Communicable disease surveillance through predictive analysis: A comparative analysis of prediction models
Author
Abstract
Suggested Citation
DOI: 10.46223/HCMCOUJS.tech.en.13.2.2944.2023
Download full text from publisher
References listed on IDEAS
- Felipe J Colón-González & Leonardo Soares Bastos & Barbara Hofmann & Alison Hopkin & Quillon Harpham & Tom Crocker & Rosanna Amato & Iacopo Ferrario & Francesca Moschini & Samuel James & Sajni Malde &, 2021. "Probabilistic seasonal dengue forecasting in Vietnam: A modelling study using superensembles," PLOS Medicine, Public Library of Science, vol. 18(3), pages 1-30, March.
- Oswaldo Santos Baquero & Lidia Maria Reis Santana & Francisco Chiaravalloti-Neto, 2018. "Dengue forecasting in São Paulo city with generalized additive models, artificial neural networks and seasonal autoregressive integrated moving average models," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-12, April.
- Naizhuo Zhao & Katia Charland & Mabel Carabali & Elaine O Nsoesie & Mathieu Maheu-Giroux & Erin Rees & Mengru Yuan & Cesar Garcia Balaguera & Gloria Jaramillo Ramirez & Kate Zinszer, 2020. "Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 14(9), pages 1-16, September.
- Jiucheng Xu & Keqiang Xu & Zhichao Li & Fengxia Meng & Taotian Tu & Lei Xu & Qiyong Liu, 2020. "Forecast of Dengue Cases in 20 Chinese Cities Based on the Deep Learning Method," IJERPH, MDPI, vol. 17(2), pages 1-14, January.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Zhichao Li, 2022. "Forecasting Weekly Dengue Cases by Integrating Google Earth Engine-Based Risk Predictor Generation and Google Colab-Based Deep Learning Modeling in Fortaleza and the Federal District, Brazil," IJERPH, MDPI, vol. 19(20), pages 1-16, October.
- Panja, Madhurima & Chakraborty, Tanujit & Nadim, Sk Shahid & Ghosh, Indrajit & Kumar, Uttam & Liu, Nan, 2023. "An ensemble neural network approach to forecast Dengue outbreak based on climatic condition," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
- Vicente Navarro Valencia & Yamilka Díaz & Juan Miguel Pascale & Maciej F. Boni & Javier E. Sanchez-Galan, 2021. "Assessing the Effect of Climate Variables on the Incidence of Dengue Cases in the Metropolitan Region of Panama City," IJERPH, MDPI, vol. 18(22), pages 1-18, November.
- Zhao, Xinxing & Li, Kainan & Ang, Candice Ke En & Cheong, Kang Hao, 2023. "A deep learning based hybrid architecture for weekly dengue incidences forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
- Mokhalad A. Majeed & Helmi Zulhaidi Mohd Shafri & Zed Zulkafli & Aimrun Wayayok, 2023. "A Deep Learning Approach for Dengue Fever Prediction in Malaysia Using LSTM with Spatial Attention," IJERPH, MDPI, vol. 20(5), pages 1-22, February.
- Zhichao Li & Helen Gurgel & Nadine Dessay & Luojia Hu & Lei Xu & Peng Gong, 2020. "Semi-Supervised Text Classification Framework: An Overview of Dengue Landscape Factors and Satellite Earth Observation," IJERPH, MDPI, vol. 17(12), pages 1-29, June.
- Ray, Evan L. & Brooks, Logan C. & Bien, Jacob & Biggerstaff, Matthew & Bosse, Nikos I. & Bracher, Johannes & Cramer, Estee Y. & Funk, Sebastian & Gerding, Aaron & Johansson, Michael A. & Rumack, Aaron, 2023. "Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1366-1383.
- Sathi Patra & Soovoojeet Jana & Sayani Adak & T. K. Kar, 2024. "A deep learning architecture using hybrid and stacks to forecast weekly dengue cases in Laos," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 97(8), pages 1-16, August.
- Sarbhan Singh & Lai Chee Herng & Lokman Hakim Sulaiman & Shew Fung Wong & Jenarun Jelip & Norhayati Mokhtar & Quillon Harpham & Gina Tsarouchi & Balvinder Singh Gill, 2022. "The Effects of Meteorological Factors on Dengue Cases in Malaysia," IJERPH, MDPI, vol. 19(11), pages 1-24, May.
- Supreet Kaur & Sandeep Sharma & Ateeq Ur Rehman & Elsayed Tag Eldin & Nivin A. Ghamry & Muhammad Shafiq & Salil Bharany, 2022. "Predicting Infection Positivity, Risk Estimation, and Disease Prognosis in Dengue Infected Patients by ML Expert System," Sustainability, MDPI, vol. 14(20), pages 1-20, October.
More about this item
Keywords
communicable disease prediction; disease prediction; early detection; machine learning; public health planning;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bjw:techen:v:13:y:2023:i:2:p:45-54. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Vu Tuan Truong (email available below). General contact details of provider: https://journalofscience.ou.edu.vn/index.php/tech-en .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.