scholarly journals A user study on public health events detected within the medical ecosystem

2011 ◽  
Author(s):  
Avare Stewart ◽  
Eelco Herder ◽  
Matthew Smith ◽  
Wolfgang Nejdl
Keyword(s):  
Public Health ◽  
User Study ◽  
Health Events

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

2016 ◽  
Vol 22 (4) ◽  
pp. 720-722 ◽  
Author(s):  
Trong T. Ao ◽  
Mahmudur Rahman ◽  
Farhana Haque ◽  
Apurba Chakraborty ◽  
M. Jahangir Hossain ◽  
...  
Keyword(s):  
Public Health ◽  
Surveillance System ◽  
Low Cost ◽  
Health Events ◽  
National Media

scholarly journals Analyzing Cross-country Pandemic Connectedness During COVID-19 Using a Spatial-Temporal Database: Network Analysis (Preprint)

2021 ◽  
Author(s):  
Amanda MY Chu ◽  
Jacky NL Chan ◽  
Jenny TY Tsang ◽  
Agnes Tiwari ◽  
Mike KP So
Keyword(s):  
Public Health ◽  
Time Series ◽  
Large Scale ◽  
Early Recognition ◽  
Communicable Disease ◽  
Control Measure ◽  
Communicable Diseases ◽  
Civil Aviation ◽  
Temporal Database ◽  
Health Events

UNSTRUCTURED Communicable diseases including COVID-19 pose a major threat to public health worldwide. To curb the spread of communicable diseases effectively, timely surveillance and prediction of the risk of pandemics are essential. The aim of this study is to analyze free and publicly available data to construct useful travel data records for network statistics other than common descriptive statistics. This study describes analytical findings of time-series plots and spatial-temporal maps to illustrate or visualize pandemic connectedness. We analyzed data retrieved from the web-based Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, which contains up-to-date and comprehensive meta-information on civil flights from 193 national governments in accordance with the airport, country, city, latitude, and the longitude of flight origin and the destination. We used the database to visualize pandemic connectedness through the workflow of travel data collection, network construction, data aggregation, travel statistics calculation, and visualization with time-series plots and spatial-temporal maps. We observed similar patterns in the time-series plots of worldwide daily flights from January to early-March of 2019 and 2020. A sharp reduction in the number of daily flights recorded in mid-March 2020 was likely related to large-scale air travel restrictions owing to the COVID-19 pandemic. The levels of connectedness between places are strong indicators of the risk of a pandemic. Since the initial reports of COVID-19 cases worldwide, a high network density and reciprocity in early-March 2020 served as early signals of the COVID-19 pandemic and were associated with the rapid increase in COVID-19 cases in mid-March 2020. The spatial-temporal map of connectedness in Europe on March 13, 2020, shows the highest level of connectedness among European countries, which reflected severe outbreaks of COVID-19 in late March and early April of 2020. As a quality control measure, we used the aggregated numbers of international flights from April to October 2020 to compare the number of international flights officially reported by the International Civil Aviation Organization with the data collected from the Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, and we observed high consistency between the 2 data sets. The flexible design of the database provides users access to network connectedness at different periods, places, and spatial levels through various network statistics calculation methods in accordance with their needs. The analysis can facilitate early recognition of the risk of a current communicable disease pandemic and newly emerging communicable diseases in the future.

Multinational Outbreak Investigations

2019 ◽  
pp. 295-306 ◽  
Author(s):  
Frank Mahoney ◽  
James W. Le Duc
Keyword(s):  
Public Health ◽  
Decision Making ◽  
International Health ◽  
World Health ◽  
Roles And Responsibilities ◽  
Health Events ◽  
The World ◽  
Outbreak Investigations ◽  
Health Organization ◽  
Health Regulations

Multinational collaborations on international outbreak investigations and response have a long history. Development of the World Health Organization (WHO) in 1948 was closely linked to efforts by the global community to prevent, detect, and respond to outbreaks of international concern. Through the International Health Regulations (IHR) of 2005, a legally binding instrument requiring countries to report certain outbreaks and public health events, WHO outlined a strategy for disease threat response. Efforts by global partners to strengthen cooperation have evolved over the years, including roles and responsibilities of WHO, its Member States, and other partners. Among the challenges faced by Member State and WHO in implementing the IHRs are limited funding to support staffing and operational support as well as sometimes conflicting multijurisdictional decision-making. The response to recent outbreaks provides evidence that much work remains to be done to strengthen IHR mechanisms.

scholarly journals A New Geo-Propagation Model of Event Evolution Chain Based on Public Opinion and Epidemic Coupling

2020 ◽  
Vol 17 (24) ◽  
pp. 9235
Author(s):  
Yan Zhang ◽  
Nengcheng Chen ◽  
Wenying Du ◽  
Shuang Yao ◽  
Xiang Zheng
Keyword(s):  
Public Health ◽  
Public Opinion ◽  
Spatial Dimension ◽  
Real Space ◽  
Propagation Model ◽  
Health Events ◽  
Model Complex ◽  
Epidemic Area ◽  
Before And After ◽  
The Impact

The online public opinion is the sum of public views, attitudes and emotions spread on major public health emergencies through the Internet, which maps out the scope of influence and the disaster situation of public health events in real space. Based on the multi-source data of COVID-19 in the context of a global pandemic, this paper analyzes the propagation rules of disasters in the coupling of the spatial dimension of geographic reality and the dimension of network public opinion, and constructs a new gravity model-complex network-based geographic propagation model of the evolution chain of typical public health events. The strength of the model is that it quantifies the extent of the impact of the epidemic area on the surrounding area and the spread of the epidemic, constructing an interaction between the geographical reality dimension and online public opinion dimension. The results show that: The heterogeneity in the direction of social media discussions before and after the “closure” of Wuhan is evident, with the center of gravity clearly shifting across the Yangtze River and the cyclical changing in public sentiment; the network model based on the evolutionary chain has a significant community structure in geographic space, divided into seven regions with a modularity of 0.793; there are multiple key infection trigger nodes in the network, with a spatially polycentric infection distribution.

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