E-Research and GeoComputation in Public Health

Computer technology advancements are further shaping research, practice and related training in science and engineering. The chapter is seen as a key e-science and e-research study. With the rapid development of the Internet of Things (IoT) and wearable technology, huge numbers of health-related data are generated every moment. Recent technologies like grid and cloud computing have resulted in even more than an increase in treatment efficiency. The features of this new e-research approach are discussed using the related examples in this chapter. It is argued that in a geospatial sense, specific approaches can be beneficial. The last portion of the chapter focused on various ways of growing availability of powerful e-research resources for GeoComputing and expanding.

[1]  P. Farmer,et al.  Community-Based Health Financing and Child Stunting in Rural Rwanda. , 2016, American journal of public health.

[2]  P. Zandbergen,et al.  Influence of geocoding quality on environmental exposure assessment of children living near high traffic roads , 2007, BMC public health.

[3]  E. S. Page CONTINUOUS INSPECTION SCHEMES , 1954 .

[4]  Tanesh Kumar,et al.  Blockchain Utilization in Healthcare: Key Requirements and Challenges , 2018, 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom).

[5]  A. Craft,et al.  INVESTIGATION OF LEUKAEMIA CLUSTERS BY USE OF A GEOGRAPHICAL ANALYSIS MACHINE , 1988, The Lancet.

[6]  Zhenlong Li,et al.  Geospatial Service Web: towards integrated cyberinfrastructure for GIScience , 2012, Geo spatial Inf. Sci..

[7]  Anuj Karpatne,et al.  Spatio-Temporal Data Mining , 2017, ACM Comput. Surv..

[8]  Vipin Kumar,et al.  A Comparative Study Of Algorithms For Land Cover Change , 2010, CIDU.

[9]  Pierre Goovaerts,et al.  International Journal of Health Geographics Geostatistical Analysis of Disease Data: Visualization and Propagation of Spatial Uncertainty in Cancer Mortality Risk Using Poisson Kriging and P-field Simulation , 2022 .

[10]  Noel A Cressie,et al.  Statistics for Spatio-Temporal Data , 2011 .

[11]  G. Rushton,et al.  Exploratory spatial analysis of birth defect rates in an urban population. , 1996, Statistics in medicine.

[12]  L. Anselin Local Indicators of Spatial Association—LISA , 2010 .

[13]  Alan H. Strahler,et al.  Fuzzy Neural Network Classification of Global Land Cover from a 1° AVHRR Data Set , 1999 .

[14]  Shawn T. Brown,et al.  FRED (A Framework for Reconstructing Epidemic Dynamics): an open-source software system for modeling infectious diseases and control strategies using census-based populations , 2013, BMC Public Health.

[15]  Joaquín B. Ordieres Meré,et al.  Activity-aware essential tremor evaluation using deep learning method based on acceleration data. , 2019, Parkinsonism & related disorders.

[16]  L. De Cola Spatial Forecasting of Disease Risk and Uncertainty , 2002 .

[17]  Fulvio Corno,et al.  A Healthcare Support System for Assisted Living Facilities: An IoT Solution , 2016, 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC).

[18]  Shaowen Wang,et al.  A communication-aware framework for parallel spatially explicit agent-based models , 2013, Int. J. Geogr. Inf. Sci..

[19]  Daniel A. Griffith,et al.  Impacts of Positional Error on Spatial Regression Analysis: A Case Study of Address Locations in Syracuse, New York , 2007, Trans. GIS.

[20]  Leiyu Shi,et al.  Public Health, Social Determinants of Health, and Public Policy , 2009 .

[21]  Song Guo,et al.  Adaptive and Fault-Tolerant Data Processing in Healthcare IoT Based on Fog Computing , 2020, IEEE Transactions on Network Science and Engineering.

[22]  Andreas Menychtas,et al.  Recommender Systems for IoT Enabled m-Health Applications , 2018, AIAI.

[23]  Shawn T. Brown,et al.  School closure as an influenza mitigation strategy: how variations in legal authority and plan criteria can alter the impact , 2012, BMC Public Health.

[24]  M. Kwan Space-time and integral measures of individual accessibility: a comparative analysis using a point-based framework , 2010 .

[25]  Wei Xiang,et al.  Internet of Things for Smart Healthcare: Technologies, Challenges, and Opportunities , 2017, IEEE Access.

[26]  E. S. Gardner EXPONENTIAL SMOOTHING: THE STATE OF THE ART, PART II , 2006 .

[27]  Mohan M. Trivedi,et al.  Learning trajectory patterns by clustering: Experimental studies and comparative evaluation , 2009, CVPR.

[28]  E. Lesaffre,et al.  Disease mapping and risk assessment for public health. , 1999 .

[29]  Sandeep K. Sood,et al.  Exploring Temporal Analytics in Fog-Cloud Architecture for Smart Office HealthCare , 2019, Mob. Networks Appl..

[30]  Farrokh Alemi,et al.  Enhancing spatial detection accuracy for syndromic surveillance with street level incidence data , 2010, International journal of health geographics.

[31]  S. Openshaw Ecological Fallacies and the Analysis of Areal Census Data , 1984, Environment & planning A.

[32]  Seungmin Rho,et al.  Fog Computing-Based IoT for Health Monitoring System , 2018, J. Sensors.

[33]  Martin Charlton,et al.  A Mark 1 Geographical Analysis Machine for the automated analysis of point data sets , 1987, Int. J. Geogr. Inf. Sci..

[34]  Graham Clarke,et al.  Examining the factors associated with depression at the small area level in Ireland using spatial microsimulation techniques , 2010 .

[35]  Vipin Kumar,et al.  Discovery of climate indices using clustering , 2003, KDD '03.

[36]  Kay Caldwell,et al.  Developing a framework for critiquing health research: an early evaluation. , 2011, Nurse education today.

[37]  P A Zandbergen,et al.  Error propagation models to examine the effects of geocoding quality on spatial analysis of individual-level datasets. , 2012, Spatial and spatio-temporal epidemiology.

[38]  Vipin Kumar,et al.  Monitoring global forest cover using data mining , 2011, TIST.

[39]  Zhiliang Zhu,et al.  Mapping Mountain Pine Beetle Mortality through Growth Trend Analysis of Time-Series Landsat Data , 2014, Remote. Sens..

[40]  David G Steel,et al.  Constraint Choice for Spatial Microsimulation , 2016 .

[41]  Gerard Rushton,et al.  Modeling the probability distribution of positional errors incurred by residential address geocoding , 2007 .

[42]  Birgit Müller,et al.  Simulation Models for Socioeconomic Inequalities in Health: A Systematic Review , 2013, International journal of environmental research and public health.

[43]  Bahae Abidi,et al.  Wireless Sensor Networks in Biomedical: Wireless Body Area Networks , 2017 .

[44]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[45]  R. Logesh,et al.  Fog computing-based intelligent healthcare system for the detection and prevention of mosquito-borne diseases , 2019, Comput. Hum. Behav..

[46]  R. Webster,et al.  Kriging: a method of interpolation for geographical information systems , 1990, Int. J. Geogr. Inf. Sci..

[47]  Paul A. Longley,et al.  Geocomputation: a primer , 1998 .

[48]  Victor I. Chang,et al.  Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare , 2018, Future Gener. Comput. Syst..

[49]  Logesh Ravi,et al.  Fog-assisted personalized healthcare-support system for remote patients with diabetes , 2019, J. Ambient Intell. Humaniz. Comput..

[50]  Xi Chen,et al.  Global Monitoring of Inland Water Dynamics: State-of-the-Art, Challenges, and Opportunities , 2016, Computational Sustainability.

[51]  Geoffrey M Jacquez,et al.  Local indicators of geocoding accuracy (LIGA): theory and application , 2009, International journal of health geographics.

[52]  Haeng-Kon Kim,et al.  From Cloud to Fog and IoT-Based Real-Time U-Healthcare Monitoring for Smart Homes and Hospitals , 2016 .

[53]  E. Ghysels,et al.  Detecting Multiple Breaks in Financial Market Volatility Dynamics , 2002 .

[54]  David Palma,et al.  Fog Computing in Healthcare–A Review and Discussion , 2017, IEEE Access.

[55]  Dino Pedreschi,et al.  Trajectory pattern mining , 2007, KDD '07.

[56]  Xun Shi,et al.  Selection of bandwidth type and adjustment side in kernel density estimation over inhomogeneous backgrounds , 2010, Int. J. Geogr. Inf. Sci..

[57]  Liangpei Zhang,et al.  Classification of High Spatial Resolution Imagery Using Improved Gaussian Markov Random-Field-Based Texture Features , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[58]  Juan C. Duque,et al.  Supervised Regionalization Methods: A Survey , 2007 .

[59]  Jae-Gil Lee,et al.  Trajectory clustering: a partition-and-group framework , 2007, SIGMOD '07.

[60]  Thomas O Talbot,et al.  Positional error in automated geocoding of residential addresses , 2003, International journal of health geographics.

[61]  Lee De Cola,et al.  Spatial Forecasting of Disease Risk and Uncertainty , 2002 .

[62]  Hyeonjoon Moon,et al.  A Survey on Internet of Things and Cloud Computing for Healthcare , 2019, Electronics.

[63]  L. Valinsky,et al.  Near real-time space-time cluster analysis for detection of enteric disease outbreaks in a community setting. , 2016, The Journal of infection.

[64]  ZhengXiaochen,et al.  The development of intelligent healthcare in China. , 2015 .

[65]  Danny Dorling,et al.  Using geographical information systems and spatial microsimulation for the analysis of health inequalities , 2006, Health Informatics J..

[66]  Mojtaba Alizadeh,et al.  The application of internet of things in healthcare: a systematic literature review and classification , 2018, Universal Access in the Information Society.

[67]  Gerard Rushton,et al.  Geocoding accuracy and the recovery of relationships between environmental exposures and health , 2008, International journal of health geographics.

[68]  Kimberley L Edwards,et al.  The design and validation of a spatial microsimulation model of obesogenic environments for children in Leeds, UK: SimObesity. , 2009, Social science & medicine.

[69]  Travis C Porco,et al.  Surveillance Tools Emerging From Search Engines and Social Media Data for Determining Eye Disease Patterns. , 2016, JAMA ophthalmology.

[70]  James D Sargent,et al.  Alcohol retail density and demographic predictors of health disparities: a geographic analysis. , 2010, American journal of public health.

[71]  Hongming Cai,et al.  The design of an m-Health monitoring system based on a cloud computing platform , 2017, Enterp. Inf. Syst..

[72]  Neeraj Kumar,et al.  Fog computing for Healthcare 4.0 environment: Opportunities and challenges , 2018, Comput. Electr. Eng..

[73]  Mingzhe Jiang,et al.  Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: A fog computing approach , 2018, Future Gener. Comput. Syst..

[74]  Jiawei Han,et al.  CLARANS: A Method for Clustering Objects for Spatial Data Mining , 2002, IEEE Trans. Knowl. Data Eng..

[75]  Kyung-Sup Kwak,et al.  The Internet of Things for Health Care: A Comprehensive Survey , 2015, IEEE Access.

[76]  Geza Benke,et al.  Artificial Intelligence and Big Data in Public Health , 2018, International journal of environmental research and public health.

[77]  Kaarin J Anstey,et al.  Can social dancing prevent falls in older adults? a protocol of the Dance, Aging, Cognition, Economics (DAnCE) fall prevention randomised controlled trial , 2013, BMC Public Health.

[78]  Shashi Shekhar,et al.  Identifying patterns in spatial information: A survey of methods , 2011, WIREs Data Mining Knowl. Discov..

[79]  P. Elliott,et al.  Geographical epidemiology of prostate cancer in Great Britain , 2002, International journal of cancer.

[80]  Richard D. Mrozinski,et al.  Subject loss in spatial analysis of breast cancer. , 1999, Health & place.

[81]  Joanne S Colt,et al.  Positional Accuracy of Two Methods of Geocoding , 2005, Epidemiology.

[82]  L. K. Hansen,et al.  On Clustering fMRI Time Series , 1999, NeuroImage.

[83]  Yaniv Assaf,et al.  Cluster analysis of resting-state fMRI time series , 2009, NeuroImage.

[84]  Nabil Ahmed Sultan,et al.  Making use of cloud computing for healthcare provision: Opportunities and challenges , 2014, Int. J. Inf. Manag..

[85]  Vipin Kumar,et al.  Discovering Groups of Time Series with Similar Behavior in Multiple Small Intervals of Time , 2014, SDM.

[86]  R. Spear,et al.  Integrating uncertainty and interindividual variability in environmental risk assessment. , 1987, Risk analysis : an official publication of the Society for Risk Analysis.

[87]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[88]  T. Talbot,et al.  Data quality and the spatial analysis of disease rates: congenital malformations in New York State. , 2002, Health & place.

[89]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[90]  Rémi Bastide,et al.  Engineering IoT Healthcare Applications: Towards a Semantic Data Driven Sustainable Architecture , 2016, eHealth 360°.

[91]  Andrew Harvey,et al.  Forecasting, structural time series models and the Kalman filter: Selected answers to exercises , 1990 .

[92]  Wei Shen,et al.  A Smart Gateway for Health Care System Using Wireless Sensor Network , 2010, 2010 Fourth International Conference on Sensor Technologies and Applications.

[93]  P. Nilsen Making sense of implementation theories, models and frameworks , 2015, Implementation Science.

[94]  Harry H. Kelejian,et al.  A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model , 1999 .

[95]  Aron Culotta,et al.  Towards detecting influenza epidemics by analyzing Twitter messages , 2010, SOMA '10.

[96]  Sandeep K. Sood,et al.  A Fog-Based Healthcare Framework for Chikungunya , 2018, IEEE Internet of Things Journal.

[97]  Julian Besag,et al.  The Detection of Clusters in Rare Diseases , 1991 .

[98]  Empirical Process of the Squared Residuals of an ARCH Sequence , 2001 .

[99]  P. Elliott,et al.  Spatial Epidemiology: Current Approaches and Future Challenges , 2004, Environmental health perspectives.

[100]  Jun Chen,et al.  Advances in Photogrammetry, Remote Sensing and Spatial Information Sciences: 2008 ISPRS Congress Book , 2008 .

[101]  Peter Corcoran,et al.  Mobile-Edge Computing and the Internet of Things for Consumers: Extending cloud computing and services to the edge of the network , 2016, IEEE Consumer Electronics Magazine.

[102]  Jiang Gui,et al.  Mapping Disease at an Approximated Individual Level Using Aggregate Data: A Case Study of Mapping New Hampshire Birth Defects , 2013, International journal of environmental research and public health.

[103]  Marimuthu Palaniswami,et al.  Internet of Things (IoT): A vision, architectural elements, and future directions , 2012, Future Gener. Comput. Syst..

[104]  Mark Monmonier,et al.  Cartography: uncertainty, interventions, and dynamic display , 2006 .

[105]  Xun Shi,et al.  INTERNATIONAL JOURNAL OF HEALTH GEOGRAPHICS METHODOLOGY Density estimation and adaptive bandwidths: , 2022 .

[106]  Joe Weber,et al.  Individual accessibility and distance from major employment centers: An examination using space-time measures , 2003, J. Geogr. Syst..

[107]  Sateesh Addepalli,et al.  Fog computing and its role in the internet of things , 2012, MCC '12.

[108]  P. Shit,et al.  Geospatial Analysis of Public Health , 2018 .

[109]  Andrew Harvey,et al.  Forecasting, Structural Time Series Models and the Kalman Filter , 1990 .

[110]  Marisol García-Valls,et al.  Accelerating smart eHealth services execution at the fog computing infrastructure , 2020, Future Gener. Comput. Syst..

[111]  Shaowen Wang,et al.  Computational and data sciences for health-GIS , 2015, Ann. GIS.

[112]  Richard J Jackson,et al.  Economic gains resulting from the reduction in children's exposure to lead in the United States. , 2002, Environmental health perspectives.

[113]  Sylvia Richardson,et al.  Improving ecological inference using individual‐level data , 2006, Statistics in medicine.

[114]  Dae-Hyeong Kim,et al.  Multifunctional wearable devices for diagnosis and therapy of movement disorders. , 2014, Nature nanotechnology.

[115]  N. Arunkumar,et al.  Enabling technologies for fog computing in healthcare IoT systems , 2019, Future Gener. Comput. Syst..

[116]  M. Kulldorff A spatial scan statistic , 1997 .

[117]  Jeremy Ginsberg,et al.  Detecting influenza epidemics using search engine query data , 2009, Nature.

[118]  Stan Openshaw,et al.  Using a Geographical Explanations Machine to Explore Spatial Factors relating to Primary School Performance , 2001 .

[119]  L. Pickle,et al.  Geographic bias related to geocoding in epidemiologic studies , 2005, International journal of health geographics.

[120]  Andrew B. Lawson,et al.  Statistical Methods in Spatial Epidemiology , 2001 .

[121]  S. Fotheringham,et al.  Geographically Weighted Regression , 1998 .

[122]  Ju Ren,et al.  Fog-Enabled Smart Health: Toward Cooperative and Secure Healthcare Service Provision , 2019, IEEE Communications Magazine.

[123]  Amit P. Sheth,et al.  Toward Practical Privacy-Preserving Analytics for IoT and Cloud-Based Healthcare Systems , 2018, IEEE Internet Computing.

[124]  M. Kwan,et al.  Evaluating the Effects of Geographic Contexts on Individual Accessibility: A Multilevel Approach1 , 2003 .

[125]  David Lillethun,et al.  Mobile fog: a programming model for large-scale applications on the internet of things , 2013, MCC '13.

[126]  Elsie R. Pamuk,et al.  Health, United States, 2001; with Urban and rural health chartbook , 2001 .

[127]  C. Woodcock,et al.  Continuous monitoring of forest disturbance using all available Landsat imagery , 2012 .

[128]  M. P. van den Heuvel,et al.  Normalized Cut Group Clustering of Resting-State fMRI Data , 2008, PloS one.