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Year : 2020  |  Volume : 64  |  Issue : 6  |  Page : 168-171  

COVID-19 and lockdown: Insights from Mumbai

Professor, School of Health Systems Studies, Tata Institute of Social Sciences, Mumbai, Maharashtra, India

Date of Submission04-May-2020
Date of Decision11-May-2020
Date of Acceptance20-May-2020
Date of Web Publication2-Jun-2020

Correspondence Address:
Kanchan Mukherjee
School of Health Systems Studies, Tata Institute of Social Sciences, V.N. Purav Marg, Deonar, Mumbai - 400 088, Maharashtra
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/ijph.IJPH_508_20

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Background: Mumbai is facing the full brunt of the COVID-19 pandemic epidemiologically and economically. Objectives: The objective was to understand the spatial distribution and trends of the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) in Mumbai during the lockdown period and draw insights for effective actions. Methods: Spatial and trend analysis was conducted to trace the spread of the virus during the lockdown period in April 2020. The administrative divisions of Mumbai, in the form of wards and zones, have been used as units of analysis. Results: Greater Mumbai area occupies only 0.015% of the landmass of India, but is contributing to over 20% of the SARS-CoV-2 cases in India. Cases of SARS-CoV-2 infections have increased over 375 times within 50 days of the lockdown. An analysis of trends across the wards during the 3-week period (April 4 to April 25) shows a skewed pattern, with three zones out of six contributing to the vast majority of cases in Mumbai. The wards with higher formal economic activity are relatively less affected than the other wards. The test positivity rate in Mumbai is much higher than the rest of India. Conclusion: The study suggests that the virus had already spread to the community in Mumbai before the lockdown started.

Keywords: Administrative wards, data triangulation, economics, effectuation theory, epidemiology, India, policy entrepreneurship, severe acute respiratory syndrome-coronavirus-2, testing rate

How to cite this article:
Mukherjee K. COVID-19 and lockdown: Insights from Mumbai. Indian J Public Health 2020;64, Suppl S2:168-71

How to cite this URL:
Mukherjee K. COVID-19 and lockdown: Insights from Mumbai. Indian J Public Health [serial online] 2020 [cited 2022 Nov 28];64, Suppl S2:168-71. Available from:

   Introduction Top

On May 1, 2020, the state of Maharashtra completed 60 years of its existence but had very little to celebrate due to the ongoing COVID-19 pandemic. On that day, the state earned the dubious distinction of carrying the burden of one-third of the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infections in the country. The capital city of the state, Mumbai, contributed to 66% of the cases in Maharashtra and over 20% of all cases in India. Mumbai and Maharashtra went into lockdown from March 22, 2020, as a continuation of the nationwide curfew and as of May 10, completed 50 days of lockdown.

The Greater Mumbai region, which comprises Mumbai City and suburban areas, occupies only 483 km2 but has an estimated population of 12.8 million.[1] It is one of the most densely populated urban agglomerations in the world, with a population density more than double of New York City, which is currently the epicenter of the COVID-19 pandemic in the USA. The governing civic body for this region is the Municipal Corporation of Greater Mumbai also known as Brihanmumbai Municipal Corporation (BMC), which is the richest municipal corporation in Asia. Greater Mumbai is divided into 24 wards for administrative purposes, among six zones. Of these, nine are city wards, six are eastern suburb wards, and nine are western suburb wards.[1] The classification of wards and zones along with the localities in Mumbai is shown in [Table 1].[1],[2]
Table 1: Classification of Mumbai wards and zones (compiled from the Municipal Corporation of Greater Mumbai Civic Diary, 2020, and Municipal Corporation of Greater Mumbai Disaster Management Department ward maps, 2018)

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This study was conceptualized to understand the trends and spatial distribution of the SARS-CoV-2 in Mumbai through its administrative units, the wards, and zones. Each ward in Mumbai has a ward office, which is responsible for the implementation of the public health and economic activities in its respective ward. The ward officers are grassroot decision makers in Mumbai. Although ward boundaries are very porous, the lockdown had brought in mechanisms of isolating them through suspension of public transport, enforcement of social distancing, and creation of containment zones and red zones. In this context, this study was conducted to provide insights on understanding the spatial spread of the virus in Mumbai across these administrative units, which would be useful for local-level decision makers in planning and implementing control efforts.

   Materials and Methods Top

This was an observational study using secondary data publicly available from the BMC. Data mining was performed from records of the disaster management department of the BMC to obtain the demographic and geographical characteristics of the wards in Mumbai. Coronavirus updates provided by the BMC health department through its press releases and websites were used to obtain information on cases and testing. Based on the available data, a trend analysis was conducted to plot the growth trajectory of SARS-CoV-2 cases from day 1 to day 50 of the lockdown. The disaggregated ward-wise data on SARS-CoV-2 cases, which were available from the BMC for the period April 4 to April 25, were used to chart the ward- and zone-wise trends and distribution. Furthermore, the available testing data in Mumbai were used to conduct an incremental analysis of the cases detected per tests in Mumbai to compare it with that of the rest of India. Data triangulation was done from the above data sources to provide insights for the situation in Mumbai.

   Results Top

Mumbai saw over 375 times' increase of cases within 50 days of the lockdown [Figure 1]. The ward-wise and zone-wise SARS-CoV-2 trend data for 3 weeks (April 4 to April 25) are shown in [Figure 2], which shows an increasing trend across all wards of the city. However, some wards are much more affected than the others. Zone 2 has contributed the maximum number of cases, and G/S (includes Worli and neighboring areas) is the ward with the maximum cases. However, the maximum increase in cases (87 times) during this time period occurred in G/N ward (includes Dharavi). In Mumbai, the overall increase in cases during this 3-week period was 14.75 times, with the slowest increase of 3.2 times (T ward). The wards with the lowest number of cases (<35) and the slowest progression were R/N, C, and T as on April 25. These wards include the areas of the National Park (R/N ward), Marine Lines (C ward), and Mulund (T ward). Three zones (Zones 1, 2, and 3) were contributing to more than 70% of the SARS-CoV-2 cases in Mumbai, whereas wards in Zones 4 and 6 have the least number of cases and have shown the slowest progression. The map in [Figure 3] depicts the geographical distribution and shows the spread of cases among the wards in Mumbai. Among the 24 wards, four wards are areas of higher formal economic activities relative to the other wards. These wards are H/E (Bandra Kurla Complex), K/E (MIDC Marol and SEEPZ special economic zone), M/W (BPCL refinery, RCF, and Tata Power), and R/S (Charkop industrial estate). As of April 25, these four wards have been relatively less affected in terms of cases and progression rate.
Figure 1: Trends in severe acute respiratory syndrome-coronavirus-2 cases in Mumbai (day 1 to day 50 of the lockdown).

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Figure 2: Severe acute respiratory syndrome-coronavirus-2 trends in Mumbai during the April 2020 lockdown.

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Figure 3: Spread of severe acute respiratory syndrome-coronavirus-2 in Mumbai (Source: Brihanmumbai Municipal Corporation).

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In terms of testing, Mumbai has been doing better compared to the national average. On May 6, BMC had completed over 100,000 tests (7692 tests/million), which was about eight times higher than the national average of 982/million on the same day. An incremental analysis of cases detected per tests conducted between April 23 and May 6 showed that 12 cases of SARS-CoV-2 infection were detected for every 100 tests performed in Mumbai, whereas the rest of India detected only three cases for every 100 tests performed during the same period.

   Discussion Top

The wards of Mumbai are very porous in terms of movement of people as people travel to work, cutting across ward boundaries. However, the lockdown provided a natural experiment in which movements across wards were curtailed and limited only to essential and emergency services. Within this environment of restricted people movement, trend and spatial analyses show that the virus increased its geographic spread across different wards. With around 13 million people crammed in 483 km2 space, one out of every five cases of SARS-CoV-2 infection in India is in Mumbai.

This study shows that the cases are not evenly distributed across the 24 wards of Mumbai. The R/N ward, which had the lowest number of cases, has a large area uninhabited by humans in the form of the national park. T ward, which showed the lowest progression of cases during this period, is the least densely populated ward in Greater Mumbai. However, C Ward, which had <35 cases and showed very slow increase, is the most densely populated ward in Mumbai. This ward includes a very small area of 1.8 km2 around Marine Drive and is home to the higher income groups of Mumbai, with no slum population. C Ward is 2.5 times more densely populated than ward G/S (which had contributed to the maximum number of cases) and 1.5 times more densely populated than G/N ward (which showed the maximum increase in cases). A closer look at the source of cases within the wards revealed a huge cluster of chawls in G/S ward and Asia's largest slum (Dharavi) in G/N ward. With community latrines, overcrowded living spaces, and negligible boundaries between neighbors, the concept of physical and social distancing remains a theory in these settings. Hence, it is the social and living conditions and not just population density, which seems to have facilitated the virus spread. This is a structural and relatively fixed variable that is contributing to the spread of SARS-CoV-2 infection in Mumbai.

The other variable closely associated with SARS-CoV-2 cases during this lockdown period was the amount of testing being conducted. This was a changing variable as the number of tests conducted changed every day. An incremental analysis of cases detected per tests during April 23 to May 6 was conducted and compared with the rest of India during the same period. This showed that Mumbai was detecting four times more number of cases than the rest of India for the same number of tests during that period. This suggests that the increase in cases in Mumbai is not merely a function of increased testing, but also reflects a much higher density of infection in this city.

The spatial analysis of trends in Mumbai suggests that SARS-CoV-2 had spread across the community in Mumbai before the lockdown started and now with increased testing, these cases are being detected. The spread of the virus in Mumbai in April showed a linear growth, and it is possible that the lockdown prevented an exponential growth. However, the virus seems to be ahead of the testing curve and as testing rates improve, a clearer picture will emerge. Moreover, the number of cases in the four most economically critical wards (H/E, K/E, M/W, and R/S) is less, and they also have a slower spread of infection. These wards have a large inflow of population during working hours from other wards as people travel to work in the offices and industries located in these wards. As the lockdown was already in place when this study was conducted, it is possible that the lockdown measures prevented a larger spread of virus within these wards.

This analysis captures a short period in the journey of the virus in Mumbai and is limited by the availability of ward-wise data. It could not be ascertained if the wards that showed low virus cases in April had lesser testing than the wards that showed higher virus cases. A longitudinal study of the ward-wise trends will provide a clearer picture with reference to the community spread of this virus over time. The evidence from this analysis can be used by ward-level decision makers to prioritize actions. The study also highlights how testing has played an important role in Mumbai and its importance to achieve an accurate understanding of the ground-level situation.

The insights from Mumbai can be applied to larger administrative regions in India, such as other districts and states. Spatial and trend analyses using disaggregated epidemiological and economic data across districts and states in India will provide for a more data-informed discussion for decision-making. As testing rates increase, decision makers across all levels need to integrate epidemiological data with economic data in a constructive manner to plan ahead. Because knowledge related to this virus is limited at present and changing constantly, the future is unpredictable. In this situation, “policy entrepreneurship” is the need of the hour, with a theoretical shift from the logic of prediction to the logic of control so that one can focus on the controllable aspects of an unpredictable future. The theory of effectuation[3] needs to be implemented. This includes identification and use of contingencies through cooperative strategies and alliances, with the decision-making criteria based on affordable loss or acceptable risks.

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Conflicts of interest

There are no conflicts of interest.

   References Top

Municipal Corporation of Greater Mumbai. Civic Diary 2020. 62nd Annual Publication. Available from: [Last accessed on 2020 May 23].  Back to cited text no. 1
Municipal Corporation of Greater Mumbai. Disaster Management Department. Ward Maps 2018. Available from: [Last accessed on 2020 May 23].  Back to cited text no. 2
Sarasvathy SD. Causation and effectuation: Towards a theoretical shift from economic inevitability to entrepreneurial contingency. Acad Manage Rev 2001;26:243-63.  Back to cited text no. 3


  [Figure 1], [Figure 2], [Figure 3]

  [Table 1]

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