Several Recent Papers from Risks-X Published on Top Journals
including Physical Review Letters

Recently, several papers have been published by Risks-X, SUSTech in internationally high-impact academic journals, including Physical Review Letters, Nonlinear Dynamics, Earth and Planetary Science Letters, and IEEE Internet of things Journal. The research areas vary from physics, COVID-19, earthquakes to the Internet of Things.

1  The research team led by Didier Sornette, Dean of Risks-X, SUSTech, published a paper titled Non-universal Power Law Distribution of Intensities of the Self-excited Hawkes Process: a Field-theoretical Approach in Physical Review Letter. 

In this paper, the Hawkes self-excited point process provided an efficient representation of the bursty intermittent dynamics of many physical, biological, geological and economic systems. By expressing the probability for the next event per unit time (called "intensity"), say of an earthquake, as a sum over all past events of (possibly) long-memory kernels, the Hawkes model was non-Markovian. By mapping the Hawkes model onto stochastic partial differential equations that were Markovian, researchers developed a field theoretical approach in terms of probability density functionals. Solving the steady-state equations, they predicted a power law scaling of the probability density function (PDF) of the intensities close to the critical point n=1 of the Hawkes process, with a non-universal exponent, function of the background intensity ν0 of the Hawkes intensity, the average time scale of the memory kernel and the branching ration. Their theoretical predictions were confirmed by numerical simulations.

Kiyoshi Kanazawa, assistant professor at the University of Tsukuba, Japan was the first author of the paper, with Prof. Didier Sornette as the correspondent author. The Physical Review Letters is a top journal in physics worldwide (IF 8.385, JCR Q1).

A paper with a detailed model was published in an extended version in Physical Review Research, a new journal of Physical Review series.

References:

1. Kanazawa K, Sornette D. Nonuniversal Power Law Distribution of Intensities of the Self-Excited Hawkes Process: A Field-Theoretical Approach[J]. Physical Review Letters, 2020, 125(13). 

https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.125.138301

2. Kanazawa K, Sornette D. Field master equation theory of the self-excited Hawkes process[J]. Physical Review Research, 2020, 2(3).

https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.2.033442

2  The research team led by Prof. Didier Sornette, Dean of Risks-X SUSTech, consecutively published three papers analyzing COVID-19 worldwide development in Nonlinear Dynamics, which is a renown journal enjoying a worldwide reputation in engineering with its IF indexed 4.867 in 2019 and JCR in Q1.

With the unfolding of the COVID-19 pandemic, mathematical modeling of epidemics has been perceived and used as a central element in understanding, predicting, and governing the pandemic event. However, soon it becomes clear that long-term predictions are extremely challenging to address. Also, it is still unclear which metric shall be used for a global description of the evolution of the outbreaks. Yet a robust modeling of pandemic dynamics and a consistent choice of the transmission metric is crucial for an in-depth understanding of the macroscopic phenomenology and better-informed mitigation strategies. The research team led by Prof. Didier Sornette published three papers and put forward three different modeling frameworks and methods, giving an all-round analysis of the global evolution of COVID-19.

"Generalized Logistic Growth Modeling of the COVID-19 Outbreak: Comparing the Dynamics in the 29 Provinces in China and in the Rest of the World" 

In this paper, the research team calibrated the logistic growth model, the generalized logistic growth model, the generalized Richards model, and the generalized growth model to the reported number of infected cases for the whole of China, 29 provinces in China, and 33 countries and regions that had been or were undergoing major outbreaks. The research team dissected the development of the epidemics in China and the impact of the drastic control measures both at the aggregate level and within each province. They quantitatively documented four phases of the outbreak in China with a detailed analysis on the heterogeneous situations across provinces. The extreme containment measures implemented by China were very effective with some instructive variations across provinces. Borrowing from the experience of China, they made scenario projections on the development of the outbreak in other countries. They identified that outbreaks in 14 countries (mostly in western Europe) had ended, while "first waves" of epidemic had been identified in 19 countries. In this paper, after-peak trajectories in different countries were measured, and the results clearly showed the differences between western countries and China. Also, the paper proposed an outbreak progress index, which quantified and categorized the outbreak progress in different countries.

Wu Ke, assistant professor of Risks-X, SUSTech, was the first author, Didier Darcet from Gavekal Intelligence was the second author, while Wang Qian, Senior Researcher of Risks-X, SUStech was the third author, with Didier Sornette, Dean of Risks-X, SUSTech, as the correspondent author.

"Interpreting, Analysing and Modelling COVID-19 Mortality Data"

The paper presented results on the mortality statistics of the COVID-19 epidemic in a number of countries. The data analysis suggested classifying countries into five groups, 1) Western countries, 2) East Block, 3) developed South East Asian countries, 4) Northern Hemisphere developing countries and 5) Southern Hemisphere countries. Comparing the number of deaths per million inhabitants, a pattern emerged in which the Western countries exhibited the largest mortality rate. Furthermore, comparing the running cumulative death tolls as the same level of outbreak progress in different countries revealed several subgroups within the Western countries and further emphasized the difference between the five groups. Analyzing the relationship between deaths per million and life expectancy in different countries, taken as a proxy of the preponderance of elderly people in the population, a main reason behind the relatively more severe COVID-19 epidemic in the Western countries was found to be their larger population of elderly people, with exceptions such as Norway, for which other factors seemed to dominate. In the paper, the comparison between countries at the same level of outbreak progress allowed researchers to identify and quantify a measure of efficiency of the level of stringency of confinement measures. They found that increasing the stringency from 20 to 60 decreased the death count by about 50 lives per million in a time window of 20 days. Finally, they performed logistic equation analyses of deaths as a means of tracking the dynamics of outbreaks in the "first wave" and estimating the associated ultimate mortality, using four different models to identify model error and robustness of results. This quantitative analysis allowed them to assess the outbreak progress in different countries, further assisting in fighting against the pandemic.

Didier Sornette, Dean of Risks-X, SUSTech, Michael Schatz (also the correspondent author) and Euan Mearns from ETH-Zurich, and Wu Ke, assistant professor of Risks-X, SUSTech were joint first authors, while Didier Darcet from Gavekal Intelligence was the fifth author. 

"The Dynamics of Entropy in the COVID-19 Outbreaks"

The paper proposed a Markovian stochastic framework designed for describing the evolution of entropy during the COVID-19 pandemic together with the instantaneous reproductive ratio. Then, it introduced and used entropy-based metrics of global transmission to measure the impact and the temporal evolution of a pandemic event. In the formulation of the model, the temporal evolution of the outbreak was modeled by an equation governing the probability distribution that described a nonlinear Markov process of a statistically averaged individual, leading to a clear physical interpretation. The time-dependent parameters were formulated by adaptive basis functions, leading to a parsimonious representation. In addition, the researchers provided a full Bayesian inversion scheme for calibration together with a coherent strategy to address data unreliability. The time evolution of the entropy rate, the absolute change in the system entropy, and the instantaneous reproductive ratio were natural and transparent outputs of this framework. The framework had the appealing property of being applicable to any compartmental epidemic model. As an illustration, they applied the proposed approach to a simple modification of the SEIR (susceptible–exposed–infected–removed) model. Applying the model to the Hubei region, South Korean, Italian, Spanish, German, and French COVID-19 datasets, they discovered significant difference in the absolute change of entropy but highly regular trends for both the entropy evolution and the instantaneous reproductive ratio.

Wang Ziqi, assistant professor of Guangzhou University, and Marco Broccardo, associate professor of the University of Trento worked as the first and second author, respectively, and were both the correspondent authors. Arnaud Mignan, associate professor of Risks-X, SUSTech was the third author, while Didier Sornette, chair professor of Risks-X, SUSTech was the fourth author. 

References:

1. Wu K, Darcet D, Wang Q, et al. Generalized logistic growth modeling of the COVID-19 outbreak: comparing the dynamics in the 29 provinces in China and in the rest of the world[J]. Nonlinear Dynamics, 2020, 101: 1561–1581.

https://doi.org/10.1007/s11071-020-05862-6

2. Sornette D, Mearns E, Schatz M, et al. Interpreting, analysing and modelling COVID-19 mortality data[J]. Nonlinear Dynamics, 2020, 101: 1751–1776

https://doi.org/10.1007/s11071-020-05966-z

3. Wang Z, Broccardo M, Mignan A, et al. The dynamics of entropy in the COVID-19 outbreaks[J]. Nonlinear Dynamics, 2020, 101: 1847–1869

https://doi.org/10.1007/s11071-020-05871-5

3  Arnaud Mignan, associate professor published a comment paper, titled Comment on “Elastic strain energy and pore-fluid pressure control of aftershocks” by Terakawa et al. on Earth and Planetary Science Letters, a renown journal in earth and planetary science.

The comment mentioned that aftershock occurrence was the dominant phenomenon observed in seismicity, offering a wealth of data for the statistical learning of the earthquake nucleation process. However, physical interpretability could be biased by a number of well-known pitfalls. In this comment, the authors showed that the study of Terakawa et al. (2020), who concluded that the spatial distribution of aftershocks was best explained by a combination of elastic strain energy and pore-fluid pressure change (ΔEFS), was beset by both confirmation bias and data leakage. Using the same data (1992 Landers mainshock), procedure (binary classification), and metrics (Area Under the Curve, AUC), the research team led by Arnaud Mignan found that the simple empirical model based on aftershock-distance-to-mainshock-rupture (Mignan and Broccardo, 2019) performed to a level (AUC = 0.76) similar to the one obtained by ΔEFS (AUC = 0.75 [0.70-0.77]). Including the Big Bear rupture improved the distance-based model performance up to AUC = 0.86. From a statistical perspective, they further showed that including pore-fluid pressure change, being calculated from earthquake focal mechanisms, was equivalent to implicitly propagating aftershock location information into the model. Such type of data leakage clearly improved any classifier performance.

Arnaud Mignan, associate professor of Risks-X and Department of Earth and Space Sciences, SUSTech, was the first and correspondent author of this comment, while Marco Broccardo from ETH-Zurich was the second author. 

Earth and Planetary Science Letters is  a well-known journal in earth and planetary science enjoying a global reputation, with its IF reaching 4.823 in 2019 and JCR in Q1.

Reference:

1. Mignan A, Broccardo M. Comment on “Elastic strain energy and pore-fluid pressure control of aftershocks” by Terakawa et al.[Earth Planet. Sci. Lett. 535 (2020) 116103][J]. Earth and Planetary Science Letters, 2020, 544: 116402.

https://doi.org/10.1016/j.epsl.2020.116402

4  Chen Kejie, assistant professor, jointly published a review paper titled Toward Location-Enabled IoT (LE-IoT): IoT Positioning Techniques, Error Sources, and Error Mitigation in IEEE Internet of things Journal, the top journal in computer science.

The review pointed out that the Internet of Things (IoT) had started to empower the future of many industrial and mass-market applications. Localization techniques were becoming key to add location context to IoT data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) technologies had advantages such as long-range, low power consumption, low cost, massive connections, and the capability for communication in both indoor and outdoor areas. These features made LPWAN signals strong candidates for mass-market localization applications. However, there were various error sources that had limited localization performance by using such IoT signals. This paper reviewed the IoT localization system through the following sequence: IoT localization system review -- localization data sources -- localization algorithms -- localization error sources and mitigation -- localization performance evaluation. Compared to the related surveys, this paper had a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, the survey provided comprehensive guidance for peers who were interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors.

The main authors of this paper were from the University of Calgary, Wuhan University, Beijing University of Posts and Telecommunications, China University of Geosciences, University of Exeter, Sun Yat-sen University, Shanghai Jiao Tong University, National University of Defense Technology , and The University of Edinburgh. Chen Kejie, assistant professor of Risks-X and Department of Earth and Space Sciences, SUSTech was one of the co-authors. 

IEEE Internet of things Journal tops the world in computer science, as its JCR ranks Q1 and IF indexed 9.936 in 2019. 

Reference:

1. Li Y, Zhuang Y, Hu X, et al. Toward Location-Enabled IoT (LE-IoT): IoT Positioning Techniques, Error Sources, and Error Mitigation[J]. IEEE Internet of Things Journal, 2020.

https://doi.org/10.1109/JIOT.2020.3019199