PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

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• Aarthi, A. D., & Gnanappazham, L. (2018). Urban growth prediction using neural network coupled agents-based cellular automata model for Sriperumbudur taluk, Tamil Nadu, India. The Egyptian Journal of Remote Sensing and Space Science, 21(3), 353-362. https://doi.org/10.1016/j.ejrs.2017.12.004

• Abedini, M., Ghasemyan, B., & Rezaei Mogaddam, M. H. (2017). Landslide susceptibility mapping in Bijar city, Kurdistan province, Iran: A comparative study by logistic regression and AHP models. Environmental Earth Sciences, 76(8), Article 308. https://doi.org/10.1007/s12665-017-6502-3

• Allwood, B. W., Koegelenberg, C. F., Ngah, V. D., Sigwadhi, L. N., Irusen, E. M., Lalla, U., Yalew, A., Tamuzi, J. L., McAllister, M., Zemlin, A. E., Jalavu, T. P., Erasmus, R., Chapanduka, Z. C., Matsha, T. E., Fwemba, I., Zumla, A., & Nyasulu, P. S. (2022). Predicting COVID-19 outcomes from clinical and laboratory parameters in an intensive care facility during the second wave of the pandemic in South Africa. IJID Regions, 3, 242-247. https://doi.org/10.1016/j.ijregi.2022.03.024

• Arabi, Y. M., Murthy, S., & Webb, S. (2020). COVID-19: A novel coronavirus and a novel challenge for critical care. Intensive Care Medicine, 46(5), 833-836. https://doi.org/10.1007/s00134-020-05955-1

• Bae, S., Sung, E., & Kwon, O. (2021). Accounting for social media effects to improve the accuracy of infection models: Combatting the COVID-19 pandemic and infodemic. European Journal of Information Systems, 30(3), 342-355. https://doi.org/10.1080/0960085x.2021.1890530

• Bird, J. J., Barnes, C. M., Premebida, C., Ekárt, A., & Faria, D. R. (2020). Country-level pandemic risk and preparedness classification based on COVID-19 data: A machine learning approach. PLoS ONE, 15(10), Article e0241332. https://doi.org/10.1371/journal.pone.0241332

• Dong, E., Du, H., & Gardner, L. (2020). An interactive web-based dashboard to track COVID-19 in real-time. The Lancet Infectious Diseases, 20(5), 533-534. https://doi.org/10.1016/s1473-3099(20)30120-1

• Estenssoro, E., Loudet, C. I., Dubin, A., Kanoore Edul, V. S., Plotnikow, G., Andrian, M., Romero, I., Sagardía, J., Bezzi, M., Mandich, V., Groer, C., Torres, S., Orlandi, C., Rubatto Birri, P. N., Valenti, M. F., Cunto, E., Sáenz, M. G., Tiribelli, N., Aphalo, V., Bettini, L., Rios, F. G., & Reina, R. (2022). Clinical characteristics, respiratory management, and determinants of oxygenation in COVID-19 ards: A prospective cohort study. Journal of Critical Care, 71, Article 154021. https://doi.org/10.1016/j.jcrc.2022.154021

• Franch-Pardo, I., Napoletano, B. M., Rosete-Verges, F., & Billa, L. (2020). Spatial analysis and GIS in the study of COVID-19. A review. Science of the Total Environment, 739, Article 140033. https://doi.org/10.1016/j.scitotenv.2020.140033

• Garrido, J., Martínez-Rodríguez, D., Rodríguez-Serrano, F., Pérez-Villares, J., Ferreiro-Marzal, A., Jiménez-Quintana, M., & Villanueva, R. (2022). Mathematical model optimized for prediction and health care planning for COVID-19. Medicina Intensiva (English Edition), 46(5), 248-258. https://doi.org/10.1016/j.medine.2022.02.020

• Grasselli, G., Pesenti, A., & Cecconi, M. (2020). Critical care utilization for the COVID-19 outbreak in Lombardy, Italy. JAMA, 323(16), Article 1545. https://doi.org/10.1001/jama.2020.4031

• He, X., Zhou, C., Wang, Y., & Yuan, X. (2021). Risk assessment and prediction of COVID-19 based on epidemiological data from spatiotemporal geography. Frontiers in Environmental Science, 9, Article 634156. https://doi.org/10.3389/fenvs.2021.634156

• Klyushin, D. A. (2020). Nonparametric analysis of tracking data in the context of COVID-19 pandemic. In A. E. Hassanien, N. Dey & S. Elghamrawy (Eds.), Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach (pp. 35-50). Springer. https://doi.org/10.1007/978-3-030-55258-9_3

• Li, J., Li, S., Cai, Y., Liu, Q., Li, X., Zeng, Z., Chu, Y., Zhu, F., & Zeng, F. (2020). Epidemiological and clinical characteristics of 17 hospitalized patients with 2019 novel coronavirus infections outside Wuhan, China. MedRxiv. https://doi.org/10.1101/2020.02.11.20022053

• Liu, D., Clemente, L., Poirier, C., Ding, X., Chinazzi, M., Davis, J., Vespignani, A., & Santillana, M. (2020). Real-time forecasting of the COVID-19 outbreak in Chinese provinces: Machine learning approach using novel digital data and estimates from mechanistic models. Journal of Medical Internet Research, 22(8), Article e20285. https://doi.org/10.2196/20285

• Liu, Q. Y., Kwong, C. F., Zhang, S., & Li, L. (2018, November 4). A hybrid fuzzy-MADM based decision-making scheme for QoS aware handover. [Paper presentation]. IET Doctoral Forum on Biomedical Engineering, Healthcare, Robotics and Artificial Intelligence 2018 (BRAIN 2018), Ningbo, China. https://doi.org/10.1049/cp.2018.1728

• Looi, M. (2020). COVID-19: Is a second wave hitting Europe? BMJ, 371, Article 4113. https://doi.org/10.1136/bmj.m4113

• Lu, R., Zhao, X., Li, J., Niu, P., Yang, B., Wu, H., Wang, W., Song, H., Huang, B., Zhu, N., Bi, Y., Ma, X., Zhan, F., Wang, L., Hu, T., Zhou, H., Hu, Z., Zhou, W., Zhao, L., ... & Tan, W. (2020). Genomic characterisation and epidemiology of 2019 novel coronavirus: Implications for virus origins and receptor binding. The Lancet, 395(10224), 565-574. https://doi.org/10.1016/s0140-6736(20)30251-8

• Mahdavi, M., Choubdar, H., Zabeh, E., Rieder, M., Safavi-Naeini, S., Jobbagy, Z., Ghorbani, A., Abedini, A., Kiani, A., Khanlarzadeh, V., Lashgari, R., & Kamrani, E. (2021). A machine learning based exploration of COVID-19 mortality risk. PLoS ONE, 16(7), Article e0252384. https://doi.org/10.1371/journal.pone.0252384

• Mogensen, I., Hallberg, J., Björkander, S., Du, L., Zuo, F., Hammarström, L., Pan-Hammarström, Q., Ekström, S., Georgelis, A., Palmberg, L., Janson, C., Bergström, A., Melén, E., Kull, I., Almqvist, C., Andersson, N., Ballardini, N., Bergström, A., Björkander, S., ... & Schwenk, J. M. (2022). Lung function before and after COVID-19 in young adults: A population-based study. Journal of Allergy and Clinical Immunology: Global, 1(2), 37-42. https://doi.org/10.1016/j.jacig.2022.03.001

• Mudenda, S., Mukosha, M., Mwila, C., Saleem, Z., Kalungia, A. C., Munkombwe, D., Daka, V., Witika, B. A., Kampamba, M., Chileshe, M., Hikaambo, C., Kasanga, M., Mufwambi, W., Mfune, R. L., Matafwali, S. K., Bwalya, A. G., Banda, D. C., Gupta, A., Phiri, M. N., ... & Kazonga, E. (2021). Impact of the coronavirus disease (COVID-19) on the mental health and physical activity of pharmacy students at the University of Zambia: A cross-sectional study. MedRxiv. https://doi.org/10.1101/2021.01.11.21249547

• Muhammad, M. A., & Al-Turjman, F. (2021). Application of IoT, AI, and 5G in the fight against the COVID-19 pandemic. In F. Al-Turhman (Ed.), Artificial Intelligence and Machine Learning for COVID-19 (pp. 213-234). Springer. https://doi.org/10.1007/978-3-030-60188-1_10

• Olszewski, R., Pałka, P., & Wendland, A. (2021, December 13-16). Agent-based modeling as a tool for predicting the spatial-temporal diffusion of the COVID-19 pandemic. [Paper presentation]. 2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Singapore. https://doi.org/10.1109/IEEM50564.2021.9672878

• Pan, W., Deng, Q., Li, J., Wang, Z., & Zhu, W. (2021, July 18-22). STSIR: A spatial temporal pandemic model with mobility data-A COVID-19 study. [Paper presentation]. 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China. https://doi.org/10.1109/IJCNN52387.2021.9533596

• Pullano, G., Pinotti, F., Valdano, E., Boëlle, P., Poletto, C., & Colizza, V. (2020). Novel coronavirus (2019-nCoV) early-stage importation risk to Europe, January 2020. Eurosurveillance, 25(4), Article 2000057. https://doi.org/10.2807/1560-7917.es.2020.25.4.2000057

• Quah, P., Li, A., & Phua, J. (2020). Mortality rates of patients with COVID-19 in the intensive care unit: A systematic review of the emerging literature. Critical Care, 24, Article 285. https://doi.org/10.1186/s13054-020-03006-1

• Rustam, F., Reshi, A. A., Mehmood, A., Ullah, S., On, B., Aslam, W., & Choi, G. S. (2020). COVID-19 future forecasting using supervised machine learning models. IEEE Access, 8, 101489-101499. https://doi.org/10.1109/access.2020.2997311

• Shaukat, K., Masood, N., Shafaat, A., Jabbar, K., Shabbir, H., & Shabbir, S. (2015). Dengue fever in perspective of clustering algorithms. Journal of Data Mining in Genomics & Proteomics, 6(3), Article 1000176. https://doi.org/10.4172/2153-0602.1000176

• Shilo, S., Rossman, H., & Segal, E. (2020). Axes of a revolution: Challenges and promises of big data in healthcare. Nature Medicine, 26(1), 29-38. https://doi.org/10.1038/s41591-019-0727-5

• Woolf, S. H., Chapman, D. A., & Lee, J. H. (2021). COVID-19 as the leading cause of death in the United States. Jama, 325(2), 123-124. https://doi.org/10.1001/jama.2020.24865

• World Health Organization. (2020). Dashboard of the Coronavirus Disease (COVID-19) Outbreak Situation. World Health Organization. https://covid19/who.int/

• Wynants, L., Van Calster, B., Collins, G. S., Riley, R. D., Heinze, G., Schuit, E., Bonten, M. M., Dahly, D. L., Damen, J. A., Debray, T. P., de Jong, V. M., De Vos, M., Dhiman, P., Haller, M. C., Harhay, M. O., Henckaerts, L., Heus, P., Kammer, M., Kreuzberger, N., ... & van Smeden, M. (2020). Prediction models for diagnosis and prognosis of COVID-19: Systematic review and critical appraisal. BMJ, 369, Article 1328. https://doi.org/10.1136/bmj.m1328

• Zhao, D., & Zhang, H. (2022, March 25-27). Comparison of the SVR and ARIMA models for prediction of daily imported new cases of COVID-19 in Shanghai, China. [Paper presentation]. 2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML), Hangzhou, China. https://doi.org/10.1109/CACML55074.2022.00048