We are seeking up to five highly skilled and quantitative researcher to join multiple projects seeking to analyse, model and predict the dynamics of a range of infectious diseases. Ideal candidate would have a strong background in mathematical or statistical modelling of infectious diseases, experience with matching models to data or an understanding of genomics applied to disease transmission.
The application details can be found here on the Warwick website.
The five positions are:
You will work on a research programme, funded by the NIHR, aimed at integrating epidemiological and bio-informatics concepts and bringing such modern methodologies to East African researchers. To do this GeMVi will focus on the application of whole genome sequence (WGS) data and predictive models to support intervention decisions. It will extend and enhance the collaboration between University of Warwick and KEMRI Wellcome Trust Research Programme, and create a network of research centres in Kenya, Uganda and Tanzania. Technical advances in the integration of genome sequence data and models of infectious disease transmission will be made.
The technical methods will be developed as the project advances in response to feedback from consultation with public health decision makers and will relate to (a) the priority pathogens, (b) policy questions, (c) data availability and (d) prevailing technical advances and gaps. You will have day-to-day responsibility for the development of novel mathematical and statistical methodologies to bring together predictive epidemiology with bioinformatics approaches.
You will also be expected to help with the research training of East African researchers who wish to develop advanced skills in predictive modelling, helping them to address a range of important public-health policy issues through mathematical and statistical models. As such you will be an integral part of developing the Zeeman Institute into a centre of expertise for integrated mathematical and genomic epidemiology, with meaningful ties to multiple East African Institutes.
The fixed term contract associated with these 2 posts is currently restricted to end on 30.06.2019 with an extension to 31.03.21, subject to confirmation of the funding, this might have to be shortened up to a maximum of 29.5 months dependent upon the starting salary of the successful candidate.
The team aims to develop and parameterise a series of transmission dynamic models of priority pathogens that will allow us to simulate different vaccine trials and determine which designs are most likely to be successful. We also intend to use the same models to assess how such vaccines could be used to mitigate outbreaks if licensed products become available. This will inform the policy-making process in outbreak-prone countries and in global health organisations.
We will conduct detailed reviews of the epidemiology of the target diseases that will cover the distribution of outbreak sizes and geographical extent, key transmission routes, relevant pathogenicity and vaccine candidate characteristics (vaccine platform, likely schedule etc). We will conduct a series of analyses on the reviewed and compiled epidemiological data, including estimation of the basic reproduction number and assess factors underlying variation in this (e.g. transmission routes, proportion of infections that are clinically detectable).
Building on these epidemiological analyses, we will construct a suite of stochastic epidemic models that will be fitted to the available data using Bayesian methods. Outbreak scenarios will also be constructed that will cover the plausible range of future epidemiological patterns for the target diseases. These models will be used to simulate various vaccine trial designs. These simulation studies will assess the randomisation units, endpoints and sample size requirements for the different designs and the likelihood of success (i.e. the power to detect efficacy, taking into account the likelihood of extinction of the epidemic). Logistical and ethical issues will also be reviewed and integrated into our assessment of the utility of the different designs. The models will also be used to assess possible post-license study designs and how a licenced product could best complement existing control measures.
Work in Warwick will focus on models of Bubonic Plague and Rift Valley Fever, although there is expected to be strong interaction between all involved groups.
The fixed term contract associated with this post is restricted to end on 31.03.20 and is subject to the funding being confirmed because the contract is still being reviewed; this might have to be shortened up to a maximum 22 months depending on the starting salary of the successful candidate.
The work in the wider consortium focuses on seven neglected tropical diseases (human African trypanosomiasis, Lymphatic filariasis, Onchocerciasis, Schistosomiasis, Soil-transmitted helminthiasis, Trachoma and Visceral leishmaniasis), with the aim of supporting the development of NTD policy. Mathematical modelling and quantitative analyses are essential tools in developing mid- to long-term strategies for the control or elimination of NTDs. Over the last two and a half years, the NTD modelling consortium has developed a reputation for providing timely analyses which are closely linked to relevant policy questions. Here we outline the types of questions which the modelling would address. The stages of control and elimination for NTDs is loosely characterized by the following four aims: Achieving the 2020 goals; Maintaining gains; Elimination; and Deployment of new tools to speed control. It is important to acknowledge that different diseases are at different stages in this process and so one of the important activities of the grant will be to identify where the priority questions – the ones which will push the programs forward most rapidly - lie for each disease.
For HAT, there is already a strong modelling team within Warwick, with experience in developing models and analysing the data from the WHO HAT Atlas. Key questions remain around the most appropriate control methods in low-endemicity areas: When is a good time to reduce or stop active screening in such settings and move to a more reactive strategy? Is the availability of passive health facilities with HAT diagnostics good enough? What are the risks of also scaling back current ‘enhanced’ passive surveillance?
The fixed term contract associated with this post is restricted to end on 15.11.21 and is funded through external grants therefore any offer is contingent upon these awards.
The research will expand and test data science methods and their applications to monitor and predict the emergence, spread and impact of infections and other diseases. We will focus on diseases with short and identifiable induction times which link exposure to onset, and with strong spatiotemporal patterns of occurrence. Infectious and non-infectious diseases with variable seasonal or outbreak patterns produce the necessary strong and potentially informative spatio-temporal signals. Systematic analysis of these patterns can support prediction of the emergence, increase or spread of these infections. This in turn can allow for interventions to be planned, including information to patients, preventative care, and enhancement of treatment capacity. The work will jointly analyse data on causes, diseases, and the effects of disease, and will use a range of individual- and population-level health data including pathogen genomes, as well as publicly available sources.
In addition to developing the necessary methodology, the approaches will be tested against a range of pressing public-health problems including:
Signatures of an Outbreak: Combining machine learning & mechanistic models to optimise syndromic surveillance; Assessment of the benefit of including additional data sources; Inferring transmission events through integration of pathogen genomics and observational data.
Antimicrobial Resistance in Communities and Hospitals: Incorporation of the external environment; Practical use of patient-risk prediction; Access to electronic patient reported outcome.
Assessing Mental Health: Linking to NHS mental health data will allow analysis of combined individual patient data and contextual social media data to predict mental health in adolescent populations where social media use is particularly high.
Forecasting Asthma: Devising a robust prediction model to inform individuals, interventions and healthcare services; Aetiological epidemiology to inform personalised prediction and interventions; Strategies that inform patients and service providers when risks are high; A learning surveillance and response system within local and national healthcare.
The fixed term contract associated with this post is restricted to end on 31.03.23 and is funded through external grants therefore any offer is contingent upon these awards.
The successful candidates will be based in the highly successful and expanding Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER), but will work closely with other researchers within University of Warwick and at our partner institutions. Close working and regular communication between the post holder and the wider team will be crucial to the successful delivery of the research programme.
Initial enquiries should be directed to M.J.Keeling@warwick.ac.uk