The tale of two disciplines: Economics versus(?) Public Health in the crisis of virus in India

The Dilemma: Humanity is facing a defining crisis, fighting against an invisible enemy with every resource in our intellectual, socio-political and emotional toolkit. Unfortunately, a dynamic has developed in this conversation which undermines our common goal. The fields of Public Health and Economics often find themselves pitted against one another, with experts and non-experts engaged in vigorous debates over the relative importance of health and economics. Such partisanship is misplaced. We all wish to minimize human suffering and promote well-being. We need to save lives and save livelihoods. An omnibus solution derived in a complex-systems, cost-benefit framework is needed. Experts in all field must collaborate to generate credible information, actionable knowledge and feasible solutions.

As many countries begin to reopen, public health experts are participating in charting a strategic path, even if this path involves suboptimal mitigation from a public health perspective. We must help pave the way to economic recovery. Putting political biases aside, we need to titrate policy and resources to balance between the public health goal of mitigation and the economic goal of reactivating the workforce. We must recognize that policymakers are riding a “Schrodinger’s seesaw,” changing swiftly from one position while preparing to return to the original if needed, to maintain balance in the face of uncertainty. As we lift the national lockdown version n.0 in India, and prepare for the next phase of the pandemic, we offer a brief synthesis of the situation as pragmatic public health scholars. We as stochastic modelers have been following the course of the virus closely in India with no crystal ball in front of us but a voice guided by humanitarian data science.

What do we know now? In the middle of March, when the coronavirus outbreak started to explode in multiple countries around the world, India adopted draconian prevention measures. On orders from the national government, we hunkered down to buy time and to prepare ourselves.

The virus caught us almost completely off-guard. Now, however, we know more about the virus. Scientists are working round-the-clock to learn more. We have seen successes in flattening the curve by strict adherence to social distancing. We know more about person-to-person and person-to-object-to-person transmissions. With the exception of few hotspots, hospital capacity is not saturated. Promising drugs for severely ill patients and possibly an accelerated timeline for the vaccine is in the horizon. We know we need to ramp up testing capacity, possibly using a dual strategy of using both RT-PCR and serology tests. We know that there has probably been a much larger number of asymptomatic infections that were never reported. We know open areas and sunlight may reduce transmission while confined spaces are particularly conducive to the spread. Properties of the virus that we now know provide critical information for an exit strategy. Though there is still a great degree of uncertainty and despair, there is hope.

What can we expect? The lockdown was rolled out quickly but the remobilization process must be slow to provide maximal benefits. We have to wait until we see steady decline in the number of cases in hotspots, get closer to the tail of the incidence curve and have a well-thought out disease surveillance plan to fully re-open. While the lockdown was national to stop the movement of cases, lifting policies will be more regional and depend on the extent of the local outbreak. We need to be prepared to deal with the number of new cases and need to be able to curb any new surges in their nascent phases — this can be achieved by following the triple T principle: test, trace and treat. There may be a second peak and resurge after we reopen. If we have an intelligent testing and an aggressive surveillance strategy we will know when the number of infected individuals reaches closer to the outbreak threshold in any community. We need to treat the infected people and contain the growth through contact tracing, isolation and localized shutdown. Unless we are prepared for this next phase, we risk forfeiting the successes of the people and government of India in beating the initial, dire projections. A coordinated, long-term strategy for the states and the nation is required.

What can we do to help? The public has a serious role to play in public health. Community participation and engagement is a key in this coming phase. It is probably unreasonable to expect that we will scale up testing to even test 1% of the population in the next one month or so and then keep testing a given person repeatedly every two weeks. As of May 11, India has conducted a total number of roughly 1.7 million tests with an average of 80000 tests over the last one week and have tested roughly 0.12% of the population. US has done about 9.4 million tests so far, testing roughly 2.82% of the population. Testing 3% of the Indian population or conducting another 40 million tests will take a long time for India.

We will not be able to follow the other extreme, the approach of Sweden, where the government determined to proceed with life as normal and largely rely on herd immunity. For India, it will likely be an intermediate compromise. We need to decide on an optimal allocation plan for a fixed number of tests that may be available daily at each state. How to distribute it to most vulnerable (frontline health workers, essential workers, migrant workers, high-density and high-contact populations) and also carry out some random testing. We need to use surrogates or proxies to track the pulse of the virus: implement syndromic surveillance, symptom recording/reporting, temperature scan, mine twitter/social media data for words in COVID-19 ontology, and monitor hospital admissions and insurance claims related to respiratory and flu-like illnesses.

We will have to wear masks in public, practice social distancing as much as possible, avoid large gatherings, restrict travel, and follow hand hygiene. We need to roll out the culture of carrying out contact survey, promote daily practice of contact recording, and use cell phone network data to trace contacts if needed. Employing a large contact tracing task force can help both economy and public health. Every organization and employer should be drawn to the mission of reducing the spread of the virus, disinfecting workplaces, ensure the safety of the employees, incentivize testing, and implementing self-quarantine and isolation, while maintaining core functionalities. Those of us who are privileged to work from home have to think how to retain purpose and meaning in our work with limited physical interactions. We need to find effective ways of combining online and face-to-face interaction in our classrooms and workplaces.

Did projection models mislead us? Statistical models are wrinkled with uncertainties and assumptions. When we report a number from a model, we rarely report the associated uncertainty. All models are wrong, but some are useful. Regardless of the variation in the projections from different models, the takeaway messages were robust and consistent, we needed the lockdown to prepare our infrastructure and to slow down the spread of the virus. When interventions work, the afterthought is always that we overreacted, but we will never (thankfully) know the counterfactual.

Finally, we have to remember that models rely on data. To arrive at the best models, we need accurate case counts and death counts. Thus, it is critical that individuals do not hide symptoms or possible exposure for the fear of being stigmatized or quarantined and the government does not hide true case/death data in order to maintain a projected image. Additionally, granular information on contact network, migration/mobility patterns and population density should be easily accessible to modelers along with accurate cause-specific death counts. Data has a huge role to play in this pandemic.

The massive inequities of loss: The COVID-19 pandemic has underscored the structural inequities and disparities that exist in societies around the world. Be it economics, education or health, the distribution of loss and suffering has not been uniform across all strata of society. From Detroit, Michigan to Dharavi, Mumbai the narrative is the same. The impact of the pandemic has been more devastating among the poor and socially marginalized groups. This indicates that the existing social fabric matters. The whole world can learn from the success of Kerala where societal values, focus on literacy, health awareness and historic tenets led to remarkable outcomes. Policymakers need to lead with efficiency, science, humanity and empathy to protect the most vulnerable.

Finally, for those that are saying social distancing interventions have not been effective in India, we present this plot of the basic reproduction number (R) over time, that clearly shows that the stay at home orders have had an effect. The nationwide data may mask the state level heterogeneity and we plan to write on that in our next story. We have a lot more work to do to get R closer to unity. For that we need partnership of the government with the common people in embracing social distancing and other preventive measures. Community engagement and participation is going to be critical in navigating the next phase of the pandemic. Beating the virus also holds the key to reviving business and economy. We have to stand up as a nation, and as scholars, together.

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Figure 1. A plot of the basic reproduction number for India averaged across the three lockdown periods. The numbers in brackets are the 95% confidence intervals.


Bhramar Mukherjee, Departments of Biostatistics and Epidemiology and Center for Precision Health Data Science, University of Michigan

Maxwell Salvatore, Departments of Biostatistics and Epidemiology and Center for Precision Health Data Science, University of Michigan

Xuelin Gu, Departments of Biostatistics and Center for Precision Health Data Science, University of Michigan

Jiacong Du, Departments of Biostatistics and Center for Precision Health Data Science, University of Michigan

An interdisciplinary group of scholars and data scientists who use of data and modeling to generate timely reports and recommendations about COVID-19 in India

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