Time Series Clustering Applied to Eco-Epidemiology: the case of Aedes aegypti in Córdoba, Argentina

Spatial Distribution of Aedes aegypti Oviposition Temporal Patterns and Their Relationship with Environment and Dengue Incidence

“…Within this variability, our time series clustering approach, allowed us to identify 3 different temporal patterns over the city in all 3 periods considered (full period, 2017–2018 and 2018–2019). Most algorithms, however, identified one large group and two smaller ones, similarly to the preliminary results presented in [ 32 ].…”

“…The approach we propose here is aimed at contributing to the understanding of mosquito activity patterns within urban areas in contrast to other studies that aggregate both mosquito and environmental data and consider cities as homogeneous packs and predict mean oviposition or abundance values [ 22 , 23 , 43 , 50 , 51 ]. In this regard, the only studies assuming differences within cities regarding different temporal patterns are [ 24 , 32 ]. The former, however, does not use time series clustering but a regular k-means in order to group data before training deep learning LSTM networks and forecasting adult mosquito abundance by group.…”

“…Examples in the literature commonly include the use of Dynamic Time Warping (DTW) distance combined with a clustering method to assess patterns in economic time series [ 28 ], crop classification [ 29 ], hydrodynamic behavior of ground water level [ 30 ] and phenological regions delineation [ 31 ]. Remarkably, only once has clustering been applied to time series of vector data in an attempt to understand if there might be different temporal patterns that could explain the spatial differences usually observed in timing of cases occurrence and transmission risk [ 32 ].…”

Dengue Vector Population Forecasting Using Multisource Earth Observation Products and Recurrent Neural Networks

“…Figure 7 presents the location of the trap points along with color indicators showing the clusters they belong to. It can be seen that points in the same cluster are not necessarily geographically collocated, as is also the case in the results presented in [19].…”

“…aegypti at neighbourhoodlevel based on EO data inputs to RNNs. To address this question, we refer to the study presented in [19] which shows that in a group of concurrent mosquito population time series data covering a specific area, and over a sufficient amount of time, there exist multiple subgroups (or clusters) of temporally homogeneous time series in different spatial points. From the results of that study, we deduce that the temporal distribution of mosquito population data within the same cluster can be approximated as a single signal: the centroid (or mean) of this cluster.…”