Spatiotemporal statistical machine learning (ST-SML): theory, methods, and applications
Lead Research Organisation:
Imperial College London
Department Name: 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.
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.
Organisations
- Imperial College London (Lead Research Organisation)
- University of Copenhagen (Collaboration)
- Centers for Disease Control and Prevention (CDC) (Collaboration)
- Columbia University (Project Partner)
- World Food Programme (Project Partner)
- Ames Research Center (Project Partner)
- University of Oxford (Fellow)
People |
ORCID iD |
Seth Flaxman (Principal Investigator / Fellow) |
Publications
Wolock TM
(2021)
Evaluating distributional regression strategies for modelling self-reported sexual age-mixing.
in eLife
Volz E
(2021)
Assessing transmissibility of SARS-CoV-2 lineage B.1.1.7 in England.
in Nature
Vollmer MAC
(2021)
A unified machine learning approach to time series forecasting applied to demand at emergency departments.
in BMC emergency medicine
Vollmer MAC
(2021)
The impact of the COVID-19 pandemic on patterns of attendance at emergency departments in two large London hospitals: an observational study.
in BMC health services research
Unwin HJT
(2020)
State-level tracking of COVID-19 in the United States.
in Nature communications
Unwin HJT
(2021)
Using Hawkes Processes to model imported and local malaria cases in near-elimination settings.
in PLoS computational biology
Unwin HJT
(2022)
Global, regional, and national minimum estimates of children affected by COVID-19-associated orphanhood and caregiver death, by age and family circumstance up to Oct 31, 2021: an updated modelling study.
in The Lancet. Child & adolescent health
Suel E
(2021)
Multimodal deep learning from satellite and street-level imagery for measuring income, overcrowding, and environmental deprivation in urban areas.
in Remote sensing of environment
Smith T
(2021)
Temperature and population density influence SARS-CoV-2 transmission in the absence of nonpharmaceutical interventions
in Proceedings of the National Academy of Sciences
Sharma M
(2021)
Understanding the effectiveness of government interventions against the resurgence of COVID-19 in Europe.
in Nature communications
Semenova E
(2022)
PriorVAE: encoding spatial priors with variational autoencoders for small-area estimation.
in Journal of the Royal Society, Interface
Scott L
(2021)
Track Omicron's spread with molecular data.
in Science (New York, N.Y.)
Ratmann O
(2021)
Implications of a highly transmissible variant of SARS-CoV-2 for children.
in Archives of disease in childhood
Rashid T
(2021)
Life expectancy and risk of death in 6791 communities in England from 2002 to 2019: high-resolution spatiotemporal analysis of civil registration data.
in The Lancet. Public health
Monod M
(2021)
Age groups that sustain resurging COVID-19 epidemics in the United States.
in Science (New York, N.Y.)
Mohler G
(2021)
A modified two-process Knox test for investigating the relationship between law enforcement opioid seizures and overdoses
in Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
Mlcochova P
(2021)
SARS-CoV-2 B.1.617.2 Delta variant replication and immune evasion.
in Nature
Mishra S
(2021)
Changing composition of SARS-CoV-2 lineages and rise of Delta variant in England.
in EClinicalMedicine
Mishra S
(2022)
A COVID-19 Model for Local Authorities of the United Kingdom
in Journal of the Royal Statistical Society Series A: Statistics in Society
Mishra S
(2021)
Comparing the responses of the UK, Sweden and Denmark to COVID-19 using counterfactual modelling
in Scientific Reports
Meyerowitz-Katz G
(2021)
Is the cure really worse than the disease? The health impacts of lockdowns during COVID-19
in BMJ Global Health
Meng B
(2022)
Altered TMPRSS2 usage by SARS-CoV-2 Omicron impacts infectivity and fusogenicity.
in Nature
Lightley J
(2022)
Robust deep learning optical autofocus system applied to automated multiwell plate single molecule localization microscopy.
in Journal of microscopy
Krawczyk K
(2021)
Quantifying Online News Media Coverage of the COVID-19 Pandemic: Text Mining Study and Resource
in Journal of Medical Internet Research
Holbrook AJ
(2021)
Scalable Bayesian inference for self-excitatory stochastic processes applied to big American gunfire data.
in Statistics and computing
Hillis SD
(2021)
COVID-19-Associated Orphanhood and Caregiver Death in the United States.
in Pediatrics
Hillis SD
(2021)
Global minimum estimates of children affected by COVID-19-associated orphanhood and deaths of caregivers: a modelling study.
in Lancet (London, England)
Gurdasani D
(2021)
Vaccinating adolescents against SARS-CoV-2 in England: a risk-benefit analysis.
in Journal of the Royal Society of Medicine
Faria NR
(2021)
Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil.
in Science (New York, N.Y.)
Dhar MS
(2021)
Genomic characterization and epidemiology of an emerging SARS-CoV-2 variant in Delhi, India.
in Science (New York, N.Y.)
Brizzi A
(2022)
Spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals.
in Nature medicine
Brito A
(2022)
Global disparities in SARS-CoV-2 genomic surveillance
in Nature Communications
Bradley VC
(2021)
Unrepresentative big surveys significantly overestimated US vaccine uptake.
in Nature
Boland MA
(2021)
Improving axial resolution in Structured Illumination Microscopy using deep learning.
in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Ball J
(2022)
Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation
in Methods in Ecology and Evolution
Altman G
(2022)
A dataset of non-pharmaceutical interventions on SARS-CoV-2 in Europe.
in Scientific data
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 | 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 | 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 | 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 | "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 | "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/ |
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 |