scholarly journals Nextstrain: real-time tracking of pathogen evolution

2017 ◽  
Author(s):  
James Hadfield ◽  
Colin Megill ◽  
Sidney M. Bell ◽  
John Huddleston ◽  
Barney Potter ◽  
...  

AbstractSummaryUnderstanding the spread and evolution of pathogens is important for effective public health measures and surveillance. Nextstrain consists of a database of viral genomes, a bioinformatics pipeline for phylodynamics analysis, and an interactive visualisation platform. Together these present a real-time view into the evolution and spread of a range of viral pathogens of high public health importance. The visualization integrates sequence data with other data types such as geographic information, serology, or host species. Nextstrain compiles our current understanding into a single accessible location, publicly available for use by health professionals, epidemiologists, virologists and the public alike.Availability and implementationAll code (predominantly JavaScript and Python) is freely available from github.com/nextstrain and the web-application is available at nextstrain.org.

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Maureen Rebecca Smith ◽  
Maria Trofimova ◽  
Ariane Weber ◽  
Yannick Duport ◽  
Denise Kühnert ◽  
...  

AbstractBy October 2021, 230 million SARS-CoV-2 diagnoses have been reported. Yet, a considerable proportion of cases remains undetected. Here, we propose GInPipe, a method that rapidly reconstructs SARS-CoV-2 incidence profiles solely from publicly available, time-stamped viral genomes. We validate GInPipe against simulated outbreaks and elaborate phylodynamic analyses. Using available sequence data, we reconstruct incidence histories for Denmark, Scotland, Switzerland, and Victoria (Australia) and demonstrate, how to use the method to investigate the effects of changing testing policies on case ascertainment. Specifically, we find that under-reporting was highest during summer 2020 in Europe, coinciding with more liberal testing policies at times of low testing capacities. Due to the increased use of real-time sequencing, it is envisaged that GInPipe can complement established surveillance tools to monitor the SARS-CoV-2 pandemic. In post-pandemic times, when diagnostic efforts are decreasing, GInPipe may facilitate the detection of hidden infection dynamics.


2018 ◽  
Author(s):  
Joshua B Singer ◽  
Emma C Thomson ◽  
John McLauchlan ◽  
Joseph Hughes ◽  
Robert J Gifford

AbstractBackgroundVirus genome sequences, generated in ever-higher volumes, can provide new scientific insights and inform our responses to epidemics and outbreaks. To facilitate interpretation, such data must be organised and processed within scalable computing resources that encapsulate virology expertise. GLUE (Genes Linked by Underlying Evolution) is a data-centric bioinformatics environment for building such resources. The GLUE core data schema organises sequence data along evolutionary lines, capturing not only nucleotide data but associated items such as alignments, genotype definitions, genome annotations and motifs. Its flexible design emphasises applicability to different viruses and to diverse needs within research, clinical or public health contexts.ResultsHCV-GLUE is a case study GLUE resource for hepatitis C virus (HCV). It includes an interactive public web application providing sequence analysis in the form of a maximum-likelihood-based genotyping method, antiviral resistance detection and graphical sequence visualisation. HCV sequence data from GenBank is categorised and stored in a large-scale sequence alignment which is accessible via web-based queries. Whereas this web resource provides a range of basic functionality, the underlying GLUE project can also be downloaded and extended by bioinformaticians addressing more advanced questions.ConclusionGLUE can be used to rapidly develop virus sequence data resources with public health, research and clinical applications. This streamlined approach, with its focus on reuse, will help realise the full value of virus sequence data.


BMJ Open ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. e041778
Author(s):  
Maxwell Salvatore ◽  
Deepankar Basu ◽  
Debashree Ray ◽  
Mike Kleinsasser ◽  
Soumik Purkayastha ◽  
...  

ObjectivesTo evaluate the effect of four-phase national lockdown from March 25 to May 31 in response to the COVID-19 pandemic in India and unmask the state-wise variations in terms of multiple public health metrics.DesignCohort study (daily time series of case counts).SettingObservational and population based.ParticipantsConfirmed COVID-19 cases nationally and across 20 states that accounted for >99% of the current cumulative case counts in India until 31 May 2020.ExposureLockdown (non-medical intervention).Main outcomes and measuresWe illustrate the masking of state-level trends and highlight the variations across states by presenting evaluative evidence on some aspects of the COVID-19 outbreak: case fatality rates, doubling times of cases, effective reproduction numbers and the scale of testing.ResultsThe estimated effective reproduction number R for India was 3.36 (95% CI 3.03 to 3.71) on 24 March, whereas the average of estimates from 25 May to 31 May stands at 1.27 (95% CI 1.26 to 1.28). Similarly, the estimated doubling time across India was at 3.56 days on 24 March, and the past 7-day average for the same on 31 May is 14.37 days. The average daily number of tests increased from 1717 (19–25 March) to 113 372 (25–31 May) while the test positivity rate increased from 2.1% to 4.2%, respectively. However, various states exhibit substantial departures from these national patterns.ConclusionsPatterns of change over lockdown periods indicate the lockdown has been partly effective in slowing the spread of the virus nationally. However, there exist large state-level variations and identifying these variations can help in both understanding the dynamics of the pandemic and formulating effective public health interventions. Our framework offers a holistic assessment of the pandemic across Indian states and union territories along with a set of interactive visualisation tools that are daily updated at covind19.org.


Author(s):  
A. Viehweger ◽  
F. Kühnl ◽  
C. Brandt ◽  
B. König ◽  
A. C. Rodloff

AbstractEffective public health response to viral outbreaks such as SARS-CoV-2 require reliable information about the spread of the infecting agent. Often real-time PCR screening of large populations is a feasible method to generate this information. Since test capacities are usually limited, pooling of test specimens is often necessary to increase screening capacity, provided that the test sensitivity is not significantly compromised. However, when a traditional pool is tested positive, all samples in the pool need individual retesting, which becomes ineffective at a higher proportion of positive samples. Here, we report a new pooling protocol that mitigates this problem by replicating samples across multiple pools. The resulting pool set allows the sample status to be resolved more often than with traditional pooling. At 2% prevalence and 20 samples per pool, our protocol increases screening capacity by factors of 5 and 2 compared to individual testing and traditional pooling, respectively. The corresponding software to layout and resolve samples is freely available under a BSD license (https://github.com/phiweger/clonepool).


2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Jonathan Edwin ◽  
Lisa Indar ◽  
Virginia Asin-Oostburg

Objective: The new Tourism and Health Information System (THiS) was implemented for syndromic surveillance in visitor accommodations in the Caribbean region. The objective was to monitor for illnesses and potential outbreaks in visitor accommodations (hotels/guest houses) in the Caribbean in real-time using the web-based application.Introduction: Travel and tourism pose global health security risks via the introduction and spread of disease, as demonstrated by the H1N1 pandemic (2009), Chikungunya (2013), and recent Zika virus outbreak. In 2016, nearly 60 million persons visited the Caribbean. Historically no regional surveillance systems for illnesses in visitor populations existed. The Tourism and Health Information System (THiS), designed by the Caribbean Public Health Agency (CARPHA) from 2016-2017, is a new web-based application for syndromic surveillance in Caribbean accommodation settings, with real-time data analytics and aberration detection built in. Once an accommodation registers as part of the surveillance system, guests and staff can report their illness to front desk administration who then complete an online case questionnaire. Alternatively guests and staff from both registered and unregistered accommodations can self-report their illness using the online questionnaire in the THiS web application. Reported symptoms are applied against case definitions in real-time to generate the following syndromes: gastroenteritis, fever & respiratory symptoms, fever & haemorrhagic symptoms, fever & neurologic symptoms, undifferentiated fever, and fever & rash. Reported data is analyzed in real-time and displayed in a data analytic dashboard that is accessible to hotel/guest house management and surveillance officers at the Ministry of Health. Data analytics include syndrome trends over time, gender and age breakdown, and illness attack rates.Methods: Visitor accommodations from the following countries participated: Bahamas, Barbados, Belize, Bermuda, Guyana, Jamaica, Trinidad & Tobago, and Turks & Caicos Islands. National staff from the Ministry of Health, Ministry of Tourism, and/or Tourism Authority/Board engaged accommodations to participate. Participating accommodations were provided with training by national staff on how to report cases and use data analytic functions. They were asked to provide registration information to CARPHA, such as contact information to create login credentials, and data on occupancy rates for low/high seasons, number of staff, and number of lodging rooms to calculate illness attack rates. Weekly email reminders to accommodations to report cases of illness in the THiS web application, or to confirm 'nil' cases by email were sent by CARPHA staff.Results: Of the 105 accommodations engaged by national staff, 39.1% (n=41) registered to participate, accounting for 3738 lodging rooms. From epidemiological week 24-39, five cases of syndromes from three accommodations in two countries were reported in the THiS web application (Table). A case of gastroenteritis and fever & respiratory symptoms were self-reported from an unregistered accommodation. Three cases of gastroenteritis were reported by hotel administration from two registered accommodations. The average response rate to weekly emails confirming 'nil' cases was 32.1% (range: 10.5-83.3%). One accommodation reported by email a cluster of 7 cases with possible conjuctivitis. No outbreaks or aberrations were detected in the THiS web application.Conclusions: Engagement of Caribbean visitor accommodations in public health surveillance is a novel but critical undertaking for promoting health, safety, and security for both visitors and locals in the tourism dependent Caribbean region, but it will take time to establish. Confirming the absence of illness is an important public health endeavor for visitor accommodations. Preliminary results have demonstrated that it is possible for public health to work in a voluntary basis with the private accommodation sector. To establish more consistent and reliable reporting public health legislation and policies will need to be explored. As more data is gathered, assessments of the validity and sensitivity of the system will need to be conducted.


2021 ◽  
Author(s):  
Nelly Lopes de Moraes Gil ◽  
Aline Chotte de Oliveira ◽  
Gabriela Ganassin ◽  
Carolina Luca ◽  
Sandra Pelloso ◽  
...  

Background: Health decision-makers currently face the challenge of accumulating health data in time to inform evidence-based interventions to improve health outcomes. The Brazilian healthcare system is in need of daily primary care data reported in real-time to support evidence-based policy decisions. This study aims to detail the development of a solution for geospatial monitoring in public health called AUTOMAP. Its main objective is to facilitate epidemiological surveillance and promote that rapidly available data improve the provision of health services. Methods: AUTOMAP is an application that articulates concepts inherent to epidemiological surveillance, geographic information systems, and free access technologies to design a monitoring tool of health conditions. The system architecture consists of three modules: user, application, and database. They work together to collect information regarding health conditions, its processing, and dynamic viewing. AUTOMAP design uses the statistical language R, which employs literate programming through a Shiny application package to transform statistical results of health conditions into interactive maps in real-time. AUTOMAP is a web application that has two interfaces: one for loading data and another for generating dynamic epidemiological maps. Conclusion: AUTOMAP allows a variety of clinical solutions, such as risk calculators, spatial evaluation of events of interest, decision models, simulations, and epidemiological patient monitoring. The software is open-source with easy accessibility, allowing anyone to make adjustments and handle a myriad of health conditions, thus being applicable globally. AUTOMAP is a tool that will facilitate and advance data collection for evidence generation and expedite evidence-based health system improvements.


2020 ◽  
Vol 15 ◽  
pp. 4
Author(s):  
Sanju George ◽  
Jessy Fenn ◽  
Kripa Robonderdeep

Gambling is a popular pastime in India, as in most cultures across the world. Although research from India is limited, there is enough evidence to suggest that it should be of public health importance. In this brief paper, we look at the evolution of gambling in India and also discuss potential ways forward to address this issue.


2021 ◽  
Vol 8 (1) ◽  
pp. 205395172110138
Author(s):  
Erika Bonnevie ◽  
Jennifer Sittig ◽  
Joe Smyser

While public health organizations can detect disease spread, few can monitor and respond to real-time misinformation. Misinformation risks the public’s health, the credibility of institutions, and the safety of experts and front-line workers. Big Data, and specifically publicly available media data, can play a significant role in understanding and responding to misinformation. The Public Good Projects uses supervised machine learning to aggregate and code millions of conversations relating to vaccines and the COVID-19 pandemic broadly, in real-time. Public health researchers supervise this process daily, and provide insights to practitioners across a range of disciplines. Through this work, we have gleaned three lessons to address misinformation. (1) Sources of vaccine misinformation are known; there is a need to operationalize learnings and engage the pro-vaccination majority in debunking vaccine-related misinformation. (2) Existing systems can identify and track threats against health experts and institutions, which have been subject to unprecedented harassment. This supports their safety and helps prevent the further erosion of trust in public institutions. (3) Responses to misinformation should draw from cross-sector crisis management best practices and address coordination gaps. Real-time monitoring and addressing misinformation should be a core function of public health, and public health should be a core use case for data scientists developing monitoring tools. The tools to accomplish these tasks are available; it remains up to us to prioritize them.


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