The number of confirmed COVID-19 cases in the United States today is now well over 500,000 (with the true number probably 10 times higher or more due to lack of access to testing in some areas) with well over 20,000 deaths (as of April 10th). The overall performance of the US leadership and health system during this pandemic has been fragmented and dysfunctional. There was the perfect storm of failures in public health: denial of the seriousness and urgency of the pandemic; lack of large-scale testing at early stages (including a faulty start); and lack of unified and effective containment and mitigation strategies. This situation was made even worse for three vulnerable populations: the uninsured, the illegal immigrants, and those without paid sick leave as these subgroups all lack desire to be tested for the virus (positive test has potentially dire consequences). The imbroglio has made the leaders look overwhelmed and even visibly shaken (the US Surgeon General announced at one time with a frightened countenance: “This week is going to get bad” as his public statement).
Despite a prior pandemic (H1N1) that started on US soil a decade ago, the US was utterly unprepared and is near chaos. With relative conservative estimates based on an R0 of 2, an infection rate of 25-50% (depending on how much the epidemic curve is “flattened” by relatively weak and inconsistent mitigation and suppression measures), and a mortality rate of 0.5-1.0% (assuming adequate hospital staffing, space, and supplies), there is a good possibility for US to have a lower range of 100,000-250,000 to a higher range of 2 million or more deaths in the US (potentially more deaths than American lives lost during all the wars combined, including the Civil War). There is, however, the possibility that the virus may have a premature exit during the summer months and/or become attenuated due to unfavorable mutations (both characteristics of RNA viruses) so that the death toll can be significantly less than the aforementioned bleak numbers.
In short, the US is paying a very heavy price (economic and human) for an inadequate and underfunded public health infrastructure to deal with pandemics as well as excessive tolerance for the individualism ethos (that resulted in soft and ineffective interventional measures early on), and now playing an exceedingly difficult “catch up” compounded with fragmented leadership that has not engendered trust in the public. The hope is that the overloaded healthcare system with its dedicated staff can partly take on the upcoming burden and that the virus may dwindle on its own in the ensuing months to minimize the fatalities.
Part II. Global Health Lessons Learned
Three important global health lessons learned during this pandemic based on performance of the international community are discussed below with relevance to data science and artificial intelligence:
Lesson #1: Early mass testing for the infection and organization of database need to be accurate and complete for a successful containment strategy. Testing mainly in the form of polymerase chain reaction (PCR) for viral genetic material needs to be early and ubiquitous but many or most of the infected people are not tested in certain countries for SARS-CoV-2 (so it is exceedingly difficult to follow true number of new cases or to calculate the case fatality rate for this virus). Serological testing for anti-viral antibodies will be a part of recovery phase from the pandemic. Whether there will be a breakdown of the health care system or not depends on this crucial early data as it reflects a real-time status of the disease and its burden. This collected data ideally should be made available to the international community in real-time for analysis. As elaborated earlier, the earlier a country institutes these relatively heavy measures of isolation, the less number of people will get infected and therefore the less time that these measures will need to be in place. The more proactive one is about mass testing and the smarter one is about data and data organization, the less one needs interventional measures for longer periods of time (and therefore the lower your economic burden will be). The data on infected individuals will need to be coupled with public health measures to contain these individuals and trace others that may have been exposed.
Taiwan and Singapore executed aggressive case identification and surveillance coupled with smart use of data and databases to give real-time feedback to measure effectiveness of public health interventional strategies. Taiwan was exemplary in its proactive case identification approach in gathering data into a large database (combining native population with the visitor database) based on travel history (even supported by a QR code) and clinical status. This aggressive pursuit of accurate data included public health officials boarding planes traveling back from Wuhan back in December to examine patients. This pandemic data program was under the direction of Taiwan’s National Health Command Center and included an exhaustive list of 124 action items, including an aggressive tracing program (to identify all the people the infected person has come in contact with). A combination of aggressive testing and tracing has also decreased the initial exponential growth of the virus in South Korea after a super spreader infected many at an early stage. Counter to our American experts’ opinions, we need to test not just everyone but everyone serially to identify those who become infected after an initial negative test in order to contain absolutely everyone with the infection (an initial negative test does not give one immunity).
In short, we need to proactively test everyone and follow new cases during the early stage of disease with a robust database and couple this strategy with an early aggressive containment, mitigation, or suppressive strategy to minimize mortality and economic burden.
Lesson #2: Continual disease predictive modeling is essential for real-time accurate information to predict resource allocation and to minimize mortality. There has been excessive uncertainty and guesswork in projections during this pandemic, especially in the US; the best graphic the American experts can elaborate on is the now well-demonstrated one with two curves (see figure): a curve with no interventional measures that is a taller curve vs one with some measures which is a curve with lower amplitude (flattening of the first curve).
These curves, usually accompanied by a dotted line signifying hospital capacity, simply illustrate that mitigation (or more stringent measures) as an intervention can result in less mortality over a longer period of time, but there is usually no defined timeline on the x-axis nor number of people on the y-axis. Data science can be coupled, therefore, to global health crises so that there is much more certainty and less chaos (the latter promulgates public hysteria). Discrepancy between projected numbers and real on-the-ground numbers can be continually reconciled as there is a myriad of moving elements. Everyone should learn to appreciate that each hour or day can matter greatly as the growth of the number of infected people is not linear but exponential; each period of time, therefore, has immediate sequelae of many more cases and more deaths, and this bad situation is further compounded by insufficient healthcare resources (which leads then to unnecessary deaths and increased case fatality rate). These models have to accommodate many nuances such as geography and climate, population demographics, early herd immunity, number of travelers, healthcare resources, interventional measures and compliance, degree of clustering, etc; these factors will need a nonlinear approach and more modern techniques (including deep reinforcement learning) to analyze the data in a meaningful real-time fashion. A quick search into publications on the use of deep learning in COVID-19 pandemic yielded mainly publications on chest CT imaging rather than decision support.
If we project a 20% infection rate and a case fatality of 1% (both conservative estimates and assuming the health system is not overwhelmed), then China should have had 2.5 million fatalities; China is not even close to this number of fatalities (about 3,339 deaths to date) since an aggressive mitigation/suppression strategy was implemented as soon as containment failed. Of note, while the sight of Chinese citizens being forced into quarantine may seem unacceptable to Westerners, the sight of large groups of wanton American youths partying on the beach during a supposedly mitigation phase may be equally disturbing to some international public health observers. Several Asian countries like Taiwan and Singapore are particularly good at gathering healthcare data with modern technology and organizing this data into databases to effectively follow healthcare interventions with robust data analytics. These countries have learned hard lessons from the 2003 SARS pandemic (especially the epicenter of that pandemic, Hong Kong). In Western countries with ample expertise in data science and artificial intelligence, there is not only lack of sophisticated collection of data during a pandemic and insufficient tracing of infected individuals, but also no clear evidence that governmental agencies routinely implement more modern artificial intelligence methodologies during a health crisis. The Center for Systems Science and Engineering at Johns Hopkins and the Institute for Health Metrics and Evaluation (IHME) at University of Washington, however, have been productive and accurate.
In short, we need to leverage big data analytics and more sophisticated machine and deep learning for an accurate, real-time map of the pandemic to enable a more precise and individualized containment, mitigation or suppression measures with appropriate allocation of valuable resources to save most number of lives while not incurring an unnecessary economic burden.
Lesson #3: Therapeutic interventions, especially if public health measures have failed to have impact, need to be both innovative and expedient. The traditional timelines (in months and years) are no longer acceptable when the velocity of infection towards a pandemic is extremely fast (exponential) due to global connectedness; we therefore need therapies (vaccines or anti-viral medications) in hours and days. Randomized controlled trials (RCTs) with multiple phases are outdated and too time-consuming; these trials need to be accelerated in an exponential trajectory (to match that of our viral adversaries). Volunteers with full consent can be recruited to fulfill the need for safety trials that may be excessively lengthy with many lives lost to pandemics during that interim. Human cognition is still important to guide a therapeutic research program: in addition to vaccines and anti-viral agents, perhaps the observation that children seem to have much less morbidity and mortality can lead to good research questions. For example, is the lack of full maturity of the immune system or lung tissue or their recent vaccinations factors in their decreased disease burden?
Among the promising (but time-consuming) trials are the ones that involve a vaccine; the viruses (especially RNA viruses like SARS-CoV-2), however, can mutate frequently (SARS-CoV-2 has probably already done so several times) and the solutions such as vaccines or antibodies in recovered patients’ serum are rendered less effective after these viruses mutate enough times. In addition, existing anti-viral drugs can be called into action: remdesivir, a nucleotide analog antiviral drug initially used with Ebola, is already in a clinical trial. With the genomic map of the virus already online, this promulgated an international collaboration of scientists to explore therapeutic options to treat COVID-19 with various different approaches (such as attacking viral proteins or protecting host proteins) using some form of artificial intelligence and three-dimensional protein folding analytics and drug discovery.
In short, we need to disrupt the traditional approach of multiple phases of drug trials and bend the trajectory of these trials from linear to exponential while encouraging international and multidisciplinary open collaborative efforts in leveraging artificial intelligence in order to expediently save lives.
Part III. Future AI-Enabled Strategy for Epidemics
Let’s imagine our strategy against a fictional COVID-29 in the future and how artificial intelligence along with public health measures can be a tour de force dyad in the future management of pandemics:
A small novel coronavirus outbreak (SARS-CoV-7) is detected in southern France with clinical manifestation of bleeding and seizures with an R0 of 7.5 and a case fatality of greater than 50%. The AI-enabled MRI scans of the brain revealed an unusual pattern of brain inflammation and natural language processing as well as unsupervised learning (cluster analysis) are used to collect data on these patients. One-shot learning with transfer learning are deployed for ICUs around the world as an alert for these cases. In pursuit of an effective anticipation and containment strategy of the novel virus, mandatory daily testing at home (30 seconds for results) with wirelessly automated data entry immediately started for all of France and its surrounding countries.
A real-time epidemiological map is made publicly available with proactive approach for case identification and tracing of these individuals using devices for temperature monitoring (including infrared scans now required in all public areas and transportation hubs) and travel history with internet of things and everything (IoT and IoE). Public health measures are immediately implemented in the surrounding countries in a precise format using machine learning: some areas are in containment with individuals followed via their smart phones while other areas are in surveillance mode so businesses and schools are not disrupted in most surrounding regions. Drones with supplies are dispatched to people who reside in containment status.
Simulations of disease models using emulators (deep emulator network search, or DENSE) and AI are deployed to speed up simulations many times over of this small outbreak. The projected and confirmed numbers of new cases and case fatalities are reconciled using AI in the form of deep reinforcement learning to minimize the number of fatalities and take into account many changing nuances such as climate and demographics. Using crowd-sourced AI (including high school and college AI student championship teams as well as startups and NIH), and providing genomic sequencing and protein folding with structure predictions, the novel coronavirus and its complex quaternary biomolecular structure is successful delineated within 2 hours by this collective swarm intelligence and a list of top 10 anti-viral agents with highest benefit-risk ratios (using generative design algorithms) is collected within 24 hours for use in the critically-ill ICU patients. The candidate drugs are designed as well as repurposed and are immediately approved by the FDA, which had representatives as part of this process to facilitate the research. The patients and their pharmacogenomic profiles are delineated for therapy based on precision medicine and AI. In addition, a new vaccine is made available in 48 hours as there was already ongoing work on a universal coronavirus vaccine (following the success of the universal flu vaccine in 2025). This work is necessary as coronaviruses now mutate on an hourly basis.
After 2 months of this small outbreak, a total of 147 patients were infected with 25 deaths and AI (including training on synthetic data generated from generative models) is widely utilized in the management of these patients from a global database in the ICU and hospitals for the COVID-29 patients. The workers in the hospitals had access to AI-enabled 3D-printed equipment such as masks and gowns (without shortages of the past) and intelligent robots attended the COVID-29 patients while they were infectious on mechanical ventilation with weaning protocols utilizing fuzzy logic and deep reinforcement learning. A group review of COVID-29 at the international Biomedical Research and Intelligence Center (iBRAIN) and its Global Pandemic Prevention Task Force (collaborative international center formed after COVID-19 that claimed over 2 million lives, with WHO and CDC as well as representatives from 109 countries with a rotating directorship) include a discussion of the last pandemic of the current era, COVID-19, as a case history. No mitigation or suppression measures are necessary as surveillance and immediate containment with good individualized precision therapy obviated the need for such historic strategies.
Of note, some of the aforementioned technology is already available but we need to work diligently and relentlessly towards this idealized scenario to reduce the universality of suffering for generations to come. The quote from the venerable Dr. Anthony Fauci: “You don’t make the timeline, the virus makes the timeline” should be challenged this coming decade by our mankind taking control of the human vs virus eternal struggle.
In conclusion: To eradicate a pandemic, we need a proactive case identification and tracing strategy by serial mass screening coupled with sophisticated real-time data science-driven modeling as well as an innovative AI-centric therapeutic program. This overall philosophy will separate the infected individuals from the rest of the population while preserving both the hospital capacity to care for the sickest as well as the economy.
Viruses are the near perfect complex adaptive system (CAS) as these machine-like automata self-organize, pursue a common goal (finding a live host to replicate), and do this without a central leader. Albert Camus described epidemics (and even more so with pandemics) as “a shrewd, unflagging adversary; a skilled organizer, doing his work thoroughly and well.” Future viral pandemics (including a second wave of COVID-19 later this year and a possible third wave early next year) may very well be even more dangerous adversaries as these become even more contagious and lethal. We can surpass their capabilities with passion, inspiration, and creativity but we humans unfortunately also have greed, stubbornness, and hubris.
We do need, however, machines to arm us with artificial intelligence to combat these viruses. We need artificial intelligence to help guide us to execute an intervention that is effective and to devise novel therapies with a much shorter timeline. This AI-inspired strategy-outcome coupling using deep reinforcement learning as well as human swarm intelligence will minimize mortality while concomitantly preserving economy (akin to how ICU doctors titrate blood pressure and cardiac output with varying doses of combinations of inotropic medications). As Alan Turing so presciently stated: “One must design machines to fight machines”.
Just as we work towards synergy between clinical medicine and artificial intelligence, there also needs to be such a coupling between global health and artificial intelligence. COVID-19, the biggest pandemic since the 1918 Spanish flu, is the current generations’ world war. Going into battle with viruses without a sound public health strategy is like going to battle without armor, and going into war with viruses without artificial intelligence is akin to going to war without weapons; in both cases, the human toll is unacceptable.
Anthony Chang, MD, MBA, MPH, MS
Chief Intelligence and Innovation Officer
Medical Director, The Sharon Disney Lund Medical Intelligence and Innovation Institute (MI3)
Children’s Hospital of Orange County
To view the 5 part AIMed Talk series on Artificial Intelligence and Covid-19: How pandemics will be eradicated in the future presented by Dr. Anthony Chang click here.