By: Anika Nambisan
Image Credit: Pixabay
As the world battles against COVID-19 and countries are reopening the economy in phases, we need to focus on data science to balance public versus economic health. In the U.S., various state governors lay out different roadmaps to restarting the economy safely. According to medical experts, it will entail at least 20 million COVID-19 tests a day, robust contact tracing and quarantine measures. Ultimately, the development of a vaccine, antibody testing and other therapeutics will be the key to flattening the curve and maybe even ending the pandemic. In the meantime, data scientists have built data analytics and prediction models to help forecast new coronavirus hot spots, hospital capacity, ICU beds, ventilators and PPE (Personal Protective Equipment). With better data, state governments can determine where to send these resources, which counties are safe to start opening and when to return to restriction in the event of a surge in virus cases.
Computer Algorithm Sounded The Alarm
60 Minutes reported that on “New Year's Eve, a small company in Canada, BlueDot was among the first to raise the alarm about an infectious disease outbreak” using its computer algorithm. BlueDot’s algorithm powered by artificial intelligence was crunching through tons of data including medical, livestock reports, cell phone data, ticket data from 4,000 airports to predict where the virus will spread next. California Governor, Gavin Newsom, in his daily briefing made no secret that he believes in outbreak science to forecast in real time “on a daily basis, hourly basis, moment-by-moment basis if necessary, whether or not our stay-at-home orders were working. We can truly track now by census tract, not just by county.” “Data became California’s all seeing crystal ball,” as it leveraged the help of BlueDot, Esri, Facebook and others, using mapping technologies, cell phone data to predict the next hotspots and develop risk heat maps. California’s early action to mandate shelter-in-place may have saved 1,600 lives in the first month according to researchers.
Crowdsourced Symptom Data Predicts Next COVID Hotspots
While gathering information from each city in the entire nation may seem onerous, Facebook and Google have partnered with Carnegie Mellon University (CMU) to create a COVID-19 Symptom Map. There are roughly 2 billion Facebook users worldwide. Basic surveys created by researchers at CMU on coronavirus symptoms are being pushed out to Facebook’s users to voluntarily participate. To protect users’ privacy, Facebook does not share the results; in fact, participants leave Facebook's website to take the survey. To ensure anonymity, a random ID number along with a statistical weight value to correct for any sample bias are assigned to measure participation in different communities. The COVID-19 symptoms data collected by CMU will be aggregated to help predict potential coronavirus spread by county and hospital region. According to Tibshirani, co-leader of CMU’s Delphi Research Group, “This data has the potential to be extremely valuable for forecasts, because a spike in symptomatic infections might be indicative of a spike in hospitalizations to come."
Facebook has also partnered with University of Maryland to expand its survey globally and the CMU research team to develop an application programming interface so that any researchers can access the data anytime, anywhere to make informed decisions to combat COVID-19. Google has also joined forces with CMU’s research efforts by partnering with CMU to collect a one-question survey for COVID-19 through its Opinion Rewards and AdMob apps.
Turning Data into Insights
Early symptom detection can be a sign of whether or not the curve is flattening, as to where the outbreak may be spreading, and when the next wave of the virus may hit. The results from assays and online surveys can be used to predict where the virus will spread, and provide valuable insights on medical resources allocation.
Due to the limited COVID-19 testing kits, CDC and tech companies are partnering to launch self-screening tools. Apple has released an app that offers free COVID-19 screening in terms of symptom analysis, age, travel history, pre existing conditions to determine appropriate next steps: self isolate, eligible for testing, or call 911 for emergency medical services. Verily, an Alphabet company, has launched a “Project Baseline” online screener to triage people who may have a high risk of exposure for testing based on public health guidelines. Project Baseline is an initiative that engages people and scientists to uncover more medical insights and develop new health products and services. According to Project Baseline COVID-19 privacy policy, the personal health data collected will be used for a variety of purposes, including commercial product research and development. This raises privacy concerns: one medical researcher is questioning if the Google COVID-19 site is a “data mining operation”.
Studying the Spread of COVID-19 - Mobility Data
In an effort to fight COVID-19 spread, Facebook and Google track our GPS location data and share it with public health researchers and local governments to help make informed decisions on social distancing measures and travel policy. Scientists believe that social distancing can be effective in mitigating the COVID spread. In response to the COVID-19 pandemic, Facebook’s Data For Good COVID-19 program provides a number of tools such as Disease Prevention Maps, Mobility Datasets and Social Connectedness Index to help health researchers and policymakers address virus outbreak. As the virus spreads mainly from person to person, the public has resorted to physical distancing to slow down the rate of transmission in the absence of a vaccine and effective therapeutics treatment.
Facebook’s Data For Good mobility tools are helping public health professionals track and monitor to see if social distancing is being practiced. For example, the data observed low mobility in the San Francisco bay area and high mobility in places like Riverside and San Bernardino. This may be attributed to the Silicon Valley tech industry that allows tech workers to work from home remotely. In contrast, San Bernardino and Riverside have a different socioeconomic demographics with a predominately blue-collar workforce where remote working and shelter in place is not possible.
Likewise, Google’s free mobility tracking tool provides insight on how the public move around the community due to COVID-19, to do less visits to groceries, pharmacy, and retail and more park visits. Google believes that the mobility reports could help shape “recommendations on business hours or inform delivery service offerings, or add additional buses or trains in order to allow people who need to travel room to spread out for social distancing.”
Facebook’s Social Connectedness Index “shows friendships across states and countries, which can help epidemiologists forecast the likelihood of disease spread, as well as where areas hardest hit by COVID-19 might seek support.” Similarly, researchers in Facebook utilize colocation maps to “...reveal the probability that people in one area will come in contact with people in another, helping illuminate where COVID-19 cases may appear next.” This will help scientists track people from an area with a large outbreak and are likely to come in contact with a person from an area with less cases.
With the recent protests against police brutality, the COVID-19 Mobility Data Network may shed some light on whether such gatherings will cause a spike in COVID-19 cases. However, this remains to be seen and the verdict is still out on whether there is no correlation between the protests and the spread of the virus, or protest gatherings were responsible for a spike in coronavirus cases in the wake of Memorial Holiday.
Using Artificial Intelligence To Help Health Experts
Facebook AI has partnered with academic experts to improve COVID-19 forecasting tools for resource planning and allocation for health care providers and emergency responders. For example, using publicly available data and applying Multivariate Hawkes Processes, Facebook AI researchers can create daily COVID-19 forecasting models for the state of New Jersey that will help New York University “...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.”
In addition, Facebook AI is “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.” Using machine learning to learn from patients’ data such as de-identified X-rays and CT scans, will help health experts better “...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.”
COVID-19 and Income Inequality
Using Facebook’s near-real-time mobility tracking data, researchers in Italy are observing the lockdown measures and how they may be correlated to income inequality. The study found that “lockdown measures had the biggest impact on people's mobility in towns with higher financial performance… but also in municipalities with high income inequality and low income per capita, suggesting that the lockdown might exacerbate poverty and income inequality in the absence of targeted fiscal interventions.”
REAL-TIME Data is King, But Not All Data Is Created Equal
With the advent of data analytics comes insight and smart decisions. Real-time big data is the new gold in fighting the COVID battle. However, not all COVID-19 data is created equal; misinformation also spreads as fast as the virus itself. According to CMU researchers, “Nearly half of the Twitter accounts spreading messages on the social media platform about the coronavirus pandemic are likely bots.” While researchers are using machine learning and artificial intelligence to do contact tracing or to find a cure, everyone of us can do our part in fighting COVID-19. We can help bend the COVID curve by taking socially responsible measures including physical distancing, mask-wearing, hand washing, contributing to the COVID Symptom Tracker and embracing outbreak science. But most importantly, we need to stay healthy and keep others healthy. We are all in this battle together.
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