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3 Ways COVID-19 Is Transforming Advanced Analytics and AI

3d vector illustration of virus and data cloud

While the impact of artificial intelligence on COVID-19 has been widely reported in the press, the influence of the coronavirus pandemic on AI has not received as much attention.

Three key impact areas (outlined below) have helped shape the use of AI in the past five months and will continue to transform advanced analytics and AI in the months and years to come.

The spread of the pandemic — first in China and South Korea and then in Europe and the United States — was swift and caught most governments, companies and citizens off guard.

This global health crisis developed into an economic crisis and a supply chain crisis within weeks (demand for toilet paper and paper towels alone rose by 600-750 percent during the week of March 8 in the U.S.). Fewer than 100,000 global confirmed cases in early March ballooned to more than 17 million in late July.

“With business leaders needing to act quickly, the crisis provided a chance for advanced analytics and AI-based techniques to augment decision making.”

The speed of decision making leads to agile data science

With business leaders needing to act quickly, the crisis provided a chance for advanced analytics and AI-based techniques to augment decision making.

While machine learning models were a natural assistance, development time for machine learning models or advanced analytical models typically clock in at four to eight weeks — and that’s after there is a clear understanding of the scope of the use case, as well as the necessary data to train, validate and test the models.

If you add use-case evaluation before model development — and model deployment after model training — you are looking at three to four months from initial conception to production deployment.

For solutions in days — not weeks or months — minimum viable AI models (MVAIM) required development in much shorter time frames. Using agile data science methodologies, PwC was able to compress these times significantly, building a SEIRD (Susceptible-Exposed-Infected-Recovered-Death) model of COVID-19 progression for all 50 U.S. states in one week.

We then tested, validated and deployed it in another week. Once we deployed this initial model, we extended it to all counties in the U.S. and made the model more sophisticated.

“Agent-based models are one of the best techniques to capture the time- and location-dependent variations in human behavior during the pandemic.”

Uncertainty about the future leads to multi-agent simulations

Uncertainty touched every aspect of life under COVID-19 — from health to behavior to economic impact — and expedited the increased adoption of advanced analytics and AI techniques.

Uncertainty feeds emotional reactions such as fear, anger and frustration, and such emotionally driven behavior took precedence over rational decisions and actions, especially in the early days of the pandemic.

Uncertainty along the different dimensions made scenario planning the dominant framework for evaluating plans and decisions. Scenario analysis became the predominant paradigm for evaluating disease progression, economic downturn and recovery (V-, U-, L, W-shaped economic recoveries), as well as for management decision making on site openings, contingency planning, demand sensing, supply chain disruptions and workforce planning.

While qualitative scenario analysis is quite common in the business world, using AI-based simulations to quantitatively understand the causal linkages of different drivers and develop contingent plans of action became even more prevalent.

Modeling human behavior (rational and emotional) became an important aspect of the scenario analysis. For example, compliance to the stay-at-home orders was one of the primary behavioral drivers to help contain the spread of the disease, as well as economic activity.

As a result, agent-based modeling and simulation was one of the primary advanced analytics and AI techniques used to perform scenario analysis. Daily mobility data on miles driven within each zip code in the country became a proxy for the effectiveness of the stay-at-home orders.

The same data helped model the mobility behavior of people in different parts of U.S. as the pandemic progressed. Agent-based models are one of the best techniques to capture the time- and location-dependent variations in human behavior during the pandemic.

System dynamic modeling, another well-known modeling technique, was critical in integrating multiple decision-making domains (COVID-19 disease progression, government interventions, people behavior, demand sensing, supply disruptions). The Centers for Disease Control and Prevention (CDC) has used agent-based simulation to model disease progression and health behaviors. Both methods proved successful in a number of uncertain scenarios to help make strategic and operational management decisions.

“As the pandemic progressed and more data became available, data-rich and model-free approaches could combine, leading to a few key hybrid solutions.”

Some recent examples include the following:

A SEIRD (Susceptible-Exposed-Infected-Recovered-Death) disease progression model using synthetic behavioral data for the U.S. to estimate the COVID-19 risk propensity of localized population groups. This model, built by PwC, proved invaluable in the early days of the pandemic to estimate hospitalizations, ICU bed demand and ventilators.

The initial MVAIM (minimum viable AI model) then expanded to take into account the mobility of the population in different zip codes. As some states in the U.S. went past peak hospitalization points, we built agent-based demand simulation models for hospitals to help determine the demand for non-COVID-19 procedures.

The demand models incorporated the potential emotional behavior of patients, including fear and anxiety, and the urgency and severity of the existing health conditions. The primary factor driving the need for such sophisticated behavior modeling to estimate demand was the uncertainty around human behavior during the pandemic.

An integrated demand sensing, supply chain disruption and workforce planning simulation model. Our team brought multiple silos of management decision making into a single-system dynamic model — a critical requirement given the speed and uncertainty of the evolving pandemic.

This initial MVAIM then expanded to incorporate multiple macro-economic recovery scenarios like the V, U, L, and W economic recovery scenarios. Under normal, non-pandemic scenarios, demand, production (using workforce and machinery) and supply chain become independent silos for localized decision making. However, during the pandemic all three factors are analyzed together daily to ensure companies make holistic and proper decisions.

Lack of historical data leads to upsurge of model-based AI

Given the rarity of the pandemic event there was very little historical data at a global level on the disease. As a result, there was little information to power data-rich, model-free approaches to AI like deep learning.

By necessity, model-based AI (which leverages the data available) saw a resurgence. As the pandemic progressed and more data became available, data-rich and model-free approaches could combine, leading to a few key hybrid solutions.

Epidemiological models encapsulated the knowledge and experience of disease progression from previous outbreaks. As more data about the disease emerged on a daily basis, machine learning approaches combined with the epidemiological models.

The historical part of the epidemiological model synced with the actual data, and the resulting models helped project future scenarios. The daily update of disease data at a granular level (state and county level in the U.S.) provided a mechanism to compare the simulated and actual outcomes and improve these models.

“The pandemic has provided an opportunity for data scientists and AI scientists to put their advanced techniques and tools to use by helping business leaders make decisions in a challenging environment.”

In developing a hybrid workforce model for a client considering whether to open a manufacturing site, we were able to build a model to help run multiple scenarios using other data sources (absenteeism trends within the sector) and data from other models (infection and fatality rates in the county where the manufacturing site was located, using the SEIRD model).

In many ways, the pandemic has highlighted the inadequacies of our systems, processes, governance and behaviors. On the other hand, it has also provided an opportunity for data scientists and AI scientists to put their advanced techniques and tools to use by helping business leaders make decisions in a challenging environment.

In summary, as organizations manage through this pandemic and transform themselves post-pandemic, three key learnings are worth keeping in mind.

First, focus on agile data science methods that address the speed, urgency and uncertainty of decision making. Second, build and manage your business using dynamic and resilient models (scenario-based simulations using system dynamic and agent-based models) that capture the interrelationships of multiple domains (demand, production, supply, finance) and human behavior. Third, combine model-rich and data-rich approaches to obtain the best of both worlds while building AI systems.

These approaches will help you identify solutions quickly while maximizing the technologies and systems already in place.

Republished with permission, this article first appeared on World Economic Forum.

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