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PhD Studentship funded by Laboratory of Data Discovery for Health (D24H), Hong Kong (Round 1)

The London School of Hygiene & Tropical Medicine is pleased to invite applications for a PhD studentship, funded for three years by the Laboratory of Data Discovery for Health (D24H), Hong Kong, starting in September 2022.

The award will cover tuition fees only (at the overseas or home rates).

The studentships will be based in the Faculty of Epidemiology and Population Health. The Faculty is multi-disciplinary and encompasses epidemiologists, medical statisticians, medical demographers, nutritionists, social scientists and public health practitioners.

The successful candidates will spend time in London and Hong Kong, and will work with an expert team to develop, refine, and evaluate digital innovations 1) for users to obtain accurate and personalized vaccine information and to assess the veracity of vaccine information, 2) for infodemic management, with a focus on addressing misinformation, fake news, and rumours, and 3) to improve public confidence and reduce inequity around vaccine and during COVID-19 recovery. 

Using artificial intelligence to control the growing epidemic of vaccine hesitancy and refusal

The purpose of VCP is to monitor public confidence in immunization programs by building an information surveillance system for early detection of public concerns around vaccines; by applying a diagnostic tool to data collected to determine the risk level of public concerns in terms of their potential to disrupt vaccine programs; and, finally, to provide analysis and guidance for early response and engagement with the public to ensure sustained confidence in vaccines and immunization. VCP has created the Vaccine Confidence Index™ (VCI) as a tool for mapping confidence globally.

Annually, VCP reports its findings in The State of Vaccine Confidence and discusses their implications with key stakeholders including the EU and the WHO. Over the years, VCP has amassed a large database on vaccine confidence and hesitancy, including over 10,000 full text media articles, and a growing resource of global cross-platform social media data and digital analytics - which continues to be used to build an evidence base for formulating a bespoke communication framework for addressing vaccine hesitancy.

Despite its success since inception, the global impact of VCP is currently limited by (i) the availability of manpower to collate and interpret the large amount of data generated in the digital media and curate content for its audience; (ii) its lack of active presence in mainstream social media platforms such as Facebook and YouTube; and (iii) the lack of real-time interaction with and learning from its audience in terms of their concerns about vaccines and the effectiveness of VCP on addressing their concerns. As a result,VCP has low penetration among the general public.

The objective of this project is to establish VCP as the global authoritative portal for vaccine information by using artificial intelligence (AI) analytics to alleviate these limits. Building on the scientific achievements and global health influence of VCP, we propose to substantially enhance its global penetration, data management and analytic throughput using AI. Below are the three specific aims:

Specific aim 1

To develop AI analytics for continuously and comprehensively monitors the digital media for emergence and spread of vaccine misinformation and anti-vaccination propaganda.

  1. Allows lay-persons to communicate with vaccine-specific chatbots that provide realtime interactive guidance on the safety, effectiveness, and risk-benefits of different vaccines.
  2. 2. Allows lay-persons to submit any text, audio or video about vaccines to our chatbots for an evaluation of its content in terms of scientific merit, origin, pervasiveness, etc.

The AI platform will learn from the global mapping of sentiment and vaccine hesitancy across digital media. Specifically, Language Learning with BERT (Bidirectional Encoder Representations from Transformers), Tensorflow and Deep Learning are used to understand the language of social media discourse with regards to all vaccines - as well as categorize across a spectrum of sentiment (positive, ambiguous, neutral and negative) expressed in both digital texts and social media posts concerning vaccines. These data will be used to inform the baseline for building the chatbots that we will develop to engage with concerned publics seeking vaccine information. We hypothesize that the knowledge gained from the use of machine learning and AI monitoring will strengthen our ability to respond quickly to early signs and digital spread of concern, rumour and misinformation, across global digital networks.

Specific aim 2

To develop vaccine-specific chatbots that provide real-time interactive guidance on the safety, effectiveness, and risk-benefits of different vaccines as well as evaluation of the vaccine information contained in any text article, audio or video in terms of scientific merit, origin, pervasiveness, etc.

Specific aim 3

To develop an ad system that automatically identifies people who are seeking vaccine information online (using search engines and social media) and their stance towards vaccines, and provides them with information tailored to their needs, personality and stance, such that they will be more likely to vaccinate as a result of this information provisioning

Eligibility requirements

  • Relevant undergraduate and masters degrees, awarded at a high grade. Applicants with a very strong undergraduate degree and relevant experience will be exceptionally considered.
  • Demonstrable attention to detail.
  • The ability to work to deadlines.
  • Excellent oral and written communication skills.
  • An interest in digital health and vaccine confidence research in the Asia Pacific region.
  • Demonstrated working experience in social listening, machine learning, natural language processing, quantitative data analytics, computer science, and/or mathematical and statistical modelling in the areas of vaccine hesitancy and misinformation.
  • Some capacity of the Chinese language (Mandarin or Cantonese), and experience with Chinese social media analytics.

The award is available to all candidates who meet the above eligibility requirements regardless of their fee status (ie Home or Overseas fees).

The PhD Programme

Students will be mentored by their supervisors and an Advisory Committee consisting of at least two other academics, who can be from outside the School.  Students are expected to take part in the academic life of their department and can also be members of other Academic Centres - e.g. Vaccine Confidence Project, Centre for the Mathematical Modelling of Infectious Diseases, the Clinical Trials Unit, and the Centre for Statistical Methodology.  All research seminars and journal clubs are open to PhD students from across the School. Students are able to take up to four Master’s level Study Modules per academic year, subject to approval from their supervisor.

Support for research students’ future career development is covered through the supervision process, through the Transferable Skills Programme (in the School and the Bloomsbury Postgraduate Skills Network) and the School’s Careers Service.  Also important for career development are the opportunities for students to network and establish professional contacts.  The School also facilitates national and international conference attendance by students in the later stages which provides networking opportunities.

How to apply

Applicants should provide the following documents:

  • A 2-page curriculum vitae, including details of their academic achievements to date and the names of 2 referees (at least one of whom should be able to comment on your academic ability)
  • A research proposal of up to 1,000 words. The research proposal should expand on the topic listed, summarise the relevant background information (with no more than 5 key references) and should outline an appropriate research methodology by which the question can be addressed.
  • A covering letter saying why they are interested in undertaking a PhD in this area at LSHTM
  • Copies of the transcripts from their undergraduate and postgraduate qualifications

Only applications in the correct format will be considered. Applications should be submitted by email to eph.frdd@lshtm.ac.uk.

Please state clearly in the heading and text of your email that you are applying for the ‘PhD Studentship funded by Laboratory of Data Discovery for Health (D24H), Hong Kong’. 

The deadline for applications is 21st February 2022, 10 am GMT.