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Presented June 21, 2019.

In this talk, Dr. Daihai He presents his recent works on applications of likelihood-based inference with non-mechanistic and mechanistic models in infectious disease modeling. Examples include modeling of the transmission of influenza, measles, yellow-fever virus... Read more

Content type: Webinar

An increasing number of geo-coded information streams are available with possible use in disease surveillance applications. In this setting, multivariate modeling of health and non-health data allows assessment of concurrent patterns among data streams and conditioning on one another. Therefore... Read more

Content type: Abstract

Population surges or large events may cause shift of data collected by biosurveillance systems [1]. For example, the Cherry Blossom Festival brings hundreds of thousands of people to DC every year, which results in simultaneous elevations in multiple data streams (Fig. 1). In this paper, we... Read more

Content type: Abstract

The spread of infectious diseases is facilitated by human travel. Infectious diseases are often introduced into a population by travelers and then spread among susceptible individuals. Likewise uninfected susceptible travelers can move into populations sustaining the spread of an infectious... Read more

Content type: Abstract

The early detection of outbreaks of diseases is one of the most challenging objectives of epidemiological surveillance systems. In order to achieve this goal, the primary foundation is using those big surveillance data for understanding and controlling the spatiotemporal variability of disease... Read more

Content type: Abstract

The National Notifiable Disease Surveillance System (NNDSS) comprises many activities including collaborations, processes, standards, and systems which support gathering data from US states and territories. As part of NNDSS, the National Electronic Disease Surveillance System (NEDSS) provides... Read more

Content type: Abstract

The goal of disease and syndromic surveillance is to monitor and detect aberrations in disease prevalence across space and time. Disease surveillance typically refers to the monitoring of confirmed cases of disease, whereas syndromic surveillance uses syndromes associated with disease to detect ... Read more

Content type: Abstract

Assigning causes of deaths to seasonal infectious diseases is difficult in part due to laboratory testing prior to death being uncommon. Since influenza (and other common respiratory pathogens) are therefore notoriously underreported as a (contributing) cause of death in deathcause statistics... Read more

Content type: Abstract

Evidence suggests that transmission within the workplace contributes significantly to the magnitude of a pandemic flu epidemic. A significant number of large organizations have a pandemic plan in place which may help in controlling this manner of transmission. These plans typically include... Read more

Content type: Abstract

Syndromic surveillance typically involves collecting time-stamped transactional data, such as patient triage or examination records or pharmacy sales. Such records usually span multiple categorical features, such as location, age group, gender, symptoms, chief complaints, drug category and so on... Read more

Content type: Abstract

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