Using Extreme Value Theory Approaches to Forecast the Probability of Outbreak of Highly Pathogenic Influenza in Zhejiang, China

Chen, Jiangpeng; Lei, Xun; Zhang, Li; Peng, Bin

doi:10.1371/journal.pone.0118521

Cited by 32 publications

(19 citation statements)

References 11 publications

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“…EVT was also used to detect outliers (here seen as extreme events) in time series surveillance data, rather than estimate the risk of future extreme events [ 6 ]. More recently, a study applied EVT to predict monthly incidence of avian influenza cases [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Applications of Extreme Value Theory in Public Health

et al. 2016

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ObjectivesWe present how Extreme Value Theory (EVT) can be used in public health to predict future extreme events.MethodsWe applied EVT to weekly rates of Pneumonia and Influenza (P&I) deaths over 1979–2011. We further explored the daily number of emergency department visits in a network of 37 hospitals over 2004–2014. Maxima of grouped consecutive observations were fitted to a generalized extreme value distribution. The distribution was used to estimate the probability of extreme values in specified time periods.ResultsAn annual P&I death rate of 12 per 100,000 (the highest maximum observed) should be exceeded once over the next 30 years and each year, there should be a 3% risk that the P&I death rate will exceed this value. Over the past 10 years, the observed maximum increase in the daily number of visits from the same weekday between two consecutive weeks was 1133. We estimated at 0.37% the probability of exceeding a daily increase of 1000 on each month.ConclusionThe EVT method can be applied to various topics in epidemiology thus contributing to public health planning for extreme events.

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Section: Introductionmentioning

confidence: 99%

Applications of Extreme Value Theory in Public Health

et al. 2016

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“…The Mean Excess Function plot is useful for determining the appropriate thresholds for extreme values of the variable in the POT. The MEF is also a convenient visual tool for examining whether a variable has a specific distribution (Chen et al, 2015). In the research, the MEF allows a threshold (limit) of extreme values to be established.…”

Section: Methodsmentioning

confidence: 99%

The positional MEF-TOPSIS method for the assessment of complex economic phenomena in territorial units

Łuczak

Just

2020

Statistics in Transition New Series

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In this paper, the authors propose a new methodological approach to the construction of a synthetic measure, where the objects are described by variables with strong asymmetry and extreme values (outliers). Even a single extreme value (very large or very small) of a variable for the object may significantly affect the attribution of an excessively high or low rank in the final ranking of objects. This dependence is particularly apparent when using the classical TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method. The aim of the study is to present the application potential of the positional MEF-TOPSIS method for the assessment of the level of development of complex economic phenomena for territorial units. In the positional TOPSIS method, the application of the spatial median of Oja, which limits the impact of strong asymmetry, is proposed. In order to weaken the influence of extreme values, the Mean Excess Function (MEF) is used, by means of which it is possible to identify the limits of extreme values and establish model objects. The proposed approach is used to assess the financial self-sufficiency of Polish municipalities in 2016. The study finally compares the results of applications of positional MEF-TOPSIS and the classic and positional TOPSIS methods.

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“…After that, the theory of statistics of rare events was extensively studied. Extreme value theory has been used in various applications such as finance [17], appearance of epidemics [5] and climatology [15]. In [3], we can find a lot of examples of real-world applications of EVT.…”

Section: Related Workmentioning

confidence: 99%

On Choice of Hyper-parameter in Extreme Value Theory based on Machine Learning Techniques

Nakamura¹

2021

Preprint

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Extreme value theory (EVT) is a statistical tool for analysis of extreme events. It has a strong theoretical background, however, we need to choose hyper-parameters to apply EVT. In recent studies of machine learning, techniques of choosing hyper-parameters have been well-studied. In this paper, we propose a new method of choosing hyper-parameters in EVT based on machine learning techniques. We also experiment our method to real-world data and show good usability of our method.

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Using Extreme Value Theory Approaches to Forecast the Probability of Outbreak of Highly Pathogenic Influenza in Zhejiang, China

Cited by 32 publications

References 11 publications

Applications of Extreme Value Theory in Public Health

Applications of Extreme Value Theory in Public Health

The positional MEF-TOPSIS method for the assessment of complex economic phenomena in territorial units

On Choice of Hyper-parameter in Extreme Value Theory based on Machine Learning Techniques

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