A Distribution-Free Regional Cumulative Sum for Identifying Hyperendemic Periods of Disease Incidence

Leung, Clara; Patel, Mahomed; McGilchrist, C. A.

doi:10.1111/1467-9884.00183

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…Holt's method or Double Exponential Smoothing (DES) [18] uses the recursive application of an exponential filter twice and it works using the parameters like the sequence of observations (x t ), the smoothed value (s t ), and the best estimate of the trend (b t ). Equations (12)(13)(14)(15)(16) show the mathematical equations for DES model, where α is the data smoothing factor, with range 0 < α < 1 and β is the trend smoothing factor, with range 0 < β < 1. Equation ( 16) is the forecasting equation with m-steps ahead…”

Section: Methodsmentioning

confidence: 99%

“…Sparks et al [11] optimized an early outbreak detection of Influenza and Ross River virus using cumulative sum and negative binomial transitional regression. Leung et al [12] developed a distribution-free cumulative sum procedure to diagnose geographical temporal clustering events from over-time neighboring geographical areas. Fisman et al [13] developed an Incidence Decay and Exponential Adjustment (IDEA) model to aid public authorities during the outbreaks or epidemic situations and found that the model provides a more accurate estimate of the total size and duration for a given outbreak when the value of R 0 is low or moderate.…”

Section: Mathematical Modelsmentioning

confidence: 99%

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Comparative analysis of epidemiological models for COVID-19 pandemic predictions

Gupta

Pandey

Pal³

2021

Biostatistics & Epidemiology

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Epidemiological modeling is an important problem around the world. This research presents COVID-19 analysis to understand which model works better for different regions. A comparative analysis of three growth curve fitting models (Gompertz, Logistic, and Exponential), two mathematical models (SEIR and IDEA), two forecasting models (Holt's exponential and ARIMA), and four machine/deep learning models (Neural Network, LSTM Networks, GANs, and Random Forest) using three evaluation criteria on ten prominent regions around the world from North America, South America, Europe, and Asia has been presented. The minimum and median values for RMSE were 1.8 and 5372.9; the values for the mean absolute percentage error were 0.005 and 6.63; and the values for AIC were 87.07 and 613.3, respectively, from a total of 125 experiments across 10 regions. The growth curve fitting models worked well where flattening of the cases has started. Based on region's growth curve, a relevant model from the list can be used for predicting the number of infected cases for COVID-19. Some other models used in forecasting the number of cases have been added in the future work section, which can help researchers to forecast the number of cases in different regions of the world.

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Section: Methodsmentioning

confidence: 99%

Section: Mathematical Modelsmentioning

confidence: 99%

Comparative analysis of epidemiological models for COVID-19 pandemic predictions

Gupta

Pandey

Pal³

2021

Biostatistics & Epidemiology

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“…The CUSUM chart has been used to detect patterns of disease occurrence in the methods developed by Raubertas [9], Rogerson [10], Leung et al [11], and Rogerson and Yamada [12], in addition to the local Knox monitoring method. Joner et al [13] studied the ARL performance for the method developed by Rogerson and Yamada [12], but the performance of the CUSUM chart for monitoring the local Knox statistic has not previously been investigated.…”

Section: Introductionmentioning

confidence: 99%

Use of the local Knox statistic for the prospective monitoring of disease occurrences in space and time

Marshall

Spitzner

Woodall

2006

Statistics in Medicine

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The detection of clusters of events occurring close together both temporally and spatially is important in finding outbreaks of disease within a geographic region. The Knox statistic is often used in epidemiology to test for space-time clustering retrospectively. For quicker detection of epidemics, prospective methods should be used in which observed events in space and time are assessed as they are recorded. The cumulative sum (CUSUM) surveillance method for monitoring the local Knox statistic tests for space-time clustering each time there is an incoming observation. We consider the design of this control chart by determining the in-control average run length (ARL) performance of the CUSUM chart for different space and time closeness thresholds as well as for different control limit values. We also explain the effect of population density and region shape on the in-control ARL and discuss other distributional issues that should be considered when implementing this method.

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The Use of Control Charts in Health-Care and Public-Health Surveillance

Woodall

2006

Journal of Quality Technology

504

318

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A Distribution-Free Regional Cumulative Sum for Identifying Hyperendemic Periods of Disease Incidence

Cited by 3 publications

References 3 publications

Comparative analysis of epidemiological models for COVID-19 pandemic predictions

Comparative analysis of epidemiological models for COVID-19 pandemic predictions

Use of the local Knox statistic for the prospective monitoring of disease occurrences in space and time

The Use of Control Charts in Health-Care and Public-Health Surveillance

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