TY - GEN
T1 - PandemCap
T2 - 8th Annual IEEE Workshop on Visual Analytics in Healthcare, VAHC 2017
AU - Yañez, Andrea
AU - Duggan, Jim
AU - Hayes, Conor
AU - Jilani, Musfira
AU - Connolly, Maire
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/6/15
Y1 - 2018/6/15
N2 - Pandemics or high impact epidemics are one of the biggest threats facing humanity today. While a complete elimination of the occurrence of such threats is improbable, it is possible to contain their impact by efficient management which in turn depends on effective decision-making. In the event of a pandemic the data flows are enormous and pose severe cognitive overload to the public health decision-makers. In this context, this paper presents PandemCap, an innovative decision support tool that can be used by the public health officials for making better and well informed decisions in the event of pandemics or high impact epidemics. PandemCap provides an interactive, flexible platform to public health decision-makers by making extensive use of techniques from the domains of visual analytics and epidemic modeling. In addition, the tool also allows for the study of the impact of various interventions or control measures such as the use of vaccines, anti-virals, hospital beds, and ventilators.
AB - Pandemics or high impact epidemics are one of the biggest threats facing humanity today. While a complete elimination of the occurrence of such threats is improbable, it is possible to contain their impact by efficient management which in turn depends on effective decision-making. In the event of a pandemic the data flows are enormous and pose severe cognitive overload to the public health decision-makers. In this context, this paper presents PandemCap, an innovative decision support tool that can be used by the public health officials for making better and well informed decisions in the event of pandemics or high impact epidemics. PandemCap provides an interactive, flexible platform to public health decision-makers by making extensive use of techniques from the domains of visual analytics and epidemic modeling. In addition, the tool also allows for the study of the impact of various interventions or control measures such as the use of vaccines, anti-virals, hospital beds, and ventilators.
UR - https://www.scopus.com/pages/publications/85050091581
U2 - 10.1109/VAHC.2017.8387497
DO - 10.1109/VAHC.2017.8387497
M3 - Conference contribution
AN - SCOPUS:85050091581
T3 - 2017 IEEE Workshop on Visual Analytics in Healthcare, VAHC 2017
SP - 24
EP - 30
BT - 2017 IEEE Workshop on Visual Analytics in Healthcare, VAHC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 1 October 2017
ER -
