scholarly journals The Impact of Mobility Patterns on the Spread of the COVID-19 in Indonesia

2021 ◽  
Vol 7 (1) ◽  
pp. 31
Author(s):  
Syafira Fitri Auliya ◽  
Nurcahyani Wulandari
Keyword(s):  
Short Term Memory ◽  
The Novel ◽  
Mobility Patterns ◽  
Short Term ◽  
Social Distancing ◽  
Mobility Data ◽  
Rapid Transmission ◽  
Long Short Term Memory ◽  
Novel Coronavirus ◽  
The Impact

Background: The novel coronavirus disease 2019 (COVID-19) has been spreading rapidly across the world and infected millions of people, many of whom died. As part of the response plans, many countries have been attempting to restrict people’s mobility by launching social distancing protocol, including in Indonesia. It is then necessary to identify the campaign’s impact and analyze the influence of mobility patterns on the pandemic’s transmission rate.Objective: Using mobility data from Google and Apple, this research discovers that COVID-19 daily new cases in Indonesia are mostly related to the mobility trends in the previous eight days.Methods: We generate ten-day predictions of COVID-19 daily new cases and Indonesians’ mobility by using Long-Short Term Memory (LSTM) algorithm to provide insight for future implementation of social distancing policies.Results: We found that all eight-mobility categories result in the highest accumulation correlation values between COVID-19 daily new cases and the mobility eight days before. We forecast of the pandemic daily new cases in Indonesia, DKI Jakarta and worldwide (with error on MAPE 6.2% - 9.4%) as well as the mobility trends in Indonesia and DKI Jakarta (with error on MAPE 6.4 - 287.3%).Conclusion: We discover that the driver behind the rapid transmission in Indonesia is the number of visits to retail and recreation, groceries and pharmacies, and parks. In contrast, the mobility to the workplaces negatively correlates with the pandemic spread rate.

scholarly journals Renewable Energy Deployment and COVID-19 Measures for Sustainable Development

2021 ◽  
Vol 13 (8) ◽  
pp. 4418
Author(s):  
Miraj Ahmed Bhuiyan ◽  
Jaehyung An ◽  
Alexey Mikhaylov ◽  
Nikita Moiseev ◽  
Mir Sayed Shah Danish
Keyword(s):  
Renewable Energy ◽  
Short Term Memory ◽  
External Environment ◽  
Energy Markets ◽  
Energy Market ◽  
Short Term ◽  
The United Kingdom ◽  
Long Short Term Memory ◽  
Restrictive Measures ◽  
The Impact

The main goal of this study is to evaluate the impact of restrictive measures introduced in connection with COVID-19 on consumption in renewable energy markets. The study will be based on the hypothesis that similar changes in human behavior can be expected in the future with the further spread of COVID-19 and/or the introduction of additional quarantine measures around the world. The analysis also yielded additional results. The strongest reductions in energy generation occurred in countries with a high percentage (more than 80%) of urban population (Brazil, USA, the United Kingdom and Germany). This study uses two models created with the Keras Long Short-Term Memory (Keras LSTM) Model, and 76 and 10 parameters are involved. This article suggests that various restrictive strategies reduced the sustainable demand for renewable energy and led to a drop in economic growth, slowing the growth of COVID-19 infections in 2020. It is unknown to what extent the observed slowdown in the spread from March 2020 to September 2020 due to the policy’s impact and not the interaction between the virus and the external environment. All renewable energy producers decreased the volume of renewable energy market supply in 2020 (except China).

Production Forecasting with the Interwell Interference by Integrating Graph Convolutional and Long Short-Term Memory Neural Network

2021 ◽  
pp. 1-17
Author(s):  
Enda Du ◽  
Yuetian Liu ◽  
Ziyan Cheng ◽  
Liang Xue ◽  
Jing Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Production Forecasting ◽  
Temporal Correlations ◽  
Proposed Model ◽  
The Mean ◽  
Long Short Term Memory ◽  
The Impact

Summary Accurate production forecasting is an essential task and accompanies the entire process of reservoir development. With the limitation of prediction principles and processes, the traditional approaches are difficult to make rapid predictions. With the development of artificial intelligence, the data-driven model provides an alternative approach for production forecasting. To fully take the impact of interwell interference on production into account, this paper proposes a deep learning-based hybrid model (GCN-LSTM), where graph convolutional network (GCN) is used to capture complicated spatial patterns between each well, and long short-term memory (LSTM) neural network is adopted to extract intricate temporal correlations from historical production data. To implement the proposed model more efficiently, two data preprocessing procedures are performed: Outliers in the data set are removed by using a box plot visualization, and measurement noise is reduced by a wavelet transform. The robustness and applicability of the proposed model are evaluated in two scenarios of different data types with the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE). The results show that the proposed model can effectively capture spatial and temporal correlations to make a rapid and accurate oil production forecast.

scholarly journals Analysis of Groundwater Level Variations Caused by the Changes in Groundwater Withdrawals Using Long Short-Term Memory Network

Hydrology ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 64 ◽  
Author(s):  
Mun-Ju Shin ◽  
Soo-Hyoung Moon ◽  
Kyung Goo Kang ◽  
Duk-Chul Moon ◽  
Hyuk-Joon Koh
Keyword(s):  
Groundwater Level ◽  
Short Term Memory ◽  
Groundwater Resources ◽  
Prediction Performance ◽  
Groundwater Withdrawal ◽  
Short Term ◽  
Maximum Increase ◽  
Term Memory ◽  
Long Short Term Memory ◽  
The Impact

To properly manage the groundwater resources, it is necessary to analyze the impact of groundwater withdrawal on the groundwater level. In this study, a Long Short-Term Memory (LSTM) network was used to evaluate the groundwater level prediction performance and analyze the impact of the change in the amount of groundwater withdrawal from the pumping wells on the change in the groundwater level in the nearby monitoring wells located in Jeju Island, Korea. The Nash–Sutcliffe efficiency between the observed and simulated groundwater level was over 0.97. Therefore, the groundwater prediction performance of LSTM was remarkably high. If the groundwater level is simulated on the assumption that the future withdrawal amount is reduced by 1/3 of the current groundwater withdrawal, the range of the maximum rise of the groundwater level would be 0.06–0.13 m compared to the current condition. In addition, assuming that no groundwater is taken, the range of the maximum increase in the groundwater level would be 0.11–0.38 m more than the current condition. Therefore, the effect of groundwater withdrawal on the groundwater level in this area was exceedingly small. The method and results can be used to develop new groundwater withdrawal sources for the redistribution of groundwater withdrawals.

scholarly journals Impact of COVID-19 on Forecasting Stock Prices: An Integration of Stationary Wavelet Transform and Bidirectional Long Short-Term Memory

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Daniel Štifanić ◽  
Jelena Musulin ◽  
Adrijana Miočević ◽  
Sandi Baressi Šegota ◽  
Roman Šubić ◽  
...  
Keyword(s):  
Crude Oil ◽  
Stock Prices ◽  
Short Term Memory ◽  
Oil Price ◽  
Stationary Wavelet Transform ◽  
Crude Oil Price ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
The Impact

COVID-19 is an infectious disease that mostly affects the respiratory system. At the time of this research being performed, there were more than 1.4 million cases of COVID-19, and one of the biggest anxieties is not just our health, but our livelihoods, too. In this research, authors investigate the impact of COVID-19 on the global economy, more specifically, the impact of COVID-19 on the financial movement of Crude Oil price and three US stock indexes: DJI, S&P 500, and NASDAQ Composite. The proposed system for predicting commodity and stock prices integrates the stationary wavelet transform (SWT) and bidirectional long short-term memory (BDLSTM) networks. Firstly, SWT is used to decompose the data into approximation and detail coefficients. After decomposition, data of Crude Oil price and stock market indexes along with COVID-19 confirmed cases were used as input variables for future price movement forecasting. As a result, the proposed system BDLSTM + WT-ADA achieved satisfactory results in terms of five-day Crude Oil price forecast.

scholarly journals Using long short-term memory networks for river flow prediction

2020 ◽  
Vol 51 (6) ◽  
pp. 1358-1376
Author(s):  
Wei Xu ◽  
Yanan Jiang ◽  
Xiaoli Zhang ◽  
Yi Li ◽  
Run Zhang ◽  
...  
Keyword(s):  
River Flow ◽  
Short Term Memory ◽  
River Basins ◽  
Short Term ◽  
Term Memory ◽  
Learning Efficiency ◽  
Flow Prediction ◽  
Non Linear ◽  
Long Short Term Memory ◽  
The Impact

Abstract Deep learning has made significant advances in methodologies and practical applications in recent years. However, there is a lack of understanding on how the long short-term memory (LSTM) networks perform in river flow prediction. This paper assesses the performance of LSTM networks to understand the impact of network structures and parameters on river flow predictions. Two river basins with different characteristics, i.e., Hun river and Upper Yangtze river basins, are used as case studies for the 10-day average flow predictions and the daily flow predictions, respectively. The use of the fully connected layer with the activation function before the LSTM cell layer can substantially reduce learning efficiency. On the contrary, non-linear transformation following the LSTM cells is required to improve learning efficiency due to the different magnitudes of precipitation and flow. The batch size and the number of LSTM cells are sensitive parameters and should be carefully tuned to achieve a balance between learning efficiency and stability. Compared with several hydrological models, the LSTM network achieves good performance in terms of three evaluation criteria, i.e., coefficient of determination, Nash–Sutcliffe Efficiency and relative error, which demonstrates its powerful capacity in learning non-linear and complex processes in hydrological modelling.

scholarly journals Analysis of Public Opinion Sentiment on Instagram regarding Covid-19 with SVM

2021 ◽  
Vol 1 (1) ◽  
pp. 67
Author(s):  
Iin Kurniasari ◽  
Kusrini Kusrini ◽  
Hanif Al Fatta
Keyword(s):  
Support Vector Machine ◽  
Public Opinion ◽  
Short Term Memory ◽  
Support Vector ◽  
Response Ratio ◽  
Short Term ◽  
Term Memory ◽  
Social Distancing ◽  
Word Level ◽  
Long Short Term Memory

Perkembangan teknologi dewasa ini mendorong masyarakat untuk selalu tanggap teknologi, terlebih di era pandemi covid-19 yang selalu mengedepankan social distancing. Media sosial digunakan sebagai suatu alat untuk menyampaikan opini masyarakat kepada khalayak. Dalam penelitian ini, penulis melakukan penelitian tentang opini masyaraat pada media sosial instagram dengan mengguakan Support Vector Machine. Setelah dilakukan uji akurasi dan presisi ternyata SVM belum sesuai digunakan sebagai algoritma yang dapat menangkap urutan karena susunan kata yang dibolak-balik meskipun maknanya berbeda tetap bermakna sama oleh mesin SVM, hal ini dibuktikan juga dengan jumlah akurasi yang kecil.yaitu 59%. Sehingga diperlukan langkah untuk bisa diteliti dengan algoritma lain misalnya algoritma HRRN (Highest Response Ratio Next) atau LSTM (Long Short-Term Memory) yang memperhatikan urutan dan proses dengan rasio respon paling tinggi. Jika berdasarkan pendekatan ekstraksi fitur SVM dengan pendekatan count vector, tf-idf word level, tf-idf ngram level dan tf-idf char level. Dalam skenario ini nilai akurasi tertinggi terdapat pada perhitungan dengan menggunakan ekstraksi fitur count vector dan tf-idf ngram level.

scholarly journals Socioeconomic Disparities in Social Distancing During the COVID-19 Pandemic in the United States: Observational Study (Preprint)

2020 ◽  
Author(s):  
Romain Garnier ◽  
Jan R Benetka ◽  
John Kraemer ◽  
Shweta Bansal
Keyword(s):  
United States ◽  
Black People ◽  
The United States ◽  
Control Measures ◽  
Mobility Patterns ◽  
Poverty Level ◽  
Social Distancing ◽  
Mobility Data ◽  
Black Populations ◽  
The Impact

BACKGROUND Eliminating disparities in the burden of COVID-19 requires equitable access to control measures across socio-economic groups. Limited research on socio-economic differences in mobility hampers our ability to understand whether inequalities in social distancing are occurring during the SARS-CoV-2 pandemic. OBJECTIVE We aimed to assess how mobility patterns have varied across the United States during the COVID-19 pandemic and to identify associations with socioeconomic factors of populations. METHODS We used anonymized mobility data from tens of millions of devices to measure the speed and depth of social distancing at the county level in the United States between February and May 2020, the period during which social distancing was widespread in this country. Using linear mixed models, we assessed the associations between social distancing and socioeconomic variables, including the proportion of people in the population below the poverty level, the proportion of Black people, the proportion of essential workers, and the population density. RESULTS We found that the speed, depth, and duration of social distancing in the United States are heterogeneous. We particularly show that social distancing is slower and less intense in counties with higher proportions of people below the poverty level and essential workers; in contrast, we show that social distancing is intensely adopted in counties with higher population densities and larger Black populations. CONCLUSIONS Socioeconomic inequalities appear to be associated with the levels of adoption of social distancing, potentially resulting in wide-ranging differences in the impact of the COVID-19 pandemic in communities across the United States. These inequalities are likely to amplify existing health disparities and must be addressed to ensure the success of ongoing pandemic mitigation efforts.

scholarly journals A data-driven assessment of early travel restrictions related to the spreading of the novel COVID-19 within mainland China

2020 ◽  
Author(s):  
Alberto Aleta ◽  
Qitong Hu ◽  
Jiachen Ye ◽  
Peng Ji ◽  
Yamir Moreno
Keyword(s):  
Mainland China ◽  
Behavioral Changes ◽  
The Novel ◽  
Metapopulation Model ◽  
Short Term ◽  
Effective Measure ◽  
Mobility Data ◽  
Health Authorities ◽  
Travel Restrictions ◽  
Novel Coronavirus

Two months after it was firstly reported, the novel coronavirus disease COVID-19 has already spread worldwide. However, the vast majority of reported infections have occurred in China. To assess the effect of early travel restrictions adopted by the health authorities in China, we have implemented an epidemic metapopulation model that is fed with mobility data corresponding to 2019 and 2020. This allows to compare two radically different scenarios, one with no travel restrictions and another in which mobility is reduced by a travel ban. Our findings indicate that i) travel restrictions are an effective measure in the short term, however, ii) they are ineffective when it comes to completely eliminate the disease. The latter is due to the impossibility of removing the risk of seeding the disease to other regions. Our study also highlights the importance of developing more realistic models of behavioral changes when a disease outbreak is unfolding.

scholarly journals Modelling the Potential Impact of Social Distancing on the COVID-19 Epidemic in South Africa

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
F. Nyabadza ◽  
F. Chirove ◽  
C. W. Chukwu ◽  
M. V. Visaya
Keyword(s):  
South Africa ◽  
Deterministic Model ◽  
Cumulative Number ◽  
The Novel ◽  
Social Distancing ◽  
The Social ◽  
Potential Impact ◽  
Policy Direction ◽  
Novel Coronavirus ◽  
The Impact

The novel coronavirus (COVID-19) pandemic continues to be a global health problem whose impact has been significantly felt in South Africa. With the global spread increasing and infecting millions, containment efforts by countries have largely focused on lockdowns and social distancing to minimise contact between persons. Social distancing has been touted as the best form of response in managing a rapid increase in the number of infected cases. In this paper, we present a deterministic model to describe the impact of social distancing on the transmission dynamics of COVID-19 in South Africa. The model is fitted to data from March 5 to April 13, 2020, on the cumulative number of infected cases, and a scenario analysis on different levels of social distancing is presented. The model shows that with the levels of social distancing under the initial lockdown level between March 26 and April 13, 2020, there would be a projected continued rise in the number of infected cases. The model also looks at the impact of relaxing the social distancing measures after the initial announcement of the lockdown. It is shown that relaxation of social distancing by 2% can result in a 23% rise in the number of cumulative cases whilst an increase in the level of social distancing by 2% would reduce the number of cumulative cases by about 18%. The model results accurately predicted the number of cases after the initial lockdown level was relaxed towards the end of April 2020. These results have implications on the management and policy direction in the early phase of the epidemic.

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