Coronavirus: The Story of Risk and Resilience (Part 1)

What will be our exit strategy after the “enhanced community quarantine”?

This article (Part 1 of the series) “Coronavirus: The Story of Risk and Resilience (Part 1, Part 2, Part 3)” follows the “Coronavirus: Ang Maso at ang Sayaw,” an adaptation of “Coronovirus: The Hammer and the Dance.” The Filipino adaptation is dedicated to the heroes of our trying times, our valiant “frontliners.” We salute you. This article now focuses on the situation in the Philippines as of April 22, 2020.

Summary. There will always be two sides to a story. It is generally wise and safe to know both sides. Risk and resilience are both key concepts that our policymakers and citizens need to understand to craft an exit strategy amid uncertainty. In the story of coronavirus, risk and resilience should be viewed as two sides of the same coin.

What is our current situation?

The COVID-19 pandemic poses an unprecedented threat to public health, and to our way of life. In the Philippines, the National Capital Region (NCR) took the biggest hit after the country recorded its first coronavirus death in the world outside China in early February. According to the records of The Department of Health (DOH) COVID-19 tracker, more than six thousand have been confirmed to have the coronavirus and at least 400 have died. At least 570 also have recovered from the disease.

In Chart 1, the timeline of events is shown along with the reported daily cases nationwide as of April 18. The visualization of the data is taken from the dashboard developed by the Center for Systems Science and Engineering at John Hopkins University.

In retrospect, were we that “ill-prepared” to manage the crisis? Did we miss the opportunity to contain the outbreak and to communicate this emerging threat to the public? The government has faced intense criticism of handling this threat, such as its initial decision before to allow direct flights to China despite the lockdown in Hubei, the epicenter of this global pandemic. At the same time that the World Health Organization (WHO) declared the outbreak as a pandemic last March 12, the government announced its version of “suppression strategy” known as enhanced community quarantine (ECQ) to “flatten the curve.” Suppression is a well-known approach done by several countries, as discussed in the previous article to give us time to absorb the shock, especially for our healthcare system to prepare and respond effectively to the threat posed by this epidemic. The government used a “sledgehammer” intending to crush this “coronavirus thing.”

Is the epidemic curve starting to flatten after five weeks of the Luzon-wide enhanced community quarantine (ECQ)? Note that the data in Chart 1 should be interpreted with caution considering the very limited testing and inefficient contact tracing being done in the Philippines in comparison with that of other countries like Vietnam, South Korea, and Singapore. It was estimated that hundreds of thousands would be infected in Metro Manila during the peak of the epidemic period, according to epidemiological modeler Dr. Jomar Rabajante and the UP COVID-19 Pandemic Response Team.

Last April 14, the government introduced a more “aggressive” testing program for the COVID-19 (coronavirus) outbreak to locate as many as 15,000 unknown infections that were not detected yet. Before that, the Department of Health (DOH) is only allowing testing of those with severe symptoms, members of the vulnerable sector showing mild symptoms, and Philippine government officials. To date, the country has 17 testing centers and, an additional 47 laboratories are undergoing the certification process to become testing facilities. Besides, an online platform is introduced by the government to trace people who are experiencing symptoms of COVID-19 and provide them remote and instant consultations with health workers.

These are some of the measures being done by the government to manage the crisis now as we delay the inevitable. Many are already infected, and some will need hospitalization, and our healthcare system will still be overwhelmed in the next coming weeks. How would we know if these measures are effective and when is the right time to lift the “sledgehammer”?

There are several metrics we can use to monitor the process of “flattening the curve.” The death growth rate is one of the several metrics we can use. From Chart 2, the trend from the growth rate of death looks encouraging. Initially, the doubling time of reported COVID-19 death is 2 to 3 days, and now we are looking at the trend of doubling time of more than 10 to 30 days. What does it mean? Let’s say we have 400 reported deaths today. If the doubling time is two days, then two days from now, we will have 800 deaths, and ten days from now, we will have 12,800 deaths. But if the doubling time is ten days, then we have only 800 deaths. Thus, we can hypothetically “save” 12,000 lives in the process of flattening the curve. Again, we have to be cautious about how we will interpret such a trend. On a positive note, this is good news because of a higher doubling time for death growth rate from the previous weeks’ mean that “flattening” is on its way. Chart 3 shows that the reported death is decreasing, but we have to check as well the risk of death of a confirmed case in real-time, adjusted for the expected time-delay between case confirmation date and actual death. Ten days or one month from now, we can speculate what will be the trend looks like. How will we tell our story then?

Is our timing to lift the “Hammer” is just in time for a graceful “Dance”?

To find out more of the different metrics (ways to measure) to track the “story” of coronavirus from the reported data of the government (DOH), please refer to this new COVID-19 time-series dashboard developed by a recently formed consortium which I am a part of. We call ourselves the LEADS for Health Security and Resilience.

The burning question now is what will be our exit strategy if ECQ is lifted on the 30th of April. To understand the implications of any exit strategy being prepared by our government, specifically the COVID-19 Inter-Agency Task Force (IATF) on Emerging Infectious Diseases, we have to understand the concept of risk and resilience.

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.

Thus, our exit strategy should be clear on the desired outcome at different planning horizons, i.e. short-term, mid-term, and long term. What if there will be another disastrous event such as dengue outbreak, typhoons, volcanic eruptions, or earthquakes in the next couple of months, can we handle such disruption while we are managing the crisis brought by COVID-19 outbreak?

We must strengthen the coping and adaptive capacity of our community, institution, and critical infrastructures in such a way that they are also agnostic to threats. Resilience-based approaches do complement risk-based policies to optimize resources in a manner that prepares our society to deal with disruptions not only from the epidemic but also from a broad variety of threats.

The systems approach based on resilience thinking would focus on identifying the interlinkages and interdependencies within and between systems. We can then craft measures to minimize the risk of cascading failure that will lead to the system’s inoperability during and after a disruption. For example, some tools can be used to assess the impact of disruption to the economic systems and prioritize key sectors in the aftermath of disasters by considering the interdependencies of economic sectors (Yu et al, 2013). This is where equitable and transparent socio-economic stimulus package will play an important role in building resilience among the different sectors in our society.

Thus, introducing the concept of resilience in the mindset of our policymakers enriches the portfolio of strategies to mitigate the risk by providing another viewpoint that traditional risk analysis may miss. This is the ability to understand how our community, institution, or infrastructure can bounce back from a massive external shock or a series of disruptions.

In the context of the COVID-19 epidemic, this underscores how important it is to develop a roadmap for health systems resilience as described in the strategic action plans proposed to the Philippine Council for Health Research and Development (Promentilla, 2019). Systems thinking approach and a holistic conceptual framework (see Chart 5 ) are needed wherein research and innovation programs can be designed to strengthen our health security and resilience to emerging global and domestic threats.

This also emphasizes the vital role of scientists and researchers from across disciplines to provide information, methods, and tools needed to fully understand the health risk and resilience in the Philippine context. Of course, now is also the right time for our policymakers to listen to these qualified experts. This is evidence-informed policymaking in action. Doing science for the people.

And to understand further the story of risk and resilience, we need to measure both of them. If we cannot measure risk, how can we control it? If we cannot measure resilience, how can we improve it? This reminds me of the maxim on management quoted by Dr. H. James Harrington:

“ Measurement is the first step that leads to control and eventually to improvement. If you can’t measure something, you can’t understand it. If you can’t understand it, you can’t control it. If you can’t control it, you can’t improve it.”

Concluding remarks.

Part 1 of this article provides an overview of what risk and resilience are. Both are closely related to each other, and it is better for both not to be separated, even though both seem to be completely different. How do we then measure risk and resilience to manage the crisis we are in now? In Part 2 of this article, I will describe CO-INFORM in a few days or so. It is a proposed conceptual framework to develop metrics to inform our policymakers about the level of risk and resilience of our local government units (LGUs) in the context of COVID-19. This metric could provide actionable insights leading to an exit strategy that could improve our condition amid this global pandemic. Then, we can discuss our options for a well-thought exit strategy in Part 3 of the article.

To conclude this article, let’s revisit the reported story of coronavirus in the Philippines as it unfolds in both time and space by looking at Chart 6. Note that the information we have is based on the assumption that DOH provides us data in a timely and transparent manner. We have to be cautious and use our critical reasoning when we look at trends in data and read articles like this. Context is everything.

It is not surprising that the epicenter of the outbreak in the country is in Metro Manila. We can see the “burning” hotspot in the area while it spreads out in the nearby provinces for the past three weeks. When will the fire of infections die out? Will there be another “burning” hotspot within and outside the border of Luzon island? What is the risk of getting burned from this fire? If we get burned, can we heal promptly and recover from this burn soon? Are we resilient enough to bounce back and be better? Are we learning from this adversity and preparing ourselves for a postcoronavirus era?

In retrospect, it becomes an advantage that our country is an archipelago of many islands and not just one big landmass. Here is another interesting fire metaphor. The spread of “fire” throughout the country may be easily controlled as we are “physically” separated by water. Physical distancing is at our advantage. We just need to restrict the non-essential mobility of humans to avoid case importation from outside. We also have a chance to suppress this “fire” through FIRe technologies. FIRe, also known as the Fourth Industrial Revolution, enables us to find immediate and long-term solutions through the synergies of data science and digital technology, AI/Machine Learning, 3D printing, nanotechnology, biotechnology, among others.

Let’s use FIRe against “fire”. We can beat this thing.

Fast-forward to the future. If I will be allowed to imagine a preferred future 18 months from now, it is a future where we start building antibodies against coronavirus through vaccination or natural herd immunity. It is a future where we start thinking of how to build antifragile systems. Antifragility is a concept beyond robustness. It is the term coined by risk guru Nassim Nicholas Taleb to address wicked problems brought by a black swan, an extremely rare event with the impact of high severity. We can discuss this concept further at another time. In the meantime, let’s hope for resilient and antifragile healthcare systems that gain valuable lessons from our current situation and will thrive in a postcoronavirus era of this VUCA (volatile, uncertain, complex, and ambiguous) world.

Thank you for reading!

Please do share if you think this article or any similar one is informative and would help others to understand the situation at hand or change peoples’ opinions. The time to advocate for health systems resilience is NOW.

The author is a Professor at the De La Salle University, teaching risk assessment and management in the Environmental Engineering Graduate Program. He was also a 2018–2019 fellow at the Philippine Council for Health Research and Development under the ASEAN Science and Technology Fellowship program. His views are independent of the views of his affiliations. Thank you also to April Ann Tigue for her help in data encoding and visualization of Chart 6.