Estimating parameters for a dynamical dengue model using genetic algorithms

• Main Authors: Estuar, Ma. Regina Justina E, Uyheng, Joshua, Rosales, John Clifford, Espina, Kennedy E
• Format: text
• Published: Archīum Ateneo 2018
• Subjects: Applied computing; Mathematical analysis; Health informatics; Discrete mathematics; Combinatorics; Analysis; Computer Sciences; Databases and Information Systems; Discrete Mathematics and Combinatorics; Health Information Technology
• Online Access: https://archium.ateneo.edu/discs-faculty-pubs/21; https://dl.acm.org/doi/10.1145/3205651.3205716
• Institution: Ateneo De Manila University

Dynamical models are a mathematical framework for understanding the spread of a disease using various epidemiological parameters. However, in data-scarce regions like the Philippines, local estimates of epidemiological parameters are difficult to obtain because methods to obtain these values are costly or inaccessible. In this paper, we employ genetic algorithms trained with novel fitness functions as a low-cost, data-driven method to estimate parameters for dengue incidence in the Western Visayas Region of the Philippines (2011-2016). Initial results show good ht between monthly historical values and model outputs using parameter estimates, with a best Pearson correlation of 0.86 and normalized error of 0.65 over the selected 72-month period. Furthermore, we demonstrate a quality assessment procedure for selecting biologically feasible and numerically stable parameter estimates. Implications of our findings are discussed in both epidemiological and computational contexts, highlighting their application in FASSSTER, an integrated syndromic surveillance system for infectious diseases in the Philippines.