Predicting the dengue cluster outbreak dynamics in Yogyakarta, Indonesia: a modelling study

Abstract

Background: Human mobility and climate conditions are recognised key drivers of dengue transmission, but their combined and individual role in the local spatiotemporal clustering of dengue cases is not well understood. This study investigated the effects of human mobility and weather conditions on dengue risk in an urban area in Yogyakarta, Indonesia.

Methods: We established a Bayesian spatiotemporal model for neighbourhood outbreak prediction and evaluated the performances of two different approaches for constructing an adjacency matrix: one based on geographical proximity and the other based on human mobility patterns. We used population, weather conditions, and past dengue cases as predictors using a flexible distributed lag approach. The human mobility data were estimated based on proxies from social media. Unseen data from February 2017 to January 2020 were used to estimate the one-month ahead prediction accuracy of the model.

Findings: When human mobility proxies were included in the spatial covariance structure, the model fit improved in terms of the log score (from 1.748 to 1.561) and the mean absolute error (from 0.676 to 0.522) based on the validation data. Additionally, showed only few observations outside the credible interval of predictions (1.48%) and weather conditions were not found to contribute additionally to the clustering of cases at this scale.

Interpretation: The study shows that it is possible to make highly accurate predictions of the within-city cluster dynamics of dengue using mobility proxies from social media combined with disease surveillance data. These insights are important for proactive and timely outbreak management of dengue.

Funding: Swedish Research Council Formas, Umeå Centre for Global Health Research, Swedish Council for Working Life and Social Research, Swedish research council VINNOVA and Alexander von Humboldt Foundation (Germany).

Keywords: Arbovirus; Big data; Climate Variability; Climate services; DLNM; Dengue; Early warning; Epidemic; Forecasting model; INLA; Population mobility; Rainfall; Social media; Spatiotemporal model; Temperature; Twitter; Weather.

Fig. 1

Heat-map of reported dengue incidence rate (per 100,000 population) for the 45 urban villages in Yogyakarta municipality between February 2013 and January 2020.

Fig. 2

Adjacency matrix based on (a) the administrative map, (b) geotagged social media data: rows and columns identify urban villages, squares identify neighbours.

Fig. 3

The comparison of posterior predictive median and 95% credible interval to observed cases. The black line represents median values, and the gray shading represents 95% credible intervals. The dots represent the observed values: red means the values lie between credible intervals, and blue indicates otherwise.