scholarly journals Haplotype Explorer: an infection cluster visualization tool for spatiotemporal dissection of the COVID-19 pandemic

Author(s):  
Tetsuro Kawano-Sugaya ◽  
Koji Yatsu ◽  
Tsuyoshi Sekizuka ◽  
Kentaro Itokawa ◽  
Masanori Hashino ◽  
...  

Abstract Summary Many of software for network visualization are available, but existing software have not been optimized to infection cluster visualization, especially the current worldwide invasion of COVID-19 since 2019. To reach the spatiotemporal understanding of epidemics, we have developed Haplotype Explorer. In Haplotype Explorer, users can explore the network interactively with metadata like accession number, locations, and collection dates. Time dependent transition of the network can be exported as continuous sections for making a movie. Here, we introduce features and products of Haplotype Explorer, demonstrating time-dependent snapshots and a movie of haplotype networks inferred from total of 4,282 SARS-CoV-2 genomes. Abstract The worldwide eruption of COVID-19 that began in Wuhan, China in late 2019 reached 10 million cases by late June 2020. In order to understand the epidemiological landscape of the COVID-19 pandemic, many studies have attempted to elucidate phylogenetic relationships between collected viral genome sequences using haplotype networks. However, currently available applications for network visualization are not suited to understand the COVID-19 epidemic spatiotemporally due to functional limitations, that motivated us to develop Haplotype Explorer, an intuitive tool for visualizing and exploring haplotype networks. Haplotype Explorer enables to dissect epidemiological consequences via interactive node filters and provides the perspective on infectious disease dynamics depend on regions and time, such as introduction, outbreak, expansion, and containment. Here, we demonstrate the effectiveness of Haplotype Explorer by showing features and an example of visualization. The demo using SARS-CoV-2 genomes are available at https://github.com/TKSjp/HaplotypeExplorer/blob/master/Example/. There are several examples using SARS-CoV-2 genomes and Dengue virus serotype 1 E-genes sequence.

2020 ◽  
Author(s):  
Tetsuro Kawano-Sugaya ◽  
Koji Yatsu ◽  
Tsuyoshi Sekizuka ◽  
Kentaro Itokawa ◽  
Masanori Hashino ◽  
...  

AbstractThe worldwide eruption of COVID-19 that began in Wuhan, China in late 2019 reached 10 million cases by late June 2020. In order to understand the epidemiological landscape of the COVID-19 pandemic, many studies have attempted to elucidate phylogenetic relationships between collected viral genome sequences using haplotype networks. However, currently available applications for network visualization are not suited to understand the COVID-19 epidemic spatiotemporally, due to functional limitations That motivated us to develop Haplotype Explorer, an intuitive tool for visualizing and exploring haplotype networks. Haplotype Explorer enables people to dissect epidemiological consequences via interactive node filters to provide spatiotemporal perspectives on multimodal spectra of infectious diseases, including introduction, outbreak, expansion, and containment, for given regions and time spans. Here, we demonstrate the effectiveness of Haplotype Explorer by showing an example of its visualization and features. The demo using SARS-CoV-2 genome sequences is available at https://github.com/TKSjp/HaplotypeExplorerSummaryA lot of software for network visualization are available, but existing software have not been optimized to infection cluster visualization against the current worldwide invasion of COVID-19 started since 2019. To reach the spatiotemporal understanding of its epidemics, we developed Haplotype Explorer. It is superior to other applications in the point of generating HTML distribution files with metadata searches which interactively reflects GISAID IDs, locations, and collection dates. Here, we introduce the features and products of Haplotype Explorer, demonstrating the time-dependent snapshots of haplotype networks inferred from total of 4,282 SARS-CoV-2 genomes.


Epidemics ◽  
2018 ◽  
Vol 22 ◽  
pp. 56-61 ◽  
Author(s):  
Sebastian Funk ◽  
Anton Camacho ◽  
Adam J. Kucharski ◽  
Rosalind M. Eggo ◽  
W. John Edmunds

2019 ◽  
Vol 7 (8) ◽  
pp. 277
Author(s):  
Yong-jun Chen ◽  
Qing Liu ◽  
Cheng-peng Wan

Accidents occur frequently in traffic-intensive waters, which restrict the safe and rapid development of the shipping industry. Due to the suddenness, randomness, and uncertainty of accidents in traffic-intensive waters, the probability of the risk factors causing traffic accidents is usually high. Thus, properly analyzing those key risk factors is of great significance to improve the safety of shipping. Based on the analysis of influencing factors of ship navigational risks in traffic-intensive waters, this paper proposes a cloud model to excavate the factors affecting navigational risk, which could accurately screen out the key risk factors. Furthermore, the risk causal model of ship navigation in traffic-intensive waters is constructed by using the infectious disease dynamics method in order to model the key risk causal transmission process. Moreover, an empirical study of the Yangtze River estuary is conducted to illustrate the feasibility of the proposed models. The research results show that the cloud model is useful in screening the key risk factors, and the constructed causal model of ship navigational risks in traffic-intensive waters is able to provide accurate analysis of the transmission process of key risk factors, which can be used to reduce the navigational risk of ships in traffic-intensive waters. This research provides both theoretical basis and practical reference for regulators in the risk management and control of ships in traffic-intensive waters.


2020 ◽  
Vol 14 (1) ◽  
pp. 57-89 ◽  
Author(s):  
Sheryl L. Chang ◽  
Mahendra Piraveenan ◽  
Philippa Pattison ◽  
Mikhail Prokopenko

2014 ◽  
pp. 150127063140004
Author(s):  
Guo-jie Ye ◽  
Marina M Scotti ◽  
Darby L Thomas ◽  
Lijun Wang ◽  
David R. Knop ◽  
...  

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