identifying those discussions. In previous work we proposed a process by which only posts that are based on specific "important" topics are read, thus drastically reducing the amount of posts that need to be read. The process works by finding a set of "bellwether" users that act as indicators for "important" topics and only posts relating to these topics are then read. This approach does not consider the text of messages, only the patterns of user participation. Our text analysis approach follows that of Cataldi et al.[1], using the idea of semantic "energy" to identify emerging topics within Twitter posts. Authority is calculated via PageRank and used to weight each author's contribution to the semantic energy of all terms occurring in within some interval ti. A decay parameter d defines the impact of prior time steps on the current interval.
Objective
To explore how different models of user influence or authority perform when detecting emerging events within a small-scale community of infectious disease experts.
The effectiveness of public health interventions during a disease outbreak depends on rapid, accurate characterization of the initial outbreak and spread of the pathogen. Computer-based simulation using mathematical models provides a means to characterize both and enables practitioners to test intervention strategies. While compartmental differential equation models can be used to represent epidemics, they are unsuitable for early time simulations (first few days) when a small number of people are infected (and even fewer symptomatic), nor are they capable of representing spatial disease spread. Numerous models for disease propagation have been explored, including national scale network models for influenza and social network-based and probabilistic models for smallpox. To be useful in a public health context, a model for disease propagation should be efficient (e.g., simulating several weeks of real time in an hour) and flexible enough to simultaneously represent multiple diseases and attack scenarios.
Objective
This paper describes biologically-based mathematical models and efficient methods for early epoch simulation of disease outbreaks and bioterror attacks.
Though spatio-temporal patterns of influenza spread have often suggested that environmental factors, such as temperature, solar radiation and humidity play a key role, few studies have directly assessed their effect on the timing of annual epidemics. Finkelman et al observed a significant positive relationship between the latitudinal position of temperate countries and epidemic timing. It is hypothesized that during winter months, in temperate regions, decreased skin exposure to sunlight affects immune function by altering the production of certain immunomodulators (e.g. melatonin and Vitamin D3). Other studies have linked temperature and humidity conditions to the rate of transmission of the influenza virus.
Objective
To assess the strength of the association between peak influenza activity and dew point, average daily temperature, solar radiation, latitude and longitude so that we may better understand the factors that affect virus transmission and/or innate immunity and to determine whether these climate variables should be used as covariates in the surveillance of influenza.
Historical data are essential for development of detection algorithms. Spatio-temporal data, however, are difficult to come by due to variety of issues concerning patient confidentiality. Several approaches have been used to generate benchmark data using statistical methods. Here, we demonstrate how to generate benchmark data using a discrete event model simulating inter- and intra-contact network transmission dynamics of infectious diseases in space and time using publicly available population data.
OBJECTIVE
The objective of this study is to generate benchmark data from a discrete event model simulating the transmission dynamics of an infectious disease within and between contact networks in urban settings using real population data. Such data can be used to test the performance of various temporal and spatio-temporal detection algorithms when real data are scarce or cannot be shared.
Our objective in this research is to take advantage of a supercomputer grid (TeraGrid) to develop a distributed memory national scale agent-based model (ABM) to study disease outbreaks at the micro level. This has data needs at both the national data surveillance and the local community structure and outbreak levels.
Real-time syndromic surveillance systems require adapted dataflow organization and tools for supporting data processing in real time, from their acquisition until the counter-measure building process. This work explores the capabilities of a specific model based architecture for fulfilling these requisites and its results during a real-size international disease surveillance exercise.
This paper describes the value of a distributed approach to population health efforts that span clinical research, quality measurement and public health. The goal of the paper is to challenge the traditional paradigm which relies on centralized data repositories with more distributed models where data collection and analysis remains as close to local data sources as possible. We will propose that a distributed approach is desirable because it allows for information to reside more closely with those who can act upon it and it can overcome existing barriers by allowing information to be shared more rapidly and effectively while minimizing privacy risks.
Despite decades of attempts to promote judicious AU, the US has high rates of per-person antimicrobial consumption, and extremely high rates of carbapenem use. Such profligate use is a key factor in the high rate of antimicrobial-resistant infections seen in US healthcare facilities. Antimicrobial stewardship (AS) programs have been identified as a critical component of intervention strategies. A core component of AS programs is tracking AU, which is needed to monitor trends in use, focus interventions on aberrant behaviors, promote judicious use, and evaluate the effectiveness of interventions. A system designed to extend two national data models would provide a scalable platform for rapid adoption of AU reporting.
Objective:
Plan, develop, and pilot an open source system that could be integrated into the PCORnet (PCORI) and Sentinel (FDA) national common data models (CDMs) to generate antimicrobial use (AU) reports submittable to CDCâs National Healthcare Safety Network (NHSN). The system included ancillary tables, and data quality and report generation queries. The DataMIME system will allow hospitals to generate comparable AU reports for hospital inpatients.