scholarly journals Spatiotemporal analysis of COVID-19 outbreaks in Wuhan, China

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wei Liu ◽  
Dongming Wang ◽  
Shuiqiong Hua ◽  
Cong Xie ◽  
Bin Wang ◽  
...  
Keyword(s):  
Population Density ◽  
Disease Surveillance ◽  
Influence Factors ◽  
Spatiotemporal Analysis ◽  
Disease Surveillance System ◽  
Local Aggregation ◽  
Short Time ◽  
Effective Public Health ◽  
Global And Local ◽  
Spatial Transmission

AbstractFew study has revealed spatial transmission characteristics of COVID-19 in Wuhan, China. We aimed to analyze the spatiotemporal spread of COVID-19 in Wuhan and its influence factors. Information of 32,682 COVID-19 cases reported through March 18 were extracted from the national infectious disease surveillance system. Geographic information system methods were applied to analysis transmission of COVID-19 and its influence factors in different periods. We found decrease in effective reproduction number (Rt) and COVID-19 related indicators through taking a series of effective public health measures including restricting traffic, centralized quarantine and strict stay-at home policy. The distribution of COVID-19 cases number in Wuhan showed obvious global aggregation and local aggregation. In addition, the analysis at streets-level suggested population density and the number of hospitals were associated with COVID-19 cases number. The epidemic situation showed obvious global and local spatial aggregations. High population density with larger number of hospitals may account for the aggregations. The epidemic in Wuhan was under control in a short time after strong quarantine measures and restrictions on movement of residents were implanted.

scholarly journals Geographic information system methods tell spatiotemporal transmission and its drivers of COVID-19 in Wuhan, China at street-level

2020 ◽  
Author(s):  
Wei Liu ◽  
Wongming Wang ◽  
Shuiqiong Hua ◽  
Cong Xie ◽  
Bin Wang ◽  
...  
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Population Density ◽  
Urban Areas ◽  
Disease Surveillance ◽  
Influence Factors ◽  
Geographic Information ◽  
Disease Surveillance System ◽  
Local Aggregation ◽  
Global And Local

Abstract Background: No study has revealed spatial transmission characteristics of COVID-19 in Wuhan, China. We aimed to analyze the spatiotemporal spread of COVID-19 in Wuhan and its influence factors.Methods: Information of 32,682 COVID-19 cases reported through March 18 were extracted from the national infectious disease surveillance system. Geographic information system methods were applied to analysis transmission of COVID-19 and its influence factors in different periods.Results: We found decrease in effective reproduction number (Rt) and COVID-19 related indicators through taking a series of effective public health measures including restricting traffic, centralized quarantine and strict stay-at home policy. The distribution of COVID-19 cases number in Wuhan showed an obvious global aggregation and a local aggregation in central urban areas, but such aggregations was decreased in the later period of the epidemic. In addition, the analysis at streets-level suggested population density and the number of hospitals were influence factors of spatial difference.Conclusions: The epidemic situation showed obvious global and local spatial aggregations. High population density and directional flow of the Population to hospitals may account for the aggregations. Strong quarantine measures and restrictions on movement of residents in Wuhan make the epidemic under control in a short time.

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.

Incidence of acute myocardial infarction in the workforce: Findings from the Occupational Disease Surveillance System

2021 ◽  
Vol 64 (5) ◽  
pp. 338-357
Author(s):  
Natalie Troke ◽  
Chloë Logar‐Henderson ◽  
Nathan DeBono ◽  
Mamadou Dakouo ◽  
Selena Hussain ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Occupational Disease ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Disease Surveillance System

scholarly journals Apply IOT technology to practice a pandemic prevention body temperature measurement system: A case study of response measures for COVID-19

2021 ◽  
Vol 17 (5) ◽  
pp. 155014772110181
Author(s):  
Wei-Ling Lin ◽  
Chun-Hung Hsieh ◽  
Tung-Shou Chen ◽  
Jeanne Chen ◽  
Jian-Le Lee ◽  
...  
Keyword(s):  
Body Temperature ◽  
Temperature Measurement ◽  
Measurement System ◽  
Disease Surveillance ◽  
Disease Surveillance System ◽  
Temperature Detection ◽  
Wide Range ◽  
Body Temperature Measurement ◽  
Analysis System ◽  
Temperature Measurement System

Today, the most serious threat to global health is the continuous outbreak of respiratory diseases, which is called Coronavirus Disease 2019 (COVID-19). The outbreak of COVID-19 has brought severe challenges to public health and has attracted great attention from the research and medical communities. Most patients infected with COVID-19 will have fever. Therefore, the monitoring of body temperature has become one of the most important basis for pandemic prevention and testing. Among them, the measurement of body temperature is the most direct through the Forehead Thermometer, but the measurement speed is relatively slow. The cost of fast-checking body temperature measurement equipment, such as infrared body temperature detection and face recognition temperature machine, is too high, and it is difficult to build Disease Surveillance System (DSS). To solve the above-mentioned problems, the Intelligent pandemic prevention Temperature Measurement System (ITMS) and Pandemic Prevention situation Analysis System (PPAS) are proposed in this study. ITMS is used to detect body temperature. However, PPAS uses big data analysis techniques to prevent pandemics. In this study, the campus field is used as an example, in which ITMS and PPAS are used. In the research, Proof of Concept (PoC), Proof of Service (PoS), and Proof of Business (PoB) were carried out for the use of ITMS and PPAS in the campus area. From the verification, it can be seen that ITMS and PPAS can be successfully used in campus fields and are widely recognized by users. Through the verification of this research, it can be determined that ITMS and PPAS are indeed feasible and capable of dissemination. The ITMS and PPAS are expected to give full play to their functions during the spread of pandemics. All in all, the results of this research will provide a wide range of applied thinking for people who are committed to the development of science and technology.

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 Assessment of the core and support functions of the Integrated Disease Surveillance system in Maharashtra, India

2013 ◽  
Vol 13 (1) ◽  
Author(s):  
Revati K Phalkey ◽  
Sharvari Shukla ◽  
Savita Shardul ◽  
Nutan Ashtekar ◽  
Sapna Valsa ◽  
...  
Keyword(s):  
Surveillance System ◽  
Disease Surveillance ◽  
Support Functions ◽  
The Core ◽  
Disease Surveillance System
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