Study on COVID-19 in the World and Indonesia Using Regression Model of SVM, Bayesian Ridge and Gaussian

Parhusip, Hanna Arini

doi:10.35799/jis.20.2.2020.28256

Cited by 15 publications

(13 citation statements)

References 6 publications

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“…The machine learning algorithms are also used to forecast the COVID-19 epidemic situation ( Parhusip, 2020 ; Singh et al, 2020 ; Parbat & Chakraborty, 2020 ). Regarding the identification of early warning signals of COVID-19 outbreaks as a binary classification problem, we compare the performance of our combined model with the support vector machine (SVM).…”

Section: Resultsmentioning

confidence: 99%

Forecasting the COVID-19 transmission in Italy based on the minimum spanning tree of dynamic region network

Dong

Zhang

Yang

et al. 2021

PeerJ

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Background Italy surpassed 1.5 million confirmed Coronavirus Disease 2019 (COVID-19) infections on November 26, as its death toll rose rapidly in the second wave of COVID-19 outbreak which is a heavy burden on hospitals. Therefore, it is necessary to forecast and early warn the potential outbreak of COVID-19 in the future, which facilitates the timely implementation of appropriate control measures. However, real-time prediction of COVID-19 transmission and outbreaks is usually challenging because of its complexity intertwining both biological systems and social systems. Methods By mining the dynamical information from region networks and the short-term time series data, we developed a data-driven model, the minimum-spanning-tree-based dynamical network marker (MST-DNM), to quantitatively analyze and monitor the dynamical process of COVID-19 spreading. Specifically, we collected the historical information of daily cases caused by COVID-19 infection in Italy from February 24, 2020 to November 28, 2020. When applied to the region network of Italy, the MST-DNM model has the ability to monitor the whole process of COVID-19 transmission and successfully identify the early-warning signals. The interpretability and practical significance of our model are explained in detail in this study. Results The study on the dynamical changes of Italian region networks reveals the dynamic of COVID-19 transmission at the network level. It is noteworthy that the driving force of MST-DNM only relies on small samples rather than years of time series data. Therefore, it is of great potential in public surveillance for emerging infectious diseases.

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Section: Resultsmentioning

confidence: 99%

Forecasting the COVID-19 transmission in Italy based on the minimum spanning tree of dynamic region network

Dong

Zhang

Yang

et al. 2021

PeerJ

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“…Adanya wabah Covid-19 yang telah muncul didunia menjadi perhatian semua pihak disebabkan tingkat penyebaran wabah yang cepat dan banyaknya kasus kematian yang terjadi (Parhusip, 2020;Zhang et al, 2020). Pada tanggal 11 Maret 2020, Covid-19 ditetapkan sebagai pandemi oleh World Health Organization (WHO).…”

Section: Pendahuluanunclassified

Prediksi Penambahan Kasus Covid-19 di Indonesia Melalui Pendekatan Time Series Menggunakan Metode Exponential Smoothing

Gibran

Setiyawati

Liantoni

2021

JIUP

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The Covid-19 pandemic in Indonesia has emerged starting in 2020. To know the development of cases, a good calculation is needed. A prediction system can help in analyzing accurate data on positive causes, cures, and deaths. The right prediction or forecast can be the answer to the question of the impact that will occur, forecasting will provide an overview to the government and the community so that it is hoped that related parties can prepare for future impacts or even reduce the number of cases growth. In this study, the Exponential Smoothing method was used as a prediction calculation. This method is simple but effective in producing accurate predictions. Forecasting data used comes from the Indonesian government with the assumption that the data is valid and reliable. Based on research that has been carried out to predict the increase in new cases of the Indonesian National Covid-19, the best alpha (α) value is 0.33 with an SSE of 1048027,939. This shows that the number of cases is increasing. The results of forecasting in this study using the time series approach and the SES method are more suitable for predicting the percentage increase in cases than knowing the exact number.

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“…Hal ini disebabkan oleh kesalahan dalam penanganan kasus COVID-19, buruknya kemampuan pengujian atau kurangnya alat pelindung bagi petugas medis (Almuttaqi, 2020). Pemodelan peningkatan kasus COVID-19 dilakukan menggunakan model eksponensial da gaussian, namun pendekatan ini masih memerlukan perbaikan karena kasus meningkat melebihi prediksi pemodelan yang dilakukan (Parhusip, 2020). Kasus positif COVID-19 yang terkonfirmasi di Indonesia per Mei 2020 sekitar 16.574 dan masih terus meningkat (Gugus COVID-19, 2020).…”

Section: Pendahuluanunclassified

Analisis Perkembangan Pasien Covid-19 Menggunakan Segmentasi Citra Rontgen Toraks

Sumarti

2020

JFT

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Chest X-ray segmentation using the contour active method has been widely carried out and produces additional information for data analysis. This information can be used to determine the development of COVID-19 patients while in the hospital. The method in this study was divided into three, first taking X-ray images of patients COVID-19, second image segmentation using the active contour method then calculating area segmentation, and third calculating the deviation area in image segmentation then analyzed. The results showed that while patient in the hospital for 1-8 days 89% of patients had 50% decrease area clean lung, whereas only 11% of patients increased clean lung repair by about 20-31%. Patients who have improved this condition were hospitalized in a fairly severe condition and treated for 8 days. In general, days 1-8 patients determine at the hospital is when patients get worse showed by an decrease clean lung arean more than 50%, while days 8-14 is when the patient gets better or dies.

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Study on COVID-19 in the World and Indonesia Using Regression Model of SVM, Bayesian Ridge and Gaussian

Cited by 15 publications

References 6 publications

Forecasting the COVID-19 transmission in Italy based on the minimum spanning tree of dynamic region network

Forecasting the COVID-19 transmission in Italy based on the minimum spanning tree of dynamic region network

Prediksi Penambahan Kasus Covid-19 di Indonesia Melalui Pendekatan Time Series Menggunakan Metode Exponential Smoothing

Analisis Perkembangan Pasien Covid-19 Menggunakan Segmentasi Citra Rontgen Toraks

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