scholarly journals Dynamics of Health Agency Response and Public Engagement in Public Health Emergency: A Case Study of CDC Tweeting Patterns During the 2016 Zika Epidemic (Preprint)

2018 ◽  
Author(s):  
Shi Chen ◽  
Qian Xu ◽  
John Buchenberger ◽  
Arunkumar Bagavathi ◽  
Gabriel Fair ◽  
...  
Keyword(s):  
Social Media ◽  
Time Series ◽  
Public Engagement ◽  
Moving Average ◽  
Multivariate Time Series ◽  
Health Agency ◽  
The United States ◽  
Health Agencies ◽  
Us Government ◽  
Moving Average Model

BACKGROUND Social media have been increasingly adopted by health agencies to disseminate information, interact with the public, and understand public opinion. Among them, the Centers for Disease Control and Prevention (CDC) is one of the first US government health agencies to adopt social media during health emergencies and crisis. It had been active on Twitter during the 2016 Zika epidemic that caused 5168 domestic noncongenital cases in the United States. OBJECTIVE The aim of this study was to quantify the temporal variabilities in CDC’s tweeting activities throughout the Zika epidemic, public engagement defined as retweeting and replying, and Zika case counts. It then compares the patterns of these 3 datasets to identify possible discrepancy among domestic Zika case counts, CDC’s response on Twitter, and public engagement in this topic. METHODS All of the CDC-initiated tweets published in 2016 with corresponding retweets and replies were collected from 67 CDC–associated Twitter accounts. Both univariate and multivariate time series analyses were performed in each quarter of 2016 for domestic Zika case counts, CDC tweeting activities, and public engagement in the CDC-initiated tweets. RESULTS CDC sent out >84.0% (5130/6104) of its Zika tweets in the first quarter of 2016 when Zika case counts were low in the 50 US states and territories (only 560/5168, 10.8% cases and 662/38,885, 1.70% cases, respectively). While Zika case counts increased dramatically in the second and third quarters, CDC efforts on Twitter substantially decreased. The time series of public engagement in the CDC-initiated tweets generally differed among quarters and from that of original CDC tweets based on autoregressive integrated moving average model results. Both original CDC tweets and public engagement had the highest mutual information with Zika case counts in the second quarter. Furthermore, public engagement in the original CDC tweets was substantially correlated with and preceded actual Zika case counts. CONCLUSIONS Considerable discrepancies existed among CDC’s original tweets regarding Zika, public engagement in these tweets, and actual Zika epidemic. The patterns of these discrepancies also varied between different quarters in 2016. CDC was much more active in the early warning of Zika, especially in the first quarter of 2016. Public engagement in CDC’s original tweets served as a more prominent predictor of actual Zika epidemic than the number of CDC’s original tweets later in the year.

Modeling and forecasting pine sawtimber stumpage prices in the US South by various time series models

2010 ◽  
Vol 40 (8) ◽  
pp. 1506-1516 ◽  
Author(s):  
Bin Mei ◽  
Michael Clutter ◽  
Thomas Harris
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Multivariate Time Series ◽  
The United States ◽  
Percentage Error ◽  
Vector Autoregressive ◽  
Market Risks ◽  
Long Run ◽  
Timber Sales ◽  
Moving Average Model

Among the three timberland return drivers (biological growth, timber price, and land price), timber price remains the most unpredictable. It affects not only periodic dividends from timber sales but also timber production strategies embedded in timberland management. Using various time series techniques, this study aimed to model and forecast real pine sawtimber stumpage prices in 12 southern timber regions in the United States. Under the discrete-time framework, the univariate autoregressive integrated moving average model was established as a benchmark, whereas other multivariate time series methods were applied in comparison. Under the continuous-time framework, both the geometric Brownian motion and the Ornstein–Uhlenbeck process were fitted. The results revealed that (i) the vector autoregressive model forecasted more accurately in the 1-year period by the mean absolute percentage error criterion, (ii) seven out of the 12 southern timber regions played dominant roles in the long-run equilibrium, and (iii) conditional variances and covariances from the bivariate generalized autoregressive conditional heteroscedasticity model well captured market risks.

scholarly journals Identifying Influential Factors in the Discussion Dynamics of Emerging Health Issues on Social Media: Computational Study (Preprint)

2019 ◽  
Author(s):  
Lida Safarnejad ◽  
Qian Xu ◽  
Yaorong Ge ◽  
Arunkumar Bagavathi ◽  
Siddharth Krishnan ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Social Media ◽  
Time Series ◽  
Real World ◽  
Driving Forces ◽  
The United States ◽  
Web Based ◽  
Health Agencies ◽  
Influential Users

BACKGROUND Social media has become a major resource for observing and understanding public opinions using infodemiology and infoveillance methods, especially during emergencies such as disease outbreaks. For public health agencies, understanding the driving forces of web-based discussions will help deliver more effective and efficient information to general users on social media and the web. OBJECTIVE The study aimed to identify the major contributors that drove overall Zika-related tweeting dynamics during the 2016 epidemic. In total, 3 hypothetical drivers were proposed: (1) the underlying Zika epidemic quantified as a time series of case counts; (2) sporadic but critical real-world events such as the 2016 Rio Olympics and World Health Organization’s Public Health Emergency of International Concern (PHEIC) announcement, and (3) a few influential users’ tweeting activities. METHODS All tweets and retweets (RTs) containing the keyword Zika posted in 2016 were collected via the Gnip application programming interface (API). We developed an analytical pipeline, EventPeriscope, to identify co-occurring trending events with Zika and quantify the strength of these events. We also retrieved Zika case data and identified the top influencers of the Zika discussion on Twitter. The influence of 3 potential drivers was examined via a multivariate time series analysis, signal processing, a content analysis, and text mining techniques. RESULTS Zika-related tweeting dynamics were not significantly correlated with the underlying Zika epidemic in the United States in any of the four quarters in 2016 nor in the entire year. Instead, peaks of Zika-related tweeting activity were strongly associated with a few critical real-world events, both planned, such as the Rio Olympics, and unplanned, such as the PHEIC announcement. The Rio Olympics was mentioned in >15% of all Zika-related tweets and PHEIC occurred in 27% of Zika-related tweets around their respective peaks. In addition, the overall tweeting dynamics of the top 100 most actively tweeting users on the Zika topic, the top 100 users receiving most RTs, and the top 100 users mentioned were the most highly correlated to and preceded the overall tweeting dynamics, making these groups of users the potential drivers of tweeting dynamics. The top 100 users who retweeted the most were not critical in driving the overall tweeting dynamics. There were very few overlaps among these different groups of potentially influential users. CONCLUSIONS Using our proposed analytical workflow, EventPeriscope, we identified that Zika discussion dynamics on Twitter were decoupled from the actual disease epidemic in the United States but were closely related to and highly influenced by certain sporadic real-world events as well as by a few influential users. This study provided a methodology framework and insights to better understand the driving forces of web-based public discourse during health emergencies. Therefore, health agencies could deliver more effective and efficient web-based communications in emerging crises.

Estimation and Forecast Performance of a Multivariate Time Series Model of Sales

1980 ◽  
Vol 17 (4) ◽  
pp. 558-565 ◽  
Author(s):  
Mark Moriarty ◽  
Gerald Salamon
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Multivariate Time Series ◽  
Substantial Improvement ◽  
Product Line ◽  
Time Series Model ◽  
Autoregressive Moving Average ◽  
Forecast Performance ◽  
Autoregressive Moving Average Model ◽  
Moving Average Model

A unique form of a multivariate time series model—a “seemingly unrelated autoregressive moving average” model (SURARMA)—is developed in the context of forecasting unit sales of a product in four states. Data from an anonymous firm are used to test the appropriateness of the model and are found to conform to the model's constraints. The model provides substantial improvement in parameter estimation efficiency and forecast performance in comparison with individual state univariate models. SURARMA is potentially relevant to many market forecasting problems involving multiple constituent time series subunits such as states, regions, or products from a product line.

scholarly journals Identifying Influential Factors in the Discussion Dynamics of Emerging Health Issues on Social Media: Computational Study

10.2196/17175 ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. e17175
Author(s):  
Lida Safarnejad ◽  
Qian Xu ◽  
Yaorong Ge ◽  
Arunkumar Bagavathi ◽  
Siddharth Krishnan ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Social Media ◽  
Time Series ◽  
Real World ◽  
Driving Forces ◽  
The United States ◽  
Web Based ◽  
Health Agencies ◽  
Influential Users

Background Social media has become a major resource for observing and understanding public opinions using infodemiology and infoveillance methods, especially during emergencies such as disease outbreaks. For public health agencies, understanding the driving forces of web-based discussions will help deliver more effective and efficient information to general users on social media and the web. Objective The study aimed to identify the major contributors that drove overall Zika-related tweeting dynamics during the 2016 epidemic. In total, 3 hypothetical drivers were proposed: (1) the underlying Zika epidemic quantified as a time series of case counts; (2) sporadic but critical real-world events such as the 2016 Rio Olympics and World Health Organization’s Public Health Emergency of International Concern (PHEIC) announcement, and (3) a few influential users’ tweeting activities. Methods All tweets and retweets (RTs) containing the keyword Zika posted in 2016 were collected via the Gnip application programming interface (API). We developed an analytical pipeline, EventPeriscope, to identify co-occurring trending events with Zika and quantify the strength of these events. We also retrieved Zika case data and identified the top influencers of the Zika discussion on Twitter. The influence of 3 potential drivers was examined via a multivariate time series analysis, signal processing, a content analysis, and text mining techniques. Results Zika-related tweeting dynamics were not significantly correlated with the underlying Zika epidemic in the United States in any of the four quarters in 2016 nor in the entire year. Instead, peaks of Zika-related tweeting activity were strongly associated with a few critical real-world events, both planned, such as the Rio Olympics, and unplanned, such as the PHEIC announcement. The Rio Olympics was mentioned in >15% of all Zika-related tweets and PHEIC occurred in 27% of Zika-related tweets around their respective peaks. In addition, the overall tweeting dynamics of the top 100 most actively tweeting users on the Zika topic, the top 100 users receiving most RTs, and the top 100 users mentioned were the most highly correlated to and preceded the overall tweeting dynamics, making these groups of users the potential drivers of tweeting dynamics. The top 100 users who retweeted the most were not critical in driving the overall tweeting dynamics. There were very few overlaps among these different groups of potentially influential users. Conclusions Using our proposed analytical workflow, EventPeriscope, we identified that Zika discussion dynamics on Twitter were decoupled from the actual disease epidemic in the United States but were closely related to and highly influenced by certain sporadic real-world events as well as by a few influential users. This study provided a methodology framework and insights to better understand the driving forces of web-based public discourse during health emergencies. Therefore, health agencies could deliver more effective and efficient web-based communications in emerging crises.

scholarly journals Intervention Time Series Modeling of Infant Mortality: Impact of Free Maternal Health Care

2020 ◽  
pp. 38-47
Author(s):  
J. Kisabuli ◽  
J. Ong'ala ◽  
E. Odero
Keyword(s):  
Health Care ◽  
Time Series ◽  
Maternal Health ◽  
Infant Mortality ◽  
Moving Average ◽  
Maternal Health Care ◽  
The Sustainable Development ◽  
Infant Deaths ◽  
Moving Average Model ◽  
The Impact

Infant mortality is an important marker of the overall society health. The 3rd goal of the Sustainable Development Goals aims at reducing infant deaths that occur due to preventable causes by 2030. Due to increased infant mortality the Kenyan government introduced Free Maternal Health Care as an intervention towards reducing infant mortality through elimination of the cost burden of accessing medical care by the mother and the infant. The study examines the impact of Free Maternal Health Care on infant mortality using Intervention time series analysis particularly the intervention Box Jenkins ARIMA (Autoregressive Integrated Moving Average) model. There was significant support that Free Maternal Health Care had a significant impact on infant mortality which was estimated to be a decrease of 10.15% in infant deaths per month.

scholarly journals Studying monthly rainfall over Dibrugarh, Assam: Use of SARIMA approach

MAUSAM ◽  
2021 ◽  
Vol 68 (2) ◽  
pp. 349-356
Author(s):  
J. HAZARIKA ◽  
B. PATHAK ◽  
A. N. PATOWARY
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Demand Management ◽  
Arima Model ◽  
Monthly Rainfall ◽  
Series Data ◽  
Data Set ◽  
Modeling And Forecasting ◽  
Moving Average Model

Perceptive the rainfall pattern is tough for the solution of several regional environmental issues of water resources management, with implications for agriculture, climate change, and natural calamity such as floods and droughts. Statistical computing, modeling and forecasting data are key instruments for studying these patterns. The study of time series analysis and forecasting has become a major tool in different applications in hydrology and environmental fields. Among the most effective approaches for analyzing time series data is the ARIMA (Autoregressive Integrated Moving Average) model introduced by Box and Jenkins. In this study, an attempt has been made to use Box-Jenkins methodology to build ARIMA model for monthly rainfall data taken from Dibrugarh for the period of 1980- 2014 with a total of 420 points.  We investigated and found that ARIMA (0, 0, 0) (0, 1, 1)12 model is suitable for the given data set. As such this model can be used to forecast the pattern of monthly rainfall for the upcoming years, which can help the decision makers to establish priorities in terms of agricultural, flood, water demand management etc.  

scholarly journals Public engagement and dialogic accounting through social media during COVID-19 crisis: a missed opportunity?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Stefano Landi ◽  
Antonio Costantini ◽  
Marco Fasan ◽  
Michele Bonazzi
Keyword(s):  
Public Health ◽  
Social Media ◽  
New Zealand ◽  
Public Engagement ◽  
Fake News ◽  
Public Agencies ◽  
Content Type ◽  
Public Health Agencies ◽  
The Public ◽  
Health Agencies

PurposeThe purpose of this exploratory study is to investigate why and how public health agencies employed social media during coronavirus disease 2019 (COVID-19) outbreak to foster public engagement and dialogic accounting.Design/methodology/approachThe authors analysed the official Facebook pages of the leading public agencies for health crisis in Italy, United Kingdom and New Zealand and they collected data on the number of posts, popularity, commitment and followers before and during the outbreak. The authors also performed a content analysis to identify the topics covered by the posts.FindingsEmpirical results suggest that social media has been extensively used as a public engagement tool in all three countries under analysis but – because of legitimacy threats and resource scarcity – it has also been used as a dialogic accounting tool only in New Zealand. Findings suggest that fake news developed more extensively in contexts where the public body did not foster dialogic accounting.Practical implicationsPublic agencies may be interested in knowing the pros and cons of using social media as a public engagement and dialogic accounting tool. They may also leverage on dialogic accounting to limit fake news.Originality/valueThis study is one of the first to look at the nature and role of social media as an accountability tool during public health crises. In many contexts, COVID-19 forced for the first time public health agencies to heavily engage with the public and to develop new skills, so this study paves the way for numerous future research ideas.

Forecasting e-waste recovery scale driven by seasonal data characteristics: A decomposition-ensemble approach

2021 ◽  
pp. 0734242X2110614
Author(s):  
AKM Mohsin ◽  
Lei Hongzhen ◽  
Mohammed Masum Iqbal ◽  
Zahir Rayhan Salim ◽  
Alamgir Hossain ◽  
...  
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Production Capacity ◽  
Waste Recycling ◽  
Support Vector ◽  
Waste Recovery ◽  
Decomposition Procedure ◽  
Moving Average Model ◽  
The Government ◽  
Seasonal Data

Forecasting the scale of e-waste recycling is the basis for the government to formulate the development plan of circular economy and relevant subsidy policies and enterprises to evaluate resource recovery and optimise production capacity. In this article, the CH-X12 /STL-X framework for e-waste recycling scale prediction is proposed based on the idea of ‘decomposition-integration’, considering that the seasonal data characteristics of quarterly e-waste recycling scale data may lead to large forecasting errors and inconsistent forecasting results of a traditional single model. First, the seasonal data characteristics of the time series of e-waste recovery scale are identified based on Canova–Hansen (CH) test, and then the time series suitable for seasonal decomposition is extracted with X12 or seasonal-trend decomposition procedure based on loess (STL) model for seasonal components. Then, the Holt–Winters model was used to predict the seasonal component, and the support vector regression (SVR) model was used to predict the other components. Finally, the linear sum of the prediction results of each component is used to obtain the final prediction result. The empirical results show that the proposed CH-X12/STL-X forecasting framework can better meet the modelling requirements for time-series forecasting driven by different seasonal data characteristics and has better and more stable forecasting performance than traditional single models (Holt–Winters model, seasonal autoregressive integrated moving average model and SVR model).

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