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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 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 http://www.idrc.ca/acb/showdetl.cfm?&DID=6&Product_ID=495&CATID=15 Climate and Satellite Indicators to Forecast Rift Valley Fever Epidemics in Kenya Kenneth J. Linthicum, 1* Assaf Anyamba, 2* Compton J. Tucker, 2 Patrick W. Kelley, 1 Monica F. Myers, 2 Clarence J. Peters 3 The best fit to the RVF outbreak data was achieved when equatorial Pacific and Indian Ocean SST and NDVI anomaly data were used together. These data could have been used to successfully predict each of the three RVF outbreaks that occurred between 1982 and 1998 without predicting any false RVF events for an overall prediction of risk of 100%. Predictive models that use either SOI and Indian Ocean or NDVI and Indian Ocean anomaly data would have predicted all three RVF events but falsely predicted either one or two disease events, respectively. Science. 1999 Jul 16;285(5426):397-400. The Sverdlovsk Anthrax Outbreak New initiatives Global and local syndromic surveillance—human and animal Genomic characterization of species and strains of organisms Global and local micro-organism surveillance Distributed sensors Massively networked information systems Education Research/Education Agenda Dynamic Integration and Analysis of Data Sets Data Geographic Syndromic Microorganisms Climate Political alignments—state and non-state Technology--theory Sensors—hybrid systems Network communication—artificial intelligence Security—authentication, privacy Conflict—asymmetric, multi-agent game theory Education (K-12) undergraduates graduate and professional students practitioners Policy