HomelanddefenseInformation NetworksGeopoliticsClimateRemote sensorsGeographic dataInfectious diseasesUniversity of PennsylvaniaInstitute 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

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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 1982and 1998without 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.

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

Science. 1999 Jul 16;285(5426):397-400.

The Sverdlovsk Anthrax Outbreak

New initiatives

Global and local syndromic surveillancehuman 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 AgendaDynamic Integration and Analysis of Data Sets

DataGeographic Syndromic MicroorganismsClimatePolitical alignmentsstate and non-stateTechnology--theorySensorshybrid systemsNetwork communicationartificial intelligenceSecurityauthentication, privacyConflictasymmetric, multi-agent game theory

Education(K-12)undergraduatesgraduate and professional studentspractitioners

Policy