An efficient vector-borne disease prediction using SS optimization-based hybrid support vector random forest model
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s11760-023-02624-w.pdf
Reference25 articles.
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2. Robert, L.L., Debboun, M.: Arthropods of public health importance. In: Hunter’s Tropical Medicine and Emerging Infectious Diseases, pp. 1055–1062. Elsevier (2020). https://doi.org/10.1016/B978-0-323-55512-8.00146-0
3. Wilke, A.B., Beier, J.C., Benelli, G.: Complexity of the relationship between global warming and urbanization–an obscure future for predicting increases in vector-borne infectious diseases. Current Opin. Insect Sci. 35, 1–9 (2019)
4. Bartlow, A.W., Manore, C., Xu, C., Kaufeld, K.A., Del Valle, S., Ziemann, A., Fairchild, G., Fair, J.M.: Forecasting zoonotic infectious disease response to climate change: mosquito vectors and a changing environment. Vet. Sci. 6(2), 40 (2019)
5. Malone, J.B., Bergquist, R., Martins, M., Luvall, J.C.: Use of geospatial surveillance and response systems for vector-borne diseases in the elimination phase. Trop. Med. Infect Dis. 4(1), 15 (2019)
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