Covid-19: Fighting uncertainty with data | Analysis – analysis
Arguably the most important scientific statement about coronavirus disease (Covid-19) was made by Dr. Anthony Fauci, director of the National Institute of Allergy and Infectious Diseases in the United States (USA), in his testimony before the US Senate. USA very careful and, hopefully, humble to know that I don’t know everything about this disease. “This is the reality that one faces and, therefore, the biggest challenge, political and scientific, is to make decisions with uncertainty. Perhaps Our best hope in the short and medium term is that we urgently invest in developing our knowledge of the virus, while we hope that we will have a vaccine in the long term.
In the early days of the pandemic, limited data availability led to first-generation epidemiological models to predict that hundreds of millions would be infected with the virus and millions of lives would be lost. Fortunately, the reality so far has turned out to be very different. However, the situation remains worrying. Of 657 cases on March 25, India has more than 125,000 cases today. Even more worrying is that active cases continue to grow, despite a significant slowdown since the national closure.
To understand the future trajectory of the pandemic and to frame appropriate policy responses, we need granular data based on contact tracking at the city or district level to provide information on three important epidemiological parameters. One, incubation period, which is the interval between infection and symptoms. The distribution of this parameter helps the government and experts to understand the nature, scope and possible future scenarios of the outbreak. It also informs in the evaluation of the disease control strategy.
Two, the serial interval, which is the time between the onset of the disease in the primary case (infector) and the onset of the disease in the secondary case (infected). If the estimated average of the serial interval is shorter than the estimated average incubation period, then presymptomatic transmission is more likely to occur than symptomatic transmission. Research from Japan has indicated that the average serial interval for Covid-19 is 4.1 days, which is less than the average incubation period of approximately five days. The public policy implication of this is that contention through case isolation could be a difficult task. Containment, therefore, would require to be guided by aggressive testing and a rapid contact tracking strategy.
Three, the basic reproduction ratio (also popularly known as R0), which is the average number of secondary cases per primary case. Much attention has been paid to this parameter, because if R0 is greater than one, then the probability of an outbreak is extremely high.
Given the importance of parameter R0, great care must be taken in interpreting and estimating it. Most of the models that estimate this parameter assume that all individuals have a homogeneous transmission and a constant recovery rate. Therefore, a population-based R0 is estimated with the implication that if it is greater than one, outbreaks of a single infected person are highly likely to occur. However, research based on the previous Severe Acute Respiratory Syndrome (Sars) epidemic in 2003 has shown that there is great variability in individual infectiousness. For example, research on the Sars epidemic in 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 (about 6%) were highly infectious or “super spreading” with an R0> eight.
The variability of R0 plays an important role in the dynamics of an outbreak. Models that explain individual variability show that even if the population-based R0 is greater than one, an outbreak could be a low probability event. Introducing variability at the individual level into the model explains why during the Sars epidemic in 2003, several cities did not witness explosive outbreaks despite undetected exposure to infectious cases. In these models, outbreaks are generally caused by super broadcast events (SSE).
In the Indian context, this could explain why Mumbai is experiencing an explosive outbreak, while many other large and very dense cities with significant populations living in slums are not experiencing such an outbreak.
The above point becomes evident when one compares Kasaragod with Mumbai. On April 2, Kasaragod had 127 confirmed cases, while Mumbai had 185. However, by April 16, there were no new cases in Kasaragod, while Mumbai experienced a devastating outbreak. In late March, the Kasaragod police adopted an aggressive contact-tracking model and identified approximately 20,000 potential “superdiffusers” – these were primary and secondary contacts of those who returned from the Gulf countries. Police adopted a “triple lock” strategy, whereby these potential super-spreaders were subjected to a stricter household quarantine compared to the rest of the people in the district.
This prevented an ESS at Kasaragod and minimized the risk of an outbreak. A key implication of this from a political perspective is that if highly infectious people or super spreaders can predictively identify themselves, we could avoid more general blockages in the future. In the future, armed with more granular data and a better understanding of the Covid-19 virus, we could move away from a general blocking policy towards a smart blocking policy.
It is important to remind us 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 on the collective fight against the Covid-19 virus.
Shamika Ravi is a senior fellow at the Brookings Institution and a former member of the Prime Minister’s Economic Advisory Council
The opinions expressed are personal.