Description of Anthrax Outbreak Investigation in Pacitan District in 2017

2017 ◽  
Author(s):  
Veronika Ofong ◽  
Chatarina U. W ◽  
Supaat Supaat
Keyword(s):  
Outbreak Investigation

An outbreak investigation of suspected Chikungunya fever in Nalgonda District, Telangana state

2014 ◽  
Vol 3 (4) ◽  
pp. 219 ◽  
Author(s):  
Suguna Dumpala ◽  
Nagaraj Kondagunta ◽  
VarunMohan Malhotra ◽  
GuruPrasad Venna ◽  
KishoreYadav Jothula
Keyword(s):  
Outbreak Investigation ◽  
Chikungunya Fever ◽  
Telangana State ◽  
Nalgonda District

Cholera Outbreak Investigation in Sawan Area, Shuaub District, Sana'a Capital City, Yemen, October 2016

2018 ◽  
Author(s):  
Ahlam Alsaidi ◽  
M Al-Amad ◽  
N Al-Abhar ◽  
L Al-Zagar ◽  
Y Abdaulwareth
Keyword(s):  
Capital City ◽  
Outbreak Investigation ◽  
Cholera Outbreak

Cholera Outbreak Investigation in Baghdad Al-Rusafa August-November 2017

2018 ◽  
Author(s):  
Safaa Saadoon ◽  
R Hashim ◽  
Q Abdulaziz ◽  
H Ismael ◽  
M Ali ◽  
...  
Keyword(s):  
Outbreak Investigation ◽  
Cholera Outbreak

scholarly journals Outbreak investigation attributes Infectious spleen and kidney necrosis virus as a necessary cause of a mortality epidemic in farmed grouper (Epinephelus spp.) in Bali, Indonesia

2021 ◽  
Vol 20 ◽  
pp. 100723
Author(s):  
Cahya Fusianto ◽  
Paul M. Hick ◽  
Murwantoko ◽  
Afri Herlambang ◽  
Richard J. Whittington ◽  
...  
Keyword(s):  
Outbreak Investigation ◽  
Necrosis Virus

scholarly journals Outbreak investigation of symptomatic SARS-COV-2 VOC 202012/01-lineage B.1.1.7 infection in healthcare workers, Italy

2021 ◽  
Author(s):  
Daniela Loconsole ◽  
Anna Sallustio ◽  
Marisa Accogli ◽  
Angela Leaci ◽  
Antonio Sanguedolce ◽  
...  
Keyword(s):  
Healthcare Workers ◽  
Outbreak Investigation

scholarly journals Missing the Point in Point Prevalence: Harnessing EMR Data to Identify Epi-Linked Patients in an Outbreak Investigation

2020 ◽  
Vol 41 (S1) ◽  
pp. s318-s318
Author(s):  
Lisa Stancill ◽  
Lauren DiBiase ◽  
Emily Sickbert-Bennett
Keyword(s):  
At Risk ◽  
Disease Transmission ◽  
Healthcare Setting ◽  
Point Prevalence ◽  
Positive Patient ◽  
Outbreak Investigation ◽  
Population At Risk ◽  
Outbreak Investigations ◽  
Overlap Method ◽  
Prevalence Screen

Background: A critical step during outbreak investigations is actively screening for additional cases to assess ongoing transmission. In the healthcare setting, one widely used method is point-prevalence screening on the whole unit where a positive patient is housed. Although this point-prevalence approach captures the “place,” it can miss the “person” and “time” elements that define the population-at-risk. Methods: At University of North Carolina (UNC) Hospitals, we used business intelligence tools to build a query that harnesses the admission, discharge, and transfer (ADT) data from the electronic medical record (EMR). Using this data identifies every patient who overlapped in time and space with a positive patient. An additional query identifies currently admitted overlap patients and their current location. During an outbreak investigation, an analyst executes these queries in the mornings when surveillance screens are scheduled. The queries generate a list of patients to screen that are prioritized on the number of days they were in the same unit with the positive patient. This overlap methodology successfully captures the person, place, and time associated with possible disease transmission. We implemented the overlap method for the last 3 months following 1 year of point-prevalence approach screening during a novel disease outbreak at UNC Hospitals. Results: In total, 4,385 unique patients overlapped with previously identified infected or colonized patients, of which 781 (17.8%) from 40 departments were screened over 15 months. During a subsequent, currently ongoing, outbreak, we are utilizing the overlap method and in 6 weeks have already screened 161 of the 1,234 overlapping patients (13%). After 3 rounds of overlap screening, we have already been able to identify 1 additional positive patient. This patient was on the same unit as patient zero 4 months prior but was readmitted to a unit that would not have received a point-prevalence screen using the standard approach. Conclusions: Surveillance screening is a time-consuming, resource-intensive effort that requires collaboration between infection prevention, clinical staff, patients, and the laboratory. By harnessing EMR ADT data, we can better target the population at risk and more efficiently utilize resources during outbreak investigations. In addition, the overlap method fills a gap in the current CDC guidelines by focusing on patients who were on the same unit with any positive patient, including those who discharged and readmitted. Most importantly, we identified an additional positive patient that would not have been detected through a point-prevalence screen, helping us prevent further disease transmission.Funding: NoneDisclosures: None

Questions in the Role of Chest CT Scanning in TB Outbreak Investigation

CHEST Journal ◽  
2010 ◽  
Vol 138 (6) ◽  
pp. 1522-1523 ◽  
Author(s):  
Sourin Bhuniya ◽  
Pampa De
Keyword(s):  
Ct Scanning ◽  
Chest Ct ◽  
Outbreak Investigation

Development of a nosocomial outbreak investigation database

2005 ◽  
Vol 59 (3) ◽  
pp. 215-219 ◽  
Author(s):  
K. Makimoto ◽  
N. Ashida ◽  
N. Qureshi ◽  
T. Tsuchida ◽  
A. Sekikawa
Keyword(s):  
Outbreak Investigation ◽  
Nosocomial Outbreak
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