Forecasting the Trends of Covid-19 and Causal Impact of Vaccines Using Bayesian Structural time Series and ARIMA

Thorakkattle, Muhammed Navas; Farhin, Shazia; khan, Athar Ali

doi:10.1007/s40745-022-00418-4

Cited by 17 publications

(8 citation statements)

References 19 publications

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“…Recent research compared the accuracy of Bayesian structural time series (BSTS) analysis and ARIMA models and found that BSTS models tend to be more accurate (Thorakkattle et al, 2022). In addition, some have argued for using BSTS rather than ARIMA for examining trends in relatively rare events, such as serious crimes, since such data may violate ARIMA’s underlying assumptions (Ratcliff et al, 2017; Quddus, 2008).…”

Section: Resultsmentioning

confidence: 99%

A Closer Look at the Alleged “War on Cops”: Post-Ferguson Trends in Ideologically-Motivated Homicides of Police Officers, 2008–2021

Norris

2023

Police Quarterly

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Some proponents of the “war on cops” thesis have suggested that the Black Lives Matter (BLM) movement encourages people to murder police officers and is responsible for many civilian-on-police homicides. To evaluate such claims, this study examines all homicides of on-duty police officers by civilians from 2008 to 2021 ( n = 595) for ideological motives, and uses interrupted times series analysis to test for post-Ferguson trends. Ideological motivations were present in 12% of civilian-on-police homicides, and only 3% constituted terrorism. Right-wing motivations were far more common (6%) than left-wing motives (3%). Most analyses showed no significant increase in ideological civilian-on-police homicides after Ferguson. Although homicides with left-wing motives unconnected to personal revenge significantly increased after Ferguson, this only accounts for a small proportion of post-Ferguson homicides (4%). In short, there is no evidence that BLM unleashed a “war on cops” in which officers are increasingly targeted in ideological homicides.

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Section: Resultsmentioning

confidence: 99%

A Closer Look at the Alleged “War on Cops”: Post-Ferguson Trends in Ideologically-Motivated Homicides of Police Officers, 2008–2021

Norris

2023

Police Quarterly

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“…Causal impact analysis is a predictive model developed by Google, which estimates the isolated impact of not implementing an intervention 8 . This method has been generally used for online marketing analysis and has been recently applied to health care data, with use cases related to understanding the impact of the COVID‐19 pandemic, including estimating the impact of lockdown and vaccines on viral spread 23 . Causal impact analysis has also been used to understand the impact of smoking bans on cigarette sales 24 and as a framework for evaluating interventions with biomedical sensor data 25 .…”

Section: Discussionmentioning

confidence: 99%

“…8 This method has been generally used for online marketing analysis and has been recently applied to health care data, with use cases related to understanding the impact of the COVID-19 pandemic, including estimating the impact of lockdown and vaccines on viral spread. 23 Causal impact analysis has also been used to understand the impact of smoking bans on cigarette sales 24 and as a framework for evaluating interventions with biomedical sensor data. 25 The increasing availability of continuous time series data collected by health care systems, combined with economic pressures to invest in measures that prospectively reduce waste rather than retrospectively analyze it, will continue to support the utility of this analytic method.…”

Section: Discussionmentioning

confidence: 99%

Optimizing electronic blood ordering and supporting administration workflows to improve blood utilization in the pediatric hospital setting

Stoffel,

Leu,

Barry

et al. 2023

Transfusion

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BackgroundRed blood cell wastage occurs when blood is discarded rather than transfused, and ineffective ordering results in unnecessary crossmatch procedures. We describe how a multimodal approach to redesigning electronic ordering tools improved blood utilization in a pediatric inpatient setting and how using innovative application of time series data analysis provides insights into intervention effectiveness, which can guide future process improvement cycles.MethodsA multidisciplinary team used best practices and Toyota Production System methodology to redesign electronic blood ordering and improve administration processes. We analyzed crossmatch to transfusion ratio and red blood cell wastage time series data extracted from our laboratory information system and electronic health record. We used changepoint analysis to identify statistically discernible breaks in each time series, compatible with known interventions. We performed causal impact analysis on red blood cell wastage time series data to estimate blood wastage avoided due to the interventions.ResultsChangepoint analysis estimated an 11% decrease in crossmatch to transfusion ratio and a 77% decrease in red blood cell monthly wastage rate during the intervention period. Causal impact analysis estimated a 61% reduction in expected wastage compared to the scenario if the interventions had not occurred.DiscussionOur results show that electronic health record design is an important factor in reducing waste and preventing unnecessary crossmatching, and that time series analysis can be a useful tool for evaluating the long‐term impact of each stage of intervention in a longitudinal process redesign effort for the purpose of effectively targeting future improvement efforts.

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“…1 In the early stages of the pandemic, UC models were used to fit linear deterministic trends to COVID-19 case numbers and to identify structural breaks (Hartl et al 2020;Lee et al 2021;Liu et al 2021). With an increasing number of observations available, UC models with stochastic trends have been considered by Moosa (2020) and Doornik et al (2022), while (stationary) seasonal components were added by Navas Thorakkattle et al (2022) and Xie (2022), among others. However, appropriately accounting for the alternating peaks and troughs in the trend of COVID-19 case numbers remains an open challenge.…”

Section: Introductionmentioning

confidence: 99%

Modeling COVID-19 Infection Rates by Regime-Switching Unobserved Components Models

Haimerl

Hartl

2023

Econometrics

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The COVID-19 pandemic is characterized by a recurring sequence of peaks and troughs. This article proposes a regime-switching unobserved components (UC) approach to model the trend of COVID-19 infections as a function of this ebb and flow pattern. Estimated regime probabilities indicate the prevalence of either an infection up- or down-turning regime for every day of the observational period. This method provides an intuitive real-time analysis of the state of the pandemic as well as a tool for identifying structural changes ex post. We find that when applied to U.S. data, the model closely tracks regime changes caused by viral mutations, policy interventions, and public behavior.

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Forecasting the Trends of Covid-19 and Causal Impact of Vaccines Using Bayesian Structural time Series and ARIMA

Cited by 17 publications

References 19 publications

A Closer Look at the Alleged “War on Cops”: Post-Ferguson Trends in Ideologically-Motivated Homicides of Police Officers, 2008–2021

A Closer Look at the Alleged “War on Cops”: Post-Ferguson Trends in Ideologically-Motivated Homicides of Police Officers, 2008–2021

Optimizing electronic blood ordering and supporting administration workflows to improve blood utilization in the pediatric hospital setting

Modeling COVID-19 Infection Rates by Regime-Switching Unobserved Components Models

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