Publications by authors named "Liam Brierley"

11 Publications

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Predicting the animal hosts of coronaviruses from compositional biases of spike protein and whole genome sequences through machine learning.

PLoS Pathog 2021 04 20;17(4):e1009149. Epub 2021 Apr 20.

Department of Health Data Science, University of Liverpool, Brownlow Street, Liverpool, United Kingdom.

The COVID-19 pandemic has demonstrated the serious potential for novel zoonotic coronaviruses to emerge and cause major outbreaks. The immediate animal origin of the causative virus, SARS-CoV-2, remains unknown, a notoriously challenging task for emerging disease investigations. Coevolution with hosts leads to specific evolutionary signatures within viral genomes that can inform likely animal origins. We obtained a set of 650 spike protein and 511 whole genome nucleotide sequences from 222 and 185 viruses belonging to the family Coronaviridae, respectively. We then trained random forest models independently on genome composition biases of spike protein and whole genome sequences, including dinucleotide and codon usage biases in order to predict animal host (of nine possible categories, including human). In hold-one-out cross-validation, predictive accuracy on unseen coronaviruses consistently reached ~73%, indicating evolutionary signal in spike proteins to be just as informative as whole genome sequences. However, different composition biases were informative in each case. Applying optimised random forest models to classify human sequences of MERS-CoV and SARS-CoV revealed evolutionary signatures consistent with their recognised intermediate hosts (camelids, carnivores), while human sequences of SARS-CoV-2 were predicted as having bat hosts (suborder Yinpterochiroptera), supporting bats as the suspected origins of the current pandemic. In addition to phylogeny, variation in genome composition can act as an informative approach to predict emerging virus traits as soon as sequences are available. More widely, this work demonstrates the potential in combining genetic resources with machine learning algorithms to address long-standing challenges in emerging infectious diseases.
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http://dx.doi.org/10.1371/journal.ppat.1009149DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8087038PMC
April 2021

The evolving role of preprints in the dissemination of COVID-19 research and their impact on the science communication landscape.

PLoS Biol 2021 04 2;19(4):e3000959. Epub 2021 Apr 2.

Hughes Hall College, University of Cambridge, Cambridge, United Kingdom.

The world continues to face a life-threatening viral pandemic. The virus underlying the Coronavirus Disease 2019 (COVID-19), Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has caused over 98 million confirmed cases and 2.2 million deaths since January 2020. Although the most recent respiratory viral pandemic swept the globe only a decade ago, the way science operates and responds to current events has experienced a cultural shift in the interim. The scientific community has responded rapidly to the COVID-19 pandemic, releasing over 125,000 COVID-19-related scientific articles within 10 months of the first confirmed case, of which more than 30,000 were hosted by preprint servers. We focused our analysis on bioRxiv and medRxiv, 2 growing preprint servers for biomedical research, investigating the attributes of COVID-19 preprints, their access and usage rates, as well as characteristics of their propagation on online platforms. Our data provide evidence for increased scientific and public engagement with preprints related to COVID-19 (COVID-19 preprints are accessed more, cited more, and shared more on various online platforms than non-COVID-19 preprints), as well as changes in the use of preprints by journalists and policymakers. We also find evidence for changes in preprinting and publishing behaviour: COVID-19 preprints are shorter and reviewed faster. Our results highlight the unprecedented role of preprints and preprint servers in the dissemination of COVID-19 science and the impact of the pandemic on the scientific communication landscape.
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http://dx.doi.org/10.1371/journal.pbio.3000959DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8046348PMC
April 2021

Lessons from the influx of preprints during the early COVID-19 pandemic.

Authors:
Liam Brierley

Lancet Planet Health 2021 03;5(3):e115-e117

Department of Health Data Science, University of Liverpool, Liverpool L69 3GL, UK. Electronic address:

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http://dx.doi.org/10.1016/S2542-5196(21)00011-5DOI Listing
March 2021

Impact of climatic, demographic and disease control factors on the transmission dynamics of COVID-19 in large cities worldwide.

One Health 2021 Jun 3;12:100221. Epub 2021 Feb 3.

Health Protection Research Unit in Emerging and Zoonotic Infections, University of Liverpool, UK.

Approximately a year into the COVID-19 pandemic caused by the SARS-CoV-2 virus, many countries have seen additional "waves" of infections, especially in the temperate northern hemisphere. Other vulnerable regions, such as South Africa and several parts of South America have also seen cases rise, further impacting local economies and livelihoods. Despite substantial research efforts to date, it remains unresolved as to whether COVID-19 transmission has the same sensitivity to climate observed for other common respiratory viruses such as seasonal influenza. Here, we look for empirical evidence of seasonality using a robust estimation framework. For 359 large cities across the world, we estimated the basic reproduction number (R) using logistic growth curves fitted to cumulative case data. We then assess evidence for association with climatic variables through ordinary least squares (OLS) regression. We find evidence of seasonality, with lower R within cities experiencing greater surface radiation (coefficient = -0.005, < 0.001), after adjusting for city-level variation in demographic and disease control factors. Additionally, we find association between R and temperature during the early phase of the epidemic in China. However, climatic variables had much weaker explanatory power compared to socioeconomic and disease control factors. Rates of transmission and health burden of the continuing pandemic will be ultimately determined by population factors and disease control policies.
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http://dx.doi.org/10.1016/j.onehlt.2021.100221DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7857042PMC
June 2021

Global discovery of human-infective RNA viruses: A modelling analysis.

PLoS Pathog 2020 11 30;16(11):e1009079. Epub 2020 Nov 30.

Usher Institute, University of Edinburgh, Edinburgh, United Kingdom.

RNA viruses are a leading cause of human infectious diseases and the prediction of where new RNA viruses are likely to be discovered is a significant public health concern. Here, we geocoded the first peer-reviewed reports of 223 human RNA viruses. Using a boosted regression tree model, we matched these virus data with 33 explanatory factors related to natural virus distribution and research effort to predict the probability of virus discovery across the globe in 2010-2019. Stratified analyses by virus transmissibility and transmission mode were also performed. The historical discovery of human RNA viruses has been concentrated in eastern North America, Europe, central Africa, eastern Australia, and north-eastern South America. The virus discovery can be predicted by a combination of socio-economic, land use, climate, and biodiversity variables. Remarkably, vector-borne viruses and strictly zoonotic viruses are more associated with climate and biodiversity whereas non-vector-borne viruses and human transmissible viruses are more associated with GDP and urbanization. The areas with the highest predicted probability for 2010-2019 include three new regions including East and Southeast Asia, India, and Central America, which likely reflect both increasing surveillance and diversity of their virome. Our findings can inform priority regions for investment in surveillance systems for new human RNA viruses.
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http://dx.doi.org/10.1371/journal.ppat.1009079DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7728385PMC
November 2020

Tissue tropism and transmission ecology predict virulence of human RNA viruses.

PLoS Biol 2019 11 26;17(11):e3000206. Epub 2019 Nov 26.

Centre for Immunity, Infection and Evolution, Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, United Kingdom.

Novel infectious diseases continue to emerge within human populations. Predictive studies have begun to identify pathogen traits associated with emergence. However, emerging pathogens vary widely in virulence, a key determinant of their ultimate risk to public health. Here, we use structured literature searches to review the virulence of each of the 214 known human-infective RNA virus species. We then use a machine learning framework to determine whether viral virulence can be predicted by ecological traits, including human-to-human transmissibility, transmission routes, tissue tropisms, and host range. Using severity of clinical disease as a measurement of virulence, we identified potential risk factors using predictive classification tree and random forest ensemble models. The random forest approach predicted literature-assigned disease severity of test data with mean accuracy of 89.4% compared to a null accuracy of 74.2%. In addition to viral taxonomy, the ability to cause systemic infection was the strongest predictor of severe disease. Further notable predictors of severe disease included having neural and/or renal tropism, direct contact or respiratory transmission, and limited (0 < R0 ≤ 1) human-to-human transmissibility. We present a novel, to our knowledge, comparative perspective on the virulence of all currently known human RNA virus species. The risk factors identified may provide novel perspectives in understanding the evolution of virulence and elucidating molecular virulence mechanisms. These risk factors could also improve planning and preparedness in public health strategies as part of a predictive framework for novel human infections.
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http://dx.doi.org/10.1371/journal.pbio.3000206DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6879112PMC
November 2019

The effect of dapagliflozin on glycaemic control and other cardiovascular disease risk factors in type 2 diabetes mellitus: a real-world observational study.

Diabetologia 2019 04 10;62(4):621-632. Epub 2019 Jan 10.

MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, EH4 2XU, UK.

Aims/hypothesis: Dapagliflozin, a sodium-glucose cotransporter 2 (SGLT2) inhibitor, is indicated for improving glycaemic control in type 2 diabetes mellitus. Whether its effects on HbA and other variables, including safety outcomes, in clinical trials are obtained in real-world practice needs to be established.

Methods: We used data from the comprehensive national diabetes register, the Scottish Care Information-Diabetes (SCI-Diabetes) collaboration database, available from 2004 to mid-2016. Data within this database were linked to mortality data from the General Registrar, available from the Information Services Division (ISD) of the National Health Service in Scotland. We calculated crude within-person differences between pre- and post-drug-initiation values of HbA, BMI, body weight, systolic blood pressure (SBP) and eGFR. We used mixed-effects regression models to adjust for within-person time trajectories in these measures. For completeness, we evaluated safety outcomes, cardiovascular disease events, lower-limb amputation and diabetic ketoacidosis, focusing on cumulative exposure effects, using Cox proportional hazard models, though power to detect such effects was limited.

Results: Among 8566 people exposed to dapagliflozin over a median of 210 days the crude within-person change in HbA was -10.41 mmol/mol (-0.95%) after 3 months' exposure. The crude change after 12 months was -12.99 mmol/mol (-1.19%) but considering the expected rise over time in HbA gave a dapagliflozin-exposure-effect estimate of -15.14 mmol/mol (95% CI -15.87, -14.41) (-1.39% [95% CI -1.45, -1.32]) at 12 months that was maintained thereafter. A drop in SBP of -4.32 mmHg (95% CI -4.84, -3.79) on exposure within the first 3 months was also maintained thereafter. Reductions in BMI and body weight stabilised by 6 months at -0.82 kg/m (95% CI -0.87, -0.77) and -2.20 kg (95% CI -2.34, -2.06) and were maintained thereafter. eGFR declined initially by -1.81 ml min [1.73 m] (95% CI -2.10, -1.52) at 3 months but varied thereafter. There were no significant effects of cumulative drug exposure on safety outcomes.

Conclusions/interpretation: Dapagliflozin exposure was associated with reductions in HbA, SBP, body weight and BMI that were at least as large as in clinical trials. Dapagliflozin also prevented the expected rise in HbA and SBP over the period of study.
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http://dx.doi.org/10.1007/s00125-018-4806-9DOI Listing
April 2019

Epidemiological characteristics of human-infective RNA viruses.

Sci Data 2018 02 20;5:180017. Epub 2018 Feb 20.

Centre for Immunology, Infection and Evolution, University of Edinburgh, Ashworth Laboratories, Charlotte Auerbach Road, Edinburgh EH9 3FL, UK.

RNA viruses are a major threat to human health. Here, based on extensive literature searches carried out over a period of 18 years, we provide a catalogue of all 214 known human-infective RNA virus species. We link these viruses to metadata for a number of traits that influence their epidemiology, including the date of the first report of human infection, transmissibility in human populations, transmission route(s) and host range. This database can be used in comparative studies of human-infective RNA viruses to identify the characteristics of viruses most likely to pose the greatest public health threat, both now and in the future.
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http://dx.doi.org/10.1038/sdata.2018.17DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5819479PMC
February 2018

Assessing the Epidemic Potential of RNA and DNA Viruses.

Emerg Infect Dis 2016 12;22(12):2037-2044

Many new and emerging RNA and DNA viruses are zoonotic or have zoonotic origins in an animal reservoir that is usually mammalian and sometimes avian. Not all zoonotic viruses are transmissible (directly or by an arthropod vector) between human hosts. Virus genome sequence data provide the best evidence of transmission. Of human transmissible virus, 37 species have so far been restricted to self-limiting outbreaks. These viruses are priorities for surveillance because relatively minor changes in their epidemiologies can potentially lead to major changes in the threat they pose to public health. On the basis of comparisons across all recognized human viruses, we consider the characteristics of these priority viruses and assess the likelihood that they will further emerge in human populations. We also assess the likelihood that a virus that can infect humans but is not capable of transmission (directly or by a vector) between human hosts can acquire that capability.
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http://dx.doi.org/10.3201/eid2212.160123DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5189130PMC
December 2016

Quantifying Global Drivers of Zoonotic Bat Viruses: A Process-Based Perspective.

Am Nat 2016 Feb 21;187(2):E53-64. Epub 2015 Dec 21.

Emerging infectious diseases (EIDs), particularly zoonoses, represent a significant threat to global health. Emergence is often driven by anthropogenic activity (e.g., travel, land use change). Although disease emergence frameworks suggest multiple steps from initial zoonotic transmission to human-to-human spread, there have been few attempts to empirically model specific steps. We create a process-based framework to separate out components of individual emergence steps. We focus on early emergence and expand the first step, zoonotic transmission, into processes of generation of pathogen richness, transmission opportunity, and establishment, each with its own hypothesized drivers. Using this structure, we build a spatial empirical model of these drivers, taking bat viruses shared with humans as a case study. We show that drivers of both viral richness (host diversity and climatic variability) and transmission opportunity (human population density, bushmeat hunting, and livestock production) are associated with virus sharing between humans and bats. We also show spatial heterogeneity between the global patterns of these two processes, suggesting that high-priority locations for pathogen discovery and surveillance in wildlife may not necessarily coincide with those for public health intervention. Finally, we offer direction for future studies of zoonotic EIDs by highlighting the importance of the processes underlying their emergence.
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http://dx.doi.org/10.1086/684391DOI Listing
February 2016

RNA Viruses: A Case Study of the Biology of Emerging Infectious Diseases.

Microbiol Spectr 2013 Oct;1(1)

Centre for Immunity, Infection & Evolution, University of Edinburgh, Edinburgh EH9 3JT, United Kingdom.

There are 180 currently recognized species of RNA virus that can infect humans, and on average, 2 new species are added every year. RNA viruses are routinely exchanged between humans and other hosts (particularly other mammals and sometimes birds) over both epidemiological and evolutionary time: 89% of human-infective species are considered zoonotic and many of the remainder have zoonotic origins. Some viruses that have crossed the species barrier into humans have persisted and become human-adapted viruses, as exemplified by the emergence of HIV-1. Most, however, have remained as zoonoses, and a substantial number have apparently disappeared again. We still know relatively little about what determines whether a virus is able to infect, transmit from, and cause disease in humans, but there is evidence that factors such as host range, cell receptor usage, tissue tropisms, and transmission route all play a role. Although systematic surveillance for potential new human viruses in nonhuman hosts would be enormously challenging, we can reasonably aspire to much better knowledge of the diversity of mammalian and avian RNA viruses than exists at present.
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http://dx.doi.org/10.1128/microbiolspec.OH-0001-2012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6157708PMC
October 2013