1. Home
  2. Vol 7 (2023)
  3. Muthaiyah

Data Driven Models for Contact Tracing Prediction: A Systematic Review of COVID-19

Saravanan Muthaiyah, Thein Oak Kyaw Zaw, Kalaiarasi Sonai Muthu Anbananthen, Byeonghwa Park, Myung Joon Kim

Abstract


The primary objective of this research is to identify commonly used data-driven decision-making techniques for contact tracing with regards to Covid-19. The virus spread quickly at an alarming level that caused the global health community to rely on multiple methods for tracking the transmission and spread of the disease through systematic contact tracing. Predictive analytics and data-driven decision-making were critical in determining its prevalence and incidence. Articles were accessed from primarily four sources, i.e., Web of Science, Scopus, Emerald, and the Institute of Electrical and Electronics Engineers (IEEE). Retrieved articles were then analyzed in a stepwise manner by applying Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISM) that guided the authors on eligibility for inclusion. PRISM results were then evaluated and summarized for a total of 845 articles, but only 38 of them were selected as eligible. Logistic regression and SIR models ranked first (11.36%) for supervised learning. 90% of the articles indicated supervised learning methods that were useful for prediction. The most common specialty in healthcare specialties was infectious illness (36%). This was followed closely by epidemiology (35%). Tools such as Python and SPSS (Statistical Package for Social Sciences) were also popular, resulting in 25% and 16.67%, respectively.

 

Doi: 10.28991/ESJ-2023-SPER-02

Full Text: PDF


Keywords


Coronavirus; Data Driven Decision Making; Prediction; Pandemic; Covid-19; SARS-CoV-2.

References


Jain, V., Duse, A., & Bausch, D. G. (2018). Planning for large epidemics and pandemics: Challenges from a policy perspective. Current Opinion in Infectious Diseases, 31(4), 316–324. doi:10.1097/QCO.0000000000000462.

Meo, S. A., Al-Khlaiwi, T., Usmani, A. M., Meo, A. S., Klonoff, D. C., & Hoang, T. D. (2020). Biological and epidemiological trends in the prevalence and mortality due to outbreaks of novel coronavirus COVID-19. Journal of King Saud University - Science, 32(4), 2495–2499. doi:10.1016/j.jksus.2020.04.004.

Wang, R., Hu, G., Jiang, C., Lu, H., & Zhang, Y. (2020). Data Analytics for the COVID-19 Epidemic. 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). doi:10.1109/compsac48688.2020.00-83.

Zhou, C., Su, F., Pei, T., Zhang, A., Du, Y., Luo, B., Cao, Z., Wang, J., Yuan, W., Zhu, Y., Song, C., Chen, J., Xu, J., Li, F., Ma, T., Jiang, L., Yan, F., Yi, J., Hu, Y., … Xiao, H. (2020). COVID-19: Challenges to GIS with Big Data. Geography and Sustainability, 1(1), 77–87. doi:10.1016/j.geosus.2020.03.005.

Zaw, T. O. K., Muthaiyah, S., Anbananthen, K. S. M., & Soe, M. T. (2022). A Novel Approach on Covid-19 Contact Tracing – Utilization of Low Calibrated Transmission Power & Signal Captures in BLE. Emerging Science Journal, 6, 181–192. doi:10.28991/esj-2022-sper-013.

Centers for Disease Control and Prevention (CDC). (2022). Contact Tracing for COVID-19. Centers for Disease Control and Prevention, U.S. Department of Health & Human Services, Washington, United States. Available online: https://www.cdc.gov/coronavirus/2019-ncov/php/contact-tracing/contact-tracing-plan/contact-tracing.html (accessed on July 2022).

Salzberger, B., Glück, T., & Ehrenstein, B. (2020). Successful containment of COVID-19: the WHO-Report on the COVID-19 outbreak in China. Infection, 48(2), 151–153. doi:10.1007/s15010-020-01409-4.

Sun, K., Chen, J., & Viboud, C. (2020). Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study. The Lancet Digital Health, 2(4), e201–e208. doi:10.1016/S2589-7500(20)30026-1.

Heymann, D. L., & Shindo, N. (2020). COVID-19: what is next for public health? The Lancet, 395(10224), 542–545. doi:10.1016/S0140-6736(20)30374-3.

Gulyaeva, M., Huettmann, F., Shestopalov, A., Okamatsu, M., Matsuno, K., Chu, D. H., Sakoda, Y., Glushchenko, A., Milton, E., & Bortz, E. (2020). Data mining and model-predicting a global disease reservoir for low-pathogenic Avian Influenza (A) in the wider pacific rim using big data sets. Scientific Reports, 10(1), 1–11. doi:10.1038/s41598-020-73664-2.

Legendre, F., Humbert, M., Mermoud, A., & Lenders, V. (2020). Contact Tracing: An Overview of Technologies and Cyber Risks. Cryptography and Security. doi:10.48550/arXiv.2007.02806.

Rahman, M. (2021). List of countries using Google and Apple's COVID-19 contact tracing API. XDA Developers. Available online: https://www.xda-developers.com/google-apple-covid-19-contact-tracing-exposure-notifications-api-app-list-countries/ (accessed on April 2022).

Patel, S., & Patel, H. (2016). Survey of Data Mining Techniques used in Healthcare Domain. International Journal of Information Sciences and Techniques, 6(1/2), 53–60. doi:10.5121/ijist.2016.6206.

Deo, R. C. (2015). Machine learning in medicine. Circulation, 132(20), 1920–1930. doi:10.1161/CIRCULATIONAHA.115.001593.

Alodat, M. (2021). Using deep learning model for adapting and managing covid-19 pandemic crisis. Procedia Computer Science, 184, 558–564. doi:10.1016/j.procs.2021.03.070.

Rai, P. K., Sonne, C., Song, H., & Kim, K.-H. (2022). The effects of COVID-19 transmission on environmental sustainability and human health: Paving the way to ensure its sustainable management. Science of The Total Environment, 838, 156039. https://doi.org/10.1016/j.scitotenv.2022.156039.

Ferreira, A. T., Fernandes, C., Vieira, J., & Portela, F. (2021). Pervasive intelligent models to predict the outcome of COVID-19 patients. Future Internet, 13(4). doi:10.3390/fi13040102.

Maakoul, O., Boucht, S., EL HACHIMI, K., & Azzouzi, S. (2020). Towards Evaluating the COVID’19 related Fake News Problem: Case of Morocco. 2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS). doi:10.1109/icecocs50124.2020.9314517.

Podder, P., & Mondal, M. R. H. (2020). Machine Learning to Predict COVID-19 and ICU Requirement. 2020 11th International Conference on Electrical and Computer Engineering (ICECE). doi:10.1109/icece51571.2020.9393123.

Rakhra, A., Jain, I., Gupta, R., & Bhatia, M. (2021). Predicting the Prevalence Rate of COVID-19 Falsity on Temperature. 11th International Conference on Cloud Computing, Data Science & Engineering. doi:10.1109/confluence51648.2021.9377198.

Rao, V. C. S., Gampa, S., Rama, V., Anumala, H., & Gadepally, A. (2021). Extracting Insights and Prognosis of Corona Disease. 2021 5th International Conference on Computer, Communication and Signal Processing (ICCCSP2021). doi:10.1109/icccsp52374.2021.9465516.

Romadhon, M. R., & Kurniawan, F. (2021). A Comparison of Naive Bayes Methods, Logistic Regression and KNN for Predicting Healing of Covid-19 Patients in Indonesia. 2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT). doi:10.1109/eiconcit50028.2021.9431845.

Tomar, M., Arora, T., Sabitha, A. S., & Hasteer, N. (2021). Comparative Study of Deep Learning Models for COVID-19 Diagnosis. 2021 2nd International Conference on Secure Cyber Computing and Communications (ICSCCC). doi:10.1109/icsccc51823.2021.9478139.

Alballa, N., & Al-Turaiki, I. (2021). Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: A review. Informatics in Medicine Unlocked, 24, 100564. doi:10.1016/j.imu.2021.100564.

Alsmadi, T., Alqudah, N., & Najadat, H. (2021). Prediction of Covid-19 patients states using Data mining techniques. 2021 International Conference on Information Technology (ICIT). doi:10.1109/icit52682.2021.9491716.

Gupta, V. K., Gupta, A., Kumar, D., & Sardana, A. (2021). Prediction of COVID-19 confirmed, death, and cured cases in India using random forest model. Big Data Mining and Analytics, 4(2), 116–123. doi:10.26599/BDMA.2020.9020016.

Tripathi, A., Kaur, P., & Suresh, S. (2021). AI in Fighting Covid-19: Pandemic Management. Procedia Computer Science, 185, 380–386. doi:10.1016/j.procs.2021.05.039.

Villavicencio, C. N., Macrohon, J. J. E., Inbaraj, X. A., Jeng, J. H., & Hsieh, J. G. (2021). Covid-19 prediction applying supervised machine learning algorithms with comparative analysis using weka. Algorithms, 14(7). doi:10.3390/a14070201.

Jaleel, R. A., Burhan, I. M., & Jalookh, A. M. (2021). A Proposed Model for Prediction of COVID-19 Depend on K-Nearest Neighbors Classifier:Iraq Case Study. 2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE). doi:10.1109/icecce52056.2021.9514171.

Mohammad Masum, A. K., Khushbu, S. A., Keya, M., Abujar, S., & Hossain, S. A. (2020). COVID-19 in Bangladesh: A Deeper Outlook into the Forecast with Prediction of Upcoming per Day Cases Using Time Series. Procedia Computer Science, 178, 291–300. doi:10.1016/j.procs.2020.11.031.

Adhikari, S. P., Meng, S., Wu, Y.-J., Mao, Y.-P., Ye, R.-X., Wang, Q.-Z., Sun, C., Sylvia, S., Rozelle, S., Raat, H., & Zhou, H. (2020). Epidemiology, causes, clinical manifestation and diagnosis, prevention and control of coronavirus disease (COVID-19) during the early outbreak period: a scoping review. Infectious Diseases of Poverty, 9(1). doi:10.1186/s40249-020-00646-x.

Burhanuddin, A., & Kurniawan, F. (2021). Analysis of the Spread of COVID-19 in Local Areas in Indonesia. 2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT). doi:10.1109/eiconcit50028.2021.9431906.

Khan, S., & Alfaifi, A. (2020). Modeling of coronavirus behavior to predict it’s spread. International Journal of Advanced Computer Science and Applications, 11(5), 394–399. doi:10.14569/IJACSA.2020.0110552.

S, M., V.R, N., P.S, R., S, R. R. M., & L, N. (2020). Pervasive computing in the context of COVID-19 prediction with AI-based algorithms. International Journal of Pervasive Computing and Communications, 16(5), 477–487. doi:10.1108/IJPCC-07-2020-0082.

Vadyala, S. R., Betgeri, S. N., Sherer, E. A., & Amritphale, A. (2021). Prediction of the number of COVID-19 confirmed cases based on K-means-LSTM. Array, 11, 100085. doi:10.1016/j.array.2021.100085.

Albastaki, A., Naji, M., Lootah, R., Almeheiri, R., Almulla, H., Almarri, I., Alreyami, A., Aden, A., & Alghafri, R. (2021). First confirmed detection of SARS-COV-2 in untreated municipal and aircraft wastewater in Dubai, UAE: The use of wastewater based epidemiology as an early warning tool to monitor the prevalence of COVID-19. Science of The Total Environment, 760, 143350. https://doi.org/10.1016/j.scitotenv.2020.143350.

Plessen, M. G. (2020). Integrated Time Series Summarization and Prediction Algorithm and its Application to COVID-19 Data Mining. 2020 IEEE International Conference on Big Data (Big Data). doi:10.1109/bigdata50022.2020.9377823.

Cook, A. H., & Cohen, D. B. (2008). Pandemic disease: A past and future challenge to governance in the United States. Review of Policy Research, 25(5), 449–471. doi:10.1111/j.1541-1338.2008.00346.x.

Cihan, P. (2020). Fuzzy Rule-Based System for Predicting Daily Case in COVID-19 Outbreak. 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). doi:10.1109/ismsit50672.2020.9254714.

Wu, Y., Zhu, C., Li, Y., Guo, L., & Wu, X. (2020). NetNCSP: Nonoverlapping closed sequential pattern mining. Knowledge-Based Systems, 196, 105812. doi:10.1016/j.knosys.2020.105812.

Zhan, F. (2021). COVID-19 Case Number Prediction Utilizing Dynamic Clustering With Polynomial Regression. 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC). doi:10.1109/ccwc51732.2021.9375966.

Elbasi, E., Mathew, S., Topcu, A. E., & Abdelbaki, W. (2021). A Survey on Machine Learning and Internet of Things for COVID-19. 2021 IEEE World AI IoT Congress (AIIoT). doi:10.1109/aiiot52608.2021.9454241.

Buscema, P. M., Della Torre, F., Breda, M., Massini, G., & Grossi, E. (2020). COVID-19 in Italy and extreme data mining. Physica A: Statistical Mechanics and Its Applications, 557, 124991. doi:10.1016/j.physa.2020.124991.

Zhang, Y., Guo, S. L., Han, L. N., & Li, T. L. (2016). Application and exploration of big data mining in clinical medicine. Chinese Medical Journal, 129(6), 731–738. doi:10.4103/0366-6999.178019.

Alanazi, H. O., Abdullah, A. H., Qureshi, K. N., & Ismail, A. S. (2018). Accurate and dynamic predictive model for better prediction in medicine and healthcare. Irish Journal of Medical Science, 187(2), 501–513. doi:10.1007/s11845-017-1655-3.

Nithya, B., & Ilango, V. (2017). Predictive analytics in health care using machine learning tools and techniques. 2017 International Conference on Intelligent Computing and Control Systems (ICICCS). doi:10.1109/iccons.2017.8250771.

Asadzadeh, A., Pakkhoo, S., Saeidabad, M. M., Khezri, H., & Ferdousi, R. (2020). Information technology in emergency management of COVID-19 outbreak. Informatics in Medicine Unlocked, 21, 100475. doi:10.1016/j.imu.2020.100475.

Wynants, L., Van Calster, B., Collins, G. S., Riley, R. D., Heinze, G., Schuit, E., … van Smeden, M. (2020). Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ, m1328. doi:10.1136/bmj.m1328.

Kelly-Cirino, C. D., Nkengasong, J., Kettler, H., Tongio, I., Gay-Andrieu, F., Escadafal, C., Piot, P., Peeling, R. W., Gadde, R., & Boehme, C. (2019). Importance of diagnostics in epidemic and pandemic preparedness. BMJ Global Health, 4, 1–8. doi:10.1136/bmjgh-2018-001179.

Kumari, R., Kumar, S., Poonia, R. C., Singh, V., Raja, L., Bhatnagar, V., & Agarwal, P. (2021). Analysis and predictions of spread, recovery, and death caused by COVID-19 in India. Big Data Mining and Analytics, 4(2), 65–75. doi:10.26599/BDMA.2020.9020013.

M, T., M, G., S.R, S., M, A., R, K., & P.S, R. (2020). Detecting coronavirus contact using internet of things. International Journal of Pervasive Computing and Communications, 16(5), 447–456. doi:10.1108/IJPCC-07-2020-0074.

Bhatia, S., & Malhotra, J. (2021). Naïve Bayes Classifier for Predicting the Novel Coronavirus. 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV). doi:10.1109/icicv50876.2021.9388410.

Estiri, H., Strasser, Z. H., & Murphy, S. N. (2021). Individualized prediction of COVID-19 adverse outcomes with MLHO. Scientific Reports, 11(1). doi:10.1038/s41598-021-84781-x.

Oyelade, O. N., & Ezugwu, A. E. (2020). A case-based reasoning framework for early detection and diagnosis of novel coronavirus. Informatics in Medicine Unlocked, 20(May), 100395. doi:10.1016/j.imu.2020.100395.


Full Text: PDF

DOI: 10.28991/ESJ-2023-SPER-02

Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Saravanan Muthaiyah, Thein Oak Kyaw Zaw, Kalaiarasi Sonai Muthu Anbananthen, Byeonghwa Park, Myung Joon Kim