The Impact of Mobility Patterns on the Spread of the COVID-19 in Indonesia
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Background: The novel coronavirus disease 2019 (COVID-19) has been spreading rapidly across the world and infected millions of people, many of whom died. As part of the response plans, many countries have been attempting to restrict people's mobility by launching social distancing protocol, including in Indonesia. It is then necessary to identify the campaign's impact and analyze the influence of mobility patterns on the pandemic's transmission rate.
Objective: Using mobility data from Google and Apple, this research discovers that COVID-19 daily new cases in Indonesia are mostly related to the mobility trends in the previous eight days.
Methods: We generate ten-day predictions of COVID-19 daily new cases and Indonesians' mobility by using Long-Short Term Memory (LSTM) algorithm to provide insight for future implementation of social distancing policies.
Results: We found that all eight-mobility categories result in the highest accumulation correlation values between COVID-19 daily new cases and the mobility eight days before. We forecast of the pandemic daily new cases in Indonesia, DKI Jakarta and worldwide (with error on MAPE 6.2% - 9.4%) as well as the mobility trends in Indonesia and DKI Jakarta (with error on MAPE 6.4 - 287.3%).
Conclusion: We discover that the driver behind the rapid transmission in Indonesia is the number of visits to retail and recreation, groceries and pharmacies, and parks. In contrast, the mobility to the workplaces negatively correlates with the pandemic spread rate.
Worldometer, "Worldometer COVID-19 coronavirus pandemic.” [Online]. Available: https://www.worldometers.info/coronavirus/. [Accessed: 23-Nov-2020].
WHO, "Archived: WHO Timeline Covid-19”, 2020. [Online]. Available: https://www.who.int/news/item/27-04-2020-who-timeline---covid-19. [Accessed: 10-Nov-2020].
S. Roy and P. Ghosh, "Factors affecting COVID-19 infected and death rates inform lockdown-related policymaking,” PLoS One, vol. 15, no. 10, pp. 1–18, 2020, doi: 10.1371/journal.pone.0241165.
J. Lin et al., "Containing the spread of coronavirus disease 2019 (COVID-19): Meteorological factors and control strategies,” Sci. Total Environ., vol. 744, no. December 2019, p. 140935, 2020.
Cartení¬, L. Di Francesco, and M. Martino, "The role of transport accessibility within the spread of the Coronavirus pandemic in Italy,” Saf. Sci., vol. 133, no. July 2020, p. 104999, 2021.
K. T. Lulbadda, D. Kobbekaduwa, and M. L. Guruge, "The impact of temperature, population size and median age on COVID-19 (SARS-CoV-2) outbreak,” Clin. Epidemiol. Glob. Heal., no. August, pp. 0–1, 2020.
H. Fang, L. Wang, and Y. Yang, "Human mobility restrictions and the spread of the Novel Coronavirus (2019-nCoV) in China,” J. Public Econ., vol. 191, p. 104272, 2020.
Cartení¬, L. Di Francesco, and M. Martino, "How mobility habits influenced the spread of the COVID-19 pandemic: Results from the Italian case study,” Sci. Total Environ., vol. 741, p. 140489, 2020.
Aleta, Q. Hu, J. Ye, P. Ji, and Y. Moreno, "A data-driven assessment of early travel restrictions related to the spreading of the novel COVID-19 within mainland China,” Chaos, Solitons and Fractals, vol. 139, p. 110068, 2020.
W. Liu et al., "Analysis of factors associated with disease outcomes in hospitalised patients with 2019 novel coronavirus disease," Chin. Med. J. (Engl)., vol. 133, no. 9, pp. 1032–1038, 2020, doi: 10.1097/CM9.0000000000000775.
US Pharm, "Factors Affecting COVID-19 Transmission,” 2020, [Online]. Available: https://www.uspharmacist.com/article/factors-affecting-covid19-transmission. [Accessed: 10-Nov-2020].
K. Gowthami and M. R. P. Kumar, "Study on Business Intelligence Tools for Enterprise Dashboard Development,” Int. Res. J. Eng. Technol., vol. 4, no. 4, pp. 2987–2992, 2017, [Online]. Available: https://www.irjet.net/archives/V4/i4/IRJET-V4I4721.pdf. [Accessed: 10-Nov-2020].
Z. Yang et al., "Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions,” J. Thorac. Dis., vol. 12, no. 3, pp. 165–174, 2020, doi: 10.21037/jtd.2020.02.64.
L. Golestaneh et al., "The association of race and COVID-19 mortality,” EClinicalMedicine, vol. 25, p. 100455, 2020, doi: 10.1016/j.eclinm.2020.100455.
P. Wang, X. Zheng, J. Li, and B. Zhu, "Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics,” Chaos, Solitons and Fractals, vol. 139, p. 110058, 2020, doi: 10.1016/j.chaos.2020.110058.
Holmdahl, I. and Buckee, C., 2020. Wrong but useful”what covid-19 epidemiologic models can and cannot tell us. New England Journal of Medicine.
R. Nuraini, "Kasus Covid-19 Pertama, Masyarakat Jangan Panik.” [Online]. Available: https://indonesia.go.id/narasi/indonesia-dalam-angka/ekonomi/kasus-covid-19-pertama-masyarakat-jangan-panik. [Accessed: 10-Nov-2020].
B. Nugraha, L. K. Wahyuni, H. Laswati, P. Kusumastuti, A. B. Tulaar, and C. Gutenbrunner, "COVID-19 pandemic in Indonesia: Situation and challenges of rehabilitation medicine in Indonesia,” Acta Med. Indones., vol. 52, no. 3, pp. 299–305, 2020.
D. M. Purnamasari, "Capai 657.948, Covid-19 di Indonesia belum menurun.” [Online]. https://nasional.kompas.com/read/2020/12/20/06490421/capai-657948-covid-19-di-tanah-air-belum-menurun. [Accessed: 10-Nov-2020].
Pemda DKI Jakarta, "JAKARTA TANGGAP COVID-19,” 2020. [Online]. https://corona.jakarta.go.id. [Accessed: 10-Nov-2020].
J. P. BEans, "Indonesia's ‘new normal' a disaster in the making,” 2020. [Online]. https://asiatimes.com/2020/06/indonesias-new-normal-a-disaster-in-the-making/. [Accessed: 23-Nov-2020].
Prastyo, P. H., Sumi, A. S., Dian, A. W., & Permanasari, A. E. (2020). Tweets Responding to the Indonesian Government's Handling of COVID-19: Sentiment Analysis Using SVM with Normalized Poly Kernel. Journal of Information Systems Engineering and Business Intelligence, 6(2), 112-122.
Apple Inc., "Mobility Trends,” 2020. [Online]. Available: https://covid19.apple.com/mobility. [Accessed: 06-Nov-2020].
Google LLC., "Community Mobility Report,” 2020. [Online]. Available: https://www.google.com/covid19/mobility/. [Accessed: 06-Nov-2020].
Gugus Tugas Percepatan Penanganan COVID-19 Republik Indonesia, "Indonesia COVID-19 Hub Site”, 2020. [Online]. Available: https://bnpb-inacovid19.hub.arcgis.com. [Accessed: 06-Nov-2020].
M. K. Indonesia, "Beda Data BNPB vs Kemenkes,” 2020.
Kementerian Kesehatan Republik Indonesia, "Dashboard Data Kasus COVID-19 di Indonesia,” 2020. [Online]. Available: https://www.kemkes.go.id/article/view/20031900002/Dashboard-Data-Kasus-COVID-19-di-Indonesia.html. [Accessed: 06-Nov-2020].
Satuan Tugas Penanganan COVID-19. "Peta Penanganan,”. 2020. [Online]. Available: https://covid19.go.id/peta-sebaran. [Accessed: 06-Nov-2020].
Kawal COVID-19. "Informasi Terkini COVID-19 di Indonesia,”. 2020. [Online]. Available: https://kawalcovid19.id. [Accessed: 06-Nov-2020].
World Health Organization. "WHO Coronavirus Disease (COVID-19) Dashboard,”. 2020. [Online]. Available: https://covid19.who.int. [Accessed: 06-Nov-2020].
S. Hochreiter, "the Vanishing Gradient Problem during Learning Recurrent Neural Sets and Problem Solutions,” Int. J. Uncertainty, Fuzziness, Knowledge-Based Syst., vol. 6, no. 2, pp. 107–116, 1998.
S. Hochreiter. "Long short-term memory”. Neural Comput. 1997, 9, 1735–1780
Gers, F.A.; J. Schmidhuber.; F. Cummins. "Learning to Forget: Continual Prediction with LSTM”. Neural Comput. 2000, 12, 2451–2471.
Lewis, C.D. (1982). "Industrial and business forecasting methods”. London: Butterworths.
World Health Organization. "Transmission of SARS-CoV-2: implications for infection prevention precautions”. 2020. [Online]. Available: https://www.who.int/news-room/commentaries/detail/transmission-of-sars-cov-2-implications-for-infection-prevention-precautions. [Accessed: 10-Jan-2021].
Berita Satu. "Rata-rata Masa Inkubasi Covid-19 di Indonesia, 5-6 Hari”. 2020. [Online]. https://www.beritasatu.com/kesehatan/619139/ratarata-masa-inkubasi-covid19-di-indonesia-56-hari. [Accessed: 10-Jan-2021].
Detik News. "Berapa Lama Pemeriksaan Spesimen Virus Corona? Ini Kata Kemenkes”. 2020. [Online]. https://news.detik.com/berita/d-4930237/berapa-lama-pemeriksaan-spesimen-virus-corona-ini-kata-kemenkes. [Accessed: 10-Jan-2021].
Koran Tempo. "Durasi Pemeriksaan Covid di DKI Makin Cepat”. 2020. [Online]. https://koran.tempo.co/read/metro/454483/durasi-pemeriksaan-covid-di-dki-makin-cepat. [Accessed: 10-Jan-2021].
Republika. "Lamanya Hasil Swab Test Jadi Masalah di Daerah”. [Online]. https://republika.co.id/berita/q98fb5328/lamanya-hasil-tes-emswabem-jadi-masalah-di-daerah. [Accessed: 10-Jan-2021].
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