In an age of artificial intelligence and quantum computing, governments and businesses become more dependent on machine learning. Human learning is a continual requirement.
In such a processed data-rich environment, analytics are vital to understanding advanced threats. How can humans stay in control of machines as we solve complex problems? Visual analytics guided by key questions can provide common focus among analysts, planners, and operators.
This article introduces two tools to visualize data analysis, and applies them to numerical, video, and text data about COVID-19, a strain of the corona virus. Why combine analytics with the case method?
Visual analytics can complement case method inquiry of COVID-19 and other advanced threats in the information environment (IE). While the focus of the preceding paper was, how to analyze and future-cast conditions that you or your organization desires to change, our focus here is on visualizing linkages in a complex IE.
To do that, tools such as GraphXR and SavantX enable us to see what we otherwise would not. By visualizing our problem, we can consider details that matter and think holistically about systemic solutions.
We begin with a fundamental question, how do we discover what data and information are relevant to our problem?
Our challenge is to use what we know with how to learn, given the ongoing explosion of data and information. We can use analysis and analytics to learn what to know. What is the basic difference between these two terms?
Analysis is a process that breaks a whole into component parts. It’s an intellectual partner of synthesis, a cognitive activity that combines various pieces to form a coherent whole.
Analytics are methods of analysis to help us make decisions—such as how to make changes to achieve desired effects.
A good first step toward understanding an ambiguous IE is to step back and look. Try to see the whole mess, then break that down into clarifiable pieces. When machine-and-I rearrange those pieces later, we discover and sometimes create new relationships.
In our Information Environment Advanced Analysis Course, we refer to this thinking as decomposition, recomposition and synthesis, because we begin by deconstructing the IE into elemental components. We start by identifying major systems, sub-systems, objects, and attributes. All of the relevant systems and sub-systems are often not apparent and sometimes deliberately hidden. Therefore we will treat systems as “actors” too, which helps identify them via behaviors.
Here are nine case method questions that frame our problem-solving process, with our focus Question #4 in bold type:
1. Who are the major actors and systems?
2. What do the major actors and systems want, and what resources are available?
3. What are the actors’ and systems’ strategies to get what they want?
4. What are the linkages among the major actors and systems?
5. What are the patterns, trends and anomalies?
6. What are your goals and conditions to change, with respect to viral threats?
7. What incentives/capabilities do you want to influence to achieve goals/conditions?
8. What activities can you generate to influence incentives/capabilities?
9. What are your strengths/weaknesses compared to this threat and any competitors?
To get to our focus on linkages in this article, I offer partial answers to questions 1 through 3. The samples are necessarily limited to a few key international, China, and US examples. I have organized the answers such that each lettered sub-topic (a, b, c, d) under each question (1, 2, 3) correlates to one another.
For case method practitioners, imagine that we are in a physical or virtual seminar. You as the facilitator are orchestrating a learning process by asking questions that elicit answers from participants. Participants are doing the substantive thinking, and come up with the following answers.
1. Who are the major actors and systems? A variety of actors and interconnected systems are involved at global, state and community levels of interaction.
a. Health-related organizations: World Health Organization (WHO); China National Health Commission(NHC) and Center for Disease Control and Prevention (CDCP); US Centers for Disease Control (CDC); US Department of Health and Human Services (HHS); US National Institutes of Health; US Corona Virus Task Force.
b. Leading health professionals: WHO Director-General Tedros Ashanom Ghebreyesus; China NHC Minister Minister Ma Xiaowei; China CDCP leaders leaders George Gao, Li Xinhua, Liu Jianjun, Feng Zijian; US CDC Director Robert Redfield; US HHS Secretary Alex Azar, US NIH Director Francis Collins; and US Corona Virus Task Force members.
c. Heads of international entities threatened by outbreaks: United Nations Secretary-General Antonio Guterres; China Chairman Xi Jinping; US President Donald Trump.
d. State and non-state information agencies: China’s news is state-controlled by Xinhua News Agency, China Radio International, China Global Television Network, China Daily, and People’s Daily; US news is privately-owned and the top ten newspapers are The Washington Post, Tampa Bay Times,New York Post, Los Angeles Times, Seattle Times, Boston Globe, Denver Post, Wall Street Journal, and Chicago Tribune; US top ten news outlets are Yahoo, Google, Huffington Post, CNN, New York Times, Fox News, NBC News, Mail Online, Washington Post, and The Guardian. Approximately 90% of US news outlets involve six private corporations—AT&T, Comcast, Disney, 21st Century Fox, Viacom, and CBS.
2. What do the major actors and systems want, and what resources are available? Common and competing interests among the previously noted actors and systems include these corresponding samples.
a. Collaboration and transparency: WHO-China interactions reflect a balance. Director General Ghebreyesus praised Chairman Xi’s efforts to contain the virus, despite China’s systematic suppression of information. The international health system wants to integrate China’s resources into a global effort, an incentive that tolerates non-transparency.
b. Treatment and prevention: Health professionals operate under policy constraints to treat existing patients and prevent outbreaks. Chairman Xi quarantined entire cities, while President Trump banned and limited inbound travel from China. Both policies shaped a common strategy of treatment and prevention: containment.
c. Global awareness and national calm: political authorities attempt to maintain global awareness of threats while managing domestic calm. Chinese officials emphasize the latter to mitigate economic disruption and political unrest. US officials call for balance, but the simultaneous pursuit of both outcomes is politicized. When awareness leads to preparations (e.g., testing), the corresponding call for calm is portrayed as a mixed message.
d. Health-related and political agendas: health issues are prone to competing political priorities. Consider two US examples. Health officials discourage the public from buying protective masks so that medical authorities can distribute more to patients, even though some well-sealed masks can provide some protection. The initial low rate of person-to-person spread of this high virality-low fatality virus was a political talking point for national calm, even though local transmission has broader impact.
3. What are the actors’ and systems’ strategies to get what they want? Global, state, and community-level actors have different strategies.
a. Collaboration and transparency among health-related organizations: WHO strategy intends to increase collaboration by: (a) sharing details without domestic restrictions; and (b) fighting disinformation. China’s strategy seeks to contain COVID-19 by implementing mass quarantines, relying on non-ideological experts, and reasserting authoritarian controls. US strategy aims to contain and mitigate the virus with external travel restrictions and domestic quarantine bases, without triggering socio-economic disruption.
b. Treatment and prevention among leading health professionals: WHO strategy involves managing collaboration among government agencies, private companies, and research universities to inform practices and accelerate development of a vaccine. China’s strategy encompasses improving sovereign capabilities to contain, reduce and control viral threats with limited external assistance. US strategy is similar to that of the WHO, with Chinese characteristics: collaboration among scientists and building independent capabilities.
c. Global awareness and national calm among political authorities: WHO strategy pursues increased awareness by organizing the international expertise and assistance. China’s strategy emphasizes national calm and equates that with loyalty to Party directives. US strategy promotes global awareness through transparency, and national calm…somehow. How to achieve the latter is unclear as critiques and counterattacks attribute wrongful intent and attract polarizing disinformation.
d. Health-related and political agendas among health-related organizations, professionals and political authorities: the WHO agenda is an open book of international partnerships, cooperative research, and consensus-building among national and non-state agendas. China’s agenda is an opaque blend of secretive Party functionaries and problem-solving experts. The US agenda is a brew of overlapping responsibilities among federal, state and local governments, and research opportunities among academia and business.
4. What are the linkages among the major actors and ideas? This is the question that GraphXR and SavantX will help us answer in more detail.
GraphXR is a visual analytics platform used in domains ranging from counterterrorism to business intelligence. It bridges the workflows of data scientists and subject matter experts by enabling transformations, filtering, and algorithms to be performed on high dimensional and connected data in a browser-based graphical user interface.
SavantX is a HYDRA-based analytic that looks for relationships among people, places, things, and ideas in multiple geo-spatial dimensions: HYper-Dimensional Relationship Analysis. The program is a narrow AI machine learning platform that ingests unstructured data from videos with English captions and from any form of text.
GraphXR and SavantX can be used together to characterize the IE in complementary ways. In our following applications, we describe online applications of Graph XR and demonstrate SavantX.
The GraphXR application involves publicly available data on from The Johns Hopkins University’s Center for Systems Science and Engineering (CSSE).
This online tracker uses GraphXR’s dynamic modeling capabilities to present different perspectives on the CSSE’s COVID-19 data. Multiple instances of the GraphXR interface are embedded within the page alongside contextual information:
“Daily change in number” maps the locales of confirmed outbreaks to nodes. The number of new cases is indicated by node size, with each date represented as a separate property. Outbreak locations, magnitudes, and directions are immediately apparent. These vectors can be overlaid by transportation hubs and routes to get ahead of further coronavirus transmissions.
“Stats by region” maps the same dataset to a different model. Each node indicates the number of COVID-19 related deaths reported in a given location on a given date. Compared to the at-a-glance insight revealed by “daily change in number,” this model enables a time series playback for trend analysis. Different nations’ capabilities can be modeled as well, which is particularly useful in building out our answers to Framework Questions 1 through 3 above.
For instance, China’s extensive surveillance at the local level may be able to anticipate vectors that are driving community spread. While we cannot do this in the US due to Constitutional protections of civil liberties, knowing what other actors can do is important to our strategy.
Based on publicly available information, trends in the “new epicenters” of outbreak can be displayed to indicate viral growth rates and death rates. Iran’s death rate, depicted by the size of each node below, is currently the world’s highest at 7.6%. This compares to 3.6% in China, 2.6% in Italy, and .5% in South Korea:
Exploratory analysis of the same model from multiple perspectives can reveal anomalies and underlying issues in the data’s reliability. For instance, the “death per 100 cases” score could be understood to reflect a higher mortality rate of COVID-19 in certain countries. However, given the coronavirus’ mechanisms for transmission and lethality are not influenced by national boundaries, it is unlikely that COVID-19 is deadlier in the US and Iran. More probably, detection efforts have been less successful in these countries, inflating the apparent ratio of deaths to reported cases.
In this application, all data is drawn from a single source. GraphXR’s dynamic modeling also makes it possible to ingest and compare data from multiple sources, including databases and live streams. This enables subject matter experts to create bioinformatics on COVID-19 as needed, without relying on technical users to perform ETL. Correlations among symptoms and diagnoses across communities and regions can inform decisions about where to place resources and expertise.
The SavantX demonstration involves us making decisions about machine-processed relationships from a YouTube video that we chose, “Experts discuss COVID-19 at Johns Hopkins Carey Business School.”
We conducted three approaches to influencing a target, what we call a target of influence (TOI): singular search, leverage search, and radial search.
The application provides a description of each search type and goal, followed by a brief description of thinking with and using of SavantX results.
The first three basic relationships displayed were among the nodes named virus, risk and countries. We increased the fidelity of the program, which proliferated the number of nodes shown to over 60. Of these We noted risk, testing, and severe illness. We were interested in relationships between risk and testing, and risk and severe illness, for three reasons. First, we need to manage risk. Second, testing, is something we can influence. Third, we should prevent severe illness.
So we selected those three nodes, which yielded the following text-based information that explained the relationships.
With respect to risk and testing, testing is done only on people who seek treatment. This led to a recommendation that high-risk individuals ought to be tested. With respect to risk and severe illness, the need to develop a vaccine for individuals with higher risks of developing severe illness led to that being considered as a priority.
The usefulness of this singular search approach is that we are able to:
Targets, therefore, can be conditions in the IE, not just actors.
Having selected our targets to influence — risk, testing, and severe illness — we again increased the fidelity of the program. We noted infected was a node, which drew our attention because we reasoned that it adversely influenced risk, testing, and those with severe illness.
Selecting infected yielded the following text information: the majority of people infected have cold-like symptoms that would not suggest COVID-19, and would not even lead those people to seek medical attention. Therefore we need to test people with a connection to the origin of the virus (China). One person in Singapore infected 11 others.
The usefulness of this leverage search is that we could focus on:
We applied this logic to our three nodes of interest — risk, testing, and severe illness — which again generated dozens of other nodes…potential relationships. None of these, when selected, yielded common relationships with risk, testing, and severe illness. The unproductive nodes were named provider, surveillance, ill, virus, cases, and influenza. At this point, we needed to find new linkages.
So we deselected severe illness and selected symptoms instead, increasing the fidelity of the program to see if there were four-node relationships. The nodes that showed up were named infected, viruses, ill, and potentially.
Of these, the only node that yielded a relationship was infected, which contained the following text information:
Viruses that cause non-specific symptoms make it difficult to identify COVID-19 unless people seek medical attention or have other risk factors that would cause their medical providers (assuming they have one) to contact them.
The usefulness of this radial search was that it revealed the limits of relying on some information:
We also know with reasonable certainty that the risks of CoV-2 include:
Having identified linkages, the next step in our problem-solving process is to find patterns, trends and anomalies (Framework Question #5). Depending on the scope of your interests, you may be interested in different characteristics of this complex IE. Infectious disease researchers, for instance, have identified a COVID-19 pattern in patients’ CT-scans that confirms diagnosis of the virus.
While the length of this article precludes going through the remaining steps, here is a brief description. As the COVID-19 virus runs its course, tools such as GraphXR and SavantX can help identify behavioral patterns and longer term trends in displayed data and linkages, from which potential anomalies can be identified and explored (Step 5). From there, we can develop actors’ goals and desired changes in conditions (Step 6), incentives and capabilities to influence (Step 7), activities to generate (Step 8), and strengths and weaknesses among the now-specified strategies (Step 9).
Based on the preceding linkage analysis, we offer two recommendations specific to COVID-19.
First, COVID-19 is an actor itself, whose behavioral characteristics need to be fully understood. Researchers are collaborating in this global effort to streamline the trials of vaccine development. Defense Support to Civil Authority is also being proactive in US Northern Command’s mission that prepares plans to contain the disease. Given what we know about how COVID-19 behaves, treating the virus as an insurgent has its merits. Countering this threat requires good governance and legitimacy. This relates to our next conclusion.
Second, the US Corona Virus Task Force should influence the conditions that empower COVID-19’s effects. A communications campaign should convey to the public what we know and do not know. We know that the virus is highly infectious, more fatal as the common flu, and may be accompanied by common cold-type symptoms. The virus can spread very quickly via uncertain contacts, despite efforts to contain further outbreaks. Therefore self-quarantine, seeking medical attention, and communicating risk factors to care providers, are vitally important. Risk factors include vulnerable immune systems, upper respiratory issues, obesity, and diabetes.
With respect to our increasing dependence on machine learning, we offer three recommendations.
Be a Human in Charge: of machine-learning. Ask different types of analytic questions within and beyond the machine’s data set. Inquiry should include retroactive cause-and-effect questions based on historical data (machines excel at this), and proactive questions with future possibilities and probabilities in mind (humans do this).
Fit and Refute: the analytics’ results. What are assumptions, evidence, and logic that fit the results, AND, what are assumptions, evidence and logic that would refute the results? These help generate hard questions to ask of responsible organizations and individuals.
Scrutinize constructs: of thinking. Constructs include any framework at any level of responsibility or technology that structures thinking. How a video, speech, or reading is organized may not be readily apparent without the assistance of machine learning that can break it down. Narrow AI can also process simulations and exercises; a way to future-cast desired changes in conditions.
Visualized data guided by human inquiry can integrate the efforts of analysts, planners and operators in any enterprise. This effort is one of continual education made relevant by mission-oriented leadership.