How to cite this paper
Mauritsius, T. (2025). Extending the forecasting horizon of daily new COVID-19 cases using non-pharmaceutical measures and the effective reproduction number (Rt): A deep learning-based framework.Decision Science Letters , 14(2), 283-302.
Refrences
Abolmaali, S., & Shirzaei, S. (2021). A comparative study of SIR Model, Linear Regression, Logistic Function and ARIMA Model for forecasting COVID-19 cases. AIMS Public Health, 8(4), 598–613. https://doi.org/10.3934/publichealth.2021048
Absar, N., Uddin, N., Khandaker, M. U., & Ullah, H. (2022). The efficacy of deep learning based LSTM model in forecasting the outbreak of contagious diseases. Infectious Disease Modelling, 7(1), 170–183. https://doi.org/10.1016/j.idm.2021.12.005
Alassafi, M. O., Jarrah, M., & Alotaibi, R. (2022). Time series predicting of COVID-19 based on deep learning. Neurocompu-ting, 468, 335–344. https://doi.org/10.1016/j.neucom.2021.10.035
Blavatnik School of Government. (2022). COVID-19 Government Response Tracker. https://www.bsg.ox.ac.uk/research/covid-19-government-response-tracker
Bsat, R., Chemaitelly, H., Coyle, P., Tang, P., Hasan, M. R., Kanaani, Z. Al, Kuwari, E. Al, Butt, A. A., Jeremijenko, A., Kaleeckal, A. H., Latif, A. N., Shaik, R. M., Nasrallah, G. K., Benslimane, F. M., Khatib, H. A. A., Yassine, H. M., Kuwari, M. G. A., Romaihi, H. E. Al, Al-Thani, M. H., … Ayoub, H. H. (2022). Characterizing the effective reproduction number during the COVID-19 pandemic: Insights from Qatar’s experience. Journal of Global Health, 12. https://doi.org/10.7189/JOGH.12.05004
Cinaglia, P., & Cannataro, M. (2022). Forecasting COVID-19 Epidemic Trends by Combining a Neural Network with Rt Esti-mation. Entropy, 24(7), 1–17. https://doi.org/10.3390/e24070929
Cortés-Carvajal, P. D., Cubilla-Montilla, M., & González-Cortés, D. R. (2022). Estimation of the Instantaneous Reproduction Number and Its Confidence Interval for Modeling the COVID-19 Pandemic. Mathematics, 10(2). https://doi.org/10.3390/MATH10020287
Delli Compagni, R., Cheng Id, Z., Russo, S., & Van Boeckel, T. P. (2022). A hybrid Neural Network-SEIR model for forecast-ing intensive care occupancy in Switzerland during COVID-19 epidemics. PLoS ONE, 3. https://doi.org/10.1371/journal.pone.0263789
Gnanvi, J. E., Salako, K. V., Kotanmi, G. B., & Glèlè Kakaï, R. (2021). On the reliability of predictions on Covid-19 dynamics: A systematic and critical review of modelling techniques. Infectious Disease Modelling, 6, 258–272. https://doi.org/10.1016/j.idm.2020.12.008
Goel, R., Bonnetain, L., Sharma, R., & Furno, A. (2021). Mobility-based SIR model for complex networks: with case study Of COVID-19. Social Network Analysis and Mining, 11(1). https://doi.org/10.1007/S13278-021-00814-3
Google. (2022). Community Mobility Reports. https://www.google.com/covid19/mobility/
Inthamoussou, F. A., Valenciaga, F., Núñez, S., & Garelli, F. (2022). Extended SEIR Model for Health Policies Assessment Against the COVID-19 Pandemic: the Case of Argentina. Journal of Healthcare Informatics Research, 6(1), 91–111. https://doi.org/10.1007/s41666-021-00110-x
Jo, W., & Kim, D. (2023). Neural additive time-series models: Explainable deep learning for multivariate time-series predic-tion. Expert Systems with Applications, 228. https://doi.org/10.1016/j.eswa.2023.120307
John-Otumu, A. M. G., Ikerionwu, C., Olaniyi, O. O., Dokun, O., Eze, U. F., & Nwokonkwo, O. C. (2024). Advancing COVID-19 Prediction with Deep Learning Models: A Review. International Conference on Science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024. https://doi.org/10.1109/SEB4SDG60871.2024.10630186
Kermack, W. O., & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, 115(772), 700-721.
Khalifa, N. E., Mawgoud, A. A., Abu-Talleb, A., Taha, M. H. N., & Zhang, Y. D. (2023). A COVID-19 Infection Prediction Model in Egypt Based on Deep Learning Using Population Mobility Reports. International Journal of Computational In-telligence Systems, 16(1). https://doi.org/10.1007/s44196-023-00272-z
Kim, D., Ali, S. T., Kim, S., Jo, J., Lim, J. S., Lee, S., & Ryu, S. (2022). Estimation of Serial Interval and Reproduction Num-ber to Quantify the Transmissibility of SARS-CoV-2 Omicron Variant in South Korea. Viruses, 14(3). https://doi.org/10.3390/V14030533
Livieris, I. E. (2023). A novel forecasting strategy for improving the performance of deep learning models. Expert Systems with Applications, 230. https://doi.org/10.1016/j.eswa.2023.120632
Locatelli, I., Trächsel, B., & Rousson, V. (2021). Estimating the basic reproduction number for COVID-19 in Western Europe. PLOS ONE, 16(3), e0248731. https://doi.org/10.1371/JOURNAL.PONE.0248731
Lucas, B., Vahedi, B., & Karimzadeh, M. (2023). A spatiotemporal machine learning approach to forecasting COVID-19 inci-dence at the county level in the USA. International Journal of Data Science and Analytics, 15, 247–266. https://doi.org/10.1007/s41060-021-00295-9
Luqmanul, A., Achmad, H., & Purwani, S. (2021). A Susceptible-Infected-Removed Epidemiological Model for COVID-19 Spreading in Indonesia. World Scientific News, 153(January), 55–64.
Marinov, T. T., & Marinova, R. S. (2022). Inverse problem for adaptive SIR model: Application to COVID-19 in Latin Ameri-ca. Infectious Disease Modelling, 7(1), 134–148. https://doi.org/10.1016/J.IDM.2021.12.001
Masum, M., Masud, M. A., Adnan, M. I., Shahriar, H., & Kim, S. (2022). Comparative study of a mathematical epidemic mod-el, statistical modeling, and deep learning for COVID-19 forecasting and management. Socio-Economic Planning Sciences, 80(December 2021), 101249. https://doi.org/10.1016/j.seps.2022.101249
Nabi, K. N., Tahmid, M. T., Rafi, A., Kader, M. E., & Haider, M. A. (2021). Forecasting COVID-19 cases: A comparative anal-ysis between recurrent and convolutional neural networks. Results in Physics, 24, 104137. https://doi.org/10.1016/j.rinp.2021.104137
Our World in Data. (2022). Indonesia: Coronavirus Pandemic Country Profile. https://ourworldindata.org/coronavirus/country/indonesia
Qu, Z., Li, Y., Jiang, X., & Niu, C. (2023). An innovative ensemble model based on multiple neural networks and a novel heu-ristic optimization algorithm for COVID-19 forecasting. Expert Systems with Applications, 212. https://doi.org/10.1016/j.eswa.2022.118746
Satuan Tugas Penanganan COVID-19. (2022). Data Sebaran. https://covid19.go.id/
Shuai, C., Zhao, B., Chen, X., Liu, J., Zheng, C., Qu, S., Zou, J. P., & Xu, M. (2024). Quantifying the impacts of COVID-19 on Sustainable Development Goals using machine learning models. Fundamental Research, 4(4), 890–897. https://doi.org/10.1016/J.FMRE.2022.06.016
Trajanoska, M., Trajanov, R., & Eftimov, T. (2022). Dietary, comorbidity, and geo-economic data fusion for explainable COVID-19 mortality prediction. Expert Systems with Applications, 209. https://doi.org/10.1016/j.eswa.2022.118377
Verma, H., Mandal, S., & Gupta, A. (2022). Temporal deep learning architecture for prediction of COVID-19 cases in India. Expert Systems with Applications, 195. https://doi.org/10.1016/j.eswa.2022.116611
Wathore, R., Rawlekar, S., Anjum, S., Gupta, A., Bherwani, H., Labhasetwar, N., & Kumar, R. (2023). Improving performance of deep learning predictive models for COVID-19 by incorporating environmental parameters. Gondwana Research, 114, 69–77. https://doi.org/10.1016/j.gr.2022.03.014
Wei, W. W. (2018). Multivariate time series analysis and applications. John Wiley & Sons.
Wintachai, P., & Prathom, K. (2021). Stability analysis of SEIR model related to efficiency of vaccines for COVID-19 situa-tion. Heliyon, 7(4), e06812. https://doi.org/10.1016/j.heliyon.2021.e06812
Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining. In Data Mining. https://doi.org/10.1016/C2009-0-19715-5
Xu, L., Magar, R., & Barati Farimani, A. (2022). Forecasting COVID-19 new cases using deep learning methods. Computers in Biology and Medicine, 144, 105342. https://doi.org/10.1016/J.COMPBIOMED.2022.105342
Yang, X., Wang, S., Xing, Y., Li, L., Xu, R. Y. Da, Friston, K. J., & Guo, Y. (2022). Bayesian data assimilation for estimating instantaneous reproduction numbers during epidemics: Applications to COVID-19. PLoS Computational Biology, 18(2). https://doi.org/10.1371/JOURNAL.PCBI.1009807
Zhang, P., Feng, K., Gong, Y., Lee, J., Lomonaco, S., & Zhao, L. (2022). Usage of Compartmental Models in Predicting COVID-19 Outbreaks. AAPS Journal, 24(5), 1–12. https://doi.org/10.1208/s12248-022-00743-9
Zheng, N., Du, S., Wang, J., Zhang, H., Cui, W., Kang, Z., Yang, T., Lou, B., Chi, Y., Long, H., Ma, M., Yuan, Q., Zhang, S., Zhang, D., Ye, F., & Xin, J. (2020). Predicting COVID-19 in China Using Hybrid AI Model. IEEE Transactions on Cyber-netics, 50(7), 2891–2904. https://doi.org/10.1109/TCYB.2020.2990162
Absar, N., Uddin, N., Khandaker, M. U., & Ullah, H. (2022). The efficacy of deep learning based LSTM model in forecasting the outbreak of contagious diseases. Infectious Disease Modelling, 7(1), 170–183. https://doi.org/10.1016/j.idm.2021.12.005
Alassafi, M. O., Jarrah, M., & Alotaibi, R. (2022). Time series predicting of COVID-19 based on deep learning. Neurocompu-ting, 468, 335–344. https://doi.org/10.1016/j.neucom.2021.10.035
Blavatnik School of Government. (2022). COVID-19 Government Response Tracker. https://www.bsg.ox.ac.uk/research/covid-19-government-response-tracker
Bsat, R., Chemaitelly, H., Coyle, P., Tang, P., Hasan, M. R., Kanaani, Z. Al, Kuwari, E. Al, Butt, A. A., Jeremijenko, A., Kaleeckal, A. H., Latif, A. N., Shaik, R. M., Nasrallah, G. K., Benslimane, F. M., Khatib, H. A. A., Yassine, H. M., Kuwari, M. G. A., Romaihi, H. E. Al, Al-Thani, M. H., … Ayoub, H. H. (2022). Characterizing the effective reproduction number during the COVID-19 pandemic: Insights from Qatar’s experience. Journal of Global Health, 12. https://doi.org/10.7189/JOGH.12.05004
Cinaglia, P., & Cannataro, M. (2022). Forecasting COVID-19 Epidemic Trends by Combining a Neural Network with Rt Esti-mation. Entropy, 24(7), 1–17. https://doi.org/10.3390/e24070929
Cortés-Carvajal, P. D., Cubilla-Montilla, M., & González-Cortés, D. R. (2022). Estimation of the Instantaneous Reproduction Number and Its Confidence Interval for Modeling the COVID-19 Pandemic. Mathematics, 10(2). https://doi.org/10.3390/MATH10020287
Delli Compagni, R., Cheng Id, Z., Russo, S., & Van Boeckel, T. P. (2022). A hybrid Neural Network-SEIR model for forecast-ing intensive care occupancy in Switzerland during COVID-19 epidemics. PLoS ONE, 3. https://doi.org/10.1371/journal.pone.0263789
Gnanvi, J. E., Salako, K. V., Kotanmi, G. B., & Glèlè Kakaï, R. (2021). On the reliability of predictions on Covid-19 dynamics: A systematic and critical review of modelling techniques. Infectious Disease Modelling, 6, 258–272. https://doi.org/10.1016/j.idm.2020.12.008
Goel, R., Bonnetain, L., Sharma, R., & Furno, A. (2021). Mobility-based SIR model for complex networks: with case study Of COVID-19. Social Network Analysis and Mining, 11(1). https://doi.org/10.1007/S13278-021-00814-3
Google. (2022). Community Mobility Reports. https://www.google.com/covid19/mobility/
Inthamoussou, F. A., Valenciaga, F., Núñez, S., & Garelli, F. (2022). Extended SEIR Model for Health Policies Assessment Against the COVID-19 Pandemic: the Case of Argentina. Journal of Healthcare Informatics Research, 6(1), 91–111. https://doi.org/10.1007/s41666-021-00110-x
Jo, W., & Kim, D. (2023). Neural additive time-series models: Explainable deep learning for multivariate time-series predic-tion. Expert Systems with Applications, 228. https://doi.org/10.1016/j.eswa.2023.120307
John-Otumu, A. M. G., Ikerionwu, C., Olaniyi, O. O., Dokun, O., Eze, U. F., & Nwokonkwo, O. C. (2024). Advancing COVID-19 Prediction with Deep Learning Models: A Review. International Conference on Science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024. https://doi.org/10.1109/SEB4SDG60871.2024.10630186
Kermack, W. O., & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, 115(772), 700-721.
Khalifa, N. E., Mawgoud, A. A., Abu-Talleb, A., Taha, M. H. N., & Zhang, Y. D. (2023). A COVID-19 Infection Prediction Model in Egypt Based on Deep Learning Using Population Mobility Reports. International Journal of Computational In-telligence Systems, 16(1). https://doi.org/10.1007/s44196-023-00272-z
Kim, D., Ali, S. T., Kim, S., Jo, J., Lim, J. S., Lee, S., & Ryu, S. (2022). Estimation of Serial Interval and Reproduction Num-ber to Quantify the Transmissibility of SARS-CoV-2 Omicron Variant in South Korea. Viruses, 14(3). https://doi.org/10.3390/V14030533
Livieris, I. E. (2023). A novel forecasting strategy for improving the performance of deep learning models. Expert Systems with Applications, 230. https://doi.org/10.1016/j.eswa.2023.120632
Locatelli, I., Trächsel, B., & Rousson, V. (2021). Estimating the basic reproduction number for COVID-19 in Western Europe. PLOS ONE, 16(3), e0248731. https://doi.org/10.1371/JOURNAL.PONE.0248731
Lucas, B., Vahedi, B., & Karimzadeh, M. (2023). A spatiotemporal machine learning approach to forecasting COVID-19 inci-dence at the county level in the USA. International Journal of Data Science and Analytics, 15, 247–266. https://doi.org/10.1007/s41060-021-00295-9
Luqmanul, A., Achmad, H., & Purwani, S. (2021). A Susceptible-Infected-Removed Epidemiological Model for COVID-19 Spreading in Indonesia. World Scientific News, 153(January), 55–64.
Marinov, T. T., & Marinova, R. S. (2022). Inverse problem for adaptive SIR model: Application to COVID-19 in Latin Ameri-ca. Infectious Disease Modelling, 7(1), 134–148. https://doi.org/10.1016/J.IDM.2021.12.001
Masum, M., Masud, M. A., Adnan, M. I., Shahriar, H., & Kim, S. (2022). Comparative study of a mathematical epidemic mod-el, statistical modeling, and deep learning for COVID-19 forecasting and management. Socio-Economic Planning Sciences, 80(December 2021), 101249. https://doi.org/10.1016/j.seps.2022.101249
Nabi, K. N., Tahmid, M. T., Rafi, A., Kader, M. E., & Haider, M. A. (2021). Forecasting COVID-19 cases: A comparative anal-ysis between recurrent and convolutional neural networks. Results in Physics, 24, 104137. https://doi.org/10.1016/j.rinp.2021.104137
Our World in Data. (2022). Indonesia: Coronavirus Pandemic Country Profile. https://ourworldindata.org/coronavirus/country/indonesia
Qu, Z., Li, Y., Jiang, X., & Niu, C. (2023). An innovative ensemble model based on multiple neural networks and a novel heu-ristic optimization algorithm for COVID-19 forecasting. Expert Systems with Applications, 212. https://doi.org/10.1016/j.eswa.2022.118746
Satuan Tugas Penanganan COVID-19. (2022). Data Sebaran. https://covid19.go.id/
Shuai, C., Zhao, B., Chen, X., Liu, J., Zheng, C., Qu, S., Zou, J. P., & Xu, M. (2024). Quantifying the impacts of COVID-19 on Sustainable Development Goals using machine learning models. Fundamental Research, 4(4), 890–897. https://doi.org/10.1016/J.FMRE.2022.06.016
Trajanoska, M., Trajanov, R., & Eftimov, T. (2022). Dietary, comorbidity, and geo-economic data fusion for explainable COVID-19 mortality prediction. Expert Systems with Applications, 209. https://doi.org/10.1016/j.eswa.2022.118377
Verma, H., Mandal, S., & Gupta, A. (2022). Temporal deep learning architecture for prediction of COVID-19 cases in India. Expert Systems with Applications, 195. https://doi.org/10.1016/j.eswa.2022.116611
Wathore, R., Rawlekar, S., Anjum, S., Gupta, A., Bherwani, H., Labhasetwar, N., & Kumar, R. (2023). Improving performance of deep learning predictive models for COVID-19 by incorporating environmental parameters. Gondwana Research, 114, 69–77. https://doi.org/10.1016/j.gr.2022.03.014
Wei, W. W. (2018). Multivariate time series analysis and applications. John Wiley & Sons.
Wintachai, P., & Prathom, K. (2021). Stability analysis of SEIR model related to efficiency of vaccines for COVID-19 situa-tion. Heliyon, 7(4), e06812. https://doi.org/10.1016/j.heliyon.2021.e06812
Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining. In Data Mining. https://doi.org/10.1016/C2009-0-19715-5
Xu, L., Magar, R., & Barati Farimani, A. (2022). Forecasting COVID-19 new cases using deep learning methods. Computers in Biology and Medicine, 144, 105342. https://doi.org/10.1016/J.COMPBIOMED.2022.105342
Yang, X., Wang, S., Xing, Y., Li, L., Xu, R. Y. Da, Friston, K. J., & Guo, Y. (2022). Bayesian data assimilation for estimating instantaneous reproduction numbers during epidemics: Applications to COVID-19. PLoS Computational Biology, 18(2). https://doi.org/10.1371/JOURNAL.PCBI.1009807
Zhang, P., Feng, K., Gong, Y., Lee, J., Lomonaco, S., & Zhao, L. (2022). Usage of Compartmental Models in Predicting COVID-19 Outbreaks. AAPS Journal, 24(5), 1–12. https://doi.org/10.1208/s12248-022-00743-9
Zheng, N., Du, S., Wang, J., Zhang, H., Cui, W., Kang, Z., Yang, T., Lou, B., Chi, Y., Long, H., Ma, M., Yuan, Q., Zhang, S., Zhang, D., Ye, F., & Xin, J. (2020). Predicting COVID-19 in China Using Hybrid AI Model. IEEE Transactions on Cyber-netics, 50(7), 2891–2904. https://doi.org/10.1109/TCYB.2020.2990162