scholarly journals A data-driven method to detect the flattening of the COVID-19 pandemic curve and estimating its ending life-cycle using only the time-series of new cases per day.

2020 ◽  
Author(s):  
Dr. Praveen Gupta ◽  
Prof. K.K. Sharma ◽  
Prof. S.D. Joshi ◽  
Dr. S. Goyal
Keyword(s):  
Time Series ◽  
Life Cycle ◽  
Real Life ◽  
The United States ◽  
Health Care Facilities ◽  
The Future ◽  
Containment Measures ◽  
The Usa ◽  
Novel Method ◽  
Novel Coronavirus

The novel Coronavirus-19 disease (COVID-19) has emerged as a pandemic and has presented itself as an unprecedented challenge to the majority of countries worldwide. The containment measures for this disease such as the requirement of health care facilities greatly rely on estimating the future dynamics and flattening of the COVID-19 curve. However, it is always challenging to estimate the future trends and flattening of the COVID-19 curve due to the involvement of many real-life variables. Recently, traditional methods based on SIR and SEIR have been presented for predictive monitoring and detection of flattening of the COVID-19 curve. In this paper, a novel method for detection of flattening of the COVID-19 curve and its ending life-cycle using only the time-series of new cases per day is presented. Simulation results are compared to the SIR based methods in three different scenarios using COVID-19 curves for South Korea, the United States of America, and India. In this study, simulations, performed on the 26th April 2020 show that the peak of the COVID-19 curve in the USA has already arrived and situated on the 14th of April 2020, while the peak of the COVID-19 curve for India has yet to arrive.

scholarly journals Forecasting COVID-19 confirmed cases, deaths and recoveries: Revisiting established time series modeling through novel applications for the USA and Italy

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244173
Author(s):  
Emrah Gecili ◽  
Assem Ziady ◽  
Rhonda D. Szczesniak
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Prediction Interval ◽  
Health Planning ◽  
State Space Model ◽  
Policy Decision ◽  
Prediction Errors ◽  
The Future ◽  
The Usa ◽  
Novel Applications

The novel coronavirus (COVID-19) is an emergent disease that initially had no historical data to guide scientists on predicting/ forecasting its global or national impact over time. The ability to predict the progress of this pandemic has been crucial for decision making aimed at fighting this pandemic and controlling its spread. In this work we considered four different statistical/time series models that are readily available from the ‘forecast’ package in R. We performed novel applications with these models, forecasting the number of infected cases (confirmed cases and similarly the number of deaths and recovery) along with the corresponding 90% prediction interval to estimate uncertainty around pointwise forecasts. Since the future may not repeat the past for this pandemic, no prediction model is certain. However, any prediction tool with acceptable prediction performance (or prediction error) could still be very useful for public-health planning to handle spread of the pandemic, and could policy decision-making and facilitate transition to normality. These four models were applied to publicly available data of the COVID-19 pandemic for both the USA and Italy. We observed that all models reasonably predicted the future numbers of confirmed cases, deaths, and recoveries of COVID-19. However, for the majority of the analyses, the time series model with autoregressive integrated moving average (ARIMA) and cubic smoothing spline models both had smaller prediction errors and narrower prediction intervals, compared to the Holt and Trigonometric Exponential smoothing state space model with Box-Cox transformation (TBATS) models. Therefore, the former two models were preferable to the latter models. Given similarities in performance of the models in the USA and Italy, the corresponding prediction tools can be applied to other countries grappling with the COVID-19 pandemic, and to any pandemics that can occur in future.

scholarly journals Machine prediction of US imports from the PRC in the context of mutual sanctions

2020 ◽  
Vol 73 ◽  
pp. 01027
Author(s):  
Petr Šuleř ◽  
Jan Mareček
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
United States Of America ◽  
The United States ◽  
Seasonal Fluctuations ◽  
Trade Restrictions ◽  
Republic Of China ◽  
The Usa ◽  
Multilayer Perceptron Networks ◽  
Excellent Tool

The aim of this paper is to mechanically predict the import of the United States of America (USA) from the People's Republic of China (PRC). The trade restrictions of the USA and the PRC caused by the USA feeling of imbalance of trade between the two states have significantly influenced not only the trade between the two players, but also the overall climate of international trade. The result of this paper is the finding that multilayer perceptron networks (MLP) appear to be an excellent tool for predicting USA imports from the PRC. MLP networks can capture both the trend of the entire time series and its seasonal fluctuations. It also emerged that time series delays need to be applied. Acceptable results are shown to delay series of the order of 5 and 10 months. The mutual sanctions of both countries did not have a significant impact on the outcome of the machine learning prediction.

scholarly journals Targets Detection in Sea Clutter Based on Echo State Network

2012 ◽  
Vol 4 ◽  
pp. 255-258
Author(s):  
Zhan Xu ◽  
Jian Wei Wan ◽  
Gang Li ◽  
Fang Su
Keyword(s):  
Time Series ◽  
Mean Squared Error ◽  
Real Life ◽  
Radar Data ◽  
Recursive Least Squares ◽  
Echo State Network ◽  
Sea Clutter ◽  
Rls Algorithm ◽  
Real Life Data ◽  
Novel Method

A novel method to predict the sea clutter time series and detect target embedded in sea clutter is presented. The method is actually a recurrent neural network called an echo state network (ESN). A recursive least squares (RLS) algorithm is used for updating the output weights of ESN. A set of time series from IPIX radar data is tested. Numerical experiments reveal that the proposed network shows higher prediction precision in pure sea clutter data. Moreover, the mean squared error (MSE) between real-life data and prediction value by ESN can be used to detect target effectively.

scholarly journals Evolutionary Frequency of the Available Human Coronavirus Genomes

2020 ◽  
Author(s):  
Ashfaq Ahmad ◽  
Sidra Majaz ◽  
Faisal Nouroz
Keyword(s):  
Evolutionary Process ◽  
The United States ◽  
Spatial Diversity ◽  
Global Scale ◽  
The Novel ◽  
Human Coronavirus ◽  
The Usa ◽  
Severity Of The Disease ◽  
Novel Coronavirus ◽  
Insight Into

Abstract Background. A novel, human-infecting coronavirus causing CoVID-19 was first identified in Wuhan, China in late December, 2019. Within a short span of time more the virus has recorded more than 1 million deaths world-wide. This study is designed to address the overall evolutionary process of the novel Coronavirus complete genomes. Addressing the complexity and huge population size, network-based approaches are used in mapping samples to their reported locations. Results. Total of 473 complete human-coronavirus genomes representing 20 different countries are studied including 17 states from the United States and samples collected from the Cruise-diamond princess. The phylodynamic network of global-scale is classified into five clusters contained two clusters U1 and U2 of the USA samples. Cluster B is a shared cluster of China and the USA while A and C are of diverse nature. We found that Chinese samples aggregated in cluster A and B which aided in retaining the homogeneous viral genomic pool. In contrast, samples from the USA and Spain were split into distinct clusters indicating multiple port entries and a possibility in implying a delay in quarantine measures. Among the samples from the USA, we found that sequences reported from Washington and Virginia are scattered indicating evolutionary diversity.Conclusion. This report provides insight into the transmission pattern of CoV2 which is complicated to evaluate exclusively through conventional surveillance means. Our data not only identify the transmission network but also suggest that the severity of the disease is linked to the spatial diversity of infection.

scholarly journals What types of COVID-19 conspiracies are populated by Twitter bots?

First Monday ◽  
2020 ◽  
Author(s):  
Emilio Ferrara
Keyword(s):  
Social Media ◽  
Public Spaces ◽  
The United States ◽  
The Novel ◽  
Health Concerns ◽  
Online Platforms ◽  
Early Evidence ◽  
Containment Measures ◽  
Social Media Platforms ◽  
Novel Coronavirus

With people moving out of physical public spaces due to containment measures to tackle the novel coronavirus (COVID-19) pandemic, online platforms become even more prominent tools to understand social discussion. Studying social media can be informative to assess how we are collectively coping with this unprecedented global crisis. However, social media platforms are also populated by bots, automated accounts that can amplify certain topics of discussion at the expense of others. In this paper, we study 43.3M English tweets about COVID-19 and provide early evidence of the use of bots to promote political conspiracies in the United States, in stark contrast with humans who focus on public health concerns.

scholarly journals Threatcasting: a framework and process to model future operating environments

2018 ◽  
Vol 16 (1) ◽  
pp. 79-88
Author(s):  
Natalie Vanatta ◽  
Brian David Johnson
Keyword(s):  
Real Life ◽  
Technical Note ◽  
Strategic Thinking ◽  
Futures Studies ◽  
Detailed Explanation ◽  
Operating Environments ◽  
The Future ◽  
Novel Method ◽  
The Impact

Threatcasting, a new foresight methodology, draws from futures studies and military strategic thinking to provide a novel method to model the future. The methodology fills gaps in existing military futures thinking and provides a process to specify actionable steps as well as progress indicators. Threatcasting also provides an ability to anticipate future threats and develop strategies to reduce the impact of any event. This technical note provides a detailed explanation of the Threatcasting methodology. It provides the reader with its connections to the current body of work within the foresight community and then explains the four phase methodology through the use of a real-life example.

scholarly journals A Global Scale Estimate of Novel Coronavirus (COVID-19) Cases Using Extreme Value Distributions

2020 ◽  
Author(s):  
M. Aadhityaa ◽  
K. S. Kasiviswanathan ◽  
Idhayachandhiran Ilampooranan ◽  
B. Soundharajan ◽  
M. Balamurugan ◽  
...  
Keyword(s):  
Global Scale ◽  
Extreme Value ◽  
Percentage Error ◽  
Extreme Value Distributions ◽  
Intervention Measures ◽  
The People ◽  
The Future ◽  
The Usa ◽  
Novel Coronavirus ◽  
Scale Estimate

AbstractThe COVID-19 pandemic has created a global crisis and the governments are fighting rigorously to control the spread by imposing intervention measures and increasing the medical facilities. In order to tackle the crisis effectively we need to know the trajectories of number of the people infected (i.e. confirmed cases). Such information is crucial to government agencies for developing effective preparedness plans and strategies. We used a statistical modeling approach – extreme value distributions (EVDs) for projecting the future confirmed cases on a global scale. Using the 69 days data (from January 22, 2020 to March 30, 2020), the EVDs model predicted the number of confirmed cases from March 31, 2020 to April 9, 2020 (validation period) with an absolute percentage error < 15 % and then projected the number of confirmed cases until the end of June 2020. Also, we have quantified the uncertainty in the future projections due to the delay in reporting of the confirmed cases on a global scale. Based on the projections, we found that total confirmed cases would reach around 11.4 million globally by the end of June 2020.The USA may have 2.9 million number of confirmed cases followed by Spain-1.52 million and Italy-1.28 million.

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