Spatiotemporal statistical machine learning (ST-SML): theory, methods, and applications

Lead Research Organisation: Imperial College London
Department Name: Dept of Mathematics

Abstract

Machine learning (ML) is the computational beating heart of the modern Artificial Intelligence (AI) renaissance. A number of fields, from computer vision to speech recognition have been completely transformed by the successes of machine learning. But practitioners and policymakers struggle when it comes to translating the successes of ML from narrowly defined prediction problems---e.g. "is this a picture of a cat?"---to the broader and messier world of public health and public policy. This fellowship will fund research on new ML methods to enable us to better ask and answer questions concerning change over space and time, such as:

1) How does disease risk, poverty, or housing quality vary within a country and over time?
2) Can satellite data enable us to answer policy questions in a more timely and spatially localised manner?
3) Do the dynamics of violent crime differ in different cities?
4) Did the world achieve the Millennium Development Goals? Will the world achieve the Sustainable Development Goals?

Bespoke answers to these questions are not enough, because practitioners in the public sector face new challenges in real-time. They need reproducible and well-documented applied workflows to follow to enable them to tackle important public policy problems as they arise.

Planned Impact

Who might benefit from this research and how might they benefit?

The partners who have supported this fellowship, NASA, the World Food Programme, and the UNAIDS Reference Group, will all directly benefit from the proposed development of statistical machine learning methods for spatiotemporal data. These methods will be developed to directly tackle challenges faced by these organisations in understanding and improving the health and well-being of humans.

* The World Food Programme assists 86.7 million people in around 83 countries each year, and this fellowship will develop survey design, analysis methods, and data scientific workflows to better target food aid to improve food security in these countries and quickly respond to unfolding humanitarian emergencies.

* NASA is using satellite data to map air pollution in low income countries, and this fellowship will focus on new methods to make timely and fine-grained estimates and theory to support the rigorous evaluations of these methods.

* The UNAIDS Reference Group on Estimates, Modelling, and Projections is responsible for advising UNAIDS and country governments on spatiotemporal statistics and forecasts related to the estimated 37 million people worldwide living with HIV. The fellowship will develop new methods for analysis and data collection to enable the UNAIDS Reference Group to provide estimates at the right spatial and temporal scale to be policy-relevant.

Through the support of the Stan Development Team, methods and applied workflows will be disseminated as widely as possible. The Stan software, which we will extend to handle larger and more flexible spatiotemporal statistical models, is downloaded almost a million times per year. This means that there is a very large audience with the expertise to use and adopt the methods we are developing in a range of application areas, from public health and public policy to natural science, healthcare, and business analytics.

Publications

10 25 50

Related Projects

Project Reference Relationship Related To Start End Award Value
EP/V002910/1 30/09/2020 30/08/2021 £1,373,776
EP/V002910/2 Transfer EP/V002910/1 31/08/2021 29/09/2025 £1,175,699
 
Description Computational statistical machine learning methods can have a large impact on public policy. Due to the Covid-19 pandemic, the focus of the applied work was on epidemiological, statistical, and demographic modelling to understand the characteristics and spread of novel variants, the impact of non pharmaceutical interventions, and to quantify orphanhood. Underlying the applied studies, which had major policy impacts, were novel computational tools we developed, to enable flexible and scalable spatiotemporal statistical modelling.
Exploitation Route The methods that we developed form the basis for further funding proposals to charities and research councils. The applied work underpins very large-scale funding requests to support orphans and vulnerable children in low and middle-income countries. These requests are being considered by US government at this moment.
Sectors Education,Healthcare

 
Description Our Lancet publication, "Global minimum estimates of children affected by COVID-19-associated orphanhood and deaths of caregivers: a modelling study," Hillis et al (2021), for which I was the senior author, is a landmark paper in guiding public policy response towards children during and in the recovery from the Covid-19 pandemic. Hundreds of pieces appeared in the media covering our work. It was discussed within the Biden administration in Washington, the Vatican, by the World Health Organization and the World Bank. When the paper was published we also produced a policy report, "Children:The Hidden Pandemic 2021" with the US Centers for Disease Control and Prevention which was widely disseminated to country governments, charities, and international organizations. We also produced a web tool, the Imperial College Orphanhood Calculator (https://imperialcollegelondon.github.io/orphanhood_calculator) to provide up to date estimates.
First Year Of Impact 2021
Sector Education,Government, Democracy and Justice
Impact Types Societal,Policy & public services

 
Description The Global Reference Group on Children Affected by COVID-19
Geographic Reach Multiple continents/international 
Policy Influence Type Participation in a advisory committee
Impact The Global Reference Group on Children Affected by COVID-19 advises governments and charities on providing care and support for Covid-19 orphans and vulnerable children and their surviving family members.
URL https://www.spi.ox.ac.uk/the-global-reference-group-on-children-affected-by-covid-19
 
Description Updated CDC guidance
Geographic Reach National 
Policy Influence Type Citation in other policy documents
Impact CDC guidance on mask wearing during the Covid-19 pandemic changed twice in 2021. First, after the Alpha wave subsided, the CDC recommended that vaccinated individuals no longer needed to wear masks indoors. In the summer of 2021, as Delta surged, the CDC reversed course and recommended that masks be worn by all indoors. Our study, combining epidemiological analysis of Delta in India with lab studies, was cited as justifying these guidelines, in support of the finding that, "Fully vaccinated people with Delta variant breakthrough infections can spread the virus to others."
URL https://www.idsociety.org/globalassets/idsa/public-health/covid-19/cdc-renewing-our-efforts.pdf
 
Description Copenhagen/Oxford 
Organisation University of Copenhagen
Department Department of Public Health
Country Denmark 
Sector Academic/University 
PI Contribution I collaborate closely with two members of this department on a number of ongoing and completed research projects.
Collaborator Contribution Two members of this department collaborate closely with myself and my two postdocs on ongoing and completed research projects.
Impact Major publications: 1) Volz et al, "Assessing transmissibility of SARS-CoV-2 lineage B.1.1.7 in England," Nature 2021 2) Faria et al, "Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil," Science 2021 3) Mishra et al, "Changing composition of SARS-CoV-2 lineages and rise of Delta variant in England," EClinicalMedicine 2021. Multi-disciplinary: epidemiology, biostatistics, and computational statistics / machine learning
Start Year 2021
 
Description "At Least Two Million Children Have Lost a Parent or Grandparent Caregiver to COVID" 
Form Of Engagement Activity A press release, press conference or response to a media enquiry/interview
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Public/other audiences
Results and Impact Co-authored an editorial in Scientific American on Covid-19 orphanhood to coincide with publication of my senior-authored publication in the Lancet "Global minimum estimates of children affected by COVID-19-associated orphanhood and deaths of caregivers: a modelling study."
Year(s) Of Engagement Activity 2021
URL https://www.scientificamerican.com/article/at-least-two-million-children-have-lost-a-parent-or-grand...
 
Description "Orphanhood due to covid-19: data and the rights of the child" 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact Webinar for the "International Association of Family and Child Judges and Magistrates" on Covid-19 orphanhood
Year(s) Of Engagement Activity 2021
URL https://www.youtube.com/watch?v=3_z7I9D42iQ
 
Description "The million Covid orphans must not be left behind" 
Form Of Engagement Activity A press release, press conference or response to a media enquiry/interview
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Public/other audiences
Results and Impact Authored an editorial in the Financial Times on Covid-19 orphanhood to coincide with publication of my senior-authored publication in the Lancet "Global minimum estimates of children affected by COVID-19-associated orphanhood and deaths of caregivers: a modelling study."
Year(s) Of Engagement Activity 2021
URL https://www.ft.com/content/60981a11-135e-4771-a9e1-e97550ca401b