Spatiotemporal statistical machine learning (ST-SML): theory, methods, and applications
Lead Research Organisation:
University of Oxford
Department Name: Computer Science
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.
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.
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.
People |
ORCID iD |
Seth Flaxman (Principal Investigator / Fellow) |
Publications
Ball J
(2022)
Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation
in Methods in Ecology and Evolution
Bennett JE
(2023)
Changes in life expectancy and house prices in London from 2002 to 2019: hyper-resolution spatiotemporal analysis of death registration and real estate data.
in The Lancet regional health. Europe
Boland MA
(2021)
Improving axial resolution in Structured Illumination Microscopy using deep learning.
in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Bradley VC
(2021)
Unrepresentative big surveys significantly overestimated US vaccine uptake.
in Nature
Brizzi A
(2022)
Spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals.
in Nature medicine
Charles G
(2023)
Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference
in Proceedings of the AAAI Conference on Artificial Intelligence
Cluver L
(2023)
Reauthorise PEPFAR to prevent death, orphanhood, and suffering for millions of children
in The Lancet
Dhar MS
(2021)
Genomic characterization and epidemiology of an emerging SARS-CoV-2 variant in Delhi, India.
in Science (New York, N.Y.)
Description | Computational statistical machine learning methods can have a large impact on public policy. In the context of 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 pandemic-associated orphanhood. Underlying the applied studies, which had major policy impacts, were novel computational tools we developed, to enable flexible and scalable spatiotemporal statistical modelling. These tools combined statistical approaches to quantifying uncertainty called Bayesian inference with the most exciting area of Artificial Intelligence (which underlies ChatGPT and DALL-E): deep generative 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 currently being considered by international partners and country governments. |
Sectors | Healthcare Government Democracy and Justice |
URL | https://www.cdc.gov/globalhealth/covid-19/orphanhood/index.html |
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. Subsequent studies building on this foundational work have appeared in Lancet Child & Adolescent Health, Pediatrics, and JAMA Pediatrics. |
First Year Of Impact | 2021 |
Sector | Healthcare,Government, Democracy and Justice |
Impact Types | Societal Policy & public services |
Description | Citation in FDA presentation |
Geographic Reach | North America |
Policy Influence Type | Citation in other policy documents |
Impact | VRBPAC voted to approve new childhood vaccines for COVID-19 in 2021. Our work was cited in the presentation on epidemiology. |
URL | https://www.fda.gov/media/159222/download |
Description | The Global Reference Group on Children Affected by COVID-19 |
Geographic Reach | Multiple continents/international |
Policy Influence Type | Participation in a guidance/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 | 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 | US CDC/Oxford |
Organisation | Centers for Disease Control and Prevention (CDC) |
Country | United States |
Sector | Public |
PI Contribution | We have worked closely with the US Centers for Disease Control and Prevention on estimating the global burden of pandemic-associated orphanhood. |
Collaborator Contribution | CDC colleagues have been an integral part of our research collaboration and have provided data for our US analyses. |
Impact | See Hillis et al, Lancet 2021, Hillis et al, JAMA Pediatrics 2022, and Flaxman et al, Science 2023 |
Start Year | 2020 |
Description | WFP/Oxford |
Organisation | World Food Programme (Italy, Sudan, Senegal) |
Country | Italy |
Sector | Charity/Non Profit |
PI Contribution | We are working with the World Food Programme to improve the representativeness of their Hungermap estimates of food insecurity (https://hungermap.wfp.org/) |
Collaborator Contribution | WFP is providing us with ongoing access to individual-level food security data from their mobile phone survey programme in Zimbabwe. |
Impact | Papers are in preparation |
Start Year | 2019 |
Description | WHO/Oxford |
Organisation | World Health Organization (WHO) |
Country | Global |
Sector | Public |
PI Contribution | WHO has funded our work on estimating global orphanhood |
Collaborator Contribution | WHO has provided age-disaggregated estimates of excess mortality, which we rely on to estimate global orphanhood |
Impact | Our papers are currently in preparation. |
Start Year | 2022 |
Description | "Covid-19 is leaving millions of orphaned children 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 | Media (as a channel to the public) |
Results and Impact | I was a guest on the STAT News podcast _First Opinion_: "Covid-19 is leaving millions of orphaned children behind". |
Year(s) Of Engagement Activity | 2022 |
URL | https://www.statnews.com/2022/06/01/millions-orphans-covid-is-leaving-behind/ |