The nature, degree and timing of collective action to contain Covid-19 has generated intense debate around the world. Decision-making in the sphere of collective action was never a matter of black or white; public policy always hummed under a shade of grey.
Covid-19 has made decision-making even more complex due to ‘known unknowns’ as well as ‘unknown unknowns’. Never before have so many been vulnerable to the pitfalls of public policy under conditions of such uncertainty. At the time of the Spanish flu, the last pandemic of such global reach, the entire world had as many people as South Asia alone has today.
While information about the novel coronavirus is limited, the knowledge of infectious diseases is not. While our decision-makers cannot be held accountable for the missing information, our generation will definitely be tried in the court of history if we fail to capitalize upon the existing knowledge of epidemiology, public health and those two formidable multi-disciplinary fields of inquiry: behaviour sciences and data sciences. Neither our laxity in using available information (big data) nor our delay in generating new information via mass testing will be adjudged lightly.
When information is sharp in the grey world of public policy, one can live with blunt analytics; when information is not sharp, blunt analytics are a recipe for disaster. As of today, we are pursuing a policy of countrywide non-essential lockdowns, a practice of reactive sealing of ‘neighbourhoods with positive cases’ and less than efficient contact tracing. Reactive sealing of such neighbourhoods is akin to fighting the Greek Hydra: you cut one head and two grow elsewhere. Until you cauterize the stumps (the neighbourhoods with asymptomatic cases) as Heracles’ nephew did, the monster of Covid-19 will not be contained.
Despite best intentions on the part of the authorities, somehow we are missing the primary algorithm of Covid-19. Like most infectious diseases, it diffuses through human networks; and social learning that catalyses preventive human behaviour too flows through the same networks. If neighbourhoods and villages are seen as human networks with individuals and links (nodes and edges as in graph theory) that connect such individuals, then the theory of networks throws up powerful insights to help us fight the current pandemic.
First insight: a few individuals in a network (a neighbourhood or an office space or a factory floor) are socially connected to a fairly large number of individuals but the rest of the individuals are connected to only a few. Second insight: an individual in a network adopts preventive behaviour once a particular threshold of adoption pressure is obtained. Third insight: movement of individuals (however small in number) from an infected network to a healthy network has an exponential effect on cross-network disease diffusion as long as the infectivity ratio is greater than one.
The first insight suggests that infected sub-networks (except perhaps highly globalized neighbourhoods) can be disconnected and isolated without an overwhelming socioeconomic cost. The second insight implies that confounding signals from offices of authority adversely affect adoption of preventive behaviour and therefore the luxury of preaching the false binary of ‘life versus livelihood’ must stop forthwith.
The third insight precludes any viability of sector-wide (inter-network) opening of the economy until a sector is a self-contained geographic cluster with only a few nodes linked to the outside world. In our policy and practice, we are neglecting the first-order implications of network theory, disease diffusion model and social learning model. Still, of late, we have started using the jargon of ‘smart lockdown’ without embracing the essential elements of a smart strategy, powered by a multi-disciplinary toolkit.
Could it be just an error of judgement that we have adopted the jargon without embracing the essence? Perhaps not. It is, in fact, a survival technique that most inefficient organizations use to buy legitimacy without going through the hard route of adaptive transformation.
A professor at the Harvard Kennedy School once taught us that organizational survival depends either on actual value creation or on the ability to weave a narrative of legitimacy. Building on the concept of isomorphism from the world of biology, he would tell us the evolutionary story of the scarlet king snake (a non-venomous snake) which had developed red, yellow and black bands in the style and fashion of the eastern coral snake, an iconic snake capable of twenty milligram of venom per bite, four times a lethal dose. The evolved colour of bands has served the scarlet king snake well in the ruthless struggle for survival of the fittest. This phenomenon in the world of biology is known as isomorphic mimicry.
A lockdown does not become smart by rechristening it so. Lockdowns can neither be relaxed nor declared smart till three prior conditions are met: extensive testing regime (asymptomatic persons inclusive); an industrial-scale contact tracing; and a differentiated local response matrix. Otherwise, rebranding our current strategy as smart is nothing more than an exercise in isomorphic mimicry, a technique of successful failure at best.
The writer is a practitioner of design thinking and implementation in the public sector.