Analysis of the Restrictive Measures Impact on the Disease Spread

Andriyanov, Nikita; Korovin, D. I.

doi:10.1109/itnt52450.2021.9649210

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Moreover, Indonesia was also reported to have more than 4 million confirmed positive cases of COVID-19, with over 130 thousand deaths [2]. The transmission of COVID-19 was also found to have increased in one of the provinces of Indonesia, specifically in segmented model for the analysis of changing patterns in different geographic areas in China [23]; spatiotemporal analysis and reflections on health geography in Argentina [24]; spatiotemporal analysis of COVID-19 at national and provincial levels in Mexico [25]; spatiotemporal analysis and reflections on the usefulness of GIS in the pandemic [26]; and the analysis of restrictive measures during the pandemic [27]. A Susceptible-Infected-Recovered (SIR) model for estimating COVID-19 reproduction number in East Kalimantan and Samarinda [28].…”

Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Epidemiology of COVID-19 Using a Geographically and Temporally Weighted Regression Model

et al. 2022

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This article describes the application of spatial statistical epidemiological modeling and its inference and applies it to COVID-19 case data, looking at it from a spatial perspective, and considering time-series data. COVID-19 cases in Indonesia are increasing and spreading in all provinces, including Kalimantan. This study uses applied mathematics and spatiotemporal analysis to determine the factors affecting the constant rise of COVID-19 cases in Kalimantan. The spatiotemporal analysis uses the Geographically Temporally Weighted Regression (GTWR) model by developing a spatial and temporal interaction distance function. The GTWR model was applied to data on positive COVID-19 cases at a scale of 56 districts/cities in Kalimantan between the period of January 2020 and August 2021. The purpose of the study was to determine the factors affecting the cumulative increase in COVID-19 cases in Kalimantan and map the spatial distribution for 56 districts/cities based on the significant predictor variables. The results of the study show that the GTWR model with the development of a spatial and temporal interaction distance function using the kernel Gaussian fixed bandwidth function is a better model compared to the Ordinary Least Squares (OLS) model. According to the significant variables, there are various factors affecting the rise in cases of COVID-19 in the region of Kalimantan, including the number of doctors, the number of TB cases, the percentage of elderly population, GRDP, and the number of hospitals. The highest factors that affect COVID-19 cases are the high number of TB cases, population density, and the lack of health services. Furthermore, an area map was produced on the basis of the significant variables affected by the rise in COVID-19 cases. The results of the study provide local governments with decision-making recommendations to overcome COVID-19-related issues in their respective regions.

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Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Epidemiology of COVID-19 Using a Geographically and Temporally Weighted Regression Model

et al. 2022

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ConvLSTM Coupled Economics Indicators Quantitative Trading Decision Model

Jiang

et al. 2022

Symmetry

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Time series prediction methods based on deep learning have been widely used in quantitative trading. However, the price of virtual currency represented by Bitcoin has random fluctuation characteristics, which is extremely misleading for time series prediction. In this paper, a virtual currency quantitative trading model is established, which uses a convolution long short term memory (ConvLSTM) deep learning method to predict the transaction price, and uses the evaluation model composed of Chandler momentum oscillator (CMO), percentage price oscillator (PPO), stop and reverse(SAR) and other economic indicators to make further decisions. The model quantitatively classifies the random wandering characteristics by fusing economic indicators and extracts the symmetric economic laws among them, making full use of deep learning methods to extract spatial and temporal features within the data. The 2016–2021 Bitcoin value dataset published on Kaggle was used for simulated investment. The results show that compared with other existing decision models, it shows better performance and robustness, and shows good stability in dealing with the interdependence of long-term and short-term data. Our work provides a new idea for short-term prediction of long time series data affected by multiple complex factors: coupling deep learning methods with prior knowledge to complete prediction and decision making.

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Analysis of the Restrictive Measures Impact on the Disease Spread

Cited by 2 publications

References 9 publications

Spatial-Temporal Epidemiology of COVID-19 Using a Geographically and Temporally Weighted Regression Model

Spatial-Temporal Epidemiology of COVID-19 Using a Geographically and Temporally Weighted Regression Model

ConvLSTM Coupled Economics Indicators Quantitative Trading Decision Model

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