Homeland defense Information Networks Geopolitics Climate Remote sensors Geographic data Infectious diseases University of Pennsylvania University of Pennsylvania itute for Strategic Threat Analysis and Response (I tute for Strategic Threat Analysis and Response (I
University of Pennsylvania Institute for Strategic Threat Analysis and Response (ISTAR). Climate. Information Networks. Geopolitics. Homeland defense. Infectious diseases. Geographic data. Remote sensors. Contents Foreword — Don de Savigny, Luc Loslier, and Jim Chauvin - PowerPoint PPT Presentation
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Homelanddefense
Information NetworksGeopolitics
Climate
Remote sensors
Geographic dataInfectious diseases
University of PennsylvaniaUniversity of PennsylvaniaInstitute for Strategic Threat Analysis and Response (ISTAR)Institute for Strategic Threat Analysis and Response (ISTAR)
Contents Foreword — Don de Savigny, Luc Loslier, and Jim Chauvin Preface — Don de Savigny, Lori Jones-Arsenault, and Pandu Wijeyaratne Context •The present state of GIS and future trends — Steven Reader •GIS from a health perspective — Luc Loslier •Spatial and temporal analysis of epidemiological data — Flavio Fonseca Nobre and Marilia Sa Carvalho Case studies from the South •Towards a rural information system — David le Sueur, Sipho Ngxongo, Maria Stuttaford, Brian Sharp, Rajendra Maharaj, Carrin Martin, and Dawn Brown •A GIS approach to the determination of catchment populations around Local Health Facilities in Developing Countries — H.M. Oranga •GIS management tools for the control of tropical diseases: applications in Botswana, Senegal, and Morocco
— Isabelle Nuttall, D.W. Rumisha, T.R.K. Pilatwe, H.I. Ali, S.S. Mokgweetsinyana, A.H. Sylla, and I. Talla •The use of low-cost remote sensing and GIS for identifying and monitoring the environmental factors associated with vector-borne disease transmission — S.J. Connor, M.C. Thompson, S. Flasse, and J.B. Williams •GIS for the study and control of malaria — Gustavo Bretas •Spatial analysis of malaria risk in an endemic region of Sri Lanka — D.M. Gunawardena, Lal Muthuwattac, S. Weerasingha, J. Rajakaruna, Wasantha Udaya Kumara, Tilak Senanayaka, P. Kumar Kotta, A.R. Wickremasinghe, Richard Carter, and Kamini N. Mendis •Diagnostic features of malaria transmission in Nadiad using remote sensing and GIS — M.S. Malhotra and Aruna Srivastava •Monitoring zoonotic cutaneous leishmaniasis with GIS — L. Mbarki, A. Ben Salah, S. Chlif, M.K. Chahed, A. Balma, N. Chemam, A. Garraoui, and R. Ben-Ismail •Use of RAISON for rural drinking water sources management — C.W. Wang
The bestThe best fit to the RVF outbreak data was achieved when equatorial fit to the RVF outbreak data was achieved when equatorial PacificPacific and Indian Ocean SST and NDVI anomaly data were used and Indian Ocean SST and NDVI anomaly data were used together.together.These data could have been used to successfully predict each ofThese data could have been used to successfully predict each of the the three RVF outbreaks that occurred between 1982 and 1998 withoutthree RVF outbreaks that occurred between 1982 and 1998 without
predicting any false RVF events for an overall prediction of riskpredicting any false RVF events for an overall prediction of risk of of 100%. 100%. Predictive models that use either SOI and Indian OceanPredictive models that use either SOI and Indian Ocean or NDVI and or NDVI and Indian Ocean anomaly data would have predicted allIndian Ocean anomaly data would have predicted all three RVF events three RVF events but falsely predicted either one or two diseasebut falsely predicted either one or two disease events, respectively.events, respectively.
Climate and Satellite Indicators to Forecast Rift Valley Fever Climate and Satellite Indicators to Forecast Rift Valley Fever Epidemics in Kenya Epidemics in Kenya Kenneth J. Linthicum, Kenneth J. Linthicum, 1*1* Assaf Anyamba, Assaf Anyamba, 2*2* Compton J. Tucker, Compton J. Tucker, 22 Patrick W. Kelley, Patrick W. Kelley, 11 Monica F. Monica F. Myers, Myers, 22 Clarence J. Peters Clarence J. Peters 33
Global and local syndromic surveillance—human and Global and local syndromic surveillance—human and animalanimal
Genomic characterization of species and strains of Genomic characterization of species and strains of organismsorganisms
Global and local micro-organism surveillanceGlobal and local micro-organism surveillance
Distributed sensorsDistributed sensors
Massively networked information systemsMassively networked information systems
EducationEducation
Research/Education AgendaResearch/Education AgendaDynamic Integration and Analysis of Data SetsDynamic Integration and Analysis of Data Sets
DataDataGeographic Geographic Syndromic Syndromic MicroorganismsMicroorganismsClimateClimatePolitical alignments—state and non-statePolitical alignments—state and non-state
Technology--theoryTechnology--theorySensors—hybrid systemsSensors—hybrid systemsNetwork communication—artificial intelligenceNetwork communication—artificial intelligenceSecurity—authentication, privacySecurity—authentication, privacyConflict—asymmetric, multi-agent game theoryConflict—asymmetric, multi-agent game theory
EducationEducation(K-12)(K-12)undergraduatesundergraduatesgraduate and professional studentsgraduate and professional studentspractitionerspractitioners