Author : Oommen C. Kurian

Expert Speak Health Express
Published on Jun 27, 2020
A Tale of Five Cities: The Journey of the Pandemic through Mumbai, Delhi, Ahmedabad, Chennai and Bengaluru

As of 26 June 2020, three states and the national capital territory—namely Maharashtra, Tamil Nadu, Gujarat and Delhi respectively—account for two-thirds of India’s COVID-19 cases and four-fifths of the total deaths from the pandemic. Within these places, more than 80% of cases as well as deaths have been concentrated in a handful of cities. Right from the initial phases of the ongoing pandemic, it has been observed that the spread of the novel coronavirus has been predominantly in urban areas around the world. Due to a range of reasons, including high population density, shared services, higher exposure to travellers and systemic constraints on social distancing, surges in COVID-19 cases and deaths have been common in urban hotspots, quickly overwhelming the existing healthcare delivery capacity in cities across the globe.

The changing epidemiological profile of the population, particularly in urban areas, makes citizens more vulnerable to COVID-19 due to the increasing burden of non-communicable diseases (NCDs). Apart from Delhi, the three states with the highest number of cases—Maharashtra, Tamil Nadu and Gujarat—are also amongst the most urbanised states in India. Even in states that are less urbanised but with a high number of cases, like Rajasthan, Madhya Pradesh and Uttar Pradesh, the primary spread has been focused within their cities—Jaipur, Indore and NCR region respectively. The relatively lower impact COVID-19 has had on many parts of India could be explained, in part, by the country’s lower levels of urbanisation. With its devastating effect on economic stability, this pandemic has particularly called urban health resilience into question.

This article attempts a quick analysis of COVID-19 trends across five Indian cities, from Mid-April onwards, when regular sub-national data became available. As consistent time series data is only available at the district level, we treat district numbers as a valid proxy for the respective cities. Apart from Mumbai, Delhi, Ahmedabad and Chennai, Bengaluru has been added into the analysis as a benchmark for efficient management of the epidemic in the initial phase, despite having a relatively high number of cases to start with. In fact, in the last week of March, Karnataka (along with Kerala and Maharashtra) was one of the three Indian states with the highest number of COVID-19 cases, with many being reported from Bengaluru.

However, by the time Lockdown 1 ended in mid-April, three cities, namely Mumbai, Delhi and Chennai, emerged as distinct hotspots. By the middle of Lockdown 2, at the end of April, cities like Ahmedabad, Jaipur and Indore had overtaken Chennai in terms of case numbers to emerge as the new hotspots. Up until the last week of Lockdown 4, at the end of May, Ahmedabad was ahead of Chennai in terms of recorded number of COVID-19 patients as Graph 1 shows.

A Tale of Five Cities: Cumulative Recorded Cases

Mumbai, Delhi, Ahmedabad, Chennai and Bengaluru 

Source: https://www.orfonline.org/covid19-tracker/

Later on, Ahmedabad showed a relative reduction in the pace of the spread and the rate of growth of case numbers declined as compared to Delhi and Chennai. It is likely that the decline in case numbers in Ahmedabad was a direct result of the relatively low number of tests conducted. According to available information, for every 1 million people in Gujarat, only 5,082 have been tested, against 13,329 per million people in Tamil Nadu. This suspicion is substantiated by the fact that Ahmedabad has consistently shown a much higher number of fatalities than Chennai from the beginning (Graph 2). Interestingly, till the second week of June, Ahmedabad had a higher number of COVID-19 deaths than Delhi as Graph 2 shows, despite having a much lower number of cases (Graph 1).

A Tale of Five Cities: Cumulative Deaths

Mumbai, Delhi, Ahmedabad, Chennai and Bengaluru

 Source: https://www.orfonline.org/covid19-tracker/

Partly thanks to a highly ramped up testing capability (Graph 3) in Tamil Nadu, Chennai has shown an explosive increase in new cases in the last few weeks (Graph 1). However, the city has been able to limit the mortality rate despite a significant load of new cases, starting from the end of May, and thus, we see the correspondingly high recovery rates accompanying the low mortality rates. Triage and treatment protocols are followed strictly by almost all hospitals, and this has improved treatment outcomes.

Daily Testing Numbers from Tamil Nadu (Graph 3)

Source: https://www.covid19india.org/state/TN

Bengaluru has been able to keep the mortality rate extremely low, but unlike Chennai, it has also been able to limit virus transmission itself—a great achievement in view of the high number of international travellers in the city. Despite having a comparable population size, Bengaluru only has under 4% of  Chennai’s case load. The city achieved this with aggressive quarantining of positive cases and retrospective tracing of contacts within 24 hours of a patient testing positive. Bengaluru managed to put the primary and secondary contacts of people who tested positive in institutional quarantine, since the size of the spread was kept small and manageable by initial interventions that were timely. Many other cities could not do the same because they started with strict containment measures rather late, when numbers had already ballooned through a silent spread.

COVID-19 Test Positivity Rates Across Respective States (Table 1)

cases tests test positivity rate
Maharashtra 1,47,741 8,73,570 16.91%
Delhi 73780 4,38,012 16.84%
Gujarat 29,578 3,45,278 8.57%
Tamil Nadu 70,977 10,08,974 7.03%
Karnataka 10,560 5,53,325 1.91%

Source: https://www.orfonline.org/covid19-tracker/

Although at the national level, lockdown was a clear example of a pre-emptive and decisive strategy, cities with heightened exposure risk from a high volume of international travellers needed to bring in mobility control measures early on. Mumbai and Delhi refused to intervene aggressively in the initial phases of the pandemic. These cities are now suffering the consequences in terms of high mortality rates and a ballooning, unmanageable spread of the novel coronavirus. Table 1 shows the high levels of test positivity across four major states, barring Karnataka, which hints at the high rate at which Karnataka is testing and tracing contacts, despite having a low number of cases.

Even within Mumbai, there are examples of strong public health measures yielding results: Dharavi’s containment measures and infection management in the early phase offers lessons for high-density and low income urban residential areas, which are often not regularised. More than 548 thousand people were screened in Dharavi and very strict containment measures deployed, as a result of which the case numbers have come down to single digits.

Case Fatality Rates (CFR) are of limited use given the extent of possible under-reporting of both cases and deaths. However, they provide us with a useful layer of information in a comparative analysis. Table 2 presents comparative CFRs across these five cities, and shows Chennai with the lowest rate, explained by the aggressive testing and case finding, supported by timely treatment that saves lives. Despite its very low levels of fatalities, a concerning trend is emerging in Bengaluru in terms of burgeoning deaths; it is noted that 67 of the 78 deaths in Bengaluru were during this month alone, perhaps requiring a change in strategy during the Unlock 1 phase.

Covid-19 Case Fatality Rate Across Indian Cities (Table 2)

recorded cases recorded deaths case fatality rate
Mumbai 70878 4062 5.73%
Delhi 73780 2429 3.29%
Ahmedabad 19839 1390 7.01%
Chennai 47650 691 1.45%
Bengaluru 1791 78 4.36%
All India 491179 15308 3.12%

 Source: https://www.orfonline.org/covid19-tracker/

Delhi, on the other hand, is a case study of under-preparedness and overconfidence doing an administration in. In early May, Delhi’s Chief Minister had made an appeal to the Central Government to open up the city, as he believed the system was ready to take the novel coronavirus head on. In fact, for most of May, Delhi was not amongst the top three states for either number of COVID-19 cases or deaths.  However, the surge in cases that followed was not exactly expected. To the administration’s credit, despite the obvious setbacks, Delhi has kept the mortality rate relatively low, at about the national average (Table 2). How the neighbouring cities of the National Capital Region (NCR) have managed to contain the spillover spread from Delhi has been impressive too, unlike what was witnessed in Maharashtra, where Thane and Pune also had secondary surges along with Mumbai. Up until now, the NCR region has been managing the spread well despite Delhi showing a huge surge.

Proportion of Recoveries across Mumbai, Delhi, Ahmedabad, Chennai and Bengaluru (Graph 4)

  Source: https://www.orfonline.org/covid19-tracker/

Lastly, Graph 4 provides a comparative perspective of the recovery rates across the five cities. The counterintuitively low rates in Bengaluru are explained by the fact that 80% of the city’s cases were found in Unlock phase 1, that is, in June alone. With management of reverse migration proving to be an issue driving up case numbers, suppression of recovery rates may be visible across different regions in the coming weeks.

Despite COVID-19 being a largely urban, concentrated emergency up until now, basic real-time information on cases, deaths, tests, and hospital facilities have been hard to come by. Risk Communication has been, perhaps, limited by the fear of a hyperactive media and a politicised atmosphere. However, this needs immediate fixing. With all relevant information reaching the public on a daily basis in a transparent manner, there would be no scope for misinterpretation or speculation.

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Author

Oommen C. Kurian

Oommen C. Kurian

Oommen C. Kurian is Senior Fellow and Head of Health Initiative at ORF. He studies Indias health sector reforms within the broad context of the ...

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