Imperial College London

DrAdaYan

Faculty of MedicineDepartment of Infectious Disease

Imperial College Research Fellow
 
 
 
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a.yan Website

 
 
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Location

 

421Praed StreetSt Mary's Campus

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Summary

 

Publications

Publication Type
Year
to

19 results found

Challenger J, Foo C, Wu Y, Yan A, Marjaneh MM, Liew F, Thwaites R, Okell L, Cunnington Aet al., 2022, Modelling upper respiratory viral load dynamics of SARS-CoV-2, BMC Medicine, Vol: 20, ISSN: 1741-7015

Relationships between viral load, severity of illness, and transmissibility of virus, are fundamental to understanding pathogenesis and devising better therapeutic and prevention strategies for COVID-19. Here we present within-host modelling of viral load dynamics observed in the upper respiratory tract (URT), drawing upon 2172 serial measurements from 605 subjects, collected from 17 different studies. We developed a mechanistic model to describe viral load dynamics and host response, and contrast this with simpler mixed-effects regression analysis of peak viral load and its subsequent decline. We observed wide variation in URT viral load between individuals, over 5 orders of magnitude, at any given point in time since symptom onset. This variation was not explained by age, sex, or severity of illness, and these variables were not associated with the modelled early or late phases of immune-mediated control of viral load. We explored the application of the mechanistic model to identify measured immune responses associated with control of viral load. Neutralizing antibody correlated strongly with modelled immune-mediated control of viral load amongst subjects who produced neutralizing antibody. Our models can be used to identify host and viral factors which control URT viral load dynamics, informing future treatment and transmission blocking interventions.

Journal article

Peacock TP, Brown JC, Zhou J, Thakur N, Sukhova K, Newman J, Kugathasan R, Yan AWC, Furnon W, De Lorenzo G, Cowton VM, Reuss D, Moshe M, Quantrill JL, Platt OK, Kaforou M, Patel AH, Palmarini M, Bailey D, Barclay WSet al., 2022, The altered entry pathway and antigenic distance of the SARS-CoV-2 Omicron variant map to separate domains of spike protein

<jats:title>Abstract</jats:title><jats:p>The SARS-CoV-2 Omicron/BA.1 lineage emerged in late 2021 and rapidly displaced the Delta variant before being overtaken itself globally by, the Omicron/BA.2 lineage in early 2022. Here, we describe how Omicron BA.1 and BA.2 show a lower severity phenotype in a hamster model of pathogenicity which maps specifically to the spike gene. We further show that Omicron is attenuated in a lung cell line but replicates more rapidly, albeit to lower peak titres, in human primary nasal cells. This replication phenotype also maps to the spike gene. Omicron spike (including the emerging Omicron lineage BA.4) shows attenuated fusogenicity and a preference for cell entry via the endosomal route. We map the altered Omicron spike entry route and partially map the lower fusogenicity to the S2 domain, particularly the substitution N969K. Finally, we show that pseudovirus with Omicron spike, engineered in the S2 domain to confer a more Delta-like cell entry route retains the antigenic properties of Omicron. This shows a distinct separation between the genetic determinants of these two key Omicron phenotypes, raising the concerning possibility that future variants with large antigenic distance from currently circulating and vaccine strains will not necessarily display the lower intrinsic severity seen during Omicron infection.</jats:p>

Journal article

Deol AK, Scarponi D, Beckwith P, Yates TA, Karat AS, Yan AWC, Baisley KS, Grant AD, White RG, McCreesh Net al., 2021, Estimating ventilation rates in rooms with varying occupancy levels: Relevance for reducing transmission risk of airborne pathogens, PLoS One, Vol: 16, ISSN: 1932-6203

BACKGROUND: In light of the role that airborne transmission plays in the spread of SARS-CoV-2, as well as the ongoing high global mortality from well-known airborne diseases such as tuberculosis and measles, there is an urgent need for practical ways of identifying congregate spaces where low ventilation levels contribute to high transmission risk. Poorly ventilated clinic spaces in particular may be high risk, due to the presence of both infectious and susceptible people. While relatively simple approaches to estimating ventilation rates exist, the approaches most frequently used in epidemiology cannot be used where occupancy varies, and so cannot be reliably applied in many of the types of spaces where they are most needed. METHODS: The aim of this study was to demonstrate the use of a non-steady state method to estimate the absolute ventilation rate, which can be applied in rooms where occupancy levels vary. We used data from a room in a primary healthcare clinic in a high TB and HIV prevalence setting, comprising indoor and outdoor carbon dioxide measurements and head counts (by age), taken over time. Two approaches were compared: approach 1 using a simple linear regression model and approach 2 using an ordinary differential equation model. RESULTS: The absolute ventilation rate, Q, using approach 1 was 2407 l/s [95% CI: 1632-3181] and Q from approach 2 was 2743 l/s [95% CI: 2139-4429]. CONCLUSIONS: We demonstrate two methods that can be used to estimate ventilation rate in busy congregate settings, such as clinic waiting rooms. Both approaches produced comparable results, however the simple linear regression method has the advantage of not requiring room volume measurements. These methods can be used to identify poorly-ventilated spaces, allowing measures to be taken to reduce the airborne transmission of pathogens such as Mycobacterium tuberculosis, measles, and SARS-CoV-2.

Journal article

Goldhill DH, Yan A, Frise R, Zhou J, Shelley J, Cortes AG, Miah S, Akinbami O, Galiano M, Zambon M, Lackenby A, Barclay WSet al., 2021, Favipiravir-resistant influenza A virus shows potential for transmission, PLoS Pathogens, Vol: 17, Pages: 1-17, ISSN: 1553-7366

Favipiravir is a nucleoside analogue which has been licensed to treat influenza in the event of a new pandemic. We previously described a favipiravir resistant influenza A virus generated by in vitro passage in presence of drug with two mutations: K229R in PB1, which conferred resistance at a cost to polymerase activity, and P653L in PA, which compensated for the cost of polymerase activity. However, the clinical relevance of these mutations is unclear as the mutations have not been found in natural isolates and it is unknown whether viruses harbouring these mutations would replicate or transmit in vivo. Here, we infected ferrets with a mix of wild type p(H1N1) 2009 and corresponding favipiravir-resistant virus and tested for replication and transmission in the absence of drug. Favipiravir-resistant virus successfully infected ferrets and was transmitted by both contact transmission and respiratory droplet routes. However, sequencing revealed the mutation that conferred resistance, K229R, decreased in frequency over time within ferrets. Modelling revealed that due to a fitness advantage for the PA P653L mutant, reassortment with the wild-type virus to gain wild-type PB1 segment in vivo resulted in the loss of the PB1 resistance mutation K229R. We demonstrated that this fitness advantage of PA P653L in the background of our starting virus A/England/195/2009 was due to a maladapted PA in first wave isolates from the 2009 pandemic. We show there is no fitness advantage of P653L in more recent pH1N1 influenza A viruses. Therefore, whilst favipiravir-resistant virus can transmit in vivo, the likelihood that the resistance mutation is retained in the absence of drug pressure may vary depending on the genetic background of the starting viral strain.

Journal article

Menkir TF, Chin T, Hay JA, Surface ED, De Salazar PM, Buckee CO, Watts A, Khan K, Sherbo R, Yan AWC, Mina MJ, Lipsitch M, Niehus Ret al., 2021, Estimating internationally imported cases during the early COVID-19 pandemic, Nature Communications, Vol: 12, ISSN: 2041-1723

Early in the COVID-19 pandemic, predictions of international outbreaks were largely based on imported cases from Wuhan, China, potentially missing imports from other cities. We provide a method, combining daily COVID-19 prevalence and flight passenger volume, to estimate importations from 18 Chinese cities to 43 international destinations, including 26 in Africa. Global case importations from China in early January came primarily from Wuhan, but the inferred source shifted to other cities in mid-February, especially for importations to African destinations. We estimate that 10.4 (6.2 - 27.1) COVID-19 cases were imported to these African destinations, which exhibited marked variation in their magnitude and main sources of importation. We estimate that 90% of imported cases arrived between 17 January and 7 February, prior to the first case detections. Our results highlight the dynamic role of source locations, which can help focus surveillance and response efforts.

Journal article

Yan AWC, Zhou J, Beauchemin CAA, Russell CA, Barclay WS, Riley Set al., 2020, Quantifying mechanistic traits of influenza viral dynamics using in vitro data, Publisher: ELSEVIER

Working paper

Yan AWC, Zhou J, Beauchemin CAA, Russell CA, Barclay WS, Riley Set al., 2020, Quantifying mechanistic traits of influenza viral dynamics using in vitro data., Epidemics: the journal of infectious disease dynamics, Vol: 33, Pages: 1-10, ISSN: 1755-4365

When analysing in vitro data, growth kinetics of influenza virus strains are often compared by computing their growth rates, which are sometimes used as proxies for fitness. However, analogous to mathematical models for epidemics, the growth rate can be defined as a function of mechanistic traits: the basic reproduction number (the average number of cells each infected cell infects) and the mean generation time (the average length of a replication cycle). Fitting a model to previously published and newly generated data from experiments in human lung cells, we compared estimates of growth rate, reproduction number and generation time for six influenza A strains. Of four strains in previously published data, A/Canada/RV733/2003 (seasonal H1N1) had the lowest basic reproduction number, followed by A/Mexico/INDRE4487/2009 (pandemic H1N1), then A/Indonesia/05/2005 (spill-over H5N1) and A/Anhui/1/2013 (spill-over H7N9). This ordering of strains was preserved for both generation time and growth rate, suggesting a positive biological correlation between these quantities which have not been previously observed. We further investigated these potential correlations using data from reassortant viruses with different internal proteins (from A/England/195/2009 (pandemic H1N1) and A/Turkey/05/2005 (H5N1)), and the same surface proteins (from A/Puerto Rico/8/34 (lab-adapted H1N1)). Similar correlations between traits were observed for these viruses, confirming our initial findings and suggesting that these patterns were related to the degree of human adaptation of internal genes. Also, the model predicted that strains with a smaller basic reproduction number, shorter generation time and slower growth rate underwent more replication cycles by the time of peak viral load, potentially accumulating mutations more quickly. These results illustrate the utility of mathematical models in inferring traits driving observed differences in in vitro growth of influenza strains.

Journal article

Alahmadi A, Belet S, Black A, Cromer D, Flegg JA, House T, Jayasundara P, Keith JM, McCaw JM, Moss R, Ross JV, Shearer FM, Tun STT, Walker J, White L, Whyte JM, Yan AWC, Zarebski AEet al., 2020, Influencing public health policy with data-informed mathematical models of infectious diseases: Recent developments and new challenges, Epidemics: the journal of infectious disease dynamics, Vol: 32, ISSN: 1755-4365

Modern data and computational resources, coupled with algorithmic and theoretical advances to exploit these, allow disease dynamic models to be parameterised with increasing detail and accuracy. While this enhances models' usefulness in prediction and policy, major challenges remain. In particular, lack of identifiability of a model's parameters may limit the usefulness of the model. While lack of parameter identifiability may be resolved through incorporation into an inference procedure of prior knowledge, formulating such knowledge is often difficult. Furthermore, there are practical challenges associated with acquiring data of sufficient quantity and quality. Here, we discuss recent progress on these issues.

Journal article

Goldhill D, Yan A, Frise R, Zhou J, Shelley J, Cort├ęs AG, Miah S, Akinbami O, Galiano M, Zambon M, Lackenby A, Barclay Wet al., 2020, Favipiravir-resistant influenza A virus shows potential for transmission, Publisher: Cold Spring Harbor Laboratory

Abstract Favipiravir is a nucleoside analogue which has been licensed to treat influenza in the event of a new pandemic. We previously described a favipiravir resistant influenza A virus generated by in vitro passage in presence of drug with two mutations: K229R in PB1, which conferred resistance at a cost to polymerase activity, and P653L in PA, which compensated for the cost of polymerase activity. However, the clinical relevance of these mutations is unclear as the mutations have not been found in natural isolates and it is unknown whether viruses harbouring these mutations would replicate or transmit in vivo. Here, we infected ferrets with a mix of wild type p(H1N1) 2009 and corresponding favipiravir-resistant virus and tested for replication and transmission in the absence of drug. Favipiravir-resistant virus successfully infected ferrets and was transmitted by both contact transmission and respiratory droplet routes. However, sequencing revealed the mutation that conferred resistance, K229R, decreased in frequency over time within ferrets. Modelling revealed that due to a fitness advantage for the PA P653L mutant, reassortment with the wild-type virus to gain wild-type PB1 segment in vivo resulted in the loss of the PB1 resistance mutation K229R. We demonstrated that this fitness advantage of PA P653L in the background of our starting virus A/England/195/2009 was due to a maladapted PA in first wave isolates from the 2009 pandemic. We show there is no fitness advantage of P653L in more recent pH1N1 influenza A viruses. Therefore, whilst favipiravir-resistant virus can transmit in vivo, the likelihood that the resistance mutation is retained in the absence of drug pressure may vary depending on the genetic background of the starting viral strain. Author Summary In the event of a new influenza pandemic, drugs will be our first line of defence against the virus. However, drug resistance has proven to be particularly problematic to drugs against influenza. Favipir

Working paper

Yan AWC, Zaloumis SG, Simpson JA, McCaw JMet al., 2019, Sequential infection experiments for quantifying innate and adaptive immunity during influenza infection, PLoS Computational Biology, Vol: 15, ISSN: 1553-734X

Laboratory models are often used to understand the interaction of related pathogens via host immunity. For example, recent experiments where ferrets were exposed to two influenza strains within a short period of time have shown how the effects of cross-immunity vary with the time between exposures and the specific strains used. On the other hand, studies of the workings of different arms of the immune response, and their relative importance, typically use experiments involving a single infection. However, inferring the relative importance of different immune components from this type of data is challenging. Using simulations and mathematical modelling, here we investigate whether the sequential infection experiment design can be used not only to determine immune components contributing to cross-protection, but also to gain insight into the immune response during a single infection. We show that virological data from sequential infection experiments can be used to accurately extract the timing and extent of cross-protection. Moreover, the broad immune components responsible for such cross-protection can be determined. Such data can also be used to infer the timing and strength of some immune components in controlling a primary infection, even in the absence of serological data. By contrast, single infection data cannot be used to reliably recover this information. Hence, sequential infection data enhances our understanding of the mechanisms underlying the control and resolution of infection, and generates new insight into how previous exposure influences the time course of a subsequent infection.

Journal article

Yan AWC, Black AJ, McCaw JM, Rebuli N, Ross J, Swan AJ, Hickson Ret al., 2018, The distribution of the time taken for an epidemic to spread between two communities, MATHEMATICAL BIOSCIENCES, Vol: 303, Pages: 139-147, ISSN: 0025-5564

Journal article

Yan AWC, Cao P, Heffernan JM, McVernon J, Quinn KM, La Gruta NL, Laurie KL, Mccaw JMet al., 2017, Corrigendum to "Modelling cross-reactivity and memory in the cellular adaptive immune response to influenza infection in the host" [J.Theor. Biol. 413 (2017) 34–49], Journal of Theoretical Biology, Vol: 419, Pages: 394-394, ISSN: 0022-5193

Journal article

Yan AWC, Cao P, Heffernan JM, McVernon J, Quinn KM, La Gruta NL, Laurie KL, McCaw JMet al., 2017, Modelling cross-reactivity and memory in the cellular adaptive immune response to influenza infection in the host, Journal of Theoretical Biology, Vol: 413, Pages: 34-49, ISSN: 0022-5193

The cellular adaptive immune response plays a key role in resolving influenza infection. Experiments where individuals are successively infected with different strains within a short timeframe provide insight into the underlying viral dynamics and the role of a cross-reactive immune response in resolving an acute infection. We construct a mathematical model of within-host influenza viral dynamics including three possible factors which determine the strength of the cross-reactive cellular adaptive immune response: the initial naive T cell number, the avidity of the interaction between T cells and the epitopes presented by infected cells, and the epitope abundance per infected cell. Our model explains the experimentally observed shortening of a second infection when cross-reactivity is present, and shows that memory in the cellular adaptive immune response is necessary to protect against a second infection.

Journal article

Cao P, Wang Z, Yan AWC, McVernon J, Xu J, Heffernan JM, Kedzierska K, McCaw JMet al., 2016, On the role of CD8(+) T cells in determining recovery time from influenza virus infection, Frontiers in Immunology, Vol: 7, Pages: 1-13, ISSN: 1664-3224

Myriad experiments have identified an important role for CD8+ T cell response mechanisms in determining recovery from influenza A virus infection. Animal models of influenza infection further implicate multiple elements of the immune response in defining the dynamical characteristics of viral infection. To date, influenza virus models, while capturing particular aspects of the natural infection history, have been unable to reproduce the full gamut of observed viral kinetic behavior in a single coherent framework. Here, we introduce a mathematical model of influenza viral dynamics incorporating innate, humoral, and cellular immune components and explore its properties with a particular emphasis on the role of cellular immunity. Calibrated against a range of murine data, our model is capable of recapitulating observed viral kinetics from a multitude of experiments. Importantly, the model predicts a robust exponential relationship between the level of effector CD8+ T cells and recovery time, whereby recovery time rapidly decreases to a fixed minimum recovery time with an increasing level of effector CD8+ T cells. We find support for this relationship in recent clinical data from influenza A (H7N9) hospitalized patients. The exponential relationship implies that people with a lower level of naive CD8+ T cells may receive significantly more benefit from induction of additional effector CD8+ T cells arising from immunological memory, itself established through either previous viral infection or T cell-based vaccines.

Journal article

Yan AWC, Cao P, McCaw JM, 2016, On the extinction probability in models of within-host infection: the role of latency and immunity, JOURNAL OF MATHEMATICAL BIOLOGY, Vol: 73, Pages: 787-813, ISSN: 0303-6812

Journal article

Laurie KL, Guarnaccia TA, Carolan LA, Yan AWC, Aban M, Petrie S, Cao P, Heffernan JM, McVernon J, Mosse J, Kelso A, Mccaw JM, Barr IGet al., 2015, Interval Between Infections and Viral Hierarchy Are Determinants of Viral Interference Following Influenza Virus Infection in a Ferret Model, JOURNAL OF INFECTIOUS DISEASES, Vol: 212, Pages: 1701-1710, ISSN: 0022-1899

Journal article

Cao P, Yan AWC, Heffernan JM, Petrie S, Moss RG, Carolan LA, Guarnaccia TA, Kelso A, Barr IG, McVernon J, Laurie KL, McCaw JMet al., 2015, Innate Immunity and the Inter-exposure Interval Determine the Dynamics of Secondary Influenza Virus Infection and Explain Observed Viral Hierarchies, PLOS COMPUTATIONAL BIOLOGY, Vol: 11

Journal article

Yan AWC, D'Alfonso AJ, Morgan AJ, Putkunz CT, Allen LJet al., 2014, Fast Deterministic Ptychographic Imaging Using X-Rays, MICROSCOPY AND MICROANALYSIS, Vol: 20, Pages: 1090-1099, ISSN: 1431-9276

Journal article

D'Alfonso AJ, Morgan AJ, Yan AWC, Wang P, Sawada H, Kirkland AI, Allen LJet al., 2014, Deterministic electron ptychography at atomic resolution, PHYSICAL REVIEW B, Vol: 89, ISSN: 2469-9950

Journal article

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