{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T15:37:58Z","timestamp":1772725078972,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":38,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,1,4]],"date-time":"2024-01-04T00:00:00Z","timestamp":1704326400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,1,4]]},"DOI":"10.1145\/3631461.3631959","type":"proceedings-article","created":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T18:08:30Z","timestamp":1705946910000},"page":"322-327","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["ATT-LSTM: An Interpretable Deep Learning Framework for COVID Outbreak Prediction"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8078-1809","authenticated-orcid":false,"given":"Neeraj","family":"Choudhary","sequence":"first","affiliation":[{"name":"CSE, Mahindra University, IN"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8247-9040","authenticated-orcid":false,"given":"Jimson","family":"Mathew","sequence":"additional","affiliation":[{"name":"IIT Patna, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7018-8067","authenticated-orcid":false,"given":"Ranjan Kumar","family":"Behera","sequence":"additional","affiliation":[{"name":"IIT Patna, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7162-8346","authenticated-orcid":false,"given":"Zenin Easa","family":"Panthakkalakath","sequence":"additional","affiliation":[{"name":"IIT Patna, India"}]}],"member":"320","published-online":{"date-parts":[[2024,1,22]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.18520\/cs\/v114\/i11\/2281-2291"},{"key":"e_1_3_2_1_2_1","volume-title":"Spatiotemporal dengue fever hotspots associated with climatic factors in taiwan including outbreak predictions based on machine-learning. Geospatial health 14, 2","author":"Anno Sumiko","year":"2019","unstructured":"Sumiko Anno, Takeshi Hara, Hiroki Kai, Ming-An Lee, Yi Chang, Kei Oyoshi, Yousei Mizukami, and Takeo Tadono. 2019. Spatiotemporal dengue fever hotspots associated with climatic factors in taiwan including outbreak predictions based on machine-learning. Geospatial health 14, 2 (2019)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.109860"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/infdis\/jiy569"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2019.135491"},{"key":"e_1_3_2_1_6_1","volume-title":"Consensus and conflict among ecological forecasts of Zika virus outbreaks in the United States. Scientific reports 8, 1","author":"Carlson J","year":"2018","unstructured":"Colin\u00a0J Carlson, Eric Dougherty, Mike Boots, Wayne Getz, and Sadie\u00a0J Ryan. 2018. Consensus and conflict among ecological forecasts of Zika virus outbreaks in the United States. Scientific reports 8, 1 (2018), 1\u201315."},{"key":"e_1_3_2_1_7_1","volume-title":"Estimation of COVID-19 prevalence in Italy, Spain, and France. Science of The Total Environment","author":"Ceylan Zeynep","year":"2020","unstructured":"Zeynep Ceylan. 2020. Estimation of COVID-19 prevalence in Italy, Spain, and France. Science of The Total Environment (2020), 138817."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.109850"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(20)30154-9"},{"key":"e_1_3_2_1_10_1","volume-title":"China","author":"Chen Ye","year":"2010","unstructured":"Ye Chen, Kunkun Leng, Ying Lu, Lihai Wen, Ying Qi, Wei Gao, Huijie Chen, Lina Bai, Xiangdong An, Baijun Sun, 2020. Epidemiological features and time-series analysis of influenza incidence in urban and rural areas of Shenyang, China, 2010\u20132018. Epidemiology & Infection 148 (2020)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.envint.2017.11.032"},{"key":"e_1_3_2_1_12_1","volume-title":"Development of genetic programming-based model for predicting oyster norovirus outbreak risks. Water research 128","author":"Chenar Shima\u00a0Shamkhali","year":"2018","unstructured":"Shima\u00a0Shamkhali Chenar and Zhiqiang Deng. 2018. Development of genetic programming-based model for predicting oyster norovirus outbreak risks. Water research 128 (2018), 20\u201337."},{"key":"e_1_3_2_1_13_1","volume-title":"Solitons & Fractals","author":"Kumar\u00a0Reddy Chimmula Vinay","year":"2020","unstructured":"Vinay Kumar\u00a0Reddy Chimmula and Lei Zhang. 2020. Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos, Solitons & Fractals (2020), 109864."},{"key":"e_1_3_2_1_14_1","volume-title":"On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259","author":"Cho Kyunghyun","year":"2014","unstructured":"Kyunghyun Cho, Bart Van\u00a0Merri\u00ebnboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014. On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259 (2014)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.01.007"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.109971"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.109761"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12879-020-4930-2"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.3390\/v12020135"},{"key":"e_1_3_2_1_20_1","volume-title":"Long short-term memory. Neural computation 9, 8","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735\u20131780."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(20)30183-5"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1111\/geb.12754"},{"key":"e_1_3_2_1_23_1","volume-title":"Time-series analysis in the medical domain: A study of Tacrolimus administration and influence on kidney graft function. Computers in biology and medicine 50","author":"Kurbalija Vladimir","year":"2014","unstructured":"Vladimir Kurbalija, Milo\u0161 Radovanovi\u0107, Mirjana Ivanovi\u0107, Danilo Schmidt, Gabriela\u00a0Lindemann von Trzebiatowski, Hans-Dieter Burkhard, and Carl Hinrichs. 2014. Time-series analysis in the medical domain: A study of Tacrolimus administration and influence on kidney graft function. Computers in biology and medicine 50 (2014), 19\u201331."},{"key":"e_1_3_2_1_24_1","volume-title":"January-April 2020: a travel network-based modelling study. medRxiv","author":"Lai Shengjie","year":"2020","unstructured":"Shengjie Lai, Isaac\u00a0I Bogoch, Nick\u00a0W Ruktanonchai, Alexander Watts, Xin Lu, Weizhong Yang, Hongjie Yu, Kamran Khan, and Andrew\u00a0J Tatem. 2020. Assessing spread risk of Wuhan novel coronavirus within and beyond China, January-April 2020: a travel network-based modelling study. medRxiv (2020)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1111\/tbed.13424"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.2147\/IDR.S207809"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.35833\/MPCE.2020.000626"},{"key":"e_1_3_2_1_28_1","volume-title":"Power load forecasting based on long short term memory-singular spectrum analysis. Energy Systems","author":"Mathew Jimson","year":"2022","unstructured":"Neeraj, Jimson Mathew, and Ranjan\u00a0Kumar Behera. 2022. Power load forecasting based on long short term memory-singular spectrum analysis. Energy Systems (2022), 1\u201323."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00202-020-01135-y"},{"key":"e_1_3_2_1_30_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. In Advances in neural information processing systems. 8026\u20138037.","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, 2019. Pytorch: An imperative style, high-performance deep learning library. In Advances in neural information processing systems. 8026\u20138037."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12879-020-4902-6"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.37268\/mjphm\/vol.19\/no.2\/art.176"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijsu.2020.02.034"},{"key":"e_1_3_2_1_34_1","volume-title":"Comparative evaluation of time series models for predicting influenza outbreaks: Application of influenza-like illness data from sentinel sites of healthcare centers in Iran. BMC research notes 12, 1","author":"Tapak Leili","year":"2019","unstructured":"Leili Tapak, Omid Hamidi, Mohsen Fathian, and Manoochehr Karami. 2019. Comparative evaluation of time series models for predicting influenza outbreaks: Application of influenza-like illness data from sentinel sites of healthcare centers in Iran. BMC research notes 12, 1 (2019), 353."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-3156.2006.01630.x"},{"key":"e_1_3_2_1_36_1","volume-title":"Solitons & Fractals","author":"Torrealba-Rodriguez O","year":"2020","unstructured":"O Torrealba-Rodriguez, RA Conde-Guti\u00e9rrez, and AL Hern\u00e1ndez-Javier. 2020. Modeling and prediction of COVID-19 in Mexico applying mathematical and computational models. Chaos, Solitons & Fractals (2020), 109946."},{"key":"e_1_3_2_1_37_1","volume-title":"Long-term forecasting using tensor-train RNNs. arXiv preprint arXiv:1711.00073","author":"Yu Rose","year":"2017","unstructured":"Rose Yu, Stephan Zheng, Anima Anandkumar, and Yisong Yue. 2017. Long-term forecasting using tensor-train RNNs. arXiv preprint arXiv:1711.00073 (2017)."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0063116"}],"event":{"name":"ICDCN '24: International Conference on Distributed Computing and Networking","location":"Chennai India","acronym":"ICDCN '24"},"container-title":["Proceedings of the 25th International Conference on Distributed Computing and Networking"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3631461.3631959","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3631461.3631959","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T00:40:30Z","timestamp":1755909630000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3631461.3631959"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,4]]},"references-count":38,"alternative-id":["10.1145\/3631461.3631959","10.1145\/3631461"],"URL":"https:\/\/doi.org\/10.1145\/3631461.3631959","relation":{},"subject":[],"published":{"date-parts":[[2024,1,4]]},"assertion":[{"value":"2024-01-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}