Model-Based Monitoring of Dengue Spreading

Abstract

The problem of designing a system for monitoring the spread of dengue in a mixed population of humans and mosquitos is addressed. For this purpose a model–based observer approach is employed based on a model that describes the actual number of infected humans (with constant population size) and mosquitos (with time–varying population size) and which has been previously validated with data from different regions of the state of Morelos in Mexico using the number of new reported cases as available measurement. This model has time–varying reproduction parameters for the mosquitos and thus enables to account for different climatic conditions in the course of the year (in particular the rain and dry periods) that affect the mosquito population size. An analysis of the structural observability, based on the preliminary assumption of a continuous measurement of the accumulative number of reported cases of human infections is carried out to motivate a reduced order model that is structurally observable. For this reduced order model a continuous–discrete extended Kalman Filter is designed as basis for the monitoring and prediction scheme. The approach is validated using real data from Cuernavaca, Morelos, Mexico, showing a high potential for future developments towards automated monitoring schemes.

Keywords

• Epidemics
• vector-borne disease modeling
• forecast of dengue cases
• population dynamics
• system identification
• Kalman filter