Applications of Likelihood-based inference with non-mechanistic and mechanistic models in infectious disease modeling

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, Zika virus, and Lassa-fever virus. Combined non-mechanistic and mechanistic models, we gain new insight into the mechanisms under the transmission of infectious diseases. 

June 21, 2019

Cross Disciplinary Consultancy: Negation Detection Use Case

Despite considerable effort since the turn of the century to develop Natural Language Processing (NLP) methods and tools for detecting negated terms in chief complaints, few standardised methods have emerged. Those methods that have emerged (e.g. the NegEx algorithm) are confined to local implementations with customised solutions.

June 18, 2019

Performance of machine learning method to classify free-text medical causes of death

Mortality is an indicator of the severity of the impact of an event on the population. In France mortality surveillance is part of the syndromic surveillance system SurSaUD and is carried out by Santé publique France, the French public health agency. The set-up of an Electronic Death Registration System (EDRS) in 2007 enabled to receive in real-time medical causes of death in free-text format. This data source was considered as reactive and valuable to implement a reactive mortality surveillance system using medical causes of death (1).

June 18, 2019

Data capture and visualization for a canine influenza outbreak - New York City, 2018

Data-driven decision-making is a cornerstone of public health emergency response; therefore, a highly-configurable and rapidly deployable data capture system with built-in quality assurance (QA; e.g., completeness, standardization) is critical. Additionally, to keep key stakeholders informed of developments during an emergency, data need to be shared in a timely and effective manner.

June 18, 2019

Precarious Data: Crack, Opioids, and Visualizing a Drug Abuse Epidemic

In late 2015, two economists studying health-related data inadvertently discovered an alarming trend: death rates for middle-aged, white Americans were dramatically increasing from drug overdoses (Kolata, 2015), particularly opioids (CDC, 2015). The opioid epidemic has since been widely publicized in the media. However, as critics have argued, the government's response to the crack epidemic differs dramatically from an arguably equally devastating œdrug epidemic that hit many inner US cities thirty years ago ”the influx of crack cocaine.

June 18, 2019

Development of a Custom Spell-Checker for Emergency Department Data

Emergency department (ED) syndromic surveillance relies on a chief complaint, which is often a free-text field, and may contain misspelled words, syntactic errors, and healthcare-specific and/or facility-specific abbreviations. Cleaning of the chief complaint field may improve syndrome capture sensitivity and reduce misclassification of syndromes. We are building a spell-checker, customized with language found in ED corpora, as our first step in cleaning our chief complaint field.

June 18, 2019

Root-cause analysis of bacteraemia increase and surveillance data in hemodialysis

In 2017, the dialysis centre of East Reunion Hospital Group (ERHG) based in Saint-Benoit highlighted an increase in bacteraemia's rates. It was a significant rising compared to previous years. Indeed, ERHG is participating since 2013 to the France haemodialysis infections network surveillance (DIALIN) [1], created in 2005 and that is allowing assessing bacteraemia. DIALIN is a multicentre prospective permanent survey that has followed six voluntary centres in 2005 and forty-two in 2016.

June 18, 2019

Development of Text-Based Algorithm for Opioid Overdose Identification in EMS Data

Opioid overdoses have emerged within the last five to ten years to be a major public health concern. The high potential for fatal events, disease transmission, and addiction all contribute to negative outcomes. However, what is currently known about opioid use and overdose is generally gathered from emergency room data, public surveys, and mortality data. In addition, opioid overdoses are a non-reportable condition.

June 18, 2019

Selection of a geographic area of interest for syndromic surveillance

Since 1 January 2016, the Auvergne and Rhone-Alpes regions have merged as part of the territorial reform. The new region is composed of 12 departments and accounts for more than 8 million inhabitants. Its territory is heterogeneous in population density with very urban areas (Clermont-Ferrand, Grenoble, Lyon and Saint-Etienne) and important mountainous areas (Arc Alpin, Massif Central).

June 18, 2019

Enhancing Syndromic Surveillance with Procedure Data: A 2017-8 Influenza Case Study

Syndromic surveillance achieves timeliness by collecting prediagnostic data, such as emergency department chief complaints, from the start of healthcare interactions. The tradeoff is less precision than from diagnosis data, which takes longer to generate. As the use and sophistication of electronic health information systems increases, additional data that provide an intermediate balance of timeliness and precision are becoming available. Information about the procedures and treatments ordered for a patient can indicate what diagnoses are being considered.

June 18, 2019

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The National Syndromic Surveillance Program (NSSP) is a collaboration among states and public health jurisdictions that contribute data to the BioSense Platform, public health practitioners who use local syndromic surveillance systems, CDC programs, other federal agencies, partner organizations, hospitals, healthcare professionals, and academic institutions.

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