Dear Sir,
Science is most oftentimes portrayed as a methodical, painstaking search for fact, and good policymaking as the translation of those evidence-based realities into action for an overall good of everyone. In prepandemic times, i.e., before the current ongoing pandemic of Corona Virus Disease-2019 (COVID-19), these assumptions every now and then held true, but the complexity of science and policymaking in the background of uncertainty has been brought into shrill attention by the COVID-19 pandemic.[1]
The disease was totally new and unknown to the whole mankind. Some recent research findings can possibly be given the status of facts, but overall, the evidence base for the effectiveness of interventions against the COVID-19 pandemic, either preventive or therapeutic, remains patchy and limited. The extent to which research findings from other diseases (and even other coronaviruses) can be extrapolated to COVID-19 is also challenged.
COVID-19: A Complex Problem in a Complex System
As each country’s COVID-19 response shifts from an acute national disaster to a chronic policy crisis, we all—clinicians, scientists, policymakers, and citizens—also need to move on from envisioning that the uncertainties can be resolved. They may could never be.
This is for the reason that COVID-19 is quite a complex problem in a complex system.[2] Complex systems are, by definition, made up of multiple machineries interacting with each other. Such systems have open boundaries and are unsolidified and difficult to outline, evolving dynamically (elements in the system feedback, positively or negatively, on other elements), unpredictable (a fixed input to the system does not have a fixed output), and self-organizing (the system responds adaptively to interventions). Complex systems can be properly understood only in their entirety; isolating a part of the system in order to “solve” it does not produce a solution that could work across the system for all time. Uncertainty, tension, and paradox are intrinsic to a complex system; they must be acknowledged and accommodated rather than hided and ignored.[3]
Problem of Data Availability for COVID-19 and Approaches Required
In circumstances like this, uncontested information—things that are ascertainable, reproducible, transferable, and predictable—tends to be very slim. Most of the decisions may be based on information that is defective (imperfectly measured, with missing data), uncertain (contested, perhaps with low sensitivity or specificity), proximate (relating to something one stage removed from the real phenomenon of interest), or scant (only available for some aspects of the problem).[4]
Data that are trustworthy, certain, definitive, and plentiful can be presented as facts and evidence-based decisions can be stemmed from them. These are the data we long for and hunt for, the science that will inform the eventual exit strategy from this pandemic.[5] But seeing the current prevailing situation, the current pandemic requires us to work with the kinds of imperfect data, described above, using many different approaches.[4]
All of us making use of such data should be careful of our own confirmatory biases, avoiding group thinking and applying the same standards of scrutiny to findings that appear to support our prior beliefs or personal biases as to those which challenge them. In such conditions, we all may need to make decisions based on “balance of probabilities” rather than “evidence beyond reasonable doubt” and consider how it fit together with existing interpretations, values, and priorities.[6]
Instead of seeking—or faking—certainty, we should be wide open and honest about the prevailing uncertainty and should be transparent in the ways in which we acknowledge the limitations of the imperfect data we have no choice but to use. Teams should be urged to admit a lack of knowledge, explore paradoxes, and reflect collectively.[7] This would improvise the quality of decision-making by encouraging constructive scrutiny and make us more open toward revising our decisions once new data and evidence emerge.
Even when an evidence base appears to settle up, different people will reach different conclusions based on the same evidence. When the evidence base is at best incipient stage, differences will be greater. When epistemological conflicts remain unacknowledged and are tried to suppress, they can be annihilating. But if surfaced and debated, competing explanations can help us productively by accepting all alternatives as faulty and requiring negotiation between a range of actors in the complex system.[8] If there is a mutual respect for different opinion holders and space for dialog and negotiations, such conflicts can be directed into multifaceted solutions and adaptive actions.[9]
Conclusion
We may all face the same pandemic, but our knowledge, worldviews, and values may differ. Rather than demonizing others for their alternative interpretations, we should celebrate the different perspectives that those who engage rigorously with the science can bring to bear on the unavoidably inconsistent data we have to work with. In this context, purist pursuit of an illusory one-dimensional truth is certainly going to fail. Instead, we must collaborate to achieve “practical inelegant solutions.”
Managing Uncertainty in a Pandemic Situation: Golden Rules to Be Followed
- Most of the available data may be flawed or incomplete. Be honest and transparent about this fact.
- For some questions, certainty could never be achieved. Consider carefully whether to wait for the definitive evidence or act on the available evidence which you have.
- Make sense of complex situations by acknowledging the complexity, admitting a lack of knowledge, exploring paradoxes, and reflecting collectively.
- Different stakeholder groups interpret data differently. Deliberation among the various stakeholders should occur to generate multifaceted solutions.
- Practical interventions, meticulously observed and compared in real-world settings, can generate valuable data to complement the findings of interventional studies such as randomized controlled trials.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
References
1. Lancaster K, Rhodes T, Rosengarten M. Making evidence and policy in public health emergencies: Lessons from COVID-19 for adaptive evidence-making and intervention. Evidence & Policy: A Journal of Research, Debate and Practice 2020;16:477-90
2. Hawe P, Shiell A, Riley T. Theorising interventions as events in systems. Am J Community Psychol 2009;43:267-76
3. Greenhalgh T, Papoutsi C. Studying complexity in health services research: Desperately seeking an overdue paradigm shift. BMC Med 2018;16:95
4. Wolpert M, Rutter H. Using flawed, uncertain, proximate and sparse (FUPS) data in the context of complexity: Learning from the case of child mental health. BMC Med 2018;16:82
5. Farrar J. This virus isn’t going away. The only way to beat it is to work together. London: Wellcome TrustAvailable from: https://wellcome.ac.uk/news/virus-isnt-going-away-only-way-beat-it-work-together?utm_source=twitter&utm_medium=o-wellcome. [Last accessed on 5 Jul 2020]
6. Fischer AJ, Threlfall A, Meah S, Cookson R, Rutter H, Kelly MP. The appraisal of public health interventions: An overview. J Public Health (Oxf) 2013;35:488-94
7. Lanham HJ, Leykum LK, Taylor BS, McCannon CJ, Lindberg C, Lester RT. How complexity science can inform scale-up and spread in health care: Understanding the role of self-organization in variation across local contexts. Soc Sci Med 2013;93:194-202
8. Tsoukas H. Don’t simplify, complexify: From disjunctive to conjunctive theorizing in organization and management studies. J Manag Stud 2013;54:132-53
9. Mouffe C. Agonistics: Thinking the world politically. New York: Verso Books; 2013
