scholarly journals Spatiotemporal analysis of the first wave of COVID-19 hospitalisations in Birmingham, UK

BMJ Open ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. e050574
Author(s):  
Samuel I Watson ◽  
Peter J Diggle ◽  
Michael G Chipeta ◽  
Richard J Lilford
Keyword(s):  
Disease Control ◽  
Real Time ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Temporal Correlation ◽  
Spatiotemporal Analysis ◽  
Control Measures ◽  
University Hospitals ◽  
Disease Surveillance System ◽  
Local Areas

ObjectivesTo evaluate the spatiotemporal distribution of the incidence of COVID-19 hospitalisations in Birmingham, UK during the first wave of the pandemic to support the design of public health disease control policies.DesignA geospatial statistical model was estimated as part of a real-time disease surveillance system to predict local daily incidence of COVID-19.ParticipantsAll hospitalisations for COVID-19 to University Hospitals Birmingham NHS Foundation Trust between 1 February 2020 and 30 September 2020.Outcome measuresPredictions of the incidence and cumulative incidence of COVID-19 hospitalisations in local areas, its weekly change and identification of predictive covariates.ResultsPeak hospitalisations occurred in the first and second weeks of April 2020 with significant variation in incidence and incidence rate ratios across the city. Population age, ethnicity and socioeconomic deprivation were strong predictors of local incidence. Hospitalisations demonstrated strong day of the week effects with fewer hospitalisations (10%–20% less) at the weekend. There was low temporal correlation in unexplained variance. By day 50 at the end of the first lockdown period, the top 2.5% of small areas had experienced five times as many cases per 10 000 population as the bottom 2.5%.ConclusionsLocal demographic factors were strong predictors of relative levels of incidence and can be used to target local areas for disease control measures. The real-time disease surveillance system provides a useful complement to other surveillance approaches by producing real-time, quantitative and probabilistic summaries of key outcomes at fine spatial resolution to inform disease control programmes.

Disease Surveillance System for Big Climate Data Processing and Dengue Transmission

2017 ◽  
Vol 8 (2) ◽  
pp. 88-105 ◽  
Author(s):  
Gunasekaran Manogaran ◽  
Daphne Lopez
Keyword(s):  
Climate Change ◽  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Sensor Data ◽  
Climate Data ◽  
The Real ◽  
Disease Surveillance System

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.

scholarly journals Electronic Integrated Disease Surveillance System and Pathogen Asset Control System

2012 ◽  
Vol 79 (2) ◽  
Author(s):  
Tom G. Wahl ◽  
Aleksey V. Burdakov ◽  
Andrey O. Oukharov ◽  
Azamat K. Zhilokov
Keyword(s):  
Control System ◽  
Real Time ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Data Exchange ◽  
Laboratory Data ◽  
World Health ◽  
Open Architecture ◽  
Case Definitions ◽  
Disease Surveillance System

Electronic Integrated Disease Surveillance System (EIDSS) has been used to strengthen and support monitoring and prevention of dangerous diseases within One Health concept by integrating veterinary and human surveillance, passive and active approaches, case-based records including disease-specific clinical data based on standardised case definitions and aggregated data, laboratory data including sample tracking linked to each case and event with test results and epidemiological investigations. Information was collected and shared in secure way by different means: through the distributed nodes which are continuously synchronised amongst each other, through the web service, through the handheld devices. Electronic Integrated Disease Surveillance System provided near real time information flow that has been then disseminated to the appropriate organisations in a timely manner. It has been used for comprehensive analysis and visualisation capabilities including real time mapping of case events as these unfold enhancing decision making. Electronic Integrated Disease Surveillance System facilitated countries to comply with the IHR 2005 requirements through a data transfer module reporting diseases electronically to the World Health Organisation (WHO) data center as well as establish authorised data exchange with other electronic system using Open Architecture approach. Pathogen Asset Control System (PACS) has been used for accounting, management and control of biological agent stocks. Information on samples and strains of any kind throughout their entire lifecycle has been tracked in a comprehensive and flexible solution PACS.Both systems have been used in a combination and individually. Electronic Integrated Disease Surveillance System and PACS are currently deployed in the Republics of Kazakhstan, Georgia and Azerbaijan as a part of the Cooperative Biological Engagement Program (CBEP) sponsored by the US Defense Threat Reduction Agency (DTRA).

scholarly journals Implementing the Health Early Warning System based on syndromic and event-based surveillance at the 2019 Hajj

2020 ◽  
Vol 26 (12) ◽  
pp. 1570-1575
Author(s):  
Kingsley Lezor Bieh ◽  
Anas Khan ◽  
Saber Yezli ◽  
Ahmed El Ganainy ◽  
Sari Asiri ◽  
...  
Keyword(s):  
Early Warning ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Early Warning System ◽  
Situational Awareness ◽  
Warning System ◽  
Control Measures ◽  
Health Events ◽  
Disease Surveillance System ◽  
Event Based

Background: During the 2019 Hajj, the Ministry of Health in Saudi Arabia implemented for the first time a health early warning system for rapid detection and response to health threats. Aims: This study aimed to describe the early warning findings at the Hajj to highlight the pattern of health risks and the potential benefits of the disease surveillance system. Methods: Using syndromic surveillance and event-based surveillance data, the health early warning system generated automated alarms for public health events, triggered alerts for rapid epidemiological investigations and facilitated the monitoring of health events. Results: During the deployment period (4 July–31 August 2019), a total of 121 automated alarms were generated, of which 2 events (heat-related illnesses and injuries/trauma) were confirmed by the response teams. Conclusion: The surveillance system potentially improved the timeliness and situational awareness for health events, including non-infectious threats. In the context of the current COVID-19 pandemic, a health early warning system could enhance case detection and facilitate monitoring of the disease geographical spread and the effectiveness of control measures.

scholarly journals Success in Revitalizing Weekly Disease Surveillance System in Zimbabwe Using Cell-phone Mediated Data Transmission, 2009-2013

2014 ◽  
Vol 6 (1) ◽  
Author(s):  
Henry Chidawanyika ◽  
Ponesai Nyika ◽  
Joshua Katiyo ◽  
Anthony Sox ◽  
Tongai Chokuda ◽  
...  
Keyword(s):  
Real Time ◽  
Data Transmission ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Cell Phone ◽  
Cell Phones ◽  
Decision Makers ◽  
Surveillance Data ◽  
Innovative Approach ◽  
Disease Surveillance System

Innovative approach to revitalizing Disease Surveillance System in Zimbabwe using cell-phone mediated data transmission has been a huge success. Cell phones have been successfully integrated into disease surveillance system resulting in expansion of surveillance coverage, improved completeness and timeliness. Decision makers are now able to access disease surveillance data in near real-time.

Disease Surveillance System for Big Climate Data Processing and Dengue Transmission

Web Services ◽  
2019 ◽  
pp. 490-509 ◽  
Author(s):  
Gunasekaran Manogaran ◽  
Daphne Lopez
Keyword(s):  
Climate Change ◽  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Sensor Data ◽  
Climate Data ◽  
The Real ◽  
Disease Surveillance System

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.

Disease Surveillance System for Big Climate Data Processing and Dengue Transmission

2018 ◽  
pp. 427-446 ◽  
Author(s):  
Gunasekaran Manogaran ◽  
Daphne Lopez
Keyword(s):  
Climate Change ◽  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Sensor Data ◽  
Climate Data ◽  
The Real ◽  
Disease Surveillance System

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.

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