A combination of incidence data and mobility proxies from social media predicts the intra-urban spread of dengue in Yogyakarta, Indonesia

Abstract

Only a few studies have investigated the potential of using geotagged social media data for predicting the patterns of spatio-temporal spread of vector-borne diseases. We herein demonstrated the role of human mobility in the intra-urban spread of dengue by weighting local incidence data with geo-tagged Twitter data as a proxy for human mobility across 45 neighborhoods in Yogyakarta city, Indonesia. To estimate the dengue virus importation pressure in each study neighborhood monthly, we developed an algorithm to estimate a dynamic mobility-weighted incidence index (MI), which quantifies the level of exposure to virus importation in any given neighborhood. Using a Bayesian spatio-temporal regression model, we estimated the coefficients and predictiveness of the MI index for lags up to 6 months. Specifically, we used a Poisson regression model with an unstructured spatial covariance matrix. We compared the predictability of the MI index to that of the dengue incidence rate over the preceding months in the same neighborhood (autocorrelation) and that of the mobility information alone. We based our estimates on a volume of 1·302·405 geotagged tweets (from 118·114 unique users) and monthly dengue incidence data for the 45 study neighborhoods in Yogyakarta city over the period from August 2016 to June 2018. The MI index, as a standalone variable, had the highest explanatory power for predicting dengue transmission risk in the study neighborhoods, with the greatest predictive ability at a 3-months lead time. The MI index was a better predictor of the dengue risk in a neighborhood than the recent transmission patterns in the same neighborhood, or just the mobility patterns between neighborhoods. Our results suggest that human mobility is an important driver of the spread of dengue within cities when combined with information on local circulation of the dengue virus. The geotagged Twitter data can provide important information on human mobility patterns to improve our understanding of the direction and the risk of spread of diseases, such as dengue. The proposed MI index together with traditional data sources can provide useful information for the development of more accurate and efficient early warning and response systems.

Fig 1

The map (panel a) and the adjacency matrix (panel b) of the 45 study neighborhoods (rows and columns identify areas; squares identify neighborhoods) in Yogyakarta municipality, Indonesia.

Fig 2. Time-series of reported dengue cases (Den) between August 2016 and June 2018 for the 45 study neighborhoods in Yogyakarta municipality, Indonesia.

Fig 3. The TW index capturing the temporal pattern of the aggregated total monthly mobility into each of the 45 study neighborhoods in Yogyakarta municipality, Indonesia, August 2016—June 2018.

Fig 4. The MI index estimating the temporal pattern of the aggregated importations into each of the 45 study neighborhoods in Yogyakarta, Indonesia, August 2016—June 2018.

Fig 5. The crude and adjusted coefficients for the Den and MI models for lag times 1 to 6.