An Integrated Approach for Spatio-Temporal Cholera Disease Hotspot Relation Mining for Public Health Management in Punjab, Pakistan
Public health management can generate actionable results when diseases are studied in context with other candidate factors contributing to disease dynamics. In order to fully understand the interdependent relationships of multiple geospatial features involved in disease dynamics, it is important to...
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doaj-7f3c0b90106a401e9ef1f97a6e6028d02020-11-25T02:33:00ZengMDPI AGInternational Journal of Environmental Research and Public Health1661-78271660-46012020-05-01173763376310.3390/ijerph17113763An Integrated Approach for Spatio-Temporal Cholera Disease Hotspot Relation Mining for Public Health Management in Punjab, PakistanFatima Khalique0Shoab Ahmed Khan1Wasi Haider Butt2Irum Matloob3Department of Computer and Software Engineering, National University of Sciences and Technology, Islamabad 44000, PakistanDepartment of Computer and Software Engineering, National University of Sciences and Technology, Islamabad 44000, PakistanDepartment of Computer and Software Engineering, National University of Sciences and Technology, Islamabad 44000, PakistanDepartment of Computer and Software Engineering, National University of Sciences and Technology, Islamabad 44000, PakistanPublic health management can generate actionable results when diseases are studied in context with other candidate factors contributing to disease dynamics. In order to fully understand the interdependent relationships of multiple geospatial features involved in disease dynamics, it is important to construct an effective representation model that is able to reveal the relationship patterns and trends. The purpose of this work is to combine disease incidence spatio-temporal data with other features of interest in a mutlivariate spatio-temporal model for investigating characteristic disease and feature patterns over identified hotspots. We present an integrated approach in the form of a disease management model for analyzing spatio-temporal dynamics of disease in connection with other determinants. Our approach aligns spatio-temporal profiles of disease with other driving factors in public health context to identify hotspots and patterns of disease and features of interest in the identified locations. We evaluate our model against cholera disease outbreaks from 2015–2019 in Punjab province of Pakistan. The experimental results showed that the presented model effectively address the complex dynamics of disease incidences in the presence of other features of interest over a geographic area representing populations and sub populations during a given time. The presented methodology provides an effective mechanism for identifying disease hotspots in multiple dimensions and relation between the hotspots for cost-effective and optimal resource allocation as well as a sound reference for further predictive and forecasting analysis.https://www.mdpi.com/1660-4601/17/11/3763cholera dynamicshealth data analyticintegrated health modeling frameworkpublic healthspatio-temporal analysis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Fatima Khalique Shoab Ahmed Khan Wasi Haider Butt Irum Matloob |
spellingShingle |
Fatima Khalique Shoab Ahmed Khan Wasi Haider Butt Irum Matloob An Integrated Approach for Spatio-Temporal Cholera Disease Hotspot Relation Mining for Public Health Management in Punjab, Pakistan International Journal of Environmental Research and Public Health cholera dynamics health data analytic integrated health modeling framework public health spatio-temporal analysis |
author_facet |
Fatima Khalique Shoab Ahmed Khan Wasi Haider Butt Irum Matloob |
author_sort |
Fatima Khalique |
title |
An Integrated Approach for Spatio-Temporal Cholera Disease Hotspot Relation Mining for Public Health Management in Punjab, Pakistan |
title_short |
An Integrated Approach for Spatio-Temporal Cholera Disease Hotspot Relation Mining for Public Health Management in Punjab, Pakistan |
title_full |
An Integrated Approach for Spatio-Temporal Cholera Disease Hotspot Relation Mining for Public Health Management in Punjab, Pakistan |
title_fullStr |
An Integrated Approach for Spatio-Temporal Cholera Disease Hotspot Relation Mining for Public Health Management in Punjab, Pakistan |
title_full_unstemmed |
An Integrated Approach for Spatio-Temporal Cholera Disease Hotspot Relation Mining for Public Health Management in Punjab, Pakistan |
title_sort |
integrated approach for spatio-temporal cholera disease hotspot relation mining for public health management in punjab, pakistan |
publisher |
MDPI AG |
series |
International Journal of Environmental Research and Public Health |
issn |
1661-7827 1660-4601 |
publishDate |
2020-05-01 |
description |
Public health management can generate actionable results when diseases are studied in context with other candidate factors contributing to disease dynamics. In order to fully understand the interdependent relationships of multiple geospatial features involved in disease dynamics, it is important to construct an effective representation model that is able to reveal the relationship patterns and trends. The purpose of this work is to combine disease incidence spatio-temporal data with other features of interest in a mutlivariate spatio-temporal model for investigating characteristic disease and feature patterns over identified hotspots. We present an integrated approach in the form of a disease management model for analyzing spatio-temporal dynamics of disease in connection with other determinants. Our approach aligns spatio-temporal profiles of disease with other driving factors in public health context to identify hotspots and patterns of disease and features of interest in the identified locations. We evaluate our model against cholera disease outbreaks from 2015–2019 in Punjab province of Pakistan. The experimental results showed that the presented model effectively address the complex dynamics of disease incidences in the presence of other features of interest over a geographic area representing populations and sub populations during a given time. The presented methodology provides an effective mechanism for identifying disease hotspots in multiple dimensions and relation between the hotspots for cost-effective and optimal resource allocation as well as a sound reference for further predictive and forecasting analysis. |
topic |
cholera dynamics health data analytic integrated health modeling framework public health spatio-temporal analysis |
url |
https://www.mdpi.com/1660-4601/17/11/3763 |
work_keys_str_mv |
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