scholarly journals Hawkes process modeling of COVID-19 with mobility leading indicators and spatial covariates

2020 ◽  
Author(s):  
Wen-Hao Chiang ◽  
Xueying Liu ◽  
George Mohler
Keyword(s):  
Reproduction Number ◽  
Leading Indicators ◽  
Hawkes Process ◽  
Conditional Intensity ◽  
Short Term ◽  
Hawkes Processes ◽  
Event Clustering ◽  
Short Term Forecasting ◽  
Demographic Covariates ◽  
The U.S

AbstractHawkes processes are used in machine learning for event clustering and causal inference, while they also can be viewed as stochastic versions of popular compartmental models used in epidemiology. Here we show how to develop accurate models of COVID-19 transmission using Hawkes processes with spatial-temporal covariates. We model the conditional intensity of new COVID-19 cases and deaths in the U.S. at the county level, estimating the dynamic reproduction number of the virus within an EM algorithm through a regression on Google mobility indices and demographic covariates in the maximization step. We validate the approach on short-term forecasting tasks, showing that the Hawkes process outperforms several benchmark models currently used to track the pandemic, including an ensemble approach and a SEIR-variant. We also investigate which covariates and mobility indices are most important for building forecasts of COVID-19 in the U.S.

scholarly journals Modeling COVID-19 Transmissions and Evaluation of Large Scale Social Restriction in Jakarta, Indonesia

2020 ◽  
Author(s):  
A. Hasan ◽  
Y. Nasution ◽  
H. Susanto ◽  
E.R.M. Putri ◽  
V.R. Tjahjono ◽  
...  
Keyword(s):  
Quantitative Evaluation ◽  
Large Scale ◽  
Reproduction Number ◽  
World Health ◽  
Transmission Model ◽  
Time Varying ◽  
Short Term ◽  
Social Restriction ◽  
Short Term Forecasting ◽  
Health Organization

AbstractThis paper presents mathematical modeling and quantitative evaluation of Large Scale Social Restriction (LSSR) in Jakarta between 10 April and 4 June 2020. The special capital region of Jakarta is the only province among 34 provinces in Indonesia with an average Testing Positivity Rate (TPR) below 5% recommended by the World Health Organization (WHO). The transmission model is based on a discrete-time compartmental epidemiological model incorporating suspected cases. The quantitative evaluation is measured based on the estimation of the time-varying effective reproduction number (ℛt). Our results show the LSSR has been successfully suppressed the spread of COVID-19 in Jakarta, which was indicated by ℛt < 1. However, once the LSSR was relaxed, the effective reproduction number increased significantly. The model is further used for short-term forecasting to mitigate the course of the pandemic.

scholarly journals Forecasting the spread of COVID19 in Hungary

2020 ◽  
Author(s):  
Owais Mujtaba Khanday ◽  
Samad Dadvandipour ◽  
Mohd. Aaqib Lone
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Reproduction Number ◽  
Sir Model ◽  
Exponential Smoothing ◽  
Short Term ◽  
Smoothing Methods ◽  
Series Analysis ◽  
Short Term Forecasting

AbstractTime series analysis of the COVID19/ SARS-CoV-2 spread in Hungary is presented. Different methods effective for short-term forecasting are applied to the dataset, and predictions are made for the next 20 days. Autoregression and other exponential smoothing methods are applied to the dataset. SIR model is used and predicted 64% of the population could be infected by the virus considering the whole population is susceptible to be infectious Autoregression, and exponential smoothing methods indicated there would be more than a 60% increase in the cases in the coming 20 days. The doubling of the number of total cases is found to around 16 days using an effective reproduction number.

The causes of poverty in Russia

2020 ◽  
Vol 13 (1) ◽  
pp. 21-36
Author(s):  
I.S. Ivanchenko
Keyword(s):  
Regression Analysis ◽  
Regional Development ◽  
Financial Market ◽  
Russian Federation ◽  
Short Term ◽  
Financial Situation ◽  
Macroeconomic Variables ◽  
Development Plans ◽  
The Russian Federation ◽  
Short Term Forecasting

Subject. This article analyzes the changes in poverty of the population of the Russian Federation. Objectives. The article aims to identify macroeconomic variables that will have the most effective impact on reducing poverty in Russia. Methods. For the study, I used the methods of logical, comparative, and statistical analyses. Results. The article presents a list of macroeconomic variables that, according to Western scholars, can influence the incomes of the poorest stratum of society and the number of unemployed in the country. The regression analysis based on the selected variables reveals those ones that have a statistically significant impact on the financial situation of the Russian poor. Relevance. The results obtained can be used by the financial market mega-regulator to make anti-poverty decisions. In addition, the models built can be useful to the executive authorities at various levels for short-term forecasting of the number of unemployed and their income in drawing up regional development plans for the areas.

Very short-term forecasting of wind power generation using hybrid deep learning model

2021 ◽  
Vol 296 ◽  
pp. 126564
Author(s):  
Md Alamgir Hossain ◽  
Ripon K. Chakrabortty ◽  
Sondoss Elsawah ◽  
Michael J. Ryan
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Power ◽  
Learning Model ◽  
Wind Power Generation ◽  
Short Term ◽  
Short Term Forecasting ◽  
Deep Learning Model
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