Predictive Surveillance Program for Disease Outbreaks
Author Information
Author(s): Witt Clara J, Richards Allen L, Masuoka Penny M, Foley Desmond H, Buczak Anna L, Musila Lillian A, Richardson Jason H, Colacicco-Mayhugh Michelle G, Rueda Leopoldo M, Klein Terry A, Anyamba Assaf, Small Jennifer, Pavlin Julie A, Fukuda Mark M, Gaydos Joel, Russell Kevin L
Primary Institution: Armed Forces Health Surveillance Center
Hypothesis
The AFHSC-GEIS predictive surveillance program aims to provide advanced awareness of emerging infectious disease threats to support timely public health actions.
Conclusion
The program has successfully predicted outbreaks of Rift Valley fever and is expanding its capabilities to other diseases.
Supporting Evidence
- The program has been effective in predicting Rift Valley fever outbreaks in multiple African countries.
- Data integration from various sources enhances the accuracy of disease outbreak predictions.
- Early alerts from the program have led to timely interventions that contained outbreaks.
Takeaway
This program helps to predict when and where diseases might break out, so we can be ready to stop them before they spread.
Methodology
The program integrates data from remote sensing, vector surveillance, animal disease-host monitoring, and human disease surveillance to generate alerts on disease outbreaks.
Limitations
The program's effectiveness depends on the quality and integration of data from various sources.
Digital Object Identifier (DOI)
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