June 18, 2020
AI is an important tool to support public health experts around the world in their efforts to keep people safe and informed amid the coronavirus pandemic. Facebook AI is partnering with academic researchers and other experts on a range of initiatives related to COVID-19. We are sharing overviews of several of these now, and we will add updates and more information in the days and weeks to come.
More information is available here about Facebook’s broader efforts related to this pandemic.
In collaboration with the Faculty of Mathematics and the Data Science research platform at the University of Vienna, we are using AI to generate district-level projections of where and how quickly COVID-19 is spreading in Austria. These sets of local predictions could help authorities and health-care providers better understand how the pandemic is evolving as some areas begin to ease restrictions and local conditions and regulations change.
We use public data shared by the Austrian government about confirmed COVID-19 cases and then generate weekly seven-day forecasts. To build adaptive models that can respond to rapid changes in each given area, we leverage a variety of techniques, including multivariate Hawkes processes, deep relational autoregression, and neural jump stochastic differential equations. All our models account for relationships between different districts, so for example an uptick in one area could impact predictions for adjacent districts. We provide these projections to our partners at the University of Vienna, who use this information to analyze trends and then share results with health officials.
This initiative builds upon our localized COVID-19 forecasting work in the United States for New York and New Jersey. These forecasts can inform planning decisions for allocating resources such as ventilators and masks, as well as forecasting ICU demand. In the future, we may evaluate other sources of data, like mobility maps from Facebook’s Data for Good team, to see whether they help improve the model’s performance.
In late March, we launched the Community Help hub for COVID-19 as a place for people to request or offer help to neighbors. Posts can be created directly on the hub, and we are using natural language processing (NLP) to help make the feature more visible so that more people can receive support or provide support to others. When our model detects a request to get help or an intent to provide help in a public News Feed post, we surface a suggestion to publish it on Community Help so it can reach more people. We’ve internationalized this model using our XLM pretraining method to support more than a dozen languages to start: English, Korean, Japanese, Turkish, Dutch, Swedish, French, Spanish, German, Thai, Portuguese, Arabic, Urdu, Russian, Chinese, Vietnamese, Hindi, Filipino, and Indonesian. We will continue adding more languages. Fifty percent of posts on the hub are coming from this NLP model today. We use similar NLP and intent detection technology to power our Blood Donations feature, where it helps connect donors to people who need blood, and in our Charitable Giving feature, where it suggests adding a “donate” button to posts seeking to raise funds for a particular nonprofit.
Separately, we have joined the Translation Initiative for COVID-19 (TICO-19), a consortium that aims to help enable the translation about the virus in a wide range of languages, including very low-resource languages. Engineers, researchers, and translation managers from a wide range of institutions, including Translators without Borders, Carnegie Mellon University, Johns Hopkins University, Appen, Google, and Translated are contributing to TICO-19.
Facebook AI’s role is to provide translations of specialized terms and phrases related to the pandemic so that professional translators like those with Translators without Borders have dictionaries and reference tools to expedite their work and ensure consistency and accuracy. Together with our industry partners, we are also contributing professional translations of a curated data set (about 68K words) related to COVID-19 and other medical terminology, which will serve as a benchmark for researchers and help them build specialized, state of the art translation tools that can be quickly deployed when future crises occur. The set will include 37 languages, and we hope this will be helpful to advance the state-of-the-art in low-resource languages such as Dari, Dinka, Hausa, Luganda, Pashto, and Zulu.
April 20, 2020 — Facebook AI has partnered with New York University’s Courant Institute of Mathematical Sciences to create localized forecasting models of the spread of COVID-19. These local predictions can help health-care providers and emergency responders in a specific county determine how best to allocate their resources (for example, deciding when to adjust a clinic’s staffing schedule to prepare for an expected increase in patients). It is challenging to create forecasts at the county level because the patterns in the data are complex and rapidly evolving. But AI is well suited for this challenge. Facebook AI researchers are using publicly available data published by the State of New Jersey and applying Multivariate Hawkes Processes to create daily COVID-19 predictions for the state. Our colleagues at NYU leverage this information in their models to estimate how progression of the disease will affect hospitals, bed and ICU capacity, and local demand for ventilators, masks, and other PPE needs at a hospital and county level. This information is collectively being shared on a daily basis with the State of New Jersey. Similarly, we have started a collaboration with Cornell University using public data published by the State of New York to model the predicted spread of coronavirus in New York, and we are working with other academic experts to scale these techniques.
We are also collaborating with NYU Langone Health’s Predictive Analytics Unit and Department of Radiology to build hospital-specific forecasts for COVID-19, using reinforcement learning, causal modeling, and supervised/self-supervised learning techniques. These models, which learn from de-identified X-rays and CT scans, as well as other de-identified and aggregated clinical data shared with Facebook in accordance with HIPAA, will help experts better allocate resources for clinical needs and optimize workflow across local hospital systems. For example, using these models, they can predict the number of patients whose condition is likely to improve or worsen in a given time period; how many people are likely to be admitted, transferred to ICUs, or discharged; and the number of ventilators, types of tests, and treatments that might be needed. Facebook AI is neither making nor recommending diagnoses for individual patients.
Similarly, we are partnering with the Mila research institute in Montreal to share predictive, causal, and decision algorithms for analyzing clinical data. No data is being shared in this collaboration, but the project will enable Mila to help hospitals in Montreal use their own patient data to better forecast what resources they will need to treat people with COVID-19.
With these joint efforts with NYU Langone and Mila, our immediate focus is on developing models that can learn from de-identified clinical data and help hospitals determine how to use their resources most effectively. As we refine and build on these techniques, we would like to explore ways to quickly scale the benefits to other organizations. This could include open-sourcing code so that other institutions can train models on their own data.
It’s crucial that public health experts understand the spread of the coronavirus and how best to deploy their resources to help people with COVID-19. We are building on the work described here and looking for more ways to use AI to help address this global crisis.
This blog post was updated on June 18. We will continue sharing information here on Facebook AI’s work related to COVID-19.