13 results found
Alahmadi A, Belet S, Black A, et 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.
Yan AWC, Zaloumis SG, Simpson JA, et 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.
Yan AWC, Black AJ, McCaw JM, et 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
Yan AWC, Cao P, Heffernan JM, et al., 2017, Modelling cross-reactivity and memory in the cellular adaptive immune response to influenza infection in the host (vol 413, pg 34, 2017), JOURNAL OF THEORETICAL BIOLOGY, Vol: 419, Pages: 394-394, ISSN: 0022-5193
Yan AWC, Cao P, Heffernan JM, et 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
Cao P, Wang Z, Yan AWC, et al., 2016, On the Role of CD8(+) T Cells in Determining Recovery Time from Influenza Virus Infection, FRONTIERS IN IMMUNOLOGY, Vol: 7, ISSN: 1664-3224
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
Laurie KL, Guarnaccia TA, Carolan LA, et 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
Cao P, Yan AWC, Heffernan JM, et 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
Yan AWC, D'Alfonso AJ, Morgan AJ, et al., 2014, Fast Deterministic Ptychographic Imaging Using X-Rays, MICROSCOPY AND MICROANALYSIS, Vol: 20, Pages: 1090-1099, ISSN: 1431-9276
D'Alfonso AJ, Morgan AJ, Yan AWC, et al., 2014, Deterministic electron ptychography at atomic resolution, PHYSICAL REVIEW B, Vol: 89, ISSN: 2469-9950
Yan AWC, Zhou J, Beauchemin CAA, et al., Cellular reproduction number, generation time and growth rate differ between human- and avian-adapted influenza strains
When analysing in vitro data, growth kinetics of influenza strains are oftencompared by computing their growth rates, which are sometimes used as proxiesfor fitness. However, analogous to mechanistic epidemic models, the growth ratecan be defined as a function of two parameters: the basic reproduction number(the average number of cells each infected cell infects) and the meangeneration time (the average length of a replication cycle). Using amechanistic model, previously published data from experiments in human lungcells, and newly generated data, we compared estimates of all three parametersfor six influenza A strains. Using previously published data, we found that thetwo human-adapted strains (pre-2009 seasonal H1N1, and pandemic H1N1) had alower basic reproduction number, shorter mean generation time and slower growthrate than the two avian-adapted strains (H5N1 and H7N9). These same differenceswere then observed in data from new experiments where two strains wereengineered to have different internal proteins (pandemic H1N1 and H5N1), butthe same surface proteins (PR8), confirming our initial findings and implyingthat differences between strains were driven by internal genes. Also, the modelpredicted that the human-adapted strains underwent more replication cycles thanthe avian-adapted strains by the time of peak viral load, potentiallyaccumulating mutations more quickly. These results suggest that the in vitroreproduction number, generation time and growth rate differ betweenhuman-adapted and avian-adapted influenza strains, and thus could be used toassess host adaptation of internal proteins to inform pandemic risk assessment.
Goldhill DH, Yan A, Frise R, et al., Favipiravir-resistant influenza A virus shows potential for transmission
<jats:title>Abstract</jats:title><jats:p>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.</jats:p><jats:sec><jats:title>Author Summary</jats:title><jats:p>In the event of a new influenza pandemic, drugs will be our first line of
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.