IN A CRISIS SITUATION, FINDING THE RIGHT BALANCE BETWEEN KNOWN AND UNKNOWNS IS CRITICAL TO MOVE FORWARD

Most of us are nearly in the third week of lockdown. We all want to know how long this will last and when will the new normal start. We are all in information overload phase, we have more access than ever before to the latest numbers and there are new intriguing facts that dominate our attention. The most recent one is, “flattening of the curve”. As we all are in a state of intrigue for the next number release and confusion for not being sure what the numbers mean, the two of us connected. We both did our undergraduate in Engineering at Indian Institute of Technology, Kharagpur, India, lived in Louisville, Kentucky but have not been touch for nearly fifteen years.

A sharing in Facebook rekindled our old friendship and soon we were zooming nearly every day to make sense of all the data. As we broke through all the clutter, we realized that most of us want to find two things:

1. Signs of hope and assurance in the numbers, so we can feel that will be over one day, soon, and

2. Be the best prepared for the new normal when it arrives.

Of course, there is a very important group of stellar individuals who have been fighting the biggest global battle to save mankind, who need every detailed number. We are grateful to every individual and their family members for their sacrifice.

During the process of discussions, we created a model to understand the challenges of model builders in today’s world. We also realized that being model creators did not give us access to additional answers that will assure us or prepare us for the future. But more importantly, we enjoyed every discussion we had, and we learned that learning is an unplanned collaborative journey where you share and listen; and wait for wisdom to light up new areas of darkness.

This document is not a research white paper, even though we did do a lot of research. It is simply a summary of our shared learning in our journey together, something that has made both of us wiser. We are excited to share this with the world.

HOW DID THIS UNRAVEL SO FAST

Even though it is a four-month crisis, the big challenge is just over a month old. Here are some key dates.

DEC 31, 2019: As the whole world was getting ready to party and welcome in a new decade, China reported a cluster of cases of pneumonia in people associated with the Huanan Seafood Wholesale Market in Wuhan, Hubei Province. Next morning the nearly 8 Billion people woke up with hope and smiles not knowing what was ahead.

Jan 7, 2020: Chinese authorities identify a new type of novel coronavirus

JAN 11, 2020: China records its first death

Jan 17, 2020: First coronavirus case outside of China is reported in Thailand

Jan 13, 2020: First US case is reported: a 35-year-old man in Snohomish County, Washington

Jan 30: 2020: WHO declares a global public-health emergency

Jan 31, 2020: First death outside China is recorded in the Philippines

Feb 8, 2020: First American citizen dies in Wuhan

Feb 11, 2020: WHO announces that the new coronavirus disease will be called “COVID-19”

Feb 12, 2020: Coronavirus cases start to spike in South Korea

Feb 19, 2020: Iran outbreak begins

Feb 21, 2020: Italy outbreak begins

Feb 29, 2020: US reports first death on American soil

Mar 8, 2020: Over 100 countries report cases of COVID-19

Mar 11, 2020: WHO made the assessment that COVID-19 can be characterized as a pandemic

Mar 19, 2020: California Orders Lockdown for State’s 40 Million Residents

Mar 29, 2020: US extends social distancing until April 30

Mar 30, 2020: Johnson & Johnson announces the selection of a lead COVID-19 vaccine candidate and expects to start human trials by September, at the latest, and anticipates the first batches of a vaccine could be available for U.S. Food and Drug Administration “emergency use authorization” in early 2021

April 2, 2020: Cases of COVID-19 surpass 1 million

Apr 6, 2020: Almost 90% of students globally are affected by school closures -over 1.5 billion children and young people, according to WHO

As of April 12, six weeks since first death on American soil, the numbers stand at:

Currently 86% of respondents were aware of the Coronavirus outbreak, per a global study by Premise. Going from the first death because of it to being one of the words with global awareness, the rate of spread of information about the disease has been unprecedented. On March 18th, Covid-19 was added as a New Dictionary Word.

UNPRECEDENTED OVERLOAD OF DATA AND ANALYSIS

Floodgates have opened up about data sources. World Health Organization (WHO), Centers for Disease Control & Prevention (CDC), U.S. Department of Health & Human Services, NextStrain, UN OCHA, Organisation for Economic Co-operation & Development (OECD), Allen Institute for AI, Candid.org, Kaiser Family Foundation, Covid Act Now, The COVID Tracking Project among others have been providing nearly real time update on key numbers or statistics on the spread of the disease.

And it is not just data, we have the best of the best analyzing data real time, provide us the insights. Then add the number of news stories on Covid-19 24–7, where most news sources are trying to fight for our attention with both news, commentary and analysis. If that was not enough there are resources like Tableau that have created opportunities for all of us to be home office analysts.

WITH ALL THE DATA, WE STILL DO NOT HAVE ALL THE ANSWERS

Even with all the data and analysis, it seems that there is something still missing. Somehow individual US residents, Healthcare Industry, Government Agencies, Support organizations all feel they do not know everything they need to act.

Even though we have all the data we think we need, there seems to be more unknowns than known. Here are some commonly mentioned unknowns

1. Where exactly did the coronavirus come from?

2. How many people actually have a COVID-19 infection?

3. What makes the coronavirus so good at spreading?

4. What actually drives mortality in people infected by the coronavirus?

5. What percent of people infected by the coronavirus die?

6. Why are young people at the least risk of dying?

7. Can you get re-infected by the coronavirus?

8. How seasonal is the coronavirus?

9. Are there any safe and effective drugs to treat COVID-19?

10. Will there be a safe and effective vaccine for the coronavirus, and when?

11. When will it end? And how?

To understand why the current state of uncertainty exists in the middle of all the data and the analysis, we started building our own model.

METHODOLOGY

The estimation model is based on researches, assumptions and data sets available through CDC (Github) within a less effective containment regime than Wuhan, China. It also incorporates non-pharmaceutical interventions including governmental policies, social distancing, stay-at-home orders, lock downs. The model pivots on the day first death is reported within the country and establishes when the incubation may have happened. Also, it applies the median fatality rate to estimate the population that has been infected by the time the first death is reported, using a lean implementation of SIR (Susceptible, Infected, Recovery) model for spread of disease. The initial daily growth of infections is exponential based on initial R0 (basic reproduction rate) and no herd immunity. It gets reflected on the projected death toll based on median number of days from incubation to death. As the interventions start to take effect and slow down R0 based on magnitude and speed of the interventions, the model adapts to estimate new infections and death toll. The full model along with the methodology, assumption, limitations and simulation is described in this article.

EVERY PROJECTION IS RIGHT BUT EACH TELL A DIFFERENT STORY

Just like most other models, our model too showed the severity of the situation. But soon we realized that

1. Projections are based on variables for some of which data does not exist and the desired data is being replaced with available data. Here are some examples:

? # of people Infected: The only number we have is the # of reported case of people infected. Even though it is essential to use this number to predict the spread of the disease, this is flawed as # of reported cases is:

o A subset of the # of people infected.

o Variance in the percentage of the actual people tested. The variance is driven by

? The number of tests available. This number too varies from country to country, over time after the disease spreads to a country. Even though availability of tests increases with the outbreak, we do not know the relationship between the rate of increase of cases and the rate of increase of availability of the testing. The rate of increase of testing is primarily driven by supply factors, whereas the rate of increase of cases in driven by the actual spreading of the virus.

? Difference in testing availability by country and the time it takes to get results.

? Non-uniformity of how to classify patients during the extended time it takes to get test results.

? Hospitalization record: This could potentially provide a more accurate picture of the infection as we expect a certain % of infected cases to get hospitalized due to severity. However, this comes with its own set of challenges. Hospitalization records have not been easily available and whatever is available, there has been a time lag. Secondly, it is unknown at times whether the patient is hospitalized due to COVID-19 or has contracted COVID-19 afterwards.

? Rate of spread: Every model has an underlying assumption of initial R0 (basic reproduction rate) and as interventions take effect, the rate of spread diminishes. It has been particularly challenging in a country like the US, where this rate varies wildly based on the state, metropolitan city or rural county, along with many other factors.

? Incubation period: The median incubation period is estimated to be 5.1 days, based on publicly reported cases. However, that may be an over-representation of more severe cases.

? Fatality Rate: We assumed a fatality rate based on our understanding of the fatality rates previously reported in Asian countries and Europe. One could potentially think that US may experience a lower fatality rate due to advanced healthcare system. But the reality is turning out to be quite different.

? Speed and magnitude of Interventions: This is one of the most important yet least studied factor so far. While we tend to make assumptions based on impact of interventions in other countries, it barely gives us great confidence how each one of the non-pharmaceutical interventions could help. We will perhaps know the impact in hindsight and adjust the model as we go.

? Flattening the curve: The “curve” researchers are talking about refers to the projected number of people who will contract COVID-19 over a period of time. (This is is not a hard prediction of how many people will definitely be infected, but a theoretical number to model the virus’ spread.)

2. There is no absolute information: Just when we thought assured on an information, it changes. Once piece of information that was universally accepted was the way to practice social distancing; be 6 feet away from other people. 6 feet has become the universally accepted distance even though measures called for by the Centers for Disease Control and Prevention and the World Health Organization call for six and three feet of space respectively.

On March 31st, Lydia Bourouiba, an associate professor at MIT, has researched the dynamics of exhalations (coughs and sneezes, for instance) for years at The Fluid Dynamics of Disease Transmission Laboratory and found exhalations cause gaseous clouds that can travel up to 27 feet. The scariest part of her paper is where she states that peak exhalation speeds can reach 33 to 100 feet per second and “currently used surgical and N95 masks are not tested for these potential characteristics of respiratory emissions.”

Now that research, if validated will change the risk factor for everyone and will need complete reevaluation of all predictive models.

PAUSE THE SEARCH FOR COMPLETE WHOLE PICTURE, ACT ON PREDICTWORTHY WHOLE

Somewhere at the end of March we realized that we are getting more accurate numbers every day, but we were no closer to learning the true impact of this crisis. That was the time we chose to hit the pause button and started asking ourselves, “what do we know already?” and “what decisions should we be able to make based on the current information.”

This was a turning point in our conversation. First and foremost, we felt heart felt appreciation for the following groups who have been marching forward without all the information:

? The healthcare personnel and the first responders for their effort to take care of those impacted by the virus.

? Those who are trying to fast track the treatment options and the vaccine; and those who are working on getting the medical supplies and testing to every corner of the country.

But for the rest of us, do we really need hourly and daily update of the full picture of the world as a result of Covid-19? Can we sit inside a crisis and get a complete picture of the situation, a picture that is reliable, stable and sustains over time?

The complete picture may only be seen in hindsight months or years after this is over. Instead we realized that we should focus getting guidance from the predictions and start to act. We felt that it is actions that will get us out of this faster, and not going from a 70% accurate model to a 71.54% model.

Hence it is urgent for the world to stop searching for complete picture now before we start acting. We should just be happy with a predictworthy whole and see how it can help us take the next step in our journey forward. In the next sections, we have discussed the path to move forward in today’s world with no more data.

HOW TO USE DATA AND NOT USE DATA

As statistician George E.P. Box would put it, “all models are wrong, but some are useful”. We also realized that every model used today is less “right than its updated version tomorrow.” That does mean that our model building and analyzing different models did not help us at all. We learned that it is important to recognize that estimates are estimates but it helps you to assess the damage, from human or economic losses perspective. We could make better decision and be adaptable based on the continuously improved estimations, rather than the raw and sensational numbers that we see from TV and other media.

Here is some guidance on using the data:

What not to use, at least not now:

Confirmed cases — this is the biggest and most talked about number. Social media got flooded when US crossed China on this number. The necessity of interventions and our outlook should not be based on this number. The real number of infections is potentially 10 to 30 times higher than this number. If you have to process the confirmed cases mentally, you should at least apply the multiplier first.

Recovery — while this parameter can help us in determining what % of population may have developed antibodies, it may not be ready for any effective use for some time for a) it is a low number compared to how many actually recovered; b) we may need to have large scale testing separately to understand the actual recovered population.

Race & ethnicity — there was an initial social distancing with Asian people as the virus was believed to have traveled from China. In a recent article in NY Times, that corona virus started to circulate in NY area was brought by travelers from Europe, not Asia. Virus does not care about your national origin, race or ethnicity. In the world of social distancing, you need to apply that with any human.

Drug & pharmaceutical intervention - these are simply anticipation of future actions. The politicians estimate of a vaccine is totally different from the projected date by physicians, and the threshold for approval even in an accelerated testing world rests solely with physicians.

What to use:

Death toll — death is the shadow of COVID-19 pandemic and is the one irreversible impact of the virus. A time series analysis of death counts has been the most useful in estimating the real number of infections, rate of spread, healthcare system needs and future death toll, given multiple scenarios under the impact of interventions. A single death could indicate there may have been a thousand or two are infected already.

Hospitalization — this could potentially be a good indicator in couple of different ways:

o If more people are getting infected, a % of them would show up in the hospital and when it starts to climb down, it could indicate a lower rate of spread;

o Based on understanding of existing healthcare capacity (regular beds, ICU beds, ventilators), you could estimate when the hospitals would start to see overloads and potentially fail to service every patient’s needs.

Testing capacity and effectiveness — the testing capacity and logistics have come a long way from mid-March. However, there remains kinks to iron out. States are operating in different ways, leading to a varied degree of % of positive cases. False negatives (test result is negative but actually have the virus) are high and have been particularly lowering the effectiveness. Once new testing procedures like Abbott Labs new molecular point-of-care test is approved and rapidly deployed, it could change all curves overnight.

Mobility maps and location data — an aggregated understanding of mobility maps from Google or Facebook may be helpful to understand the high risks and prevention strategies at a state or local level.

THE BIG LEARNING: HOW TO LIVE DURING THIS PHASE

At the end, the COVID-19 has showed us how limited we as individuals are in the face of rapidly evolving pandemic. Even though the disease forced most of us to be in house arrests, we, the human beings, have realized how strong is our desire to live, both as individuals and as a group. We have ways to socialize and communicate with each other more effectively than ever before from our homes. We have taken to Zoom, a totally business tool to now being a household communication tool, where families and social groups are using to connect face to face. We are seeing a rapid evolution that gives us hope of a better world when we come out of this.

We will come out of this. We all believe that. But how we come out of this is in our hands now. We must take good care of ourselves and be ready physically and mentally to run fast in the world when this is over. Just like our body is what we eat, the same way our mind is what we consume. During this period, it will be very important to take care of our mental health and stay as calm as possible. Here are three learning we want to share

1. Unless you are a first responder, we do not need minute by minute update, nor do we need live update. Instead we should plan for 15-minute daily update and that way we focus on living and minimizing anxiety caused by every unveiling of a new updated number. This way rest of the day is free for us to focus on building relationships and investing in making tomorrow better.

2. We do not need every possible number and every possible interpretation. We should identify a few objective sources we trust, a few key numbers to measure and have the discipline to only focus on those. We must realize that watching news of people dying is not entertainment. Instead we can take care of ourselves and then assist friends and family, following physical distancing guidelines.

3. Finally focus on ourselves and our relationships:

Just like this is a one-time life changing challenge, it also is a once in a life time opportunity. We are getting months to focus on ourselves and our families. Very few of us will get this kind of extended time with friends and family in future. Hence to maximize on that we should consider the following focus areas:

a. INVEST IN US NOW: Taking care of self and family, both physical and mental health. Use this time to create lifetime memories and build stronger connections.

b. ASSIST OTHERS AROUND US: This is the time to collaborate; hence investing in others is essential for us to all come out stronger.

c. GETTING READY FOR THE WORLD REOPENING: Finally, we should wait in excitement for the day the world opens. That day we should be cautious but share with pride the new and improved version of us. So, let us get ready.

ABOUT US

RAJ BHATTACHARYYA

Raj is a product strategy leader at Global Payments Inc., a fortune 500 fintech company. He led industry verticals and cross-functional teams for high growth startups and major public companies, delivered enterprise-grade solutions for retail, grocery, restaurant and financial services clients. Raj was Vice President at Ceridian and Comdata where he led multi-generational product initiatives from concept to operations. He is a data enthusiast at heart and seeks ways to connect data to insights and actions.

Raj holds a B.Tech in Computer Sc. & Engg. From IIT Kharagpur and an MBA from Purdue University.

ARJUN SEN

Arjun Sen, CEO and Founder of ZenMango has led his team to make big impacts on major Restaurant, Service, Retail, Non-Profit and Sports brands worldwide, both in the B2C and B2B2C space. He is a former Fortune 500 executive. At Papa John’s, he led the 3,000-restaurant chain to 4-years of record growth. He is recognized as the top Brand Whisperer in the country and one of the top growth drivers in the Brand and Customer Experience space

Blaine Hurst, Former President and CEO of Panera Bread, called him, “One of the most ‘marketing-Intelligent’ minds in the business today.” He is an acclaimed author and highly sought-after international keynote speaker.

Arjun holds a B.Tech in Aeronautical Engg. from IIT Kharagpur and an MBA from Brigham Young University.