Modelling and forecasting of growth rate of new COVID-19 cases in top nine affected countries: Considering conditional variance and asymmetric effect

Ekinci, Aykut

doi:10.1016/j.chaos.2021.111227

Cited by 21 publications

(14 citation statements)

References 49 publications

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“…Papers from the journal Chaos, Solitons, and Fractals, which specializes in mathematics and physics, composed a large portion of our final set of papers (22 out of 118 published papers) 26,51,60,64,66,73,75,[77][78][79][80][81]83,85,87,100,102,103,118,[124][125][126] . So, we conducted a separate analysis of this subset, shown in Table 5.…”

Section: Figure 4 Top 10 Journals In the Final Setmentioning

confidence: 99%

An Evaluation of Prospective COVID-19 Modeling: From Data to Science Translation

Nixon

Jindal

Parker

et al. 2022

Preprint

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SummaryBackgroundInfectious disease modeling can serve as a powerful tool for science-based management of outbreaks, providing situational awareness and decision support for policy makers. Predictive modeling of an emerging disease is challenging due to limited knowledge on its epidemiological characteristics. For COVID-19, the prediction difficulty was further compounded by continuously changing policies, varying behavioral responses, poor availability and quality of crucial datasets, and the variable influence of different factors as the pandemic progresses. Due to these challenges, predictive modeling for COVID-19 has earned a mixed track record.MethodsWe provide a systematic review of prospective, data-driven modeling studies on population-level dynamics of COVID-19 in the US and conduct a quantitative assessment on crucial elements of modeling, with a focus on the aspects of modeling that are critical to make them useful for decision-makers. For each study, we documented the forecasting window, methodology, prediction target, datasets used, geographic resolution, whether they expressed quantitative uncertainty, the type of performance evaluation, and stated limitations. We present statistics for each category and discuss their distribution across the set of studies considered. We also address differences in these model features based on fields of study.FindingsOur initial search yielded 2,420 papers, of which 119 published papers and 17 preprints were included after screening. The most common datasets relied upon for COVID-19 modeling were counts of cases (93%) and deaths (62%), followed by mobility (26%), demographics (25%), hospitalizations (12%), and policy (12%). Our set of papers contained a roughly equal number of short-term (46%) and long-term (60%) predictions (defined as a prediction horizon longer than 4 weeks) and statistical (43%) versus compartmental (47%) methodologies. The target variables used were predominantly cases (89%), deaths (52%), hospitalizations (10%), and Rt (9%). We found that half of the papers in our analysis did not express quantitative uncertainty (50%). Among short-term prediction models, which can be fairly evaluated against truth data, 25% did not conduct any performance evaluation, and most papers were not evaluated over a timespan that includes varying epidemiological dynamics. The main categories of limitations stated by authors were disregarded factors (39%), data quality (28%), unknowable factors (26%), limitations specific to the methods used (22%), data availability (16%), and limited generalizability (8%). 36% of papers did not list any limitations in their discussion or conclusion section.InterpretationPublished COVID-19 models were found to be consistently lacking in some of the most important elements required for usability and translation, namely transparency, expressing uncertainty, performance evaluation, stating limitations, and communicating appropriate interpretations. Adopting the EPIFORGE 2020 guidelines would address these shortcomings and improve the consistency, reproducibility, comparability, and quality of epidemic forecasting reporting. We also discovered that most of the operational models that have been used in real-time to inform decision-making have not yet made it into the published literature, which highlights that the current publication system is not suited to the rapid information-sharing needs of outbreaks. Furthermore, data quality was identified to be one of the most important drivers of model performance, and a consistent limitation noted by the modeling community. The US public health infrastructure was not equipped to provide timely, high-quality COVID-19 data, which is required for effective modeling. Thus, a systematic infrastructure for improved data collection and sharing should be a major area of investment to support future pandemic preparedness.

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Section: Figure 4 Top 10 Journals In the Final Setmentioning

confidence: 99%

An Evaluation of Prospective COVID-19 Modeling: From Data to Science Translation

Nixon

Jindal

Parker

et al. 2022

Preprint

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“…Karthikeyan et al used a DWT to detect autonomic nervous system activity generated from stress response to mental arithmetic, they reported a classification rate of 96.3 and 75.9% in low and high-frequency bands respectively (Karthikeyan et al, 2012). Several studies suggest that ARCH/GARCH models outperform ARIMA models in short-term forecast accuracy and asymmetric heavy-tailed distributions (Sparks and Yurova, 2006;Ekinci, 2021). Heteroscedasticity (non-constant variance of the error term) appeared in the T R time series, which has a known degrading influence on model forecast accuracy (Wang and Akabay, 1994;Schlüter and Deuschle, 2010).…”

Section: Figurementioning

confidence: 99%

A comparison of mental arithmetic performance in time and frequency domains

Abdul-Rahman

2022

Front. Psychol.

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The Heisenberg-Gabor uncertainty principle defines the limits of information resolution in both time and frequency domains. The limit of resolution discloses unique properties of a time series by frequency decomposition. However, classical methods such as Fourier analysis are limited by spectral leakage, particularly in longitudinal data with shifting periodicity or unequal intervals. Wavelet transformation provides a workable compromise by decomposing the signal in both time and frequency through translation and scaling of a basis function followed by correlation or convolution with the original signal. This study aimed to compare the accuracy of predictive models in mental arithmetic in time and frequency domains. Analysis of the author's response time at mental arithmetic using a soroban was modeled for two periods, an initial period (TI = 68 days), and a return period (TR = 170 days) both separated by an interval of 370 days. The median (min,max) response times in seconds (s) was longer for all tasks during the TI compared to the TR period (p < 0.001), for addition [CTAdd 62 (45, 127) vs 50 (38, 75) s] and summation [CTSum 68 (47, 108) vs 57(43, 109) s]. Response times were longer for errors regardless of the study period or task. There was an increasing phase difference for the addition and summation tasks during the TI period toward the end of the series 49.65o compared to the TR period where the phase difference between the two tasks was only 2.05o, indicating that both tasks are likely demonstrating similar learning rates during the latter study period. A comparison between time and time/frequency domain forecasts for an additional 100 tasks demonstrated higher accuracy of the maximum overlap discrete wavelet transform (MODWT) model, where the mean absolute percentage error ranged between 5.48 and 8.19% and that for the time domain models [autoregressive integrated moving average (ARIMA), generalized autoregressive conditional heteroscedasticity (GARCH)] was 6.16–10.80%.

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“…The COVID-19 crisis has also caused significant damage to supply chains, loss of employment, increased poverty levels, social unrest, and even, it has had significant effects on education and learning, among other manifestations of the human psyche (Ekinci, 2021). Regarding economic impacts, the COVID-19 pandemic triggered falls in global capital markets value of approximately 30%, because of the high levels of contagion and mortality of the SARS-COV-2 virus, as well as to the measures of confinement and reduction of economic activity (Ali, Alam, & Rizvi, 2020).…”

Section: Sars-cov-2 Effectsmentioning

confidence: 99%

“…For example, reduction in world economic activity, abrupt falls in international financial markets, job losses, business closings, decrease in patterns of social mobility, a significant increase in domestic violence cases, and other disorders of the human psyche as a response to confinement measures. Likewise, there is an increase in school dropout and backwardness levels before adopting new paradigms and educational strategies and the lack of information technology infrastructure in many regions of the planet, to mention a few aspects (Ekinci, 2021).…”

Section: Introductionmentioning

confidence: 99%

Tracking COVID-19 Decease Through Rolling Conditional Variance

Ríos

Preciado²

2021

RWE

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The effects of COVID-19 have been devastating globally. However, countries have essential asymmetries regarding the disease spread dynamics and the respective mortality rates. In addition to containment strategies and boosting growth and economic development in the face of the COVID-19 pandemic, society calls for solutions that allow the development of vaccines, treatments for the disease, and especially, indicators or early warnings that anticipate the evolution of new infections and deaths. This research aims to track the total deaths caused by COVID-19 in the most affected countries by the pandemics after the approval, distribution, and implementation of vaccines from 2021. We proposed an Autoregressive Integrated Moving Average (ARIMA) specification as a first adjustment. Subsequently, we estimate the conditional variance of total deaths from an Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH). Finally, we compute a rolling density backtesting within a 7-day rolling window to demonstrate the robustness estimation for COVID-19 mortality. The work's main contribution lies in exhibiting a tracking indicator for volatility and COVID-19 direction, including a weekly window to observe its evolution.

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Modelling and forecasting of growth rate of new COVID-19 cases in top nine affected countries: Considering conditional variance and asymmetric effect

Cited by 21 publications

References 49 publications

An Evaluation of Prospective COVID-19 Modeling: From Data to Science Translation

An Evaluation of Prospective COVID-19 Modeling: From Data to Science Translation

A comparison of mental arithmetic performance in time and frequency domains

Tracking COVID-19 Decease Through Rolling Conditional Variance

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