Abstract
Vector-borne diseases cause significant veterinary and public health burdens as they widely re-emerge in affected areas or emerge in new healthy areas. Understanding, predicting, and mitigating the spread of vector-borne disease in diverse hosts and geographic areas are modeling objectives still to be explored. Many scientific studies are focused on developing predictive models for vector distribution and suitability to identify high-risk transmission areas and plan disease prevention and control strategies (Nguyen et. al., 2023; Saucedo & Tien, 2022; Leta et al., 2019).
Vector distribution is driven by climatic, geographic, and environmental factors (such as temperature, precipitation, and altitude), and understanding its presence and abundance is fundamental to planning effective prevention and control strategies. Additionally, the movements of infected susceptible hosts are crucial, as they let the disease reach farthest, unaffected areas.
In this study, we combine information on vector suitability and host movement data in a raster-based compartmental epidemiological model to predict disease occurrences and critical management areas. The proposed approach incorporates network analysis features to model the health status of a location (pixel), considering both host movements and proximity-based contagion due to vector-suitable conditions. The model focuses on the “suitable paths”, the shortest paths weighted on the vector’s suitability, linking sources of infection to susceptible hosts within an area.
We used a case study to show the application of this theoretical model and demonstrate the practical utility of this approach in informing mitigation strategies: the Bluetongue Virus serotype 4 (BTV4) epidemic, that occurred in the Sardinia region in 2017.
This research offers valuable insights into the dynamic interplay between network analysis, vector suitability, host density, and host movements, enhancing our ability to predict and manage vector-borne diseases effectively. These findings are essential for public health planning and disease control strategies in a changing global environment.
References
Nguyen, A., Bartels, D. W., & Gilligan, C. A. (2023). Modelling the spread and mitigation of an emerging vector-borne pathogen: Citrus greening in the U.S. PLOS Computational Biology, 19(6). https://doi.org/10.1371/journal.pcbi.1010156
Saucedo, O., & Tien, J. H. (2022). Host movement, transmission hot spots, and vector-borne disease dynamics on spatial networks. Infectious Disease Modelling, 7(4), 742-760. https://doi.org/10.1016/j.idm.2022.10.006
Leta, S., Fetene, E., Mulatu, T., Amenu, K., Jaleta, M. B., Beyene, T. J., Negussie, H., & Revie, C. W. (2019). Modeling the global distribution of Culicoides imicola: an Ensemble approach. Scientific reports, 9(1), 14187. https://doi-org.bibliosan.idm.oclc.org/10.1038/s41598-019-50765-1