Analysis of the attention to COVID-19 epidemic based on visibility graph network

2021 ◽  
pp. 2150316
Author(s):  
Qingxiang Feng ◽  
Haipeng Wei ◽  
Jun Hu ◽  
Wenzhe Xu ◽  
Fan Li ◽  
...  
Keyword(s):  
Public Health ◽  
Time Series ◽  
Time Series Data ◽  
Search Behavior ◽  
Series Data ◽  
Global Public Health ◽  
Health Crisis ◽  
Time Space ◽  
Epidemic Period ◽  
Visibility Algorithm

Most of the existing researches on public health events focus on the number and duration of events in a year or month, which are carried out by regression equation. COVID-19 epidemic, which was discovered in Wuhan, Hubei Province, quickly spread to the whole country, and then appeared as a global public health event. During the epidemic period, Chinese netizens inquired about the dynamics of COVID-19 epidemic through Baidu search platform, and learned about relevant epidemic prevention information. These groups’ search behavior data not only reflect people’s attention to COVID-19 epidemic, but also contain the stage characteristics and evolution trend of COVID-19 epidemic. Therefore, the time, space and attribute laws of propagation of COVID-19 epidemic can be discovered by deeply mining more information in the time series data of search behavior. In this study, it is found that transforming time series data into visibility network through the principle of visibility algorithm can dig more hidden information in time series data, which may help us fully understand the attention to COVID-19 epidemic in Chinese provinces and cities, and evaluate the deficiencies of early warning and prevention of major epidemics. What’s more, it will improve the ability to cope with public health crisis and social decision-making level.

scholarly journals Time–frequency time–space LSTM for robust classification of physiological signals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuan D. Pham
Keyword(s):  
Time Series ◽  
Network Architecture ◽  
Time Series Data ◽  
Short Term Memory ◽  
Physiological Signals ◽  
Series Data ◽  
Time Frequency ◽  
Time Space ◽  
Deep Recurrent Neural Network

AbstractAutomated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here time–frequency and time–space properties of time series are introduced as a robust tool for LSTM processing of long sequential data in physiology. Based on classification results obtained from two databases of sensor-induced physiological signals, the proposed approach has the potential for (1) achieving very high classification accuracy, (2) saving tremendous time for data learning, and (3) being cost-effective and user-comfortable for clinical trials by reducing multiple wearable sensors for data recording.

scholarly journals Modelling the dynamics of a public health care system: evidence from time-series data

2011 ◽  
Vol 44 (23) ◽  
pp. 2955-2968 ◽  
Author(s):  
Fabrizio Iacone ◽  
Steve Martin ◽  
Luigi Siciliani ◽  
Peter C. Smith
Keyword(s):  
Public Health ◽  
Health Care ◽  
Time Series ◽  
Health Care System ◽  
Time Series Data ◽  
Public Health Care ◽  
Series Data ◽  
Public Health Care System ◽  
Care System

scholarly journals New statistical model for misreported data with application to current public health challenges

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
David Moriña ◽  
Amanda Fernández-Fontelo ◽  
Alejandra Cabaña ◽  
Pedro Puig
Keyword(s):  
Public Health ◽  
Time Series ◽  
Human Papillomavirus ◽  
Continuous Time ◽  
Time Series Data ◽  
Real Data ◽  
Current Data ◽  
Series Data ◽  
Proposed Model ◽  
Public Health Challenges

AbstractThe main goal of this work is to present a new model able to deal with potentially misreported continuous time series. The proposed model is able to handle the autocorrelation structure in continuous time series data, which might be partially or totally underreported or overreported. Its performance is illustrated through a comprehensive simulation study considering several autocorrelation structures and three real data applications on human papillomavirus incidence in Girona (Catalonia, Spain) and Covid-19 incidence in two regions with very different circumstances: the early days of the epidemic in the Chinese region of Heilongjiang and the most current data from Catalonia.

scholarly journals Intrinsic growth rules of patients infected, dead and recovered with 2019 novel coronavirus in mainland China

2020 ◽  
Author(s):  
Chuanliang Han ◽  
Yimeng Liu ◽  
Saini Yang
Keyword(s):  
Public Health ◽  
Time Series ◽  
Severe Acute Respiratory Syndrome ◽  
Time Series Data ◽  
Mainland China ◽  
Series Data ◽  
Cumulative Number ◽  
Descriptive Model ◽  
Infected Case ◽  
Novel Coronavirus

AbstractAn outbreak of a novel coronavirus (SARS-CoV-2)-infected pneumonia (COVID-19) was first diagnosed in Wuhan, China, in December 2019 and then spread rapidly to other regions. We collected the time series data of the cumulative number of confirmed infected, dead, and cured cases from the health commissions in 31 provinces in mainland China. A descriptive model in a logistic form was formulated to infer the intrinsic epidemic rules of COVID-19, which illustrates robustness spatially and temporally. Our model is robust (R2>0.95) to depict the intrinsic growth rule for the cumulative number of confirmed infected, dead, and cured cases in 31 provinces in mainland China. Furthermore, we compared the intrinsic epidemic rules of COVID-19 in Hubei with that of severe acute respiratory syndrome (SARS) in Beijing, which was obtained from the Ministry of Public Health of China in 2003. We found that the infected case is the earliest to be saturated and has the lowest semi-saturation period compared with deaths and cured cases for both COVID-19 and SARS. All the three types of SARS cases are later to saturate and have longer semi-saturation period than that of COVID-19. Despite the virus caused SARS (SARS-CoV) and the virus caused COVID-19 (SARS-CoV-2) are homologous, the duration of the outbreak would be shorter for COVID-19.

scholarly journals Research on the Data-Driven Quality Control Method of Hydrological Time Series Data

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1712 ◽  
Author(s):  
Qun Zhao ◽  
Yuelong Zhu ◽  
Dingsheng Wan ◽  
Yufeng Yu ◽  
Xifeng Cheng
Keyword(s):  
Time Series ◽  
Quality Control ◽  
Confidence Intervals ◽  
Time Series Data ◽  
Control Method ◽  
Data Driven ◽  
Series Data ◽  
Hydrological Data ◽  
Quality Control Method ◽  
Time Space

Ensuring the quality of hydrological data has become a key issue in the field of hydrology. Based on the characteristics of hydrological data, this paper proposes a data-driven quality control method for hydrological data. For continuous hydrological time series data, two combined forecasting models and one statistical control model are constructed from horizontal, vertical, and statistical perspectives and the three models provide three confidence intervals. Set the suspicious level based on the number of confidence intervals for data violations, control the data, and provide suggested values for suspicious and missing data. For the discrete hydrological data with large time-space difference, the similar weight topological map between the neighboring stations is established centering on the hydrological station under the test and it is adjusted continuously with the seasonal changes. Lastly, a spatial interpolation model is established to detect the data. The experimental results show that the quality control method proposed in this paper can effectively detect and control the data, find suspicious and erroneous data, and provide suggested values.

scholarly journals Improved Prediction of COVID-19 Transmission and Mortality Using Google Search Trends for Symptoms in the United States

2021 ◽  
Author(s):  
Meshrif Alruily ◽  
Mohamed Ezz ◽  
Ayman Mohamed Mostafa ◽  
Nacim Yanes ◽  
Mostafa Abbas ◽  
...  
Keyword(s):  
Public Health ◽  
Time Series ◽  
Computational Models ◽  
Time Series Data ◽  
Short Term Memory ◽  
Emerging Infectious Diseases ◽  
The United States ◽  
Series Data ◽  
Percentage Error ◽  
Google Search

ABSTRACTAccurate forecasting of emerging infectious diseases can guide public health officials in making appropriate decisions related to the allocation of public health resources. Due to the exponential spread of the COVID-19 infection worldwide, several computational models for forecasting the transmission and mortality rates of COVID-19 have been proposed in the literature. To accelerate scientific and public health insights into the spread and impact of COVID-19, Google released the Google COVID-19 search trends symptoms open-access dataset. Our objective is to develop 7 and 14 -day-ahead forecasting models of COVID-19 transmission and mortality in the US using the Google search trends for COVID-19 related symptoms. Specifically, we propose a stacked long short-term memory (SLSTM) architecture for predicting COVID-19 confirmed and death cases using historical time series data combined with auxiliary time series data from the Google COVID-19 search trends symptoms dataset. Considering the SLSTM networks trained using historical data only as the base models, our base models for 7 and 14 -day-ahead forecasting of COVID cases had the mean absolute percentage error (MAPE) values of 6.6% and 8.8%, respectively. On the other side, our proposed models had improved MAPE values of 3.2% and 5.6%, respectively. For 7 and 14 -day-ahead forecasting of COVID-19 deaths, the MAPE values of the base models were 4.8% and 11.4%, while the improved MAPE values of our proposed models were 4.7% and 7.8%, respectively. We found that the Google search trends for “pneumonia,” “shortness of breath,” and “fever” are the most informative search trends for predicting COVID-19 transmission. We also found that the search trends for “hypoxia” and “fever” were the most informative trends for forecasting COVID-19 mortality.

scholarly journals Inversion of the western Pacific subtropical high dynamic model and analysis of dynamic characteristics for its abnormality

2013 ◽  
Vol 20 (1) ◽  
pp. 131-142 ◽  
Author(s):  
M. Hong ◽  
R. Zhang ◽  
J. X. Li ◽  
J. J. Ge ◽  
K. F. Liu
Keyword(s):  
Time Series ◽  
Dynamic Model ◽  
Potential Field ◽  
Time Series Data ◽  
Subtropical High ◽  
Dynamical Model ◽  
Series Data ◽  
Nonlinear Dynamic Model ◽  
Good Correspondence ◽  
Time Space

Abstract. Based on time series data of 500 hPa potential field from NCEP/NCAR (National Center for Environmental Forecast of American/National Center for Atmospheric Research), a novel consideration of empirical orthogonal function (EOF) time–space separation and dynamic system reconstruction for time series is introduced. This method consists of two parts: first, the dynamical model inversion and model parameter optimization are carried out on the EOF time coefficient series using the genetic algorithm (GA), and, second, a nonlinear dynamic model representing the subtropical high (SH) activity and its abnormality is established. The SH activity and its abnormal mechanism is studied using the developed dynamical model. Results show that the configuration and diversification of the SH equilibriums have good correspondence with the actual short–medium term abnormal activity of the SH. Change of SH potential field brought by the combination of equilibriums is more complex than that by mutation, and their exhibition patterns are different. The mutation behavior from high-value to low-value equilibriums of the SH in summer corresponds with the southward drop of the SH in the observed weather process. The combination behavior of the two steady equilibriums corresponds with disappearance of the "double-ridge" phenomenon of the SH. Dynamical mechanisms of these phenomena are explained.

scholarly journals Design characteristics and statistical methods used in interrupted time series studies evaluating public health interventions: protocol for a review

BMJ Open ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. e024096 ◽  
Author(s):  
Simon L Turner ◽  
Amalia Karahalios ◽  
Andrew B Forbes ◽  
Monica Taljaard ◽  
Jeremy M Grimshaw ◽  
...  
Keyword(s):  
Public Health ◽  
Time Series ◽  
Statistical Methods ◽  
Time Series Data ◽  
Interrupted Time Series ◽  
Health Interventions ◽  
Series Data ◽  
Public Health Interventions ◽  
Design Characteristics ◽  
The Impact

IntroductionAn interrupted time series (ITS) design is an important observational design used to examine the effects of an intervention or exposure. This design has particular utility in public health where it may be impracticable or infeasible to use a randomised trial to evaluate health system-wide policies, or examine the impact of exposures (such as earthquakes). There have been relatively few studies examining the design characteristics and statistical methods used to analyse ITS designs. Further, there is a lack of guidance to inform the design and analysis of ITS studies.This is the first study in a larger project that aims to provide tools and guidance for researchers in the design and analysis of ITS studies. The objectives of this study are to (1) examine and report the design characteristics and statistical methods used in a random sample of contemporary ITS studies examining public health interventions or exposures that impact on health-related outcomes, and (2) create a repository of time series data extracted from ITS studies. Results from this study will inform the remainder of the project which will investigate the performance of a range of commonly used statistical methods, and create a repository of input parameters required for sample size calculation.Methods and analysisWe will collate 200 ITS studies evaluating public health interventions or the impact of exposures. ITS studies will be identified from a search of the bibliometric database PubMed between the years 2013 and 2017, combined with stratified random sampling. From eligible studies, we will extract study characteristics, details of the statistical models and estimation methods, effect metrics and parameter estimates. Further, we will extract the time series data when available. We will use systematic review methods in the screening, application of inclusion and exclusion criteria, and extraction of data. Descriptive statistics will be used to summarise the data.Ethics and disseminationEthics approval is not required since information will only be extracted from published studies. Dissemination of the results will be through peer-reviewed publications and presentations at conferences. A repository of data extracted from the published ITS studies will be made publicly available.

scholarly journals Extended SIR prediction of the epidemics trend of COVID-19 in Italy and compared with Hunan, China

2020 ◽  
Author(s):  
Jia Wangping ◽  
Han Ke ◽  
Song Yang ◽  
Cao Wenzhe ◽  
Wang Shengshu ◽  
...  
Keyword(s):  
Public Health ◽  
Time Series Data ◽  
Credible Interval ◽  
Basic Reproductive Number ◽  
Series Data ◽  
Reproductive Number ◽  
Global Public Health ◽  
Health Measures ◽  
Intervention Measures ◽  
Public Health Strategies

AbstractBackgroundCoronavirus Disease 2019 (COVID-19) is currently a global public health threat. Outside of China, Italy is one of the most suffering countries with the COVID-19 epidemic. It is important to predict the epidemics trend of COVID-19 epidemic in Italy to help develop public health strategies.MethodsWe used time-series data of COVID-19 from Jan 22,2020 to Mar 16,2020. An infectious disease dynamic extended susceptible-infected-removed (eSIR) model, which covers the effects of different intervention measures in dissimilar periods, was applied to estimate the epidemic trend in Italy. The basic reproductive number was estimated using Markov Chain Monte Carlo methods and presented using the resulting posterior mean and 95% credible interval (CI). Hunan, with similar total number of populations in Italy, was used as a comparative item.ResultsIn the eSIR model, we estimated that the basic reproductive number for COVID-19 was respectively 4.10 (95% CI: 2.15–6.77) in Italy and 3.15(95% CI: 1.71–5.21) in Hunan. There would be totally 30 086 infected cases (95%CI:7920-81 869) under the current country blockade and the endpoint would be Apr 25 (95%CI: Mar 30 to Aug 07) in Italy. If the country blockade is imposed 5 day later, the total number of infected cases would expand the infection scale 1.50 times.ConclusionItaly’s current strict measures can efficaciously prevent the further spread of COVID-19 and should be maintained. Necessary strict public health measures be implemented as soon as possible in other European countries with a high number of COVID-19 cases. The most effective strategy needs to be confirmed in further studies.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data
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