TNet: Phylogeny-Based Inference of Disease Transmission Networks Using Within-Host Strain Diversity

2020 ◽  
pp. 203-216
Author(s):  
Saurav Dhar ◽  
Chengchen Zhang ◽  
Ion Mandoiu ◽  
Mukul S. Bansal
Keyword(s):  
Disease Transmission ◽  
Host Strain ◽  
Transmission Networks ◽  
Strain Diversity

scholarly journals SharpTNI: Counting and Sampling Parsimonious Transmission Networks under a Weak Bottleneck

2019 ◽  
Author(s):  
Palash Sashittal ◽  
Mohammed El-Kebir
Keyword(s):  
Disease Transmission ◽  
Network Inference ◽  
Solution Space ◽  
Simulated Data ◽  
Theoretical Work ◽  
Likelihood Methods ◽  
Transmission Networks ◽  
Pathogen Diversity ◽  
Technological Advances ◽  
Maximum Likelihood Methods

AbstractBackgroundTechnological advances in genomic sequencing are facilitating the reconstruction of transmission histories during outbreaks in the fight against infectious diseases. However, accurate disease transmission inference using this data is hindered by a number of challenges due to within-host pathogen diversity and weak transmission bottlenecks, where multiple genetically-distinct pathogenic strains co-transmit.ResultsWe formulate a combinatorial optimization problem for transmission network inference under a weak bottleneck from a given timed phylogeny and establish hardness results. We present SharpTNI, a method to approximately count and almost uniformly sample from the solution space. Using simulated data, we show that SharpTNI accurately quantifies and uniformly samples from the solution space of parsimonious transmission networks, scaling to large datasets. We demonstrate that SharpTNI identifies co-transmissions during the 2014 Ebola outbreak that are corroborated by epidemiological information collected by previous studies.ConclusionsAccounting for weak transmission bottlenecks is crucial for accurate inference of transmission histories during outbreaks. SharpTNI is a parsimony-based method to reconstruct transmission networks for diseases with long incubation times and large inocula given timed phylogenies. The model and theoretical work of this paper pave the way for novel maximum likelihood methods to co-estimate timed phylogenies and transmission networks under a weak bottleneck.

scholarly journals Inferring disease transmission networks at a metapopulation level

2014 ◽  
Vol 2 (1) ◽  
Author(s):  
Xiaofei Yang ◽  
Jiming Liu ◽  
Xiao-Nong Zhou ◽  
William KW Cheung
Keyword(s):  
Disease Transmission ◽  
Transmission Networks

scholarly journals Reconstructing SARS-CoV-2 infection dynamics through the phylogenetic inference of unsampled sources of infection

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261422
Author(s):  
Deshan Perera ◽  
Ben Perks ◽  
Michael Potemkin ◽  
Andy Liu ◽  
Paul M. K. Gordon ◽  
...  
Keyword(s):  
New York ◽  
Disease Transmission ◽  
New York State ◽  
Disease Outbreaks ◽  
Transmission Networks ◽  
Infectious Disease Outbreaks ◽  
Infection Dynamics ◽  
Powerful Approach ◽  
Sources Of Infection

The COVID-19 pandemic has illustrated the importance of infection tracking. The role of asymptomatic, undiagnosed individuals in driving infections within this pandemic has become increasingly evident. Modern phylogenetic tools that take into account asymptomatic or undiagnosed individuals can help guide public health responses. We finetuned established phylogenetic pipelines using published SARS-CoV-2 genomic data to examine reasonable estimate transmission networks with the inference of unsampled infection sources. The system utilised Bayesian phylogenetics and TransPhylo to capture the evolutionary and infection dynamics of SARS-CoV-2. Our analyses gave insight into the transmissions within a population including unsampled sources of infection and the results aligned with epidemiological observations. We were able to observe the effects of preventive measures in Canada’s “Atlantic bubble” and in populations such as New York State. The tools also inferred the cross-species disease transmission of SARS-CoV-2 transmission from humans to lions and tigers in New York City’s Bronx Zoo. These phylogenetic tools offer a powerful approach in response to both the COVID-19 and other emerging infectious disease outbreaks.

Centrality measures for disease transmission networks

1999 ◽  
Vol 21 (1) ◽  
pp. 1-21 ◽  
Author(s):  
David C Bell ◽  
John S Atkinson ◽  
Jerry W Carlson
Keyword(s):  
Disease Transmission ◽  
Centrality Measures ◽  
Transmission Networks

scholarly journals 1836. Genomic Epidemiology of Methicillin-Susceptible Staphylococcus aureus Colonization and Infection among US Army Trainees at Fort Benning, Georgia

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S45-S45
Author(s):  
Eugene V Millar ◽  
Patrick McGann ◽  
Michael Ellis ◽  
David Tribble ◽  
Anthony Jones ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Disease Transmission ◽  
Clinical Care ◽  
Military Training ◽  
Anatomic Site ◽  
Us Army ◽  
Genomic Epidemiology ◽  
Strain Diversity ◽  
Mssa Strain ◽  
Fort Benning

Abstract Background Methicillin-susceptible Staphylococcus aureus (MSSA) is a common cause of skin and soft-tissue infection (SSTI). MSSA genomic epidemiology data are limited. We used whole-genome sequencing (WGS) to examine MSSA strain diversity among military trainees, a group known to be at high risk for S. aureus infection and carriage. Methods From July 2012 to December 2014, we conducted a prospective SSTI case–control study among US Army trainees at Fort Benning, GA. Thereafter, we identified MSSA SSTI clusters within select military training classes and performed WGS on clinical and colonizing isolates. We analyzed epidemiologic, clinical, genomic, and phylogenetic data in order to evaluate MSSA strain diversity and patterns of disease transmission. Results A total of 67 SSTI cases from 15 training classes were identified. The median (range) number of cases per class was 4 (3–10). Cases presented for care after a median of 39 (6–101) days of training. Of the 67 cases, 42 (63%) were colonized with MSSA at ≥1 anatomic site. A total of 78 MSSA colonizing isolates were identified at the time trainees presented for clinical care; colonizing isolates were found in the nares (37%), throat (31%), inguinal region (21%), and perianal region (12%). Multilocus sequence typing (MLST) assigned 128 (88%) isolates to 20 known types and 17 isolates to novel types. Among clinical isolates, 60 (90%) were assigned to known types. Sequence Type (ST) 8 was the most frequent type, accounting for 45% and 35% of clinical and colonizing isolates, respectively. The phylogenetic tree of isolates revealed seven major clusters, some of which were composed of a diversity of training classes, specimen types, and STs. These major clusters were further segregated into 15 sub-clusters where there was considerable diversity in intrahost variation. Conclusion Genomic characterization of MSSA infection and colonization isolates among congregate military trainees revealed a broad diversity of strains. There was a clear clonal origin and dissemination of MSSA isolates among close contacts within the ST-8 cluster but this transmission pattern was less apparent for MSSAs from other STs. Disclosures All Authors: No reported Disclosures.

scholarly journals Infectious disease transmission as a forensic problem: who infected whom?

2013 ◽  
Vol 10 (81) ◽  
pp. 20120955 ◽  
Author(s):  
Peter Teunis ◽  
Janneke C. M. Heijne ◽  
Faizel Sukhrie ◽  
Jan van Eijkeren ◽  
Marion Koopmans ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Kernel Functions ◽  
University Hospital ◽  
Transmission Network ◽  
Sources Of Information ◽  
Infectious Disease Transmission ◽  
Transmission Networks ◽  
Genetic Sequence ◽  
Set Up ◽  
Spatial Locations

Observations on infectious diseases often consist of a sample of cases, distinguished by symptoms, and other characteristics, such as onset dates, spatial locations, genetic sequence of the pathogen and/or physiological and clinical data. Cases are often clustered, in space and time, suggesting that they are connected. By defining kernel functions for pairwise analysis of cases, a matrix of transmission probabilities can be estimated. We set up a Bayesian framework to integrate various sources of information to estimate the transmission network. The method is illustrated by analysing data from a multi-year study (2002–2007) of nosocomial outbreaks of norovirus in a large university hospital in the Netherlands. The study included 264 cases, the norovirus genotype was known in approximately 60 per cent of the patients. Combining all the available data allowed likely identification of individual transmission links between most of the cases (72%). This illustrates that the proposed method can be used to accurately reconstruct transmission networks, enhancing our understanding of outbreak dynamics and possibly leading to new insights into how to prevent outbreaks.

scholarly journals A novel computational approach to reconstruct SARS-CoV-2 infection dynamics through the inference of unsampled sources of infection

2021 ◽  
Author(s):  
Deshan Perera ◽  
Ben Perks ◽  
Michael Potemkin ◽  
Paul Gordon ◽  
John Gill ◽  
...  
Keyword(s):  
New York ◽  
Disease Transmission ◽  
New York State ◽  
Transmission Network ◽  
Transmission Networks ◽  
Mobile Populations ◽  
Infection Dynamics ◽  
Sources Of Infection ◽  
Insight Into

ABSTRACTInfectious diseases such as the COVID19 pandemic cemented the importance of disease tracking. The role of asymptomatic, undiagnosed individuals in driving infection has become evident. Their unaccountability results in ineffective prevention. We developed a pipeline using genomic data to accurately predict a population’s transmission network complete with the inference of unsampled sources. The system utilises Bayesian phylogenetics to capture evolutionary and infection dynamics of SARS-CoV-2. It identified the effectiveness of preventive measures in Canada’s Atlantic bubble and mobile populations such as New York State. Its robustness extends to the prediction of cross-species disease transmission as we inferred SARS-CoV-2 transmission from humans to lions and tigers in New York City’s Bronx Zoo. The proposed method’s ability to generate such complete transmission networks, provides a more detailed insight into the transmission dynamics within a population. This potential frontline tool will be of direct help in “the battle to bend the curve”.

scholarly journals The construction and visualization of the transmission networks for COVID-19: A potential solution for contact tracing and assessments of epidemics

2020 ◽  
Author(s):  
Caiying Luo ◽  
Yue Ma ◽  
Pei Jiang ◽  
Tao Zhang ◽  
Fei Yin
Keyword(s):  
Disease Transmission ◽  
Health Resources ◽  
Epidemiological Data ◽  
Contact Tracing ◽  
Accurate Information ◽  
Transmission Network ◽  
Transmission Networks ◽  
Block Transmission ◽  
Imported Case ◽  
Effective Interventions

Abstract The WHO described coronavirus disease 2019 (COVID-19) as a pandemic due to the speed and scale of its transmission. Without effective interventions, the rapidly increasing number of COVID-19 cases would greatly increase the burden of clinical treatments. Identifying the transmission sources and pathways is of vital importance to block transmission and allocate limited public health resources. According to the relationships among cases, we constructed disease transmission network graphs for the COVID-19 epidemic through a visualization technique based on individual reports of epidemiological data. We proposed an analysis strategy of the transmission network with the epidemiological data in Tianjin and Chengdu. The transmission networks showed different transmission characteristics. In Tianjin, an imported case can produce an average of 2.9 secondary infections and ultimately produce up to 4 generations of infections, with a maximum of 6 cases generated before being identified. In Chengdu, 45 noninformative cases and 24 cases with vague exposure information made it difficult to provide accurate information by the transmission network. The proposed analysis framework of visualized transmission networks can trace the transmission source and contacts, assess the current situation of transmission and prevention, and provide evidence for the global response and control of the COVID-19 pandemic.

scholarly journals Distance Measurements Related to Cartesian Product of Cycles

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Xiaoli Qiang ◽  
Saima Nazeer ◽  
Yu-Ming Chu ◽  
Muhammad Awais Umar ◽  
Imrana Kousar ◽  
...  
Keyword(s):  
Social Sciences ◽  
Graph Theory ◽  
Disease Transmission ◽  
Path Length ◽  
Cartesian Product ◽  
Natural Sciences ◽  
Distance Measurements ◽  
Transmission Networks ◽  
New Era ◽  
Average Eccentricity

Graph theory and its wide applications in natural sciences and social sciences open a new era of research. Making the graph of computer networks and analyzing it with aid of graph theory are extensively studied and researched in the literature. An important discussion is based on distance between two nodes in a network which may include closeness of objects, centrality of objects, average path length between objects, and vertex eccentricity. For example, (1) disease transmission networks: closeness and centrality of objects are used to measure vulnerability to particular disease and its infectivity; (2) routing networks: eccentricity of objects is used to find vertices which form the periphery objects of the network. In this manuscript, we have discussed distance measurements including center, periphery, and average eccentricity for the Cartesian product of two cycles. The results are obtained using the definitions of eccentricity, radius, and diameter of a graph, and all possible cases (for different parity of length of cycles) have been proved.

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