Covid: Fight uncertainty with data
previous Severe Acute Respiratory Syndrome (Sars) epidemic in 2003 has shown that there is a great deal of variability in individual infectiousness. For example, research on Sars epidemic from Singapore revealed that the majority (approximately 73%) of the cases were mildly infectious; in other words, they had an R0 of less than one, while a small proportion of them (approximately 6%) was highly infectious or “super-spreaders” with an R0 > eight.
The variability of R0 plays an important role in the dynamics of an outbreak. Models that account for individual variability show that even if the population-based R0 is greater than one, an outbreak could still be a lowprobability event. Introducing individuallevel variability in the model thus explains why during the Sars epidemic in 2003, several cities did not witness explosive outbreaks despite undetected exposures to infectious cases. In these models, outbreaks are typically caused by super-spreader events (SSES).
In the Indian context, this might explain why Mumbai is experiencing an explosive outbreak, while many other large, highlydense cities with significant populations dwelling in slums, are not experiencing such an outbreak.
The above point becomes apparent when one compares Kasaragod to Mumbai. On April 2, Kasaragod had 127 confirmed cases, while Mumbai had 185. However, by April 16, there were zero new cases in Kasaragod while Mumbai experienced a devastating outbreak. In late March, the police in Kasaragod, adopted an aggressive contact tracing model, and identified approximately, 20,000 potential “super-spreaders” — these were primary and secondary contacts of those who returned from Gulf countries. A strategy of “triple lock down” was adopted by the police, whereby these potential super-spreaders were put under a more stringent home quarantine compared to the rest of the people in the district.
This prevented an SSE in Kasaragod and minimised the risk of an outbreak. A key implication of this from a policy perspective is that if highly infectious individuals or superspreaders can be predictively identified, we could avert more general lockdowns in the future. Moving forward, armed with more granular data and a better understanding of the Covid-19 virus, we could move away from a policy of general lockdown towards a policy of a smart lockdown.
It is important to remind ourselves that we know very little about the virus. Our best hope, until the vaccine is discovered, is to collect as much granular and disaggregated data as possible on the epidemiological parameters that have been outlined here. This should inform our real-time policy in the collective fight against the Covid-19 virus.