The application of geographic information system (GIS) in the field of public health

2010 ◽  
Author(s):  
Zhang Zhen ◽  
Jin Jing-min ◽  
Fang Liu
Keyword(s):  
Public Health ◽  
Information System ◽  
Geographic Information System ◽  
Geographic Information

scholarly journals Emerging cross-disciplinary profiles: The Geomatic Epidemiologist

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
M Recchioni ◽  
V Castello ◽  
S Del Vecchio ◽  
V Ciaccio ◽  
A M Donia ◽  
...  
Keyword(s):  
Public Health ◽  
Information System ◽  
Geographic Information System ◽  
Relevant Literature ◽  
Holistic Approach ◽  
Geographic Information ◽  
Health Study ◽  
Gis And Remote Sensing ◽  
Public Health Epidemiology ◽  
And Training

Abstract Issue Geographic information systems (GIS) and remote sensing technologies are increasingly used in Public Health epidemiology, showing a great potential in anticipating and responding to actual and future challenges for the public health system and in improving health services' excellence. According to the evidences collected within a wide meta-research carried on of relevant literature (”GIS geographic information system” and “GIS geographic information system and training” on Pubmed; “epidemiologist use of GIS and training” and “epidemiologist use of gis” on Google Scholar),GIS and new sensing technologies are mostly used to: map air and water pollution, map diseases prevalence, predict infection diseases and vector-spread diseases in big areas, study health service coverage and preparedness in emergencies, map cities and study urban health, study climate changes for decision making. Description of the Problem Specific skills and training are required to address the use of GIS and new sensing technologies.The specific aim of our study is to identify the professional profile of a new figure, called 'Geomatic Epidemiologist' and to define its professional and educational standards, as well as the relevant training programs. Results Data collection and analysis of INAPP and ESCO databases about existing professional profiles (starting from 2016) has allowed drafting a first qualification schema and profile. The profile has been defined according to the 4C model (elaborated by Univaq) distinguishing between Hard Skills (technical knowledge and skills),Soft Skills (cognitive, individual and social) and interpersonal behaviors. Conclusions Profile will be validated with relevant stakeholders and Public Health professionals in order to deepen the understanding of the main competences required to study health issues with GIS and related technologies; to this extent, a questionnaire has been elaborated to evaluate relevance, frequency and complexity of each component of the profile Key messages Developing cross-disciplinary profiles, (i.e. the Geomatic Epidemiologist) integrating clusters of competences (holistic approach). Public health research challenges and excellence.

scholarly journals Evolution of Diagnostic Methods and Prevalence Detection of COVID-19: A Review

2021 ◽  
Author(s):  
Hemant Bherwani
Keyword(s):  
Public Health ◽  
Information System ◽  
Geographic Information System ◽  
Clinical Research ◽  
Predictive Modeling ◽  
Diagnostic Techniques ◽  
Geographic Information ◽  
Diagnostic Methods ◽  
Or Management ◽  
Affected Area

In clinical, research, and public health laboratories, many diagnostic methods are used to detect the coronavirus. Some tests directly detect infection by detecting viral RNA, while others detect the disease indirectly by detecting host antibodies. Several studies on SARS-CoV-2 diagnostic methods have found varying throughput, batching capacity, infrastructure requirements, analytical efficiency, and turnaround times ranging from minutes to hours. Serosurvey studies have been conducted for antibodies to understand, model, and forecast the prevalence of the disease in an area. While on the research and predictive modeling side, sampling and analysis of sewage have been conducted to determine the number of RNA copies and hence the prevalence. Certain studies indicate usefulness of GIS (Geographic Information System) for understanding the pervasiveness of COVID-19 in an area as well. The current chapter deals with the evolution of diagnostic techniques for COVID-19 and discusses use of specific techniques and appropriateness in certain specified conditions. It also focuses on understanding the methods used for assessing the prevalence of COVID-19 in a particular region to extract mitigative strategies from it, either by prediction or management of the affected area.

Use of Geographic Information System Mapping in the Examination of Specialized Low Vision Resources and Areas of Need in Michigan

2021 ◽  
Vol 115 (1) ◽  
pp. 5-16
Author(s):  
Elyse Connors ◽  
Amber E. Willard ◽  
Kathleen M. Baker ◽  
Katie Debiak ◽  
Renee Beranek ◽  
...  
Keyword(s):  
Public Health ◽  
Risk Factors ◽  
Information System ◽  
Geographic Information System ◽  
Vision Loss ◽  
Low Vision ◽  
Public Health Problem ◽  
Geographic Information ◽  
Gis Mapping ◽  
Lower Peninsula

Introduction: The number of adults with visual impairments (i.e., blindness or low vision) is increasing, especially with the aging of the population. Although awareness of vision loss as a public health problem is growing, public health budgets are decreasing. This study exemplifies the use of publicly available secondary data and geographic information system (GIS) mapping to spatially map areas of potential higher risk for vision loss and identify where specialized, low vision resources are located, by county, in Michigan. Methods: County-level, publicly available data on risk factors for low vision (health and demographic) and specialized low vision resources (medical, rehabilitation, and community) are extracted from existing public health data sets and information published on the Internet. GIS mapping is applied to visually examine potential areas of disparity between need and resources. Results: Broadly speaking, counties in Michigan with the highest number of risk factors for low vision are clustered in the center of the Lower Peninsula and on the eastern and western ends of the Upper Peninsula. Areas that have fewer resources for low vision are clustered in the thumb area and the middle to upper part of the Lower Peninsula. Resources are concentrated near the state’s metropolitan areas (i.e., Detroit and suburbs, Kalamazoo, and Grand Rapids). Discussion: Maps can be helpful in locating areas of health disparities, but they need to be interpreted carefully such as by considering the county’s population size. Understanding the eligibility criteria of available services can help to uncover groups of persons not being served. Implications for practitioners: With increasing need for services and shrinking budgets, strategic planning may help alleviate anticipated shortfalls in available services. Use of publicly available data and GIS mapping may be an affordable and efficient method to identify areas of need and resources, for targeted public health efforts in vision.

scholarly journals Use of a geographic information system to map cases of measles in real-time during an outbreak in Dublin, Ireland, 2011

2012 ◽  
Vol 17 (49) ◽  
Author(s):  
G Fitzpatrick ◽  
M Ward ◽  
O Ennis ◽  
H Johnson ◽  
S Cotter ◽  
...  
Keyword(s):  
Public Health ◽  
Information System ◽  
Geographic Information System ◽  
Real Time ◽  
Vaccination Campaign ◽  
Public Health Intervention ◽  
Geographic Information ◽  
Digital Mapping ◽  
First Case ◽  
Measles Outbreak

In 2011, there was a large measles outbreak in Dublin. Nationally 285 cases were notified to the end of December 2011, and 250 (88%) were located in the Dublin region. After the first case was notified in week 6, numbers gradually increased, with 25 notified in June and a peak of 53 cases in August. Following public health intervention including a measles-mumps-rubella (MMR) vaccination campaign, no cases were reported in the Dublin region in December 2011. Most cases (82%) were children aged between 6 months and 14 years, and 46 cases (18%) were under 12 months-old. This is the first outbreak in Dublin to utilise a geographic information system for plotting measles cases on a digital map in real time. This approach, in combination with the analysis of case notifications, assisted the department of public health in demonstrating the extent of the outbreak. The digital mapping documented the evolution of two distinct clusters of 87 (35%) cases. These measles cases were infected with genotype D4-Manchester recently associated with large outbreaks across Europe. The two clusters occurred in socio-economically disadvantaged areas and were attributable to inadequate measles vaccination coverage due in part to the interruption of a school-based MMR2 vaccination programme.

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