This PhD opportunity is being offered as part of the LSTM and Lancaster University Doctoral Training Partnership. Find out more about the studentships and how to apply.
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Abstract |
Successive epidemic waves of infection are a feature of both pandemic and seasonal viruses, generating significant health and economic impacts globally. For many seasonal respiratory viruses (such as seasonal influenza, Respiratory Syncytial Virus or coronaviruses) these waves are annual in many northern and southern countries and are associated with winter periods. Critically, during such periods, there is considerable heterogeneity in infection, driven by the complex interaction of exposure, transmission, and existing immunity. During inter-seasonal periods, immunity wanes and/or pathogens evolve, such that repeated and successive epidemics generate a heterogenous but dynamic landscape of immunity within the population. This project will use routinely collected surveillance and clinical data, combined with unique datasets collected in USA and China, to identify the sociological drivers, both at the individual-level and larger scale socioeconomic structures, shaping immunity landscapes. The insight afforded will enable improved prediction of infections for forthcoming seasons, insight into optimal targeting of vaccines and improved vaccine selection (for influenza vaccines). |
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Where does the project lie on the Translational Pathway? |
T1 – Basic Research T3 Evidence into Practice |
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Expected Outputs |
We anticipate the project will generate several high-impact publications. All code will be published, and any software tools developed for susceptibility identification (particularly for SARS-CoV-2 and influenza) will also be made available for use by public health agencies. |
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Training Opportunities |
Training at Lancaster will provide the student with expertise in Bayesian model fitting, infectious disease epidemiology and modelling, and geospatial statistics |
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Skills Required |
The student should have strong quantitative skills, either mathematical modelling or statistical analysis, and ideally expertise in data science. Ability to write code (R or python) for computationally demanding applications is desirable. We are actively seeking someone from a quantitative (computer science or statistics) background for this project. |
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Key Publications associated with this project |
Fonville JM, Wilks SH, James SL, Fox A, Ventresca M, Aban M, Xue L, Jones TC, Le NM, Pham QT, Tran ND. Antibody landscapes after influenza virus infection or vaccination. Science. 2014 Nov 21;346(6212):996-1000. |
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Lessler J, Riley S, Read JM, Wang S, Zhu H, Smith GJ, Guan Y, Jiang CQ, Cummings DA. Evidence for antigenic seniority in influenza A (H3N2) antibody responses in southern China. PLoS pathogens. 2012 Jul 19;8(7):e1002802. |
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Danon L, Read JM, House TA, Vernon MC, Keeling MJ. Social encounter networks: characterizing Great Britain. Proceedings of the Royal Society B: Biological Sciences. 2013 Aug 22;280(1765):20131037. |
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Quandelacy TM, Cummings DA, Jiang CQ, Yang B, Kwok KO, Dai B, Shen R, Read JM, Zhu H, Guan Y, Riley S. Using serological measures to estimate influenza incidence in the presence of secular trends in exposure and immuno‐modulation of antibody response. Influenza and other respiratory viruses. 2021 Mar;15(2):235-44. |
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Haw DJ, Pung R, Read JM, Riley S. Strong spatial embedding of social networks generates nonstandard epidemic dynamics independent of degree distribution and clustering. Proceedings of the National Academy of Sciences. 2020 Sep 22;117(38):23636-42. |