Coronavirus: The Story of Risk and Resilience (Part 1)
By: Mike Promentilla
Tags: Philippines, Covid 19 Crisis, Resilence, Risk, Coronavirus

Chart 4 provides the backdrop of our story to put into context why risk and resilience should be part of the discussion among our experts and policymakers. This is the story I imagine from our possible futures. I am neither a health professional nor an expert in epidemiology. I am a concerned citizen just like you. I encourage my fellow scientists and researchers to validate or refute this story through critical reasoning. We will prevail in this crisis if we put our brains together and value the diversity of approach. In Parts 2 and 3 of the article, I can discuss more the implications of this story. In the meantime, what I can offer from this story is foresight instead of the forecast typically provided by models. In other words, I am making an educated guess about what the future holds for us.

Stories may come from simulations of mathematical models or manifestations of mental models. As a decision modeler, one of my favorite quotes is from the statistician George Box:

“All models are wrong but some are useful.”

Indeed, some models are useful to tell stories and give insights. It is the common pitfall of interpreting mathematical models when someone focuses on the numbers without understanding the context and the uncertainty involved. Thus, the purpose of my story is not only to give insights but also to put into context what risk and resilience are.

What is “risk”?

Let us start first with “risk.” Man has a long and rich history in trying to understand risk, as Peter Bernstein describes in his book ”Against the Gods: The Remarkable Story of Risk.” His story begins with the gamblers of ancient Greece more than 2400 years ago. The systematic way of analyzing risk has also been established as a scientific field some three decades ago (Aven, 2016). Various professions and disciplines have been fascinated with the concept of risk resulting in many versions of its operational definitions while contextualizing it to the problem domain of interest. So what is risk anyway?

According to Wikipedia, a risk is a

“potential for uncontrolled loss of something of value…an aspect of action taken despite uncertainty”.

In other words, “risk” is both a noun and a verb related to a chance of loss. It could be a loss of life, health, property, or income. To manage such risk, it is essential to understand it and measure its risk level.

In the context of disaster risk, trailblazing risk analyst Stan Kaplan suggested that risk can be better understood when we ask the following three questions about a specific hazard to which a population at risk might be exposed:

What can happen or go wrong?

How likely is it that an event may occur or go wrong?

If it does go wrong, what are the consequences?

Let us now understand the epidemic risk poses by COVID-19 if the national government lift the Luzon-wide ECQ on April 30. The reason for having this ECQ is to mitigate the risk by containing the spread of infection and thus reducing the projected losses of life by not overwhelming our healthcare system. A projected number of deaths in a given population will be avoided and will lead to a lower mortality rate and case fatality rate.

What can then happen or go wrong? Would a second or third wave of coronavirus infections possible if the exit strategy lacks foresight? The reemergence of an outbreak may occur, for example, as depicted in Chart 4. The community may be exposed to individuals who were not detected and appropriately tested for coronavirus infection. With the reemergence of the outbreak, will the healthcare system be overwhelmed by more patients resulting in higher case fatality rates?

What is the chance or likelihood that such an event will occur if ECQ is lifted? This question is data-driven, which could come from historical data or mathematical models. It is like playing Russian roulette, but the level of uncertainty is higher. Did you know your chance of losing the game and die from Russian roulette? Probabilistic risk analysis has a plethora of tools to address the second question.

If it does go wrong, what are the consequences? If it does go wrong, the adverse consequence of public health will be severe, e.g., leading to losses of life, or remaining years of life. But figures on the death toll may not fairly represent the real extent of the disaster such as this global pandemic. There could also be indirect consequences such as “collateral damages” to the economy and society. These could be a loss of job, livelihood or income, and even our freedom. Our quality of life may never be the same again. How would we quantify or define the level of impact from such loss or adverse consequences? How would we integrate these consequences to get the totality of risk not only to the individual but to the society?

Different tools could provide a qualitative or quantitative approach that addresses such questions. This may range from an unstructured ad hoc qualitative expert panel discussion to a fully quantitative decision model to assess expected losses. Thus, the typical objective of risk analysis in this situation is to provide numbers that would lead to insights to describe an epidemic risk.

The starting point of a risk mitigation concept lies in the combination of answers to these three questions posed by Kaplan. But the next important question we have to deal with is

“Now what?”.

What are the measures or strategies that can be implemented to mitigate such a risk? How would a calibrated lifting of lockdown rules mitigate the risk in both spatial and temporal scales? When will ECQ be enforced again to a community to avoid the risk of re-emergence of infections? During the transition to the new normal, would a cyclic or periodic lockdown strategy be a better approach to mitigate the risk while minimizing socio-economic cost? How would people behave during this period of transition? What is the “acceptable” or de minimis risk the society is willing to take amid this crisis?

This is the crux of risk communication and risk management. How would the risk be described and communicated to decision-makers or stakeholders? We should also know how the decision-maker or stakeholders value that “loss”.

What adverse consequences do they most prefer to avoid? It is also noteworthy to address the perceived risk amid the uncertainty. When a risk is “subjective” from the standpoint of the observer, is such risk this person might be physically exposed to is a real risk, or does he or she only feel that risk (“perceived risk”)?

Aside from the unknown and the level of novelty of a situation, psychologist Paul Slovic thinks that social disparities are factors that could influence risk perception. This is the reason why the concept of “social amplification of risk” is introduced in risk management literature.

Social vulnerability is now recognized as an important indicator, as it describes the potential risks an individual or a societal group is exposed to, as well as defining their future living conditions. Indeed, risks from disasters are highly complex and very dynamic, and a specific assessment of every hazard situation is required including factors that will affect the vulnerability of an individual or a societal group. But the level of resilience that defines the society’s potential to withstand disaster is also very indispensable.

What is “resilience”?

According to the United Nations Office for Disaster Risk Reduction (UNDRR), resilience is defined as

“the ability of a system, community or society exposed to hazards to resist, absorb, accommodate to and recover from the effects of a hazard in a timely and efficient manner, including through the preservation and restoration of its essential basic structures and functions”.

Resilience can also be considered as a mindset which focuses on the recovery post-disruption as much as the absorption of a threat and its consequences while “fortifying” the different functions of a system (e.g., society) so that it will not collapse under pressure. Our government should work with the private sectors and civil society to “fortify” our healthcare system as this critical infrastructure is our society’s lifeline against coronavirus.

The ECQ, if it is effective in flattening the curve should buy us more time to improve the healthcare capacity and cope up with the surge of demand for hospitalization, health workforce, critical beds, ventilators, personal protective equipment, among others. Moreover, the resilience capacity of the society does not disappear if one system fails from disruption but the capacity decreases gradually while maintaining its critical functions.

Also, the flexibility should be introduced in the system to allow for “graceful” degradation brought by physical distancing where the core operations are prioritized over non-essential services for as long as possible. As the community and society at large recover and adapt, its resilience increases over time as depicted in Chart 4. This is my preferred scenario among the possible futures of the new normal.