Learning the seasonality of disease incidences from empirical data

TL;DR

This paper explores how standard epidemic models can be used to understand and predict the seasonal patterns of disease incidences, using dengue data from Jakarta and applying Floquet theory for long-term behavior analysis.

Contribution

It demonstrates the application of periodic models and Floquet theory to empirical disease data, providing insights into the stability and future trends of disease incidence cycles.

Findings

Disease incidence in Jakarta is predicted to remain cyclical without improved surveillance.

Multiple optimization schemes yield consistent estimates of seasonal parameters.

Analytical and computational results suggest a stable positive orbit for disease incidence.

Abstract

Investigating the seasonality of disease incidences is very important in disease surveillance in regions with periodical climatic patterns. In lieu of the paradigm about disease incidences varying seasonally in line with meteorology, this work seeks to determine how well standard epidemic models can capture such seasonality for better forecasts and optimal futuristic interventions. Once incidence data are assimilated by a periodic model, asymptotic analysis in relation to the long-term behavior of the disease occurrences can be performed using the classical Floquet theory, which explains the stability of the existing periodic solutions. For a test case, we employed an IR model to assimilate weekly dengue incidence data from the city of Jakarta, Indonesia, which we present in their raw and moving-average-filtered versions. To estimate a periodic parameter toward performing the asymptotic analysis, eight optimization schemes were assigned returning magnitudes of the parameter that vary insignificantly across schemes. Furthermore, the computation results combined with the analytical results indicate that if the disease surveillance in the city does not improve, then the incidence will raise to a certain positive orbit and remain cyclical.