Prediction of Epidemic Trends in COVID-19 with Mann-Kendall and Recurrent Forecasting-Singular Spectrum Analysis

Shaharudin, Shazlyn Milleana; Ismail, Sarimah; Samsudin, Mohd Saiful; Azid, Azman; Tan, Mou Leong; Basri, Muhamad Afdal Ahmad

doi:10.17576/jsm-2021-5004-23

Cited by 7 publications

(6 citation statements)

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“…We analyse the trends according to the transient behaviour of the forecast as it changes from A to D. The inclination of each forecast indicates the level of infectivity, i.e. if the forecasts become steeper as they change from A to D, it gives an predict COVID-19 spread in several countries such as Saudi Arabia and Malaysia (10)(11). The salient features of SSA are that this technique is non-parametric, and it is also a datadriven approach, which does not rely on any prior modelling assumptions (11).…”

Section: Results and Trend Analysismentioning

confidence: 99%

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2021

MJMS

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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19 disease, which has become pandemic since December 2019. In the recent months, among five countries in the Southeast Asia, Malaysia has the highest per-capita daily new cases and daily new deaths. A mathematical modelling approach using a Singular Spectrum Analysis (SSA) technique was used to generate data-driven 30-days ahead forecasts for the number of daily cases in the states and federal territories in Malaysia at four consecutive time points between 27 July 2021 and 26 August 2021. Each forecast was produced using SSA prediction model of the current major trend at each time point. The objective is to understand the transition dynamics of COVID-19 in each state by analysing the direction of change of the major trends during the period of study. The states and federal territories in Malaysia were grouped in four categories based on the nature of the transition. Overall, it was found that the COVID-19 spread has progressed unevenly across states and federal territories. Major regions like Selangor, Kuala Lumpur, Putrajaya and Negeri Sembilan were in Group 3 (fast decrease in infectivity) and Labuan was in Group 4 (possible eradication of infectivity). Other states e.g. Pulau Pinang, Sabah, Sarawak, Kelantan and Johor were categorised in Group 1 (very high infectivity levels) with Perak, Kedah, Pahang, Terengganu and Melaka were classified in Group 2 (high infectivity levels). It is also cautioned that SSA provides a promising avenue for forecasting the transition dynamics of COVID-19; however, the reliability of this technique depends on the availability of good quality data.

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Section: Results and Trend Analysismentioning

confidence: 99%

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2021

MJMS

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“…Improper values selection for parameter L may yield unfinished reconstruction, which may potentially mislead the forecasting results. It has been stipulated that L should be large enough, but not greater than half of the number of observations understudy at T 2 (33). The appropriate window length selection depends on the structure of time series data and the current problems (34).…”

Section: Ssa Parameter Selectionmentioning

confidence: 99%

Short-Term Forecasting of Daily Confirmed COVID-19 Cases in Malaysia Using RF-SSA Model

Shaharudin

Ismail

Hassan

et al. 2021

Front. Public Health

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Novel coronavirus (COVID-19) was discovered in Wuhan, China in December 2019, and has affected millions of lives worldwide. On 29th April 2020, Malaysia reported more than 5,000 COVID-19 cases; the second highest in the Southeast Asian region after Singapore. Recently, a forecasting model was developed to measure and predict COVID-19 cases in Malaysia on daily basis for the next 10 days using previously-confirmed cases. A Recurrent Forecasting-Singular Spectrum Analysis (RF-SSA) is proposed by establishing L and ET parameters via several tests. The advantage of using this forecasting model is it would discriminate noise in a time series trend and produce significant forecasting results. The RF-SSA model assessment was based on the official COVID-19 data released by the World Health Organization (WHO) to predict daily confirmed cases between 30th April and 31st May, 2020. These results revealed that parameter L = 5 (T/20) for the RF-SSA model was indeed suitable for short-time series outbreak data, while the appropriate number of eigentriples was integral as it influenced the forecasting results. Evidently, the RF-SSA had over-forecasted the cases by 0.36%. This signifies the competence of RF-SSA in predicting the impending number of COVID-19 cases. Nonetheless, an enhanced RF-SSA algorithm should be developed for higher effectivity of capturing any extreme data changes.

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“…The model has been primarily employed in earth science research to characterize the fluctuation of climatic and environmental variables, such as rainfall, air temperature, and surface runoff [ 9 – 11 ]. Recently, some COVID-19 studies have used the Mann-Kendall (MK) test, which is an earlier version of the MKS test, for trend detection [ 12 , 13 ]. While the MK test is useful in detecting monotonic trends, it cannot detect changes in the trends and the corresponding change points, making it less useful for disease tracking and monitoring in the mid to long term.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, some COVID-19 studies have used the Mann-Kendall (MK) test, which is an earlier version of the MKS test, for trend detection [12,13]. While the MK test is useful in detecting monotonic trends, it cannot detect changes in the trends and the corresponding change points, making it less useful for disease tracking and monitoring in the mid to long term.…”

Section: Introductionmentioning

confidence: 99%

The Mann-Kendall-Sneyers test to identify the change points of COVID-19 time series in the United States

Chen

Wang

Lyu

et al. 2022

BMC Med Res Methodol

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Background One critical variable in the time series analysis is the change point, which is the point where an abrupt change occurs in chronologically ordered observations. Existing parametric models for change point detection, such as the linear regression model and the Bayesian model, require that observations are normally distributed and that the trend line cannot have extreme variability. To overcome the limitations of the parametric model, we apply a nonparametric method, the Mann-Kendall-Sneyers (MKS) test, to change point detection for the state-level COVID-19 case time series data of the United States in the early outbreak of the pandemic. Methods The MKS test is implemented for change point detection. The forward sequence and the backward sequence are calculated based on the new weekly cases between March 22, 2020 and January 31, 2021 for each of the 50 states. Points of intersection between the two sequences falling within the 95% confidence intervals are identified as the change points. The results are compared with two other change point detection methods, the pruned exact linear time (PELT) method and the regression-based method. Also, an open-access tool by Microsoft Excel is developed to facilitate the model implementation. Results By applying the MKS test to COVID-19 cases in the United States, we have identified that 30 states (60.0%) have at least one change point within the 95% confidence intervals. Of these states, 26 states have one change point, 4 states (i.e., LA, OH, VA, and WA) have two change points, and one state (GA) has three change points. Additionally, most downward changes appear in the Northeastern states (e.g., CT, MA, NJ, NY) at the first development stage (March 23 through May 31, 2020); most upward changes appear in the Western states (e.g., AZ, CA, CO, NM, WA, WY) and the Midwestern states (e.g., IL, IN, MI, MN, OH, WI) at the third development stage (November 19, 2020 through January 31, 2021). Conclusions This study is among the first to explore the potential of the MKS test applied for change point detection of COVID-19 cases. The MKS test is characterized by several advantages, including high computational efficiency, easy implementation, the ability to identify the change of direction, and no assumption for data distribution. However, due to its conservative nature in change point detection and moderate agreement with other methods, we recommend using the MKS test primarily for initial pattern identification and data pruning, especially in large data. With modification, the method can be further applied to other health data, such as injuries, disabilities, and mortalities.

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Prediction of Epidemic Trends in COVID-19 with Mann-Kendall and Recurrent Forecasting-Singular Spectrum Analysis

Cited by 7 publications

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Short-Term Forecasting of Daily Confirmed COVID-19 Cases in Malaysia Using RF-SSA Model

The Mann-Kendall-Sneyers test to identify the change points of COVID-19 time series in the United States

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