Graph modelling for tracking the COVID-19 pandemic spread

Alguliyev, Rasim; Alıguliyev, Ramiz M.; Yusifov, Farhad

doi:10.1016/j.idm.2020.12.002

Cited by 24 publications

(12 citation statements)

References 21 publications

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“…In the unprecedented difficulty created by the COVID-19 pandemic outbreak, [1] mathematical modeling (for recent efforts, see, e.g., refs. [2][3][4][5][6][7][8][9][10] and citations therein) developed by epidemiologists over many decades [11][12][13][14][15][16] may make an important contribution in helping politics to adopt adequate regulations to efficiently fight against the spread of SARS-CoV-2 virus while mitigating negative economic and social consequences. The latter aspect is of paramount importance [17] also because, if not adequately considered by governments currently challenged to DOI: 10.1002/adts.202000225 deciding possibly under dramatic circumstances and formidable tight schedule, it can jeopardize the health care system itself.…”

Section: Introductionmentioning

confidence: 99%

What Can We Learn from the Time Evolution of COVID‐19 Epidemic in Slovenia?

Bâldea

2021

Advcd Theory and Sims

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A recent work indicates that temporarily splitting larger populations into smaller groups can efficiently mitigate the spread of SARS‐CoV‐2 virus. The fact that, soon afterward, the two million people Slovenia was the first European country proclaiming the end of COVID‐19 epidemic within national borders may be relevant from this perspective. Motivated by this evolution, in this paper the time dynamics of coronavirus cases in Slovenia is investigated with emphasis on how efficient various containment measures act to diminish the number of COVID‐19 infections. Noteworthily, the present analysis does not rely on any speculative theoretical assumption; it is solely based on raw epidemiological data. Out of the results presented here, the most important one is perhaps the finding that, while imposing drastic curfews and travel restrictions reduce the infection rate κ by a factor of four with respect to the unrestricted state, they only improve the κ‐value by ≈15% as compared to the much bearable state of social and economic life wherein wearing face masks and social distancing rules are enforced/followed. Significantly for behavioral and social science, our analysis may reveal an interesting self‐protection instinct of the population, which became manifest even before the official lockdown enforcement.

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Section: Introductionmentioning

confidence: 99%

What Can We Learn from the Time Evolution of COVID‐19 Epidemic in Slovenia?

Bâldea

2021

Advcd Theory and Sims

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“…Moreover, the novelty of this research is the study of the course of COVID-19 pneumonia as a dynamic process using graph-probabilistic models. Probabilistic discrete-time models are often used for modelling dynamics of the COVID-19 epidemic [ 47 , 48 ], but almost never used to model the course of the disease in the hospital on the macrolevels. Abhinav Vepa and colleagues used Bayesian networks to extract probabilistic relationships and predict treatment outcomes [ 49 ]; however, their work does not research the course of the disease as a dynamic process with the analysis of all types of relationships.…”

Section: Discussionmentioning

confidence: 99%

Hybrid Bayesian Network-Based Modeling: COVID-19-Pneumonia Case

Derevitskii

Mramorov

Usoltsev

et al. 2022

JPM

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The primary goal of this paper is to develop an approach for predicting important clinical indicators, which can be used to improve treatment. Using mathematical predictive modeling algorithms, we examined the course of COVID-19-based pneumonia (CP) with inpatient treatment. Algorithms used include dynamic and ordinary Bayesian networks (OBN and DBN), popular ML algorithms, the state-of-the-art auto ML approach and our new hybrid method based on DBN and auto ML approaches. Predictive targets include treatment outcomes, length of stay, dynamics of disease severity indicators, and facts of prescribed drugs for different time intervals of observation. Models are validated using expert knowledge, current clinical recommendations, preceding research and classic predictive metrics. The characteristics of the best models are as follows: MAE of 3.6 days of predicting LOS (DBN plus FEDOT auto ML framework), 0.87 accuracy of predicting treatment outcome (OBN); 0.98 F1 score for predicting facts of prescribed drug (DBN). Moreover, the advantage of the proposed approach is Bayesian network-based interpretability, which is very important in the medical field. After the validation of other CP datasets for other hospitals, the proposed models can be used as part of the decision support systems for improving COVID-19-based pneumonia treatment. Another important finding is the significant differences between COVID-19 and non-COVID-19 pneumonia.

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“…With the recent global pandemic of the novel coronavirus disease 2019 (COVID-19), the healthcare industry has been focusing on the development of various systems that can mine insights from healthcare data to enable prolonged health through proper diagnoses, predictions, and treatments [6]. The use of graph analytics enables researchers to effectively and efficiently process large, connected graph databases in a way that is not possible using traditional methods.…”

Section: Healthmentioning

confidence: 99%

Private Graph Data Release: A Survey

Yang¹,

Purcell²,

Rakotoarivelo³

et al. 2021

Preprint

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The application of graph analytics to various domains have yielded tremendous societal and economical benefits in recent years. However, the increasingly widespread adoption of graph analytics comes with a commensurate increase in the need to protect private information in graph databases, especially in light of the many privacy breaches in real-world graph data that was supposed to preserve sensitive information. This paper provides a comprehensive survey of private graph data release algorithms that seek to achieve the fine balance between privacy and utility, with a specific focus on provably private mechanisms. Many of these mechanisms fall under natural extensions of the Differential Privacy framework to graph data, but we also investigate more general privacy formulations like Pufferfish Privacy that can deal with the limitations of Differential Privacy. A wide-ranging survey of the applications of private graph data release mechanisms to social networks, finance, supply chain, health and energy is also provided. This survey paper and the taxonomy it provides should benefit practitioners and researchers alike in the increasingly important area of private graph data release and analysis.

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Graph modelling for tracking the COVID-19 pandemic spread

Cited by 24 publications

References 21 publications

What Can We Learn from the Time Evolution of COVID‐19 Epidemic in Slovenia?

What Can We Learn from the Time Evolution of COVID‐19 Epidemic in Slovenia?

Hybrid Bayesian Network-Based Modeling: COVID-19-Pneumonia Case

Private Graph Data Release: A Survey

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