Estimating parameters for a dynamical dengue model using genetic algorithms
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.
oshua Uyheng, John Clifford Rosales, Kennedy Espina, and Ma. Regina Justina Estuar. 2018. Estimating parameters for a dynamical dengue model using genetic algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO ’18). Association for Computing Machinery, New York, NY, USA, 310–311. DOI:https://doi.org/10.1145/3205651.3205716