Spatial distribution of COVID-19 cases, epidemic spread rate, spatial pattern, and its correlation with meteorological factors during the first to the second waves

Rendana, Muhammad; Idris, Wan Mohd Razi; Rahim, Samsudin A.

doi:10.1016/j.jiph.2021.07.010

Cited by 22 publications

(7 citation statements)

References 26 publications

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“…On March 11, 2020, the WHO announced over 118,000 confirmed cases in over 110 countries around the world ( World Health Organization, 2020 ). In Indonesia, the first confirmed COVID-19 case found on 2 March 2020 ( Ministry of Health Indonesia, 2020 ; Rendana et al, 2021 ). Then, after several weeks, the total of confirmed cases was up to 5,000.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Impact of COVID-19 Pandemic on Traffic Volume and Air Quality in Urban Areas

Rendana

Idris

Rahim

et al. 2022

Air, Soil and Water Research

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The large transmission of COVID-19 has resulted in a deep impact on the surrounding urban environments, especially on air quality and traffic flows. The objective of this study was to analyze air pollutant concentrations (PM10, SO2, NO2, CO, and O3) and traffic volumes at five congested districts (Bundaran HI, Kelapa Gading, Jagakarsa, Lubang Buaya, and Kebon Jeruk) within Jakarta city impacted by the large-scale social restriction (LSSR) policy. Air quality data during three periods; before, during, and after the LSSR at five observed districts was obtained from the Department of Environment of Jakarta using the Air Quality Monitoring (AQMS) tool. While vehicle speed data were obtained from the waze data during the study period. In this study, air pollutant data during three periods; before, during, and after the LSSR were compared with vehicle speed and meteorological data using a statistical analysis. Results revealed the mean traffic volume at all five districts has greatly reduced by 19% from before to during the LSSR period. It was consistent with the mean PM10, NO2, CO, and SO2 concentrations which also dropped about 46%, 45%, 30%, and 23% respectively. In contrast, the concentrations of air pollutants significantly increased after the LSSR period. During the LSSR period, the traffic volume was negatively associated with the O3 concentration ( r = −.86, p < .01), it was different with before the LSSR periods where the traffic volume associated with CO ( r = .88, p < .01) and NO2 ( r = .89, p < .01). The broad analysis of changes in air pollutants and traffic volumes can be used by the authorities to arrange a good air quality management and an effective way for current and future scenarios.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Impact of COVID-19 Pandemic on Traffic Volume and Air Quality in Urban Areas

Rendana

Idris

Rahim

et al. 2022

Air, Soil and Water Research

Self Cite

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“…In COVID-19 modeling, different types of models are used to understand SARS-CoV-2 transmission, such as deterministic models, stochastic models [18][19][20][21][22][23][24][25], agent-based models [26,27], discrete models [28,29], spatial models [30][31][32], network models [33,34], etc. COVID-19 stochastic models are sometimes compartmental models with the introduction of white noise, such as Gaussian noise or Brownian noise, to create randomness in order to find the condition of extinction and persistence on average of the disease and to prove the existence and uniqueness of the global positive solution of the model.…”

Section: Methodsmentioning

confidence: 99%

Analytical insights of the effects of Non-Pharmaceutical Interventions on SRAS-CoV-2 Dynamic with Application to West African Data

Traore,

Houenou,

Glèlè Kakaï

2023

Contemp. Math.

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The COVID-19 pandemic was caused by the rapid spread of a new coronavirus (SARS-CoV-2) worldwide. COVID-19 pandemic mitigation measures caused significant social and economic disruption, especially in regions with weak economic and fragile healthcare systems like West Africa. Therefore, accurate knowledge of the impact of these measures on its dynamics is important in decision-making. In this study, we formulated and used a deterministic compartmental model, considering two sub-classes of susceptible individuals (S1 and S2), where S1 is the population living around the epicenter of the epidemic and S2 is the population living far from the epicenter of the epidemic. The aim was to (i) theoretically assess the impact of measures reducing the transmission rate of the disease (ψ) on the epidemic dynamics and (ii) analyze the impact of measures reducing the probability of contact between infected and susceptible individuals (detection and isolation rates, θap, θs) on the epidemic dynamics. We determined the expressions of the basic and control reproduction numbers and studied the sensitivity and elasticity of the control reproduction number with respect to ψ, θap, θs, and heterogeneity factor, k(S1/S2). Application to the COVID-19 first-wave data from West Africa revealed that the basic reproduction number was 1.85. Moreover, the results indicated that a 50% reduction in the transmission rate of COVID-19 or the detection and isolation of 10% of infected individuals per day should help to reach the peak of the epidemic. Furthermore, a 100% increase in the heterogeneity factor induces a 16% increase in the control reproduction number when θap = 0.15 and a 14% increase in the control reproduction number when θap = 0.6. These conclusions could help design control measures to curtail future epidemics.

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“…Gelombang pertama kasus Covid-19 di Sumatera Selatan adalah periode waktu permulaan diterapkannya pembatasan sosial berskala besar (PSBB) dan gelombang kedua adalah pasca PSBB (Rendana et al, 2021). Peningkatan kasus Covid-19 pada kondisi gelombang dikaitkan dengan penyebab adanya aktivitas pasca hari besar, padahal tidak setiap kondisi gelombang bertepatan dengan aktivitas hari besar.…”

Section: •109unclassified

Uji Beda Kasus Covid-19 Di Wilayah Sumatera Selatan Berdasarkan Pembagian Kelompok Periode Waktu

Cahyono

Cahyawati

Yulianti³

et al. 2023

TEOREMA

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Kasus Covid-19 bergerak dinamis dan masih cenderung meningkat. Artikel ini bertujuan untuk mendeskripsikan hasil penelitian pengujian perbedaan kasus baru Covid-19 antar kelompok periode waktu di Provinsi Sumatera Selatan. Data diamati dari 1 Januari 2021 hingga 31 Juli 2022 yang dikumpulkan dari publikasi website provinsi. Metode penelitian menggunakan uji Kruskall-Wallis sebagai alternatif uji one-way ANOVA. Periode waktu dibagi menjadi tujuh kelompok yang terdiri dari empat kelompok kondisi gelombang dan tiga kelompok kondisi melandai. Pengujian hipotesis dilakukan pada kondisi gelombang, kondisi melandai, dan kombinasi antar kondisi gelombang dan melandai. Hasil kondisi gelombang menunjukkan keempat kelompok periode waktu memiliki perbedaan yang signifikan terhadap kondisi kasus Covid-19. Pengujian hipotesis pada kasus Covid-19 antara kondisi melandai dan kombinasi gelombang dan melandai menunjukkan signifikansi hasil yang sama. Hasil penelitian memberikan informasi bahwa jumlah kasus Covid-19 pada periode waktu yang bertepatan dengan pasca hari besar keagamaan, atau hari libur nasional menunjukkan peningkatan rata-rata kasus yang signifikan berbeda secara statistik dibandingkan dengan kelompok periode waktu lainnya. Hasil tersebut memperkuat informasi bahwa jumlah kasus baru Covid-19 bergerak dinamis dan berbeda-beda berdasarkan kelompok periode waktu akibat adanya momen aktivitas tertentu. Hasil ini menyarankan untuk tetap menerapkan protokol kesehatan terutama ketika kita berada dalam beraktivitas sosial.

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Spatial distribution of COVID-19 cases, epidemic spread rate, spatial pattern, and its correlation with meteorological factors during the first to the second waves

Cited by 22 publications

References 26 publications

RETRACTED: Impact of COVID-19 Pandemic on Traffic Volume and Air Quality in Urban Areas

RETRACTED: Impact of COVID-19 Pandemic on Traffic Volume and Air Quality in Urban Areas

Analytical insights of the effects of Non-Pharmaceutical Interventions on SRAS-CoV-2 Dynamic with Application to West African Data

Uji Beda Kasus Covid-19 Di Wilayah Sumatera Selatan Berdasarkan Pembagian Kelompok Periode Waktu

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