Predicting diarrhoea outbreaks with climate change
pmc-PMC9017952.pdf · Phiên bản 1 · 18 trang
5. Kapwata T, Mathee A, Le Roux W, Wright C. Diarrhoeal Disease in Relation to Possible Household Risk Factors in South African Villages. International Journal of Environmental Research and Public health. 2018; 15(8):1665. https://doi.org/10.3390/ijerph15081665 PMID: 30082638 6. Kullin B, Meggersee R, D’Alton J, Galvao B, Rajabally N, Whitelaw A, et al. Prevalence of Gastrointesti- nal Pathogenic Bacteria in Patients with Diarrhoea Attending Groote Schuur Hospital, Cape Town, South Africa. South African Medical Journal. 2015; 105(2). https://doi.org/10.7196/SAMJ.8654 PMID: 26242530 7. Musengimana G, Mukinda F, Machekano R, Mahomed H. Temperature Variability and Occurrence of Diarrhoea in Children under Five-years-old in Cape Town metropolitan Sub-districts. International Jour- nal of Environmental Research and Public health. 2016; 13(9):859. https://doi.org/10.3390/ ijerph13090859 PMID: 27589772 8. Dhimal M, Karki K, Aryal K, Shrestha S, Pradhan B. Final Report on Assessment of Effects of Climate Factors on Diarrheal Diseases at National and Sub-national Levels in Nepal. Kathmandu: Nepal Health Research Council and Word Health Organization Country Office for Nepal. 2016;. 9. Ikeda T, Kapwata T, Behera S, Minakawa N, Hashizume M, Sweijd N, et al. Climatic Factors in Relation to Diarrhoea Hospital Admissions in Rural Limpopo, South Africa. Atmosphere. 2019; 10(9):522. https://doi.org/10.3390/atmos10090522 10. Davis CL, Vincent K. Climate Risk and Vulnerability: A Handbook for Southern Africa; 2017.
11. WHO. Protecting Health from Climate Change: Connecting Science, Policy and People. WHO, Geneva, Switzerland: WHO; 2009. 12. Yan L, Wang H, Zhang X, Li M, He J. Impact of Meteorological Factors on the Incidence of Bacillary Dysentery in Beijing, China: A Time Series Analysis (1970-2012). PLoS One. 2017; 12(8):e0182937. https://doi.org/10.1371/journal.pone.0182937 PMID: 28796834 13. Alexander K, Carzolio M, Goodin D, Vance E. Climate Change is Likely to Worsen the Public Health Threat of Diarrheal Disease in Botswana. International Journal of Environmental Research and Public Health. 2013; 10(4):1202–1230. https://doi.org/10.3390/ijerph10041202 PMID: 23531489 14. Jia W, Wan Y, Li Y, Tan K, Lei W, Hu Y, et al. Integrating Multiple Data Sources and Learning Models to Predict Infectious Diseases in China. AMIA Summits on Translational Science Proceedings. 2019; 2019:680. PMID: 31259024 15. Wang Y, Li J, Gu J, Zhou Z, Wang Z. Artificial Neural Networks for Infectious Diarrhea Prediction using Meteorological Factors in Shanghai (China). Applied Soft Computing. 2015; 35:280–290. https://doi. org/10.1016/j.asoc.2015.05.047 16. Sapankevych N, Sankar R. Time series Prediction using Support Vector Machines: A Survey. IEEE Computational Intelligence Magazine. 2009; 4(2):24–38. https://doi.org/10.1109/MCI.2009.932254 17. LeCun Y, Bengio Y, Hinton G. Deep Learning. Nature. 2015; 521(7553):436–444. https://doi.org/10. 1038/nature14539 PMID: 26017442 18. Pham T, Tran T, Phung D, Venkatesh S. Predicting Healthcare Trajectories from Medical Records: A
deep Learning Approach. Journal of Biomedical Informatics. 2017; 69:218–229. https://doi.org/10. 1016/j.jbi.2017.04.001 PMID: 28410981 19. Fuhad K, Tuba J, Sarker M, Ali R, Momen S, Mohammed N, et al. Deep Learning Based Automatic Malaria Parasite Detection from Blood Smear and Its Smartphone Based Application. Diagnostics. 2020; 10(5):329. https://doi.org/10.3390/diagnostics10050329 PMID: 32443868 20. Abideen Z, Ghafoor M, Munir K, Saqib M, Ullah A, Zia T, et al. Uncertainty Assisted Robust Tuberculo- sis Identification With Bayesian Convolutional Neural Networks. Ieee Access. 2020; 8:22812–22825. https://doi.org/10.1109/ACCESS.2020.2970023 21. Guo Y, Feng Y, Qu F, Zhang L, Yan B, Lv J. Prediction of Hepatitis E using Machine Learning Models. Plos one. 2020; 15(9):e0237750. https://doi.org/10.1371/journal.pone.0237750 PMID: 32941452 22. Wu J, Chen X, Zhang H, Xiong L, Lei H, Deng S. Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimization. Journal of Electronic Science and Technology. 2019; 17 (1):26–40. 23. O’Mahony N, Campbell S, Carvalho A, Harapanahalli S, Hernandez G, Krpalkova L, et al. Deep Learn- ing vs. Traditional Computer Vision. In: Science and Information Conference. Springer; 2019. p. 128– 144. 24. Wen Q, Sun L, Song X, Gao J, Wang X, Xu H. Time Series Data Augmentation for Deep Learning: A Survey. arXiv preprint arXiv:200212478. 2020;. 25. Esteban C, Hyland S, Ra¨ tsch G. Real-valued (medical) Time series Generation with Recurrent Condi- tional Gans. arXiv preprint arXiv:170602633. 2017;.
26. Fawaz H, Forestier G, Weber J, Idoumghar L, Muller P. Data Augmentation using Synthetic Data for Time-series Classification with Deep Residual Networks. arXiv preprint arXiv:180802455. 2018;. PLOS ONE Predicting diarrhoea outbreaks with climate change PLOS ONE | https://doi.org/10.1371/journal.pone.0262008 April 19, 2022 17 / 18