An Integrated Approach for Spatio-Temporal Cholera Disease Hotspot Relation Mining for Public Health Management in Punjab, Pakistan

Abstract

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.

Relationships among COVID-19 Prevention Practices, Risk Perception and Individual Characteristics: A Temporal Analysis

“…Such a relationship became statistically insignificant in April (lockdown stage), when age and attitude towards public health interventions started to exert stronger effects in predicting prevention behaviors. The different levels of prevention between the younger (19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34) and older age groups (55+) became more significant in June (reopening stage). Despite the general patterns, much complexity exists in how specific factors shape different types of prevention practices.…”

“…It is necessary to consider the temporal features of infectious diseases and their public health interventions in shaping prevention behaviors with different preconditions and individual characteristics [2]. However, the temporal aspect of prevention behavior and risk perception has been largely understudied [2,25] First, infectious disease prevalence carries strong spatiotemporal features that have direct impacts on local public health intervention strategies [26][27][28][29] and potentially affect individual prevention towards infectious disease [14]. The ongoing COVID-19 pandemic has highlighted the importance of bringing in a temporal perspective, since public health measures in many countries have evolved rapidly during the first wave of the pandemic.…”

Association of sociodemographic and environmental factors with spatial distribution of tuberculosis cases in Gombak, Selangor, Malaysia

“…Understanding the spatial distribution of a disease is crucial in improving interventions and guiding resource allocation in disease management. Many spatial statistic methods such as spatial autocorrelation analysis (Moran's I), hotspot analysis (Getis-Ord Gi � ), and space-time scan statistic (SaTScan) methods have been used to assess the spatial pattern of TB [34,35] and other infectious diseases [35][36][37]. However, these methods were limited to the assessment of spatial variation and they were unable to show the potential factors that influence the models.…”

Multiple Disease Hotspot Mining for Public Health Informatics in Resource Starved Settings: Study of Communicable Diseases in Punjab, Pakistan

“…Many surveillance systems support early warning techniques for public health authorities [15], [16]. Existing work is available using statistical and AI based methods to detect spatio-temporal disease hotspots including techniques eigenspot method [17], ScanStatistics [18], Mstatistic [5] and using machine learning algorithms [19]. The term, cluster alarms is also used to mean location with accessive disease intensity.…”