Scientific Reports publishes a paper by Steffen Knoblauch et al. that underscores the critical importance of integrating vector ecology and human behavior into advanced disease modeling frameworks.
The increasing availability of human movement data presents significant potential for tackling global public health challenges, especially in the context of infectious diseases. This is particularly important for vector-borne diseases like dengue fever, which is closely tied to human mobility patterns and mosquito behavior. The ability to model these dynamics can enhance predictions of disease spread and the effectiveness of control interventions. The focus of this research is on dengue, a mosquito-borne disease with a rising global incidence, largely driven by urban growth, climate change, and international travel. A better understanding of the interaction between human movement and mosquito populations is crucial for effective resource allocation and prevention strategies. However, challenges persist, including the lack of high-resolution data on mosquito abundance and human movement patterns, which are key to refining models and improving intervention strategies.
This study utilizes hourly mobile phone records of approximately 3 million urban residents and daily dengue case counts at the address level, spanning 8 years (2015–2022), to evaluate the importance of modeling human-mosquito interactions at an hourly resolution in elucidating sub-neighborhood dengue occurrence in Rio de Janeiro, Brazil. The analysis integrates knowledge of Aedes mosquito biting behavior with human movement patterns to significantly improve inferences about urban dengue dynamics.
The inclusion of spatial eigenvectors and vulnerability indicators such as healthcare access, urban centrality measures, and estimates for immunity as predictors, allowed a further fine-tuning of the spatial model. The proposed concept enabled the explanation of 77% of the deviance in sub-neighborhood DENV infections. The transfer of these results to optimize vector control in urban settings bears significant epidemiological implications, presumably leading to lower infection rates of Aedes-borne diseases in the future. It highlights how increasingly collected human movement patterns can be utilized to locate zones of potential DENV transmission, identified not only by mosquito abundance but also connectivity to high incidence areas considering Aedes peak biting hours. These findings hold particular significance given the ongoing projection of global dengue incidence and urban sprawl.
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Title image: Workflow for the sub-neighborhood spatial eigenvector mapping of urban DENV occurrence applying entomological surveillance (left) and call detail records (middle) to model daytime human-mosquito biting risk for the municipality of Rio de Janeiro in Brazil on an hourly basis. Voronoi tessellations based on mobile phone antenna locations were employed as the spatial unit for analysis. In the feature engineering process, the base model assumed a constant human-mosquito interaction throughout the day, while the proposed model accounted for the fluctuating exposure of humans to mosquito bites, considering the twilight biting activity of Aedes mosquitoes and the hourly commuting patterns of humans. Note that this workflow identifies associations at an aggregate level and should be interpreted with caution to avoid ecological fallacies, as it does not imply causation at the individual level. (CDRs: Call detail records; ORS: openrouteservice; IGBE: Brazilian Institute of Geography and Statistics; IPEA: Institute of Applied Economic Research; SMS-RJ: Municipal Health Ministry of Rio de Janeiro).
Reference: Knoblauch, S., Heidecke, J., de A. Rocha, A. A., & et al. (2025). Modeling intraday Aedes-human exposure dynamics enhances dengue risk prediction. Scientific Reports, 15, 7994. https://doi.org/10.1038/s41598-025-91950-9
