ID-Viewer: a visual analytics architecture for infectious diseases surveillance and response management in Pakistan

Ali, Md. Ahasan; Ahsan, Z.; Amin, Masoumeh; Latif, Shahid; Ayyaz, A.; Ayyaz, Muhammad Naeem

doi:10.1016/j.puhe.2016.01.006

Cited by 24 publications

(37 citation statements)

References 42 publications

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“…Instead, in a ‘case study’, the hypothetical use of the system is explained, e.g. in the id‐Viewer (recall Section 5.3 and [AAA*16]), in the PanViz system (recall Section 5.5 and [MLR*11]) and in the subpopulation discovery from cohort study data (recall Section and [AHN*17a]).…”

Section: Discussionmentioning

confidence: 99%

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A Survey of Visual Analytics for Public Health

Preim

Lawonn²

2019

Computer Graphics Forum

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We describe visual analytics solutions aiming to support public health professionals, and thus, preventive measures. Prevention aims at advocating behaviour and policy changes likely to improve human health. Public health strives to limit the outbreak of acute diseases as well as the reduction of chronic diseases and injuries. For this purpose, data are collected to identify trends in human health, to derive hypotheses, e.g. related to risk factors, and to get insights in the data and the underlying phenomena. Most public health data have a temporal character. Moreover, the spatial character, e.g. spatial clustering of diseases, needs to be considered for decision‐making. Visual analytics techniques involve (subspace) clustering, interaction techniques to identify relevant subpopulations, e.g. being particularly vulnerable to diseases, imputation of missing values, visual queries as well as visualization and interaction techniques for spatio‐temporal data. We describe requirements, tasks and visual analytics techniques that are widely used in public health before going into detail with respect to applications. These include outbreak surveillance and epidemiology research, e.g. cancer epidemiology. We classify the solutions based on the visual analytics techniques employed. We also discuss gaps in the current state of the art and resulting research opportunities in a research agenda to advance visual analytics support in public health.

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Section: Discussionmentioning

confidence: 99%

“…Ali et al . [AAA*16] mention emergency calls, school absentees and ambulatory data as further sources for infectious disease outbreak detection. Mortality data involving a precise classification of the cause of death are essential for monitoring long‐term trends, related, e.g.…”

Section: Data For Public Healthmentioning

confidence: 99%

A Survey of Visual Analytics for Public Health

Preim

Lawonn²

2019

Computer Graphics Forum

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“…• Multiple prediction maps with future values of the explanatory variables can help disaster management authorities to prepare and face all risk levels of dengue outbreaks that are likely to happen in the future [6]. • Town planning and urban development schemes usually do not consider the health impacts of urban growth.…”

Section: New Additions In Knowledgementioning

confidence: 99%

“…Weather parameters play a significant role in mosquito breeding and disease outbreaks. Therefore, in countries, where dengue spread occurs repeatedly, a variation in precipitation, temperature, and humidity can be related to dengue outbreaks [6,12]. Alkhaldy [7] established an association of dengue fever incidences with relative humidity and temperature in a city of Saudi Arabia using a generalized linear model.…”

Section: Introductionmentioning

confidence: 99%

Geostatistical modeling of dengue disease in Lahore, Pakistan

Bilal

Zaidi

2019

SN Appl. Sci.

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Dengue outbreak has become a significant mosquito-borne disease in Pakistan with the worst outbreak in the Lahore district in 2011. This situation has emerged as a serious concern for disaster management authorities, and managing risks of dengue outbreaks has become their top priority. This study investigated the relationship of the reported dengue cases in the Lahore district in 2011 with the spatial and temporal changes in climatic and environmental factors. The quantitative co-relational research method was employed to ascertain the association between dengue incidences and weather parameters, land use/cover, and demographic characteristics of the study area. Ordinary least-square and geographically weighted regression analyses were conducted to identify the influence of study variables on the dengue outbreak. Satellite and ground data were used to derive study parameters. A geostatistical dengue risk model was developed to identify the risk-prone areas in Lahore. The geographically weighted multivariate regression model indicated a statistically significant relationship between dengue cases and the built-up area and population density. These two explanatory variables explained 77.4% variance in dengue incidences. This study supported the association between the demographic parameters and distribution pattern of dengue outbreaks in the Lahore district. The dengue risk model identified the vulnerable areas that need particular attention to mitigate future outbreaks.

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“…Allen et al, found a statistically significant correlation between influenza outbreaks using the social media platform Twitter and techniques from GIS for the thirty most populated cities in the United States during the 2013-2014 influenza season, compared with national, regional, and local influenza outbreak reports [15]. A visual analytics GIS-based decision support system for early infectious diseases outbreak detection was applied in Pakistan, using real-time streaming data from emergency departments [16].…”

Section: Outlook and Conclusionmentioning

confidence: 99%

Public Health and Epidemiology Informatics

Thiébaut¹,

Thiessard²

2017

Yearb Med Inform

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SummaryObjectives: To summarize current research in the field of Public Health and Epidemiology Informatics. Methods: The complete 2016 literature concerning public health and epidemiology informatics has been searched in PubMed and Web of Science, and the returned references were reviewed by the two section editors to select 14 candidate best papers. These papers were then peer-reviewed by external reviewers to allow the editorial team an enlightened selection of the best papers. Results: Among the 829 references retrieved from PubMed and Web of Science, three were finally selected as best papers. The first one compares Google, Twitter, and Wikipedia as tools for Influenza surveillance. The second paper presents a Geographic Knowledge-Based Model for mapping suitable areas for Rift Valley fever transmission in Eastern Africa. The last paper evaluates the factors associated with the visit of Facebook pages devoted to Public Health Communication. Conclusions: Surveillance is still a productive topic in public health informatics but other very important topics in public health are appearing.

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ID-Viewer: a visual analytics architecture for infectious diseases surveillance and response management in Pakistan

Cited by 24 publications

References 42 publications

A Survey of Visual Analytics for Public Health

A Survey of Visual Analytics for Public Health

Geostatistical modeling of dengue disease in Lahore, Pakistan

Public Health and Epidemiology Informatics

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