The Significance of Dynamic COVID-19 Dashboard in Formulating School Reopening Strategies

Feby Artwodini Muqtadiroh; Eko Mulyanto Yuniarno; Supeno Mardi Susiki Nugroho; Muhammad Reza Pahlawan; Riris Diana Rachmayanti; Tsuyoshi Usagawa; Mauridhi Hery Purnomo

doi:10.23887/janapati.v13i1.76017

Authors

Feby Artwodini Muqtadiroh Institut Teknologi Sepuluh Nopember https://orcid.org/0000-0002-2304-647X
Eko Mulyanto Yuniarno Institut Teknologi Sepuluh Nopember https://orcid.org/0000-0003-1243-3025
Supeno Mardi Susiki Nugroho Institut Teknologi Sepuluh Nopember
Muhammad Reza Pahlawan Sekolah Tinggi Ilmu Ekonomi Indonesia (STIESIA), Surabaya
Riris Diana Rachmayanti Universitas Airlangga
Tsuyoshi Usagawa Kumamoto University
Mauridhi Hery Purnomo Institut Teknologi Sepuluh Nopember

DOI:

https://doi.org/10.23887/janapati.v13i1.76017

Keywords:

COVID-19, Dashboard, Schools reopen safely, Recurrent Neural Network, LSTM, Decision-making

Abstract

Experiments conducted with the COVID-19 dataset have predominantly concentrated on predicting cases fluctuating and classifying lung-related diseases. Nevertheless, the consequences of the COVID-19 pandemic have also spread to the education sector. To safeguard educational stability in response to the remote learning policy, we leverage authentic COVID-19 datasets alongside school information across 154 sub-areas in Surabaya City, Indonesia. Our focus is predicting the dynamic within these sub-areas where schools are located. The outcomes of this study, by incorporating the recurrent neural network of long- and short-term memory (RNN-LSTM) architecture and refined hyperparameters, effectively enhanced the predictive model's performance. The findings are showcased on a dashboard, visually representing the transmission of COVID-19 in schools across each sub-area. This information serves as a basis for informed decisions on the safe reopening of schools, aiming to mitigate the decline in education quality during the challenging pandemic.

References

H. Wang et al., “Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020-21,” Lancet, vol. 399, no. 10334, pp. 1513–1536, 2022, doi: 10.1016/S0140-6736(21)02796-3.

M. F. Aslan, M. F. Unlersen, K. Sabanci, and A. Durdu, “CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection,” Appl. Soft Comput., vol. 98, p. 106912, 2021, doi: 10.1016/j.asoc.2020.106912.

Mohammad H. Tayarani Najaran, “Applications of artificial intelligence in battling against covid-19: A literature review,” Chaos, Solitons and Fractals, vol. 142, no. January, pp. 1–31, 2020.

S. Dutta and S. K. Bandyopadhyay, “Machine learning approach for confirmation of COVID-19 cases: positive, negative, death and release,” Iberoam. J. Med., vol. 2, no. 3, pp. 172–177, 2020, doi: 10.53986/ibjm.2020.0031.

C. C. John, V. Ponnusamy, S. Krishnan Chandrasekaran, and N. R, “A Survey on Mathematical, Machine Learning and Deep Learning Models for COVID-19 Transmission and Diagnosis,” IEEE Rev. Biomed. Eng., vol. 14, no. 8, 2021, doi: 10.1109/RBME.2021.3069213.

J. Hopkins, “COVID-19 Dashboard,” Corona Virus Resource Center, 2022. https://coronavirus.jhu.edu/map.html (accessed Feb. 07, 2022).

D. for G. (D4G) Team, “Interactive COVID-19 Household Monitoring Dashboard,” The World Bank, 2021. https://www.worldbank.org/en/data/interactive/2020/11/11/covid-19-high-frequency-monitoring-dashboard (accessed Jan. 05, 2022).

WHO, “WHO Coronavirus (COVID-19) Dashboard,” World Health Organization, 2020. https://covid19.who.int/ (accessed Oct. 20, 2021).

J. Allen et al., “Coronavirus in the U.S.: Latest Map and Case Count,” New York Times, 2020. https://www.nytimes.com/interactive/2021/us/covid-cases.html (accessed Nov. 02, 2021).

R. Hao et al., “Control strategies and their effects on the COVID-19 pandemic in 2020 in representative countries,” J. Biosaf. Biosecurity, vol. 3, no. 2, pp. 76–81, 2021, doi: 10.1016/j.jobb.2021.06.003.

H. Patrinos, E. Vegas, and R. Carter-Rau, “An Analysis of COVID-19 Student Learning Loss,” Oxford Res. Encycl. Econ. Financ., no. May, 2023, doi: 10.1093/acrefore/9780190625979.013.893.

R. Donnelly and H. A. Patrinos, “Learning loss during Covid-19: An early systematic review,” Prospects, vol. 51, no. 4, pp. 601–609, 2022, doi: 10.1007/s11125-021-09582-6.

ADB, “Learning and Earning Losses from COVID-19 School Closures in Developing Asia,” Asian Dev. Outlook, no. April, pp. 1–25, 2021.

N. Reuge et al., “Education response to COVID 19 pandemic, a special issue proposed by UNICEF: Editorial review,” Int. J. Educ. Dev., vol. 87, p. 102485, 2021, doi: 10.1016/j.ijedudev.2021.102485.

Nasir Mustafa, “Impact of the 2019 – 20 coronavirus pandemic on education,” Int. J. Heal. Prefer. Res., pp. 1–36, 2020.

United Nations, “Policy Brief: Education during COVID-19 and beyond,” 2020. [Online]. Available: https://reliefweb.int/report/world/policy-brief-education-during-covid-19-and-beyond-august-2020.

UNESCO, “COVID-19 Education Response: Preparing the reopening of schools,” no. May, pp. 1–29, 2020, [Online]. Available: https://unesdoc.unesco.org/ark:/48223/pf0000373401.

N. Butcher, N. N. Khairina, C. Kumala, and S. Loots, “The Struggle Against COVID-19 in Indonesian Education: Responses, requirements, and policy needs for learning recovery,” 2021. [Online]. Available: www.worldbank.org.

UNICEF, “Indonesia Case Study: Situation Analysis on the Effects of and Responses to COVID-19 on the Education Sector in Asia,” 2021. doi: 10.1007/978-1-4615-5667-1_5.

D. Kekić and S. Miladinović, “Functioning of Educational System During an Outbreak of Acute Infectious Diseases,” no. November, 2016.

K. Meesters, “The Challenge and Value of Dashboard Development During the COVID-19 Pandemic,” in IEEE Global Humanitarian Technology Conference (GHTC) 2022, 2022, pp. 9–16.

J. Everts, “The dashboard pandemic,” Dialogues Hum. Geogr., vol. 10, no. 2, pp. 260–264, 2020, doi: 10.1177/2043820620935355.

UNICEF Indonesia, “The Impact of the COVID-19 Pandemic on Children ’ s Learning in Indonesia,” 2021.

UNESCO, UNICEF, World Bank, and World Food Programme, “Framework for reopening schools,” p. 5, 2020, [Online]. Available: https://www.unicef.org/media/68366/file/Framework-for-reopening-schools-2020.pdf.

A. Sherstinsky, “Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network,” Phys. D Nonlinear Phenom., vol. 404, no. March, pp. 1–43, 2020, doi: 10.1016/j.physd.2019.132306.

S. Siami-Namini, N. Tavakoli, and A. S. Namin, “The Performance of LSTM and BiLSTM in Forecasting Time Series,” Proc. - 2019 IEEE Int. Conf. Big Data, Big Data 2019, pp. 3285–3292, 2019, doi: 10.1109/BigData47090.2019.9005997.

S. Bouktif, A. Fiaz, A. Ouni, and M. A. Serhani, “Optimal deep learning LSTM model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches,” Energies, vol. 11, no. 7, 2018, doi: 10.3390/en11071636.

S. Sanders and C. Giraud-carrier, “Informing the Use of Hyperparameter Optimization Through Metalearning,” in 2017 IEEE International Conference on Data Mining, 2017, no. 1, pp. 1051–1056, doi: 10.1109/ICDM.2017.137.

T. Hinz, S. Magg, and S. Wermter, “Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks,” Int. J. Comput. Intell. Appl., vol. 17, no. 2, pp. 1850008-1-1850008–15, 2018, doi: 10.1142/S1469026818500086.

T. N. Fatyanosa and M. Aritsugi, “Effects of the Number of Hyperparameters on the Performance of GA-CNN,” Proc. - 2020 IEEE/ACM Int. Conf. Big Data Comput. Appl. Technol. BDCAT 2020, pp. 144–153, 2020, doi: 10.1109/BDCAT50828.2020.00016.

F. Hutter, L. Kotthoff, and J. Vanschoren, Automated Machine Learning. Methods, Systems, Challenges. 2020.

A. Sinha and M. Rathi, “COVID-19 prediction using AI analytics for South Korea,” Appl. Intell., 2021, doi: 10.1007/s10489-021-02352-z.

O. Castillo and P. Melin, “Forecasting of COVID-19 time series for countries in the world based on a hybrid approach combining the fractal dimension and fuzzy logic,” Chaos, Solitons and Fractals, vol. 140, 2020, doi: 10.1016/j.chaos.2020.110242.

A. Zeroual, F. Harrou, A. Dairi, and Y. Sun, “Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study,” Chaos, Solitons and Fractals, vol. 140, no. January, pp. 1–12, 2020.

F. W. Wibowo and Wihayati, “Prediction Modelling of COVID-19 on Provinces in Indonesia using Long Short-Term Memory Machine Learning,” J. Phys. Conf. Ser., vol. 1844, no. 1, 2021, doi: 10.1088/1742-6596/1844/1/012006.

CDC, “COVID Data Tracker,” Department of Health and Human Services, 2021. https://covid.cdc.gov/covid-data-tracker (accessed Mar. 31, 2023).

K. DEVAKUMAR, “COVID-19 Dataset,” Kaggle, 2021. https://www.kaggle.com/datasets/imdevskp/corona-virus-report (accessed Apr. 10, 2023).

C.-19 T. F. Indonesia, “Analysis of Recommendations Towards a Productive and Safe to COVID-19 in Indonesia,” 2020.

S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997, doi: 10.1162/neco.1997.9.8.1735.

F. A. Muqtadiroh, T. Usagawa, R. D. Rachmayanti, S. M. S. Nugroho, E. M. Yuniarno, and M. H. Purnomo, “Rules Determination Based on Time-Series Data to Classify Unsupervised Cases Based on Fuzzy Expert System,” Int. J. Intell. Eng. Syst., vol. 16, no. 3, pp. 258–268, 2023, doi: 10.22266/ijies2023.0630.20.