Mobile application to tackle infection disease in Indonesia

Leonardus Ansis Bas, Ali Shidqie Al Faruqi, Ridho Kurniwan Harefa, Harco Leslie Hendric Spits Warnars, Arief Ramadhan, Nurulhuda Noordin

Article ID: 3329
Vol 8, Issue 4, 2024

VIEWS - 367 (Abstract) 169 (PDF)

Abstract


Infectious diseases often occur, especially as diseases such as COVID-19 have claimed many lives in the years between 2019–2021. That’s why it’s called COVID-19, considering that this infectious disease outbreak started in 2019, and its consequences and effects are devastating. Like other countries’ governments, the Indonesian government always announces the latest data on this infectious disease, such as death rates and recoveries. Infectious diseases are transmitted directly through disease carriers to humans through infections such as fungi, bacteria, viruses and parasites. In this research, we offer a contagious illness monitoring application to help the public and government know the zone’s status so that people are more alert when travelling between regions. This application was created based on Web Application Programming Interface (API) data and configured on the Google Map API to determine a person’s or user’s coordinates in a particular zone. We made it using the prototype method to help users understand this application well. This research is part of the Automatic Identification System (AIS) research, where the use of mobile technology is an example of implementation options that can be made to implement this system.


Keywords


infection disease; mobile application; public health data; health monitoring

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References


Bhatt, P., Gupta, S., Singh, P., & Dhiman, P. (2017). Accident and road quality assessment using android google maps API. 2017 International Conference on Computing, Communication and Automation (ICCCA). https://doi.org/10.1109/ccaa.2017.8229952

Birenboim, A., Dijst, M., Scheepers, F. E., et al. (2019). Wearables and Location Tracking Technologies for Mental-State Sensing in Outdoor Environments. The Professional Geographer, 71(3), 449–461. https://doi.org/10.1080/00330124.2018.1547978

Chen, A., Zhu, L., Zang, H., Ding, Z., & Zhan, S. (2019). Computer-aided diagnosis and decision-making system for medical data analysis: A case study on prostate MR images. Journal of Management Science and Engineering, 4(4), 266–278. https://doi.org/10.1016/j.jmse.2020.01.002

Convissar, D. L., Gibson, L. E., Berra, L., et al. (2020). Application of Lung Ultrasound During the COVID-19 Pandemic: A Narrative Review. Anesthesia & Analgesia, 131(2), 345–350. https://doi.org/10.1213/ane.0000000000004929

El-Rashidy, N., El-Sappagh, S., Islam, S. M. R., et al. (2020). End-To-End Deep Learning Framework for Coronavirus (COVID-19) Detection and Monitoring. Electronics, 9(9), 1439. https://doi.org/10.3390/electronics9091439

Fadhil, M. J., Ali Fayadh, R., & Wali, M. K. (2020). Design and implementation a prototype system for fusion image by using SWT-PCA algorithm with FPGA technique. International Journal of Electrical and Computer Engineering (IJECE), 10(1), 757. https://doi.org/10.11591/ijece.v10i1.pp757-766

Hioual, O., Hioual, O., & Hemam, S. M. (2020). A Hybrid Approach for Clustering and Selecting of Cloud Services Based on User Preferences Evaluation. Intelligent and Fuzzy Techniques: Smart and Innovative Solutions, 63–70. https://doi.org/10.1007/978-3-030-51156-2_9

Iqbal, Z., Luo, D., Henry, P., et al. (2018). Accurate real time localization tracking in a clinical environment using Bluetooth Low Energy and deep learning. PLOS ONE, 13(10), e0205392. https://doi.org/10.1371/journal.pone.0205392

Lim, J., Jeon, S., Shin, H.-Y., et al. (2020). Case of the Index Patient Who Caused Tertiary Transmission of Coronavirus Disease 2019 in Korea: The Application of Lopinavir/Ritonavir for the Treatment of COVID-19 Pneumonia Monitored by Quantitative RT-PCR. Journal of Korean Medical Science, 35(6). https://doi.org/10.3346/jkms.2020.35.e79

Mahajan, A., Sivadas, N. A., & Solanki, R. (2020). An epidemic model SIPHERD and its application for prediction of the spread of COVID-19 infection in India. Chaos, Solitons & Fractals, 140, 110156. https://doi.org/10.1016/j.chaos.2020.110156

Mehraeen, E., Mehrtak, M., SeyedAlinaghi, S., et al. (2022). Technology in the Era of COVID-19: A Systematic Review of Current Evidence. Infectious Disorders - Drug Targets, 22(4). https://doi.org/10.2174/1871526522666220324090245

Mehraeen, E., SeyedAlinaghi, S., Heydari, M., et al. (2023). Telemedicine technologies and applications in the era of COVID-19 pandemic: A systematic review. Health Informatics Journal, 29(2), 146045822311674. https://doi.org/10.1177/14604582231167431

Mohammadi, S., Mohammadi, S., SeyedAlinaghi, S., et al. (2023). Artificial Intelligence in COVID-19 Management: A Systematic Review. Journal of Computer Science, 19(5), 554–568. https://doi.org/10.3844/jcssp.2023.554.568

Nindrea, R. D., Sari, N. P., Lazuardi, L., & Aryandono, T. (2020). Validation: The Use of Google Trends as an Alternative Data Source for COVID-19 Surveillance in Indonesia. Asia Pacific Journal of Public Health, 32(6–7), 368–369. https://doi.org/10.1177/1010539520940896

Pan, X. B. (2020). Application of personal-oriented digital technology in preventing transmission of COVID-19, China. Irish Journal of Medical Science (1971 -), 189(4), 1145–1146. https://doi.org/10.1007/s11845-020-02215-5

Priya, V., Sathiya Kumar, C., & Kannan, R. (2019). Resource scheduling algorithm with load balancing for cloud service provisioning. Applied Soft Computing, 76, 416–424. https://doi.org/10.1016/j.asoc.2018.12.021

Putra, I. P. G. A. A., Sediyono, E., & Setiawan, A. (2017). E-land design of mobile application for land information system using Android-based Google Maps API V2. 2017 International Conference on Innovative and Creative Information Technology (ICITech). https://doi.org/10.1109/innocit.2017.8319145

Rahmi, A., Piarsa, I. N., & Buana, P. W. (2017). FinDoctor-interactive android clinic geographical information system using Firebase and Google Maps API. International Journal of New Technology and Research, 3(7).

Shamsabadi, A., Pashaei, Z., Karimi, A., et al. (2022). Retracted: Internet of things in the management of chronic diseases during the COVID‐19 pandemic: A systematic review. Health Science Reports, 5(2). Portico. https://doi.org/10.1002/hsr2.557

Stormi, K. T., Laine, T., & Korhonen, T. (2019). Agile performance measurement system development: an answer to the need for adaptability? Journal of Accounting & Organizational Change, 15(2), 231–256. https://doi.org/10.1108/jaoc-09-2017-0076

Strahm, B., Gray, C. M., & Vorvoreanu, M. (2018). Generating Mobile Application Onboarding Insights Through Minimalist Instruction. Proceedings of the 2018 Designing Interactive Systems Conference. https://doi.org/10.1145/3196709.3196727

Weizman, Y., Tan, A. M., & Fuss, F. K. (2020). Use of wearable technology to enhance response to the Coronavirus (COVID-19) pandemic. Public Health, 185, 221–222. https://doi.org/10.1016/j.puhe.2020.06.048

Whitelaw, S., Mamas, M. A., Topol, E., & Van Spall, H. G. C. (2020). Applications of digital technology in COVID-19 pandemic planning and response. The Lancet Digital Health, 2(8), e435–e440. https://doi.org/10.1016/s2589-7500(20)30142-4

WHO (2020). Coronavirus disease (COVID-19) situation dashboard. Available online: https://who.sprinklr.com/ (accessed on 14 April 2023).




DOI: https://doi.org/10.24294/jipd.v8i4.3329

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