Cloud Hosting

Thomson, M. C. et al. Malaria early warnings based on seasonal climate forecasts from multi-model ensembles. Nature 439(7076), 576579 (2006).

Article ADS CAS PubMed Google Scholar

Hashizume, M., Terao, T. & Minakawa, N. The Indian Ocean Dipole and malaria risk in the highlands of western Kenya. Proc. Natl. Acad. Sci. 106(6), 18571862 (2009).

Article ADS CAS PubMed PubMed Central Google Scholar

Haileselassie, W. et al. Burden of malaria, impact of interventions and climate variability in Western Ethiopia: an area with large irrigation-based farming. BMC Public Health 22(1), 111 (2022).

Article Google Scholar

Zhou, G., Minakawa, N., Githeko, A. K. & Yan, G. Association between climate variability and malaria epidemics in the East African highlands. Proc. Natl. Acad. Sci. 101(8), 23752380 (2004).

Article ADS CAS PubMed PubMed Central Google Scholar

MBra, R. K. et al. Impact of climate variability on the transmission risk of malaria in northern Cte dIvoire. PLoS One 13(6), e0182304 (2018).

Article PubMed PubMed Central Google Scholar

Talapko, J., krlec, I., Alebi, T., Juki, M. & Vev, A. Malaria: the past and the present. Microorganisms 7(6), 179 (2019).

Article CAS PubMed PubMed Central Google Scholar

World Health Organization. World Malaria Report 2020 (World Health Organization, 2020).

Book Google Scholar

Ohrt, C. et al. Information systems to support surveillance for malaria elimination. Am. J. Trop. Med. Hyg. 93(1), 145 (2015).

Article PubMed PubMed Central Google Scholar

Kim, Y. et al. Malaria predictions based on seasonal climate forecasts in South Africa: A time series distributed lag nonlinear model. Sci. Rep. 9(1), 110 (2019).

Google Scholar

Santosh, T., Ramesh, D. & Reddy, D. LSTM based prediction of malaria abundances using big data. Comput. Biol. Med. 124, 103859 (2020).

Article PubMed Google Scholar

Mohapatra, P., Tripathi, N. K., Pal, I. & Shrestha, S. Comparative analysis of machine learning classifiers for the prediction of malaria incidence attributed to climatic factors.

Masinde, M. Africa's Malaria epidemic predictor: Application of machine learning on malaria incidence and climate data. Proc. of the 2020 the 4th International Conference on Compute and Data Analysis. 2937 (2020).

Mussumeci, E. & Coelho, F. C. Large-scale multivariate forecasting models for Dengue-LSTM versus random forest regression. Spatial Spatio Temporal Epidemiol. 35, 100372 (2020).

Article Google Scholar

Rajkomar, A., Dean, J. & Kohane, I. Machine learning in medicine. N. Engl. J. Med. 380(14), 13471358 (2019).

Article PubMed Google Scholar

Nkiruka, O., Prasad, R. & Clement, O. Prediction of malaria incidence using climate variability and machine learning. Inf. Med. Unlocked 22, 100508 (2021).

Article Google Scholar

Thomson, M. C., Mason, S. J., Phindela, T. & Connor, S. J. Use of rainfall and sea surface temperature monitoring for malaria early warning in Botswana. Am. J. Trop. Med. Hyg. 73(1), 214221 (2005).

Article PubMed Google Scholar

Behera, S. K. et al. Malaria incidences in South Africa linked to a climate mode in southwestern Indian Ocean. Environ. Dev.. 27, 4757 (2018).

Article Google Scholar

Eikenberry, S. E. & Gumel, A. B. Mathematical modeling of climate change and malaria transmission dynamics: A historical review. J. Math. Biol. 77(4), 857933 (2018).

Article MathSciNet PubMed Google Scholar

Kifle, M. M. et al. Malaria risk stratification and modeling the effect of rainfall on malaria incidence in Eritrea. J. Environ. Public Health 2019, 111 (2019).

Article Google Scholar

Okuneye, K. & Gumel, A. B. Analysis of a temperature-and rainfall-dependent model for malaria transmission dynamics. Math. Biosci. 287, 7292 (2017).

Article MathSciNet PubMed Google Scholar

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, . & Polosukhin, I. Attention is all you need. In: Advances in neural information processing systems. Vol 30. (2017).

Carmichael, I. & Marron, J. S. Data science vs. statistics: Two cultures?. Jpn. J. Stat. Data Sci. 1(1), 117138 (2018).

Article MathSciNet Google Scholar

Abbasimehr, H. & Baghery, F. S. A novel time series clustering method with fine-tuned support vector regression for customer behavior analysis. Expert Syst. Appl. 204, 117584 (2022).

Article Google Scholar

Xu, J. et al. Forecast of dengue cases in 20 Chinese cities based on the deep learning method. Int. J. Environ. Res. Public Health 17(2), 453 (2020).

Article PubMed PubMed Central Google Scholar

Ho, T. S. et al. Comparing machine learning with case-control models to identify confirmed dengue cases. PLoS Negl. Trop. Dis. 14(11), e0008843 (2020).

Article PubMed PubMed Central Google Scholar

Wang, M. et al. A novel model for malaria prediction based on ensemble algorithms. PloS One 14(12), e0226910 (2019).

Article CAS PubMed PubMed Central Google Scholar

Lim, B., Ark, S. ., Loeff, N. & Pfister, T. Temporal fusion transformers for interpretable multi-horizon time series forecasting. Int. J. Forecast. 37(4), 17481764 (2021).

Article Google Scholar

Susan, S. & Kumar, A. The balancing trick: Optimized sampling of imbalanced datasetsa brief survey of the recent state of the art. Eng. Rep. 3(4), e12298 (2021).

Article Google Scholar

Thickstun, J. The Transformer Model in Equations (University of Washington, 2021).

Google Scholar

Bengio, S., Vinyals, O., Jaitly, N. & Shazeer, N. Scheduled sampling for sequence prediction with recurrent neural networks. Advances in Neural Information Processing Systems. 28 (2015).

Mohapatra, P., Tripathi, N. K., Pal, I. & Shrestha, S. Determining suitable machine learning classifier technique for prediction of malaria incidents attributed to climate of Odisha. Int. J. Environ. Health Res. 32(8), 17161732 (2022).

Article CAS PubMed Google Scholar

Jdey, I., Hcini, G. & Ltifi, H. Deep learning and machine learning for Malaria detection: Overview, challenges and future directions. arXiv preprint arXiv:2209.13292. (2022).

Munir, M., Siddiqui, S. A., Chattha, M. A., Dengel, A. & Ahmed, S. Fusead: Unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019).

Article ADS PubMed PubMed Central Google Scholar

Kim, M. Prediction of COVID-19 confirmed cases after vaccination: Based on statistical and deep learning models. Sci. Med. J. 3(2), 153165 (2021).

CAS Google Scholar

Martineau, P. et al. Predicting malaria outbreaks from sea surface temperature variability up to 9 months ahead in Limpopo, South Africa, using machine learning. Front. Pub. Health 25(10), 962377 (2022).

Article Google Scholar

Adeola, A. M., Botai, J. O., Olwoch, J. M., Rautenbach, H. C., Adisa, O. M., De Jager, C., Botai, C. M. & Aaron, M. Predicting malaria cases using remotely sensed environmental variables in Nkomazi, South Africa. Geospatial Health. 14(1) (2019).

Mbunge, E., Milham, R. C., Sibiya, M. N. & Jr Takavarasha, S. Machine learning techniques for predicting malaria: Unpacking emerging challenges and opportunities for tackling malaria in sub-saharan Africa. Proc. Computer Science On-line Conference 327344. (Springer International Publishing, Cham, 2023).

Nguyen, V. H. et al. Deep learning models for forecasting dengue fever based on climate data in Vietnam. PLoS Neglect. Trop. Dis. 16(6), e0010509 (2022).

Article Google Scholar

Wu, N., Green, B., Ben, X. & O'Banion, S. Deep transformer models for time series forecasting: The influenza prevalence case. arXiv preprint arXiv:2001.08317. (2020).

Zerveas, G., Jayaraman, S., Patel, D., Bhamidipaty, A. & Eickhoff, C. A transformer-based framework for multivariate time series representation learning. Proc. of the 27th ACM SIGKDD conference on knowledge discovery & data mining 21142124 (2021).

Wang, N. & Zhao, X. Time series forecasting based on convolution transformer. IEICE Trans. Inf. Syst. 106(5), 976985 (2023).

Article Google Scholar

Xu, C., Li, J., Feng, B. & Lu, B. A financial time-series prediction model based on multiplex attention and linear transformer structure. Appl. Sci. 13(8), 5175 (2023).

Article CAS Google Scholar

Ahmed, D. M., Hassan, M. M. & Mstafa, R. J. A review on deep sequential models for forecasting time series data. Appl. Comput. Intell. Soft Comput. 3, 2022 (2022).

Google Scholar

Ahmed, S., Nielsen, I. E., Tripathi, A., Siddiqui, S., Rasool, G. & Ramachandran, R. P. Transformers in time-series analysis: A tutorial. arXiv 2022. arXiv preprint arXiv:2205.01138.

Haugsdal, E., Aune, E. & Ruocco, M. Persistence initialization: A novel adaptation of the transformer architecture for time series forecasting. Appl. Intell. 29, 16 (2023).

Google Scholar

Mohammadi Farsani, R. & Pazouki, E. A transformer self-attention model for time series forecasting. J. Electric. Comput. Eng. Innov. (JECEI) 9(1), 1 (2020).

Google Scholar

Kamana, E., Zhao, J. & Bai, D. Predicting the impact of climate change on the re-emergence of malaria cases in China using LSTMSeq2Seq deep learning model: A modelling and prediction analysis study. BMJ Open. 12(3), e053922 (2022).

Article PubMed PubMed Central Google Scholar

Teklehaimanot, H. D., Schwartz, J., Teklehaimanot, A. & Lipsitch, M. Alert threshold algorithms and malaria epidemic detection. Emerg. Infect. Dis. 10(7), 1220 (2004).

Article PubMed PubMed Central Google Scholar

Hartfield, M. & Alizon, S. Introducing the outbreak threshold in epidemiology. PLoS Pathog. 9(6), e1003277 (2013).

Article CAS PubMed PubMed Central Google Scholar

Bingham, N. H. & Fry, J. M. Regression: Linear Models in Statistics (Springer Science & Business Media, 2010).

Book Google Scholar

Das, A., Kong, W., Sen, R. & Zhou, Y. A decoder-only foundation model for time-series forecasting. arXiv preprint arXiv:2310.10688. (2023).

Radford, A. et al. Language models are unsupervised multitask learners. Open AI Blog. 1(8), 9 (2019).

Google Scholar

NOAA Physical sciences laboratory. NCEP/DOE AMIP-II Reanalysis (Reanalysis-2) Data. NOAA physical sciences laboratory. Available from: https://psl.noaa.gov/data/gridded/ data.ncep.reanalysis2.html. Accessed March 2023.

Liu, M., Ren, S., Ma, S., Jiao, J., Chen, Y., Wang, Z. & Song, W. Gated transformer networks for multivariate time series classification. arXiv preprint arXiv:2103.14438. (2021).

Chu J, Cao J, Chen Y. An ensemble deep learning model based on transformers for long sequence time-series forecasting. Proc. International Conference on Neural Computing for Advanced Applications 273286 (Springer Nature, Singapore, 2022).

Liu, C., Yu, S., Yu, M., Wei, B., Li, B., Li, G. & Huang, W. Adaptive smooth L1 loss: A better way to regress scene texts with extreme aspect ratios. Proc. 2021 IEEE Symposium on Computers and Communications (ISCC) 17 (IEEE, 2021).

Original post:
Utilizing a novel high-resolution malaria dataset for climate-informed predictions with a deep learning transformer ... - Nature.com