Forecasting the COVID-19 transmission in Italy based on the minimum spanning tree of dynamic region network

Dong, Min; Zhang, Xuhang; Yang, Kun; Liu, Rui; Chen, Pei

doi:10.7717/peerj.11603

Cited by 10 publications

(16 citation statements)

References 31 publications

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“…The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the related disease, coronavirus disease 2019 (COVID- 19), quickly generated a tragic health emergency in Italy due to the concurrent need to provide assistance to infected patients, and at the same time, to treat all the non-deferrable oncological and benign conditions [4].…”

Section: Introductionmentioning

confidence: 99%

The Impact of SARS-CoV-2 Pandemic on Time to Primary, Secondary Resection and Adjuvant Intravesical Therapy in Patients with High-Risk Non-Muscle Invasive Bladder Cancer: A Retrospective Multi-Institutional Cohort Analysis

Ferro

Giudice

Carrieri

et al. 2021

Cancers

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Background: To investigate the impact of COVID-19 outbreak on the diagnosis and treatment of non-muscle invasive bladder cancer (NMIBC). Methods: A retrospective analysis was performed using an Italian multi-institutional database of TURBT patients with high-risk urothelial NMIBC between January 2019 and February 2021, followed by Re-TURBT and/or adjuvant intravesical BCG. Results: A total of 2591 patients from 27 institutions with primary TURBT were included. Of these, 1534 (59.2%) and 1056 (40.8%) underwent TURBT before and during the COVID-19 outbreak, respectively. Time between diagnosis and TURBT was significantly longer during the COVID-19 period (65 vs. 52 days, p = 0.002). One thousand and sixty-six patients (41.1%) received Re-TURBT, 604 (56.7%) during the pre-COVID-19. The median time to secondary resection was significantly longer during the COVID-19 period (55 vs. 48 days, p < 0.0001). A total of 977 patients underwent adjuvant intravesical therapy after primary or secondary resection, with a similar distribution across the two groups (n = 453, 86% vs. n = 388, 86.2%). However, the proportion of the patients who underwent maintenance significantly differed (79.5% vs. 60.4%, p < 0.0001). Conclusions: The COVID-19 pandemic represented an unprecedented challenge to our health system. Our study did not show significant differences in TURBT quality. However, a delay in treatment schedule and disease management was observed. Investigation of the oncological impacts of those differences should be advocated.

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Section: Introductionmentioning

confidence: 99%

The Impact of SARS-CoV-2 Pandemic on Time to Primary, Secondary Resection and Adjuvant Intravesical Therapy in Patients with High-Risk Non-Muscle Invasive Bladder Cancer: A Retrospective Multi-Institutional Cohort Analysis

Ferro

Giudice

Carrieri

et al. 2021

Cancers

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“…The direct ones consist of communication networks [137], transportation networks [138], water supply networks [139], and electrical grids [140]. Other practical cases based on MST include COVID-19 pandemic transmission forecasting [141], clustering [142], constructing trees for broadcasting [143], image registration and segmentation [144], circuit design [145] and emotion recognition [146]. There do exist several primary algorithms, namely, classic algorithm and faster algorithm; however, in consideration that such MST-like models will facilitate the development of multifarious industrial areas, more and more individuals have turned to design a variety of intuitive algorithms to figure out such issues, such as reinforcement learning [147], genetic algorithm [148] and fast parallel algorithm [149].…”

Section: Minimum Spanning Treementioning

confidence: 99%

A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas

et al. 2022

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Combinatorial optimization problems (COPs) are a class of NP-hard problems with great practical significance. Traditional approaches for COPs suffer from high computational time and reliance on expert knowledge, and machine learning (ML) methods, as powerful tools have been used to overcome these problems. In this review, the COPs in energy areas with a series of modern ML approaches, i.e., the interdisciplinary areas of COPs, ML and energy areas, are mainly investigated. Recent works on solving COPs using ML are sorted out firstly by methods which include supervised learning (SL), deep learning (DL), reinforcement learning (RL) and recently proposed game theoretic methods, and then problems where the timeline of the improvements for some fundamental COPs is the layout. Practical applications of ML methods in the energy areas, including the petroleum supply chain, steel-making, electric power system and wind power, are summarized for the first time, and challenges in this field are analyzed.

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“…The copyright holder for this preprint this version posted October 31, 2022. ; https://doi.org/10.1101/2022.10.27.22281609 doi: medRxiv preprint the landscape network entropy index (27,30,31), or the minimum spanning tree (28,29). The sensitivity of this method ranged from 0.825 to 0.9, with a lead time between 10 days to 2 months (27)(28)(29)(30)(31).…”

Section: (Which Was Not Certified By Peer Review)mentioning

confidence: 99%

“…Locations were integrated into a weighted network structure using information on transport between these regions, traffic conditions and population. The correlation of the number of cases between the locations was used to calculate the landscape network entropy index (27,30,31), or the minimum spanning tree (28,29). The sensitivity of this method ranged from 0.825 to 0.9, with a lead time between 10 days to 2 months (27)(28)(29)(30)(31).…”

Section: Indicators Of Resilience and Complexitymentioning

confidence: 99%

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The potential of resilience indicators to signal the risk of disease outbreaks, a systematic review and guide

Delecroix

Nes

Leemput

et al. 2022

Preprint

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To reduce the consequences of infectious disease outbreaks, the timely implementation of public health measures is crucial. Currently used early-warning systems are highly context-dependent and require a long phase of model building. A proposed solution to anticipate the onset or termination of an outbreak is the use of so-called resilience indicators. These indicators are based on the generic theory of critical slowing down and require only incidence time series. Here we assess the potential for this approach to contribute to outbreak anticipation. We systematically reviewed studies that used resilience indicators to predict outbreaks or terminations of epidemics. We identified 37 studies meeting the inclusion criteria: 21 using simulated data and 16 real-world data. 36 out of 37 studies detected significant signs of critical slowing down before a critical transition (i.e., the onset or end of an outbreak), with a sensitivity (i.e., the proportion of true positive outbreak warnings) ranging from 0.67 to 1 and a lead time ranging from 10 days to 68 months. Challenges include low resolution and limited length of time series, a too rapid increase in cases, and strong seasonal patterns, and may hamper the sensitivity of resilience indicators. Alternative types of data, such as Google searches or social media data, have the potential to improve predictions in some cases. Resilience indicators may be useful when the risk of disease outbreaks is changing gradually. This may happen, for instance, when pathogens become increasingly adapted to an environment or evolve gradually to escape immunity. High-resolution monitoring is needed to reach sufficient sensitivity. If those conditions are met, resilience indicators could help improve the current practice of prediction, facilitating timely outbreak response. We provide a step-by-step guide on the use of resilience indicators in infectious disease epidemiology, and guidance on the relevant situations to use this approach.

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Forecasting the COVID-19 transmission in Italy based on the minimum spanning tree of dynamic region network

Cited by 10 publications

References 31 publications

The Impact of SARS-CoV-2 Pandemic on Time to Primary, Secondary Resection and Adjuvant Intravesical Therapy in Patients with High-Risk Non-Muscle Invasive Bladder Cancer: A Retrospective Multi-Institutional Cohort Analysis

The Impact of SARS-CoV-2 Pandemic on Time to Primary, Secondary Resection and Adjuvant Intravesical Therapy in Patients with High-Risk Non-Muscle Invasive Bladder Cancer: A Retrospective Multi-Institutional Cohort Analysis

A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas

The potential of resilience indicators to signal the risk of disease outbreaks, a systematic review and guide

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