Low-Cost National Media-Based Surveillance System for Public Health Events, Bangladesh

Abstract Background: In the era of a pandemic like COVID-19, monitoring the sentimental changes of the population is an urgent need, especially for the policy makers of the public health. A possible solution is to build a fast and low-cost surveillance system by using the sentiment analysis of Twitter data. Unfortunately, choosing a suitable sentiment classification model is still challenging. The general pre-trained model may be insensitive to the new specific terms of the pandemic. The early-trained model may have a bias issue due to the incomplete specific corpus. Although it is reasonable to assume the late-trained model is relatively reliable, it is usually available months after a pandemic begins. Methods: This paper conducts the sentiment analysis of Twitter data and compares different models. Furthermore, we propose a strategy for using the pre-trained, early-trained, and latetrained models in a surveillance system based on Twitter data. The first two models can be used together in the early stage, while the last model can be used in the late stage. This study also analyzes the relationship between the sentimental changes of COVID-19-related Twitter data and the public health policies and events. Results: Our results indicate that applying the pre-trained model to preprocessing early training samples may improve the early-trained model. Both models can work together by making up each other in the surveillance system in the early stage. Conclusions: A fast and low-cost surveillance system is critical to the policy makers of the public health in a pandemic. This work uses the sentiment analysis of Twitter data to evaluate people’s attitudes to public health policies and events. We propose a strategy to make the surveillance system effective since the early stage. This study also connects the sentimental changes of COVID19-related Twitter data to the public health policies and events.

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Integrating Poison Center Data into Oregon ESSENCE using a Low-Cost Solution

Online Journal of Public Health Informatics ◽

10.5210/ojphi.v9i1.7620 ◽

2017 ◽

Vol 9 (1) ◽

Cited By ~ 1

Author(s):

Robert Laing

Keyword(s):

Public Health ◽

Web Service ◽

Surveillance System ◽

Syndromic Surveillance ◽

Low Cost ◽

Primary Data ◽

Poison Center ◽

Clinical Effects ◽

Syndromic Surveillance System ◽

Related Data

ObjectiveTo enhance Oregon ESSENCE’s surveillance capabilities byincorporating data from the Oregon Poison Center using limitedresources.IntroductionOregon Public Health Division (OPHD), in collaboration with theJohns Hopkins University Applied Physics Laboratory, implementedOregon ESSENCE in 2012. Oregon ESSENCE is an automated,electronic syndromic surveillance system that captures emergencydepartment data. To strengthen the capabilities of Oregon ESSENCE,OPHD sought other sources of health-outcome information, includingOregon Poison Center (OPC). In the past, Oregon’s surveillance staffmanually monitored OPC data on the National Poison Data Service(NPDS) website. Although functional, it was not integrated intoOregon’s syndromic surveillance system and required epidemiologiststo assess alerts on individual calls. To achieve data integration,OPHD pursued an automated solution to deliver OPC data intoOregon ESSENCE. OPHD’s growing interoperability infrastructurefostered development of a low-cost, reliable solution to automate theintegration of these data sources.MethodsOPC facilitated OPHD’s access to the free-of-charge NPDS webservice with an approval request and a data use agreement. OPHDuses the Rhapsody Integration Engine 6.2.1 (Orion Health, Auckland,NZ) as its primary data transfer and translation mechanism. OPHDleveraged its existing Rhapsody installation to automatically requestdata from the NPDS web service daily. Each request contains customsearch parameters that query calls from the previous day (24 hours).The service returns an XML file containing poison center call datawith multiple nodes of related data. Rhapsody uses a JavaScript ‘filter’to parse each call and its related data. The Oregon ESSENCE backendSQL database contains a parent table for the call and child tables forthe related data (Clinical Effects, Routes, Scenarios, Therapies, andGeneric Codes). Rhapsody inserts data into each of these backendSQL tables.ResultsOregon ESSENCE displays OPC data through its web interface forinterpretation by OPHD’s syndromic surveillance epidemiologists.Integrating NPDS data into Oregon ESSENCE allows OPHD staffto timely monitor data in an automated, routine manner. Syndromicsurveillance staff first assess alerts generated by Oregon ESSENCE.Alerts that require follow-up trigger a call between OPHDepidemiologists and OPC. Oregon is the first state to use the NPDSweb service to upload poison center data into Oregon ESSENCE.ConclusionsOregon’s successful integration of the NPDS web service data intoOregon ESSENCE is the first known of its kind. It leverages OPHD’sgrowing infrastructure of interoperability software applications andstaff expertise to create a cost-effective and sustainable solution thatcan be easily adapted by other public health agencies.

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PyDOSS: An open-source Python-based communicable disease outbreak surveillance system designed and used in a COVID-19 community care facility (Preprint)

10.2196/preprints.21760 ◽

2020 ◽

Author(s):

Andrew Fang ◽

Jonathan Kia-Sheng Phua ◽

Terrence Chiew ◽

Daniel De-Liang Loh ◽

Lincoln Ming Han Liow ◽

...

Keyword(s):

Open Source ◽

Surveillance System ◽

Community Care ◽

Communicable Disease ◽

Low Cost ◽

Real Life ◽

Disease Outbreak ◽

Disease Spread ◽

Healthcare Team ◽

Increased Risk

BACKGROUND During the Coronavirus Disease 2019 (COVID-19) outbreak, community care facilities (CCF) were set up as temporary out-of-hospital isolation facilities to contain the surge of cases in Singapore. Confined living spaces within CCFs posed an increased risk of communicable disease spread among residents. OBJECTIVE This inspired our healthcare team managing a CCF operation to design a low-cost communicable disease outbreak surveillance system (CDOSS). METHODS Our CDOSS was designed with the following considerations: (1) comprehensiveness, (2) efficiency through passive reconnoitering from electronic medical record (EMR) data, (3) ability to provide spatiotemporal insights, (4) low-cost and (5) ease of use. We used Python to develop a lightweight application – Python-based Communicable Disease Outbreak Surveillance System (PyDOSS) – that was able perform syndromic surveillance and fever monitoring. With minimal user actions, its data pipeline would generate daily control charts and geospatial heat maps of cases from raw EMR data and logged vital signs. PyDOSS was successfully implemented as part of our CCF workflow. We also simulated a gastroenteritis (GE) outbreak to test the effectiveness of the system. RESULTS PyDOSS was used throughout the entire duration of operation; the output was reviewed daily by senior management. No disease outbreaks were identified during our medical operation. In the simulated GE outbreak, PyDOSS was able to effectively detect an outbreak within 24 hours and provided information about cluster progression which could aid in contact tracing. The code for a stock version of PyDOSS has been made publicly available. CONCLUSIONS PyDOSS is an effective surveillance system which was successfully implemented in a real-life medical operation. With the system developed using open-source technology and the code made freely available, it significantly reduces the cost of developing and operating CDOSS and may be useful for similar temporary medical operations, or in resource-limited settings.

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India as a Pioneer of Innovation

10.1093/oso/9780199476084.001.0001 ◽

2018 ◽

Keyword(s):

Public Health ◽

Health System ◽

Informal Economy ◽

Urban Poor ◽

Public Health System ◽

Low Cost ◽

Social Enterprises ◽

Specific Context ◽

Innovation Project ◽

Context Specific

What does innovation mean to and in India? What are the predominant areas of innovation for India, and under what situations do they succeed or fail? This book addresses these all-important questions arising within diverse Indian contexts: informal economy, low-cost settings, large business groups, entertainment and copyright-based industries, an evolving pharma sector, a poorly organized and appallingly underfunded public health system, social enterprises for the urban poor, and innovations for the millions. It explores the issues that promote and those that hinder the country’s rise as an innovation leader. The book’s balanced perspective on India’s promises and failings makes it a valuable addition for those who believe that India’s future banks heavily on its ability to leapfrog using innovation, as well as those sceptical of the Indian state’s belief in the potential of private enterprise and innovation. It also provides critical insights on innovation in general, the most important of which being the highly context-specific, context-driven character of the innovation project.

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