COVID-19 transmission risk in Surabaya and Sidoarjo: an inhomogeneous marked Poisson point process approach

Choiruddin, Achmad; Hannanu, Firdaus Fabrice; Mateu, Jorge; Fitriyanah, Vanda

doi:10.1007/s00477-023-02393-5

Cited by 7 publications

(2 citation statements)

References 24 publications

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“…Model inhomogeneous Thomas cluster process pada penelitian tersebut menghasilkan model yang baik. Penelitian tentang spatial point process lainnya [12], [13], [14], [15].…”

Section: Pendahuluanunclassified

Prediksi Resiko Gempa Menggunakan Model Spatial Point Process

Rahman,

Utami,

Fauzi

2024

variance

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Pertemuan lempeng Eurasia, lempeng Indo-Australia, dan lempeng Pasifik terjadi di sekitar Pulau Sulawesi dan Maluku. Pertemuan ketiga lempeng ini menyebabkan sering terjadi gempa bumi. Faktor-faktor yang mempengaruhi gempa bumi secara geologis adalah jarak terhadap zona subduksi, gunung berapi, dan sesar aktif. Untuk keperluan mitigasi risiko bencana gempa bumi, perlu dilakukan prediksi risiko terjadinya gempa bumi. Pada penelitian ini menggunakan model inhomogeneous thomas cluster process yang digunakan untuk memprediksi risiko kejadian gempa bumi. Variabel yang digunakan dalam penelitian ini adalah koordinat zona subduksi, sesar aktif, dan gunung api di pulau Sulawesi dan Maluku. Hasil yang diperoleh berdasarkan fungsi K gempa bumi di pulau Sulawesi dan Maluku membentuk cluster. Uji chi-square menyimpulkan kejadian gempa bumi di Pulau Sulawesi dan Maluku bersifat inhomogen (tidak homogen). Berdasarkan model estimasi inhomogeneous thomas cluster process, variabel zona subduksi, sesar aktif, dan gunung berapi mempunyai pengaruh yang signifikan terhadap terjadinya gempa bumi. Dari ketiga variabel yang mempengaruhi terjadinya gempa terbesar (1,8 kali) adalah variabel letak gunung api. Berdasarkan hasil prediksi, Provinsi Gorontalo, Sulawesi Utara, Sulawesi Tengah, Maluku, dan Maluku Utara memiliki risiko gempa bumi yang tinggi.

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“…Model inhomogeneous Thomas cluster process pada penelitian tersebut menghasilkan model yang baik. Penelitian tentang spatial point process lainnya [12], [13], [14], [15].…”

Section: Pendahuluanunclassified

Prediksi Resiko Gempa Menggunakan Model Spatial Point Process

Rahman,

Utami,

Fauzi

2024

variance

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“…In conjunction with geographical considerations, densely populated locations are subject to additional social factors that exert a notable impact on the risk of transmission. These factors include heightened workplace and places of worship interaction (Choiruddin et al, 2023).…”

Section: This Is Particularly Pertinent As Approximately One-third Of...mentioning

confidence: 99%

COVID-19 pandemic and population density problem in Indonesia: transmigration policy as an alternative program

Saleh,

Khadafi,

Nurmandi

et al. 2023

otoritas

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The objective of this study is to examine the associations between levels of population density and the transmission of COVID-19 in Indonesia. Descriptive analysis is employed to determine the data distribution of the response variable (dependent variable Y) as well as the independent variables X1 and X2. Therefore, inferential analysis is a quantitative technique that involves examining a sample in order to draw conclusions about a larger population. The present investigation has revealed a statistically significant positive connection (r = 0.954) between the incidence of COVID-19 patients and population density in Indonesia. The Model Summary provides the R value (Correlation Coefficient) of 0.959, indicating a strong positive correlation between the variables. Additionally, the multiple correlation coefficient r (Multiple R) is 0.920, which represents the determination index or the proportion of the influence of X on Y. Therefore, it can be asserted that 92% of COVID-19 cases are driven by population density and the number of individuals experiencing employment terminations, whilst the remaining 8% is affected by additional factors. The regression equation can be constructed from the Coefficients table in the following manner: The equation can be expressed as . The calculation of the Standard Error of the Estimate (SE) yields a value of 8151.076 or 8151. The standardized coefficient (Beta) of 1.381 represents the extent of association between the number of COVID-19 infection cases and population density.

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