scholarly journals One Health Surveillance with Electronic Integrated Disease Surveillance System

Author(s):  
Alexey V. Burdakov ◽  
Andrey O. Ukharov ◽  
Thomas G. Wahl
2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Veronica A. Fialkowski ◽  
Leigh M. Tyndall Snow ◽  
Kimerbly Signs ◽  
Mary Grace Stobierski

The histoplasmosis surveillance system was evaluated using the 2001Centers for Disease Control and Prevention Updated Guidelines for Evaluating Public Health Surveillance Systems. From 2004 to 2014, a total of 1,608 confirmed or probable cases were reported into MDSS, with a slight increasing trend in case numbers over time. Michigan’s histoplasmosis surveillance system is relatively simple, but the misclassification of cases is troublesome. Development of tools for LHDs to aid in classification of cases may improve the PPV and decrease case investigation time. Increasing the number of hospitals that report directly to MDSS would indicate more acceptability, and increase sensitivity.


2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Hayat Khogali ◽  
Ngozi A. Erondu ◽  
Betiel H. Haile ◽  
Scott J. McNabb

A recent assessment of the Sudan public health surveillance system found fragmented and siloed disease programs and an overburdened workforce due to vertical systems and inefficient processes. A plan of action was developed to support improving public health surveillance strengthening by: 1) implementing a strategic approach to achieving IHR (2005), 2) implementing One Health and IDSR aims, and 3) establishing an E-surveillance ICT platform for increasing public health surveillance capacity to safely and rapidly detect and report infectious diseases in Sudan.


2021 ◽  
Vol 64 (5) ◽  
pp. 338-357
Author(s):  
Natalie Troke ◽  
Chloë Logar‐Henderson ◽  
Nathan DeBono ◽  
Mamadou Dakouo ◽  
Selena Hussain ◽  
...  

2017 ◽  
Vol 8 (2) ◽  
pp. 88-105 ◽  
Author(s):  
Gunasekaran Manogaran ◽  
Daphne Lopez

Ambient intelligence is an emerging platform that provides advances in sensors and sensor networks, pervasive computing, and artificial intelligence to capture the real time climate data. This result continuously generates several exabytes of unstructured sensor data and so it is often called big climate data. Nowadays, researchers are trying to use big climate data to monitor and predict the climate change and possible diseases. Traditional data processing techniques and tools are not capable of handling such huge amount of climate data. Hence, there is a need to develop advanced big data architecture for processing the real time climate data. The purpose of this paper is to propose a big data based surveillance system that analyzes spatial climate big data and performs continuous monitoring of correlation between climate change and Dengue. Proposed disease surveillance system has been implemented with the help of Apache Hadoop MapReduce and its supporting tools.


2013 ◽  
Vol 13 (1) ◽  
Author(s):  
Revati K Phalkey ◽  
Sharvari Shukla ◽  
Savita Shardul ◽  
Nutan Ashtekar ◽  
Sapna Valsa ◽  
...  

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