Modelling Dengue Spread as Dynamic Networks of Time and Location Changes

Since local human movements can influence dengue spread, a network-based prediction model considers the dynamic relation between dengue case incidences and their location over time. Some approaches often generated the networks in a certain period with a single spanning time until several months or in one year, called static networks. However, one annual-based dengue spread model could have different simulations depending on the selected months to show different dynamicity. Other approaches do not involve any networks for generating the spread models but employ them for validating the models with simulations. Considering the evolution of dengue circumstances that could quickly change between periods, we proposed a Dengue Spread Dynamic Network (DSDN) model with some timespan and location boundaries variants. DSDN includes five network models with nodes representing localities and links showing dengue spread which varied every day depending on infections presence and environmental conditions in a certain period. With our proposed method, daily dengue spread from one location to another can be predicted based on the location-based incidence historical data as outbreaks prevention initiative. We also analyzed how dengue spreads differently in outbreak and non-outbreak periods using Dynamic Network Link Prediction (DNLP) method. From our experiments result, Neighbor Network which modeled that dengue only spreads between neighboring localities produced an accuracy of 92.54% for the entire period. When applied only to outbreak data, there was a performance increase of 3.39 points, which suggested that link prediction performs better when dengue is rapidly spreading. In addition to that, our experiments concluded that dengue potentially spreads to a location with no current infections if local incidence often occurred in the past.