Forecasting on Covid-19 infection waves using a rough set filter driven moving average models

Srivastava, Saurabh; Meena, Yogesh Kumar; Singh, Girdhari

doi:10.1016/j.asoc.2022.109750

Cited by 12 publications

(8 citation statements)

References 45 publications

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“…One of the main challenges of the identification of epidemic waves is distinguishing an upcoming wave from a temporary spike/drop in infection counts [4,7], as this noise affects the estimation of the waves. We used a discrete version of a Gaussian filter to avoid it, which allows for a more accurate identification of the waves and their peaks and valleys.…”

Section: Discussionmentioning

confidence: 99%

“…There are previous studies that systematically define, identify, and predict epidemic waves [5][6][7][8][9][10][11][12][13] but the methodology in less standardized. Our methodology is easy to apply, and aiming to be a globally used method that allows comparative studies and to have a common language to use.…”

Section: Discussionmentioning

confidence: 99%

“…The identification of epidemic waves lacks a consensual approach [3,4]. However, since the onset of the COVID-19 pandemic, there have been intensified attempts to systematically define, identify, and predict epidemic waves [5][6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…The increase in an upward period or the decrease in a downward period have to be substantial by sustaining itself over a period of time to distinguish them from an uptick, a downtick, reporting errors, or volatility in new cases. These characteristics are also part of the definition by Srivastava et al [7]. There are also studies that specifically point out that a rise in the mean number of cases between two consecutive weeks higher than 20% is indicative of an upcoming epidemic wave [14] Other studies identified epidemic waves by designing a method with an implicit definition [6], or identifying a threshold over and under which a wave starts and ends, respectively [12].…”

Section: Introductionmentioning

confidence: 99%

“…One of the main challenges of the epidemic wave identification is distinguishing an upcoming wave from a temporary spike/drop or a group of spikes/drops in infection counts [4,7]. This temporary spike/drop is the noise in the epidemic curve and is due to the demographic and environmental variability [16].…”

Section: Introductionmentioning

confidence: 99%

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EpidemicKabu a new method to identify epidemic waves and their peaks and valleys

Galvis,

Barbosa,

Cardozo

et al. 2024

Preprint

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INTRODUCTION: The dynamical behavior of epidemic curves is an oscillation between a very low and very high number of incident cases throughout the time. These oscillations are commonly called waves of the epidemic curve. The concept of epidemic waves lacks a consensual definition and a simple methodology that can be used for many diseases. OBJECTIVE: We describe in this study the EpidemicKabu method to identify the start and the end of past epidemic waves but also their peaks and valleys. METHOD: The methodology is divided into processing of the curve, waves detection, and peaks and valleys delimitation. For processing the curve, a Gaussian kernel was used to diminish the noise and to smooth the curve. The first and second derivatives of the curve were used for the detection of waves, delimitation of peaks and valleys. The methodology was derived into the open access library. The method was tested using COVID-19 daily cases reported between 2020 and 2022 for different countries. After detection of waves, we made some measures related to the size of the waves for those countries. RESULTS: The results of the method were the dates of start and end of waves, peaks, and valleys. The dates are displayed on graphs and added as a new column in a dataset. We found that Belgium was the country recording the highest ratio of incident cases per 100 people by day in a wave. CONCLUSION: The EpidemicKabu method is simple, easy to use, and very useful in estimating epidemic waves. The methodology requires expert judgment in order to set a parameter that could only have three possible values.

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Section: Discussionmentioning

confidence: 99%