Authors:
Jean-Claude Tshilenge Mfumu
;
Annabelle Mercier
;
Christine Verdier
and
Michel Occello
Affiliation:
Grenoble Alps University, France
Keyword(s):
Epidemic, District Health, Agent-based Model, Simulation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Development of Assistive Technology
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Healthcare Management Systems
;
Knowledge-Based Systems
;
Practice-based Research Methods for Healthcare IT
;
Symbolic Systems
Abstract:
Many contagious diseases occurred around sub-Saharan countries in the last decade due to the inefficiency of health structures to anticipate disease outbreaks. In a huge poorly-infrastructured country such as The Democratic Republic of Congo (DRC) with insufficient health staff and laboratory facilities, to provide quick response to an urgent case of epidemic is challenging especially facing the development of its rural areas. As DRC’s Health System has three levels (peripheral, regional and national levels), from producing health data at peripheral to national level that takes the decision, it can take time resulting in the spread of disease. The lack of communication between health centers and laboratory facilities in the same health zone does not contribute to regional riposte. This paper proposes to face this problem using an agent-centered approach to study through simulation how to improve the process. An experiment is described by agentifying two health zones on the same regio
nal level to show how it can reduce the decision time.. It consists of 2 peripheral coordination offices, 2 labs and 2 health zones the former with 12 health centers and the latter with 20 health zones. The interaction between these agents will provide a first model to be compared with the current system in other to reduce decision time.
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