DAMPAK POPULASI DAN MOBILITAS PERKOTAAN TERHADAP PENYEBARAN PANDEMI COVID-19 DI JAKARTA

Rizki Adriadi Ghiffari

Abstract


 

The Covid-19 disease outbreak has spread to more than 200 countries, including Indonesia. Epicenter of the spread of the disease is in big cities that have been designated as local transmission, including the city of Jakarta. In addition to the ability of virus transmission, the spread of infectious diseases directly is also influenced by the characteristics of the population and human mobility as a breeding ground for viruses. Based on the results of the correlation test, it was found that mobility within the city and external mobility, had an intermediate effect on the spread of COVID-19 disease. While the human development index, population growth, poor population, and vulnerable age variables also have a low impact on the spread of this disease. The ability to detect and respond to large-scale disease outbreaks is needed, and effective population mobility restrictions are needed to control the spread of this disease.

Key words: Urban Demographic, Urban Mobility, Covid-19 Pandemic 

 

Wabah penyakit Covid-19 telah menyebar ke lebih dari 200 negara, termasuk Indonesia. Episentrum penyebaran penyakit berada di kota-kota besar yang telah ditetapkan sebagai transmisi lokal, diantaranya Kota Jakarta. Selain kemampuan penularan virus, penyebaran penyakit menular langsung juga dipengaruhi oleh karakteristik populasi dan mobilitas manusia sebagai inang perkembangbiakan virus. Berdasarkan hasil uji korelasi didapatkan bahwa mobilitas di dalam kota dan mobilitas dari luar kota, berpengaruh dalam tingkat menengah terhadap penyebaran penyakit COVID-19. Sedangkan variabel indeks pembangunan manusia, pertumbuhan penduduk, penduduk usia rentan, penduduk miskin  juga berpengaruh secara rendah terhadap penyebaran penyakit ini. Diperlukan kemampuan mendeteksi dan merespon wabah penyakit skala besar, serta pembatasan mobilitas penduduk yang efektif untuk dapat mengendalikan penyebaran penyakit ini.

Kata Kunci: Karakteristik Demografi, Mobilitas Penduduk, Pandemi Covid-19


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References


Badan Pusat Statistika DKI Jakarta. (2020). Provinsi DKI Jakarta Dalam Agka. Jakarta: Badan Pusat Statistika.

Bedford, J., Enria, D., Giesecke, J., Heymann, D. L., Ihekweazu, C., Kobinger, G., … Wieler, L. H. (2020). COVID-19: towards controlling of a pandemic. The Lancet, 395(10229), 1015–1018. https://doi.org/10.1016/S0140-6736(20)30673-5

Detikcom. (2020, April 20). Ungkap Corona Masuk RI Sejak Januari , Pakar UI : Pemerintah Menyangkal Terus. Trans Media, pp. 1–6.

Dinas Kesehatan Kota Semarang. (2020). Data Informasi Coronavirus ( Covid-19 ) Kota Semarang, (April), 2020. Retrieved from https://siagacorona.semarangkota.go.id/halaman/odppdpv2

Dinas Kesehatan Provinsi DKI Jakarta. (2020). Data Pemantauan COVID-19 DKI Jakarta Tanggal 31 Mei 2020. Jakarta.

Farmer, P. (1996). Social Inequalities and Emerging Infectious Diseases. Emerging Infectious Diseases, 2(4), 259–269. https://doi.org/10.3201/eid0204.960402

Google Community. (2020). Google Community Mobility Report of Indonesia. Mountain View, California. Retrieved from https://www.google.com/covid19/mobility/

Kumar, A., Holenweger, R., & Martins, J. (2020). COVID-19 Aggregated movement patterns correlated to confirmed case counts of Indonesia. San Francisco. Retrieved from http://wwwhttps//citydash.ai/data_for_humanity/public_health/indonesia

Lee Rodgers, J., & Alan Nice Wander, W. (1988). Thirteen ways to look at the correlation coefficient. American Statistician, 42(1), 59–66. https://doi.org/10.1080/00031305.1988.10475524

Merler, S., & Ajelli, M. (2010). The role of population heterogeneity and human mobility in the spread of pandemic influenza. In Proceedings of The Royal Society (pp. 557–565). The Royal Society. https://doi.org/10.1098/rspb.2009.1605

Neiderud, C. J. (2015). How urbanization affects the epidemiology of emerging infectious diseases. African Journal of Disability, 5(1), 1–9. https://doi.org/10.3402/iee.v5.27060

Nishimura, A., Tabuchi, Y., Kikuchi, M., Masuda, R., Goto, K., & Iijima, T. (2016). The Amount of Fluid Given During Surgery That Leaks Into the Interstitium Correlates With Infused Fluid Volume and Varies Widely Between Patients. Anesthesia & Analgesia, 123, 1. https://doi.org/10.1213/ANE.0000000000001505

Oppenheim, B., Gallivan, M., Madhav, N. K., Brown, N., Serhiyenko, V., Wolfe, N. D., & Ayscue, P. (2019). Assessing global preparedness for the next pandemic: Development and application of an Epidemic Preparedness Index. BMJ Global Health, 4(1), 1–9. https://doi.org/10.1136/bmjgh-2018-001157

Sands, P., El Turabi, A., Saynisch, P. A., & Dzau, V. J. (2016). Assessment of economic vulnerability to infectious disease crises. The Lancet, 388(10058), 2443–2448. https://doi.org/10.1016/S0140-6736(16)30594-3

Schober, P., & Schwarte, L. A. (2018). Correlation coefficients: Appropriate use and interpretation. Anesthesia and Analgesia, 126(5), 1763–1768. https://doi.org/10.1213/ANE.0000000000002864

Toole, M. J., & Waldman, R. J. (1990). Prevention of Excess Mortality in Refugee and Displaced Populations in Developing Countries. JAMA: The Journal of the American Medical Association, 263(24), 3296–3302. https://doi.org/10.1001/jama.1990.03440240086021




DOI: https://doi.org/10.24114/tgeo.v9i1.18622

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