Trans. Inst. Indian Geographers
ISSN 0970-9851
Indexed in Scopus
Factors affecting COVID-19 infection and deaths in the millionplus cities of India
Purva Yadav* and Aisharya Bhattacharjee, New Delhi
Abstract
Big cities are highly complex spatial units and have diverse subpopulations and neighborhoods
with different sociocultural needs and vulnerable groups concerning public health emergencies,
such as COVID-19. When these cities face an epidemic that spreads rapidly within the urban
community, then human density and the urban setting have proven to be a tremendous
liability and a matter of concern. The present study in this context is a preliminary attempt
to understand the COVID-19 pandemic in the Indian context. The paper presents a city-level
study of 46 million-plus cities in India to identify major determining factors (demographic,
economic, environmental, infrastructural, and institutional interventions) of the COVID-19
infection and related deaths in different lockdown and unlock phases. Using the multiple
regression model we found that the impact of these factors does significantly explains the
variation in the COVID-19 infections and related deaths. However, the role of individual
indicators does seem to have a differential impact across phases of the lockdown strategy.
Indicators, such as GDP, hospital-doctor ratio, public transport usage, and administrative
status of the city have been found to be the most significant factors influencing COVID-19
cases and deaths. On the other hand, co-morbidities do not appear as consistent significant
factors, while the much-debated density parameter plays an insignificant role in the Indian
big cities. We would like to emphasize that the results are at best indicative in nature, and for
an in-depth understanding of each of these factors and spatial complexity, we require further
detailed analysis at a more disaggregated level.
Keywords: COVID-19, million-plus cities, India, lockdown, regression models
Introduction
Big cities are highly complex spatial units
and are not only dependent on each other
(regionally and globally) but are also tied with
the neighboring small towns and rural areas.
Moreover, these urban areas have diverse
subpopulations and neighborhoods with
different socio-cultural needs and vulnerable
groups concerning public health emergencies,
such as COVID-19. When these cities face
an epidemic that spreads rapidly within
the urban community, then human density
and the urban setting have proven to be a
tremendous liability and a matter of concern.
In developing countries like India with
inherent diversity and stark disparity, a crisis
such as COVID-19 highlights vulnerabilities
of the cities which are intricately tied with the
space-blind and uniform policies.
The urban risk from extreme events is
affected by the location, density, scale, and
connectivity. For example, location and scale
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(the positioning and city size) have linear
effects, primarily affecting the population
and the availability of resources. On the other
hand, density and connectivity often have
nonlinear impacts, both positive and negative.
In addition, the urban risk is also affected by
the feedback effects of interaction between
the urbanization process and climate change
(Siri et al., 2015). At this point, the vector and
velocity of modern transport networks would
play a key role in turning the epidemic into a
pandemic. And in the case of COVID-19, it has
been proven to be true. For example, a recent
study by Yadav and Bhattacharjee (2020)
has noted the impact of urbanization and
connectivity on COVID-19 spread in India for
the initial phases of the lockdown. The urban
vulnerability may arise for many extreme
events, and many times the factors that make
cities attractive are also the ones that increase
the risk. Cities are primarily high-density
zones with heavy resource dependence and
often resulting in their depletion. Land-use
change is often witnessed in and around the
cities. Hence, they are especially vulnerable
to extreme events and often play major roles
in aggravating the intensity of such events.
Evidences suggest that civilizations (and,
indeed, cities) that generate unsustainable
demand are vulnerable to rapid collapse.
If we track the spatial trajectory of the
ongoing pandemic, almost 90 percent of
COVID-19 cases are found in urban areas
and the consequent impact is most damaging
in cities. In an urban setting where risks
of transmission and its multidimensional
impacts, such as social, physical, and
economic are much higher (primarily due
to a greater concentration of people and
activities as well as better linkages with other
regions), data and investigation are crucial
228 | Transactions | Vol. 43, No. 2, 2021
for early outbreak detection and response
(testing, diagnosis, isolation, contact tracing,
and quarantine) to contain the transmission
of the virus (UN, 2020). In this backdrop,
it is important to understand the nuances of
contagion from spatial-temporal perspectives.
The current paper is a preliminary attempt in
this direction to identify the factors affecting
the transmission of this virus and the resultant
deaths with regard to the million-plus cities
in India.
Theoretical context and purpose
Multiple combinations of risk factors
have been hypothesized to have an impact
on COVID-19 cases and deaths. Yet not
many studies have analyzed the potential
confounding effects of these factors
(Priyadarsini and Suresh, 2020). The present
study with the help of multivariate analysis
attempts to identify the factors affecting
COVID-19 cases and death in the 46 millionplus cities of India (Figure 1) that have
become the epicenters of the pandemic.
The choice of the variables hypothesized
as significant factors in the spread of
COVID-19 has been guided by studies that
have tried to portray the relationship of
certain variables with COVID-19 infection
and related death rates. For example, several
studies have focused on the impact of density
on COVID-19 infection and mortality rates
(Arif and Sengupta, 2020; Hamidi, et al.,
2020; Henderson, 2020; Rocklöv and Sjödin,
2020; Shoichet and Jones, 2020). Hughes
(2020) has noted that the metropolitan region
with a higher number of counties linked with
one another has become more vulnerable
to the pandemic. This is further supported
by Papandreou (2020) who pointed out that
the connectivity factors have a direct effect
on the transmission of the pandemic in a
Fig. 1: Million plus cities in India
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local-global-local transportation pattern.
Also, the role of public transport has been
emphasized, drawing from a cluster case in
Hunan (S. Chen, 2020; Meinsenzahi, 2020).
Certain economic aspects have also
affected the infection and death rate. For
instance, a significant positive association
between GDP at the prefectural level and
confirmed cases has been identified in selected
provinces of China by Zhang et al. (2020).
Moreover, a positive association between the
poverty level and COVID-19 cases is also
traceable in some studies (Finch & Finch, 2020).
Apart from economic aspects, infrastructural
aspects like rigorous testing for COVID-19
(advocated for pandemic control) have
been found to possess a positive association
with confirmed cases and consequently the
recovery rate (Engelberg, 2020; Beaubian,
2020). However, it has also been noted that the
testing rates vary significantly across space.
For instance, according to Monnat and Cheng
(2020), testing rates are lower in areas with
more black and poor residents in the United
States. This inevitably draws attention to
certain social indicators. Low levels of literacy
have been found to pose a substantial barrier
in checking the spread of COVID-19 (Lopes
and McKay, 2020; Frieden, 2020; Paakkari
and Okan, 2020). Another important aspect
pertains to health facilities, particularly the
strength of the hospital staff. However, given
the proliferating number of cases, health
infrastructure has become burdened, and
hence a distinct relationship between them is
far from being acknowledged (Agarwal, 2020;
Sebastian, 2020; Cavallo et al., 2020; Singh et
al., 2020). Moreover, institutional factors such
as government effectiveness and the extent
to which ruling parties are well established
have also explained variations in COVID-19
transmission rates (Maor & Howlett, 2020).
230 | Transactions | Vol. 43, No. 2, 2021
Studies have also focused on
environmental factors mainly because of
“flu” like symptoms and have revealed higher
epidemic rates of COVID-19 with lower
average temperature (Alvarez-Ramirez and
Meraz, 2020; Ficetola and Rubolini, 2020;
Pirouz et al., 2020). Moreover, a positive
association between atmospheric pollution
levels and a high level of COVID-19 lethality
has been noted, since high pollutant levels
render the population more vulnerable to
respiratory diseases (Conticini, et al., 2020;
Setti, et al., 2020). In fact, the discourse
on incumbent respiratory disorders and
COVID-19 necessarily brings into context
an in-depth study of how vulnerable groups
are affected by COVID-19. WHO (2020) has
identified vulnerable groups in urban areas
that include the elderly population and persons
with underlying medical conditions and
several studies have also reflected a positive
association between underlying medical
conditions like chronic kidney disease,
obesity, cardiac issues, diabetes mellitus
to the COVID-19 infection rate and deaths
(BMJ, 2020; Centre for Disease Control
and Prevention, 2020). In this backdrop, the
present study provides a holistic viewpoint
to capture the causality of the multiple set
of factors to COVID-19 cases and deaths in
Indian cities.
Database and Methodology
To better understand the causal factors, a
multivariate regression model is used. To
deal with heteroscedasticity, we have used
robust standard errors (see Yadav, 2020 for
details). At the initial stage of the study,
22 indicators were selected, but due to the
multicollinearity, three indicators namely,
the share of the elderly population, the share
of obese/overweight population, and the
presence of international airport have been
dropped. Finally, 19 indicators have been
used in the model as outlined in Figure 2,
along with the data sources. The dependent
variables namely, COVID-19 cumulative
cases and deaths reported in the cities have
been collated from the public data platformhowindialives.com.
Regression was run on the share of
cumulative COVID-19 cases and deaths
for the starting date of each of the four
lockdowns and five phases. The government
has announced nationwide lockdown phases
and the same are mentioned below:
y
Lockdown Phase I-25th March-14th April
2020
y
Lockdown Phase II-15th April-3rd May
2020
y
y
Lockdown Phase III-4th May-17th May
2020
th
th
Lockdown Phase IV-18 May-30 May
2020
These are followed by five unlock phases as
given below:
y
Unlock phase I-1st June-30th June 2020
y
Unlock phase II-1st July-31st July 2020
y
Unlock phase III-1st August-31st August
2020
y
Unlock phase IV-1st September-30th
September 2020
Unlock phase V-1st October-30th October
2020
y
Analysis and Discussion
Results of the multivariate regression models
(Tables 1 to 4) have reflected revealed that the
set of multiple factors, such as demographic,
economic, infrastructural, institutional, and
environmental, explains about 75 percent
to around 91 percent of the variation in
COVID-19 cumulative cases reported in the
46 cities of India while it explains nearly 68
percent to 91 percent of the variation in the
share of the reported cumulative deaths across
the different lockdown and unlocks phases
considered in the paper. The analysis is divided
into sections-I and II presenting regression
results of COVID-19 cases and related deaths
respectively and is confined to the indicators
that are statistically significant in explaining
each of them. Population density, the share of
the anemic population, road connectivity to
National Highways, literacy rates, hospitals
per lakh population, and ruling political
party are the indicators that are statistically
insignificant for explaining cases and deaths
in different phases.
Section I: Factors affecting COVID-19
infection in million-plus cities in India:
The share of cases is significantly affected
by the economic size of the city and is
positively associated (Table 1 and 2). This is
directly related to the fact that big cities act as
economic giants with agglomeration effects
and transport hubs that encourage higher
flows and the associated risk of the virus
spread. In line with a similar argument, we
hypothesized that cities that are administrative
capitals will report more infection. Results
have lent support to this with a significant
positive association for lockdown phase 1.0
and unlock phase 3.0, 4.0, and 5.0.
Social infrastructure indicators, such
as hospital beds per lakh population have
significantly affected the cases at the
onset of lockdown 3.0 and 4.0. This could
primarily be because the rapid increase in
the number of cases resulted in immense
pressure on the already skewed public health
infrastructure resources. In fact, at the onset
of lockdown 3.0 and unlock phase 3.0, 4.0,
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232 | Transactions | Vol. 43, No. 2, 2021
Fig. 2: Factors affecting COVID-19 Transmission and Deaths
Table 1: Factors affecting COVID-19 transmission in Lockdown Phases - Multivariate Regression
Variables
Share of Cases (Dependent Variable)
25th March
Coef. t stat
Population Density in persons per sq. km
0.00
1.04
Share of anaemic population above 15 years
0.00 -0.01
Share of population above 15 years with high
blood sugar level
Share of population above 15 years with high
state of hypertension
GDP (Rs. Crore)
-0.01 -0.11
0.22
0.45
0.00 3.44*
HC Ratio
-0.01 -1.08
Literacy Rate
-0.02 -0.64
Hospitals per lakh population
Hospital Beds per lakh population
Hospital Doctors per lakh population
0.02
0.19
0.00
1.03
-0.02 -1.96
15th April
4th May
Coef. t stat Coef. t stat
0.00
0.59
-0.01 -0.19
0.00
0.48
19th May
Coef. t stat
0.00
0.57
0.03
0.42
0.03
0.58
0.12
0.74
0.19
0.84
-0.05
-0.27
1.43
1.86
1.38
1.7
1.42
1.86
0.00 4.35*
0.00
3.62*
0.00
4.16*
0.02
0.00
-0.18
0.00
0.17
-0.03 -0.44 -0.01
-0.09
0.05
0.72
0.14
0.91
0.17
1.5
0.01
2.74*
0.00
2.6*
-0.01 -0.95 -0.04 -2.05*
-0.03
-1.75
0.79
-0.03 -0.24
0.00
0.7
Public Transport Usage by Other Worker (%)
0.01
0.51
0.03
0.69
0.03
0.67
0.08
2.21*
Road Connectivity to National Highways
0.35
1.48
0.10
0.3
0.18
0.47
-0.07
-0.2
Average Temperature (in C)
0.02
0.39
-0.17 -1.47 -0.24
-1.91
-0.16
-1.79
Annual average PM<= 10 conc.
0.00 -0.06
1.19
0.02
1.66
-1.37
Annual average NO2 conc.
Time interval (in days)
0.01
1.56
0.01
0.00 -0.19
-0.05 -1.43 -0.06
-1.16
-0.05
-0.07 -1.35
-0.11 -1.12 -0.22
-1.94
-0.18 -2.02*
Administrative Status of City
1.74 2.74*
2.01
1.98
2.72
1.81
2.02
1.71
Ruling Political Party in State
0.03
0.27
0.36
0.41
0.44
0.21
0.28
-1.14 -1.67 -1.53
-1.61
-0.85
-1.08
0.29
-3.39
-0.47
Number of Testing Centres
0.08
-0.65 -1.29
_cons
0.58
2
0.14
R = 0.74
*statistically significant at 5 percent; Coef. = Coefficients
and 5.0, the association between hospital
doctor ratio and the COVID-19 cases has
emerged significantly with negative relation
indicating that cases surged up tremendously
owing to fewer resources in terms of health
care professionals and the existing huge
gap between the demand and supply of the
medical staff.
3.70
2
0.46
R = 0.80
2.78
2
R = 0.75
2
R = 0.80
Source: Based on Authors’ calculations
If we look at the transport aspect and
its role in the spread of the contagion, it is
interesting to note that with the start of the
lockdown phase 4.0 and unlock phase 1.0,
2.0, and 3.0, usage of public transport by
“other” workers category1 has emerged
significantly with a positive association.
The share of public transport usage is
1 Census of India (2011) provides data for the mode of travel to the top workplace by only “Other Workers”
category i.e. other than cultivators, agricultural labourers and household industry workers. The defense forces and
similar paramilitary personnel are not included. If the person was engaged in more than one economic activity during
the last year, travel to the main economic activity is considered.
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Table 2: Factors affecting COVID-19 transmission in Unlock Phases-Multivariate Regression
Results
Variables
Share of Cases (Dependent
Variable)
1st June
Coef.
t stat
1st July
Coef.
t stat
1st August
Coef.
t stat
1st September
Coef.
t stat
1st October
Coef.
t stat
Population Density in
persons per sq. km
0.00
0.63
0.00
1.23
0.00
1.69
0.00
1.58
0.00
1.38
Share of anaemic
population above 15 years
0.05
0.69
0.00
0.05
-0.01
-0.25
0.00
0.03
0.00
0.15
Share of population above
15 years with high blood
sugar level
-0.17
-0.68
Share of population above
15 years with high state of
hypertension
1.38
1.51
1.63
1.66
0.42
0.80
0.06
0.16
-0.06
-0.19
GDP (Rs. Crore)
0.00
3.52*
0.00
8.97*
0.00
6.62*
0.00
4.92*
0.00
4.84*
-0.01
-0.20
0.01
0.43
-0.02
-1.61
Literacy Rate
0.07
0.93
0.10
1.42
0.02
0.52
0.00
0.04
0.00
0.00
Hospitals per lakh
population
0.25
1.91
0.14
1.77
0.09
1.53
0.05
0.82
0.05
0.83
Hospital Beds per lakh
population
0.00
0.97
0.00
-0.66
0.00
-0.74
0.00
-0.86
0.00
-1.30
Hospital Doctors per lakh
population
-0.03
-1.49
0.00
-0.02
PublicTransport Usage by
Other Worker(%)
0.13
2.70*
0.13
3.61*
0.06
2.64*
0.02
0.94
0.00
0.07
Road Connectivity to
National Highways
-0.10
-0.23
-0.45
-1.16
0.00
-0.02
0.13
0.83
0.19
1.41
Average Temperature (in C)
-0.15
-1.50
-0.04
-0.61
-0.01
-0.37
-0.02
-0.66
-0.03
-1.13
Annual average PM<= 10
conc.
0.02
1.49
0.01
1.55
-0.01
-1.43
-0.01 -2.61*
Annual average NO2 conc.
-0.06
-1.30
-0.03
-1.54
0.02
1.48
Time interval (in days)
-0.23 -2.15*
-0.07
-1.24
HC Ratio
-0.47 -2.19*
-0.33 -2.74*
-0.01 -2.31*
-0.08 -2.10*
-0.20 -2.38*
-0.02 -2.22*
-0.02 -3.51*
0.02
2.30*
-0.07 -2.24*
-0.15 -2.11*
-0.02 -2.12*
-0.01 -3.61*
-0.01 -2.93*
0.02
2.56*
-0.06 -2.24*
Administrative Status of
City
2.38
1.61
1.06
1.42
1.05
2.36*
0.83
2.84*
0.67
2.77*
Ruling Political Party in
State
0.44
0.46
-0.19
-0.24
-0.10
-0.21
-0.04
-0.13
-0.01
-0.03
Number of Testing Centres
-0.82
-0.86
0.11
0.17
0.02
0.05
0.09
0.40
0.11
0.57
_cons
-5.87
-0.70
-8.56
-1.43
-0.30
-0.07
1.90
0.53
2.22
0.69
R2 = 0.79
R2 = 0.88
*statistically significant at 5 percent; Coef. = Coefficients
234 | Transactions | Vol. 43, No. 2, 2021
R2 = 0.91
R2 = 0.89
R2 = 0.89
Source: Based on Authors’ calculations
Table 3: Factors affecting COVID-19 deaths in Lockdown Phases - Multivariate Regression
Results
Variables
Share of Deaths (Dependent Variable)
15th April
Coef. t stat
4th May
Coef. t stat
19th May
Coef. t stat
Population Density in persons per sq. km
0.00
0.15
0.00
-0.20
0.00
-0.26
Share of anaemic population above 15 years
0.12
0.55
0.05
0.54
0.07
0.81
Share of population above 15 years with high blood sugar
level
Share of population above 15 years with high state of
hypertension
GDP (Rs. Crore)
0.83
1.26
0.52
1.73
0.60
2.18*
2.84
1.16
1.33
1.21
0.99
1.03
0.00
2.86*
0.00
2.74*
0.00
2.71*
HC Ratio
-0.08
-1.11
-0.03
-0.75
-0.01
-0.36
Literacy Rate
-0.15
-0.72
-0.03
-0.30
0.01
0.10
0.40
0.95
0.19
0.99
0.23
1.31
0.02
3.39*
0.02
4.73*
0.01
4.96*
Hospitals per lakh population
Hospital Beds per lakh population
Hospital Doctors per lakh population
-0.17 -3.03*
Public Transport Usage by Other Workers(%)
-0.04
-0.29
0.01
0.15
0.03
0.69
1.55
1.47
0.54
1.10
0.39
0.89
-0.37 -2.14*
-0.30
-1.99
Road Connectivity to National Highways
Average Temperature (in C)
Annual average PM <= 10 conc.
-0.87 -2.29*
-0.07 -2.99*
-0.06 -2.87*
0.00
0.15
0.01
0.78
0.01
1.12
Annual average NO2 conc.
-0.10
-0.70
-0.04
-0.71
-0.04
-0.70
Time interval (in days)
-0.85 -2.53*
Administrative Status of City
Ruling Political Party in State
9.31
2.13*
1.21
0.48
Number of Testing Centres
-6.75 -2.46*
_cons
27.54
1.05
R2 = 0.68
*statistically significant at 5 percent; Coef. = Coefficients
tremendously high for the metropolitan cities
and the risk of COVID-19 infection has a
direct connection to it, as triggered by inhaled
aerosols, or tiny particles, breathed out by the
infected passengers onboard (Chen, 2020).
Also, the adherence of the passengers to
the COVID-19 related protocols in public
transport modes has remained a daunting task
for the authorities to enforce. It is interesting
that with the staggered easing of mobility
restrictions since lockdown phase 4.0, there
was a fear of the increase in the number
-0.37 -2.41*
-0.33 -2.43*
4.11
2.01*
0.67
0.57
0.66
0.64
-2.83 -2.19*
-1.97
-1.68
0.28
0.03
6.91
0.58
R2 = 0.71
3.21
1.79
R2 = 0.74
Source: Based on Authors’ calculations
of cases in these cities, and the same was
supported by the regression results.
Furthermore, the time gap between
implementation of the lockdown by
respective states and that of the Central
government has emerged significantly with
the negative association since the onset of
lockdown phase 4.0 because the states which
imposed a total lockdown, days preceding
the national lockdown were better able to
check the spread of the pandemic. Last but
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Table 4: Factors affecting COVID-19 deaths in Unlock Phases - Multivariate Regression
Results
Variables
1st June
t stat
Coef.
t stat
1st September
Population Density in
persons per sq. km
0.00
-0.16
0.00
0.40
0.00
1.07
0.00
1.09
0.00
0.75
Share of anaemic
population above 15 years
0.08
1.07
0.08
1.13
0.04
0.76
0.02
0.71
0.03
0.92
Share of population above
15 years with high blood
sugar level
0.34
1.30
-0.01
-0.03
-0.23
-1.44
-0.16
-1.50
-0.13
-1.39
Share of population above
15 years with high state of
hypertension
0.95
1.06
1.00
1.29
0.72
1.22
0.34
0.80
0.01
0.03
GDP (Rs. Crore)
0.00
3.23*
0.00
4.18*
0.00
4.88*
0.00
4.47*
0.00
4.29*
-0.01
-0.24
0.01
0.34
-0.01
-0.80
-0.02
-1.73
Literacy Rate
0.03
0.32
0.06
0.86
0.04
0.77
0.03
0.89
0.04
1.36
Hospitals per lakh
population
0.29
1.88
0.21
1.61
0.14
1.39
0.12
1.39
0.18
2.63*
Hospital Beds per lakh
population
0.01
4.58*
0.00
1.03
0.00
0.72
0.00
0.31
0.00
-0.83
-0.06 -2.70*
-0.03
-1.40
-0.03 -2.41*
Coef.
t stat
1st October
Coef.
Hospital Doctors per lakh
population
Coef.
1st August
Share of Deaths
(Dependent Variable)
HC Ratio
t stat
1st July
-0.03 -3.21*
Coef.
t stat
-0.03 -2.38*
-0.02 -3.30*
PublicTransport Usage by
Other Worker (%)
0.09
1.75
0.11
2.53*
0.11
3.69*
0.07
2.95*
0.04
2.30*
Road Connectivity to
National Highways
0.32
0.73
-0.01
-0.02
0.17
0.65
0.24
1.27
0.21
1.25
-0.26 -2.04*
-0.13
-1.31
-0.03
-0.42
-0.04
-0.78
Average Temperature (in C)
-0.09 -2.18*
Annual average PM <= 10
conc.
0.02
1.29
0.02
1.77
0.01
0.78
0.00
-0.40
0.00
-1.22
Annual average NO2 conc.
-0.04
-0.83
-0.07
-1.35
-0.02
-0.78
0.00
0.17
0.01
0.65
Time interval (in days)
-0.33 -2.61*
-0.20
-1.84
-0.16 -2.45*
-0.14 -3.03*
-0.15 -3.66*
Administrative Status of
City
3.04
1.80
2.18
1.51
2.55
2.67*
1.89
3.03*
0.89
2.00*
Ruling Political Party in
State
0.78
0.79
0.65
0.74
0.58
0.97
0.54
1.27
0.47
1.28
Number of Testing Centres
-1.80
-1.62
-0.88
-0.95
-0.99
-1.58
-0.66
-1.61
-0.06
-0.22
_cons
-2.51
-0.26
-7.79
-0.96
-5.21
-0.93
-2.68
-0.63
-1.29
-0.35
R2 = 0.79
R2 = 0.83
*statistically significant at 5 percent; Coef. = Coefficients
236 | Transactions | Vol. 43, No. 2, 2021
R2 = 0.89
R2 = 0.90
R2 = 0.91
Source: Based on Authors’ calculations
not the least, environmental factors like PM
10 concentration have been found to have
a significant (negative association) while
NO2 concentration has a significant positive
association since the onset of unlocking phase
4.0 and 5.0. Although the reason behind the
former is unclear, the latter is directly related
to the incumbent risk of getting affected by
respiratory diseases.
Section II: Factors affecting COVID-19
deaths in million-plus cities in India
Tables 3 and 4 relate to the factors affecting
COVID-19 related deaths in the big cities of
India. Economic size (positive association),
hospital doctors ratio and time gap (negative
association) have been found statistically
significant in affecting the share of COVID-19
related deaths in different lockdown phases.
At the onset of lockdown phase 4.0, a positive
association between co-morbidity i.e. share
of high blood pressure and death cases is
evident. The same has been noted in research
conducted by the Centre for Disease Control
and Prevention (2020) where patients with
high blood pressure were associated with an
increased risk of COVID-19 deaths.
Similar to COVID-19 infection, the
share of public transport usage by the
“other” worker category has emerged to have
significantly affected the COVID-19 related
deaths since the onset of the unlock phase 2.0.
This could mainly be because workplaces
have gradually opened up, particularly for
those activities where work from home option
is not viable or are part of essential services;
hence to meet the mobility demand public
transport started operating in a staggered
manner, which has increased the contagion
risk and the consequent deaths. Furthermore,
the environmental factor i.e. average
temperature has emerged significantly with a
negative association in lockdown phase 2.0,
3.0, and unlock phase 1.0 and 5.0; however,
requires further detailed analysis for a
better understanding of this. Other factors
that have been found to yield a significant
effect on COVID-19 deaths, are capital city
status (positive association) at the onset of
lockdown phase 2.0, 3.0 and unlock phase
3.0, 4.0, and 5.0, and the number of testing
centers (negative association) in phase 2.0
and 3.0 of the lockdown phase.
Conclusions
In the Indian context, the impact of the set
of multiple factors, such as, demographic,
economic, infrastructural, environmental,
and institutional does significantly explains
the variation in the COVID-19 infections and
related deaths. However, the role of individual
indicators does seem to have a differential
impact on the dependent variables in different
phases of the lockdown strategy. Indicators,
such as GDP, hospital-doctor ratio, public
transport usage, and administrative status
of the city have been found to be the most
significant factors influencing COVID-19
cases and deaths. On the other hand, for
example, co-morbidities do not appear as
consistently significant factors, while the
much-debated density parameter plays an
insignificant role in the Indian big cities.
Among others, these are few interesting
empirical observations based on our
preliminary understanding of the COVID-19
pandemic in India from the spatial-temporal
perspective.
When we started working on this study,
the COVID-19 situation was very new, and
the availability of consistent comparable
data across time did pose a major challenge,
besides the lack of real-time comparable data
on some of the parameters we have considered
Transactions | Vol. 43, No. 2, 2021 | 237
in the paper. Hence the study is limited in
its scope but can be a useful contribution
to the COVID-19 literature. The results are
at best indicative in nature, and for an indepth understanding of each of these factors
and spatial complexity, we require further
detailed analysis at a more disaggregated
level. Since a pandemic may cause sudden,
widespread morbidity and social, political,
and economic disruption, studies reflecting
on regional nuances are much useful though
missing in contemporary research and in the
policy framework.
References:
Agarwal, N. (2020, 12-June). Lack of hospital
beds amid rising coronavirus COVID-19
cases in Delhi a cause of concern. Retrieved
2020, 12-July from https://zeenews.india.
com/india/lack-of-hospital-beds-amidrising-coronavirus-covid-19-cases-in-delhia-cause-of-concern-2289431.html
Alvarez-Ramirez, & Meraz. (2020, 23-March).
medRxiv. Retrieved 2020, 6-May from
Role of meteorological temperature and
relative humidity in the January-February
2020 propagation of 2019-nCoV in
Wuhan, China: https://www.medrxiv.org/
content/10.1101/2020.03.19.20039164v1
Arif, M., & Sengupta, S. (2020). Nexus between
population density and COVID 19 pandemic
in the south Indian states: A geo-statistical
approach. Environment, Development and
Sustainability, 23(3), 1-29.
Retrieved 2020, 12-July from Science
Daily:
https://www.sciencedaily.com/
releases/2020/06/200601101308.htm
Cavallo, J. J., Donoho, D. A., & Howard P.
Forman. (2020). Hospital Capacity and
Operations in the Coronavirus Disease 2019
(COVID-19) Pandemic—Planning for the
Nth Patient. JAMA Health Forum, 1(3), 1-16.
Centre for Disease Control and Prevention.
(2020, 25-June). People with Certain
Medical Conditions. Retrieved 2020, 12July from COVID-19: https://www.cdc.
gov/coronavirus/2019-ncov/need-extraprecautions/people-with-medical-conditions.
html
Chen, S. (2020, 20-April). Coronavirus can
travel twice as far as official ‘safe distance’.
Retrieved 2020, 11-July from South China
Morning Post: https://www.scmp.com/news/
china/science/article/3074351/coronaviruscan-travel-twice-far-official-safe-distanceand-stay
Conticini, E., Frediani, B., & Caro, D. (2020).
Can atmospheric pollution be considered
a co-factor in extremely high level of
SARS-CoV-2 lethality in Northern Italy?
Environmental Pollution, 261.
Engelberg, S. (2020, 23-March). The Coronavirus
Testing Paradox. Retrieved 2020, 12-July
from ProPublica: https://www.propublica.
org/article/covid-19-coronavirus-testingparadox-united-states
Ficetola, & Rubolini. (2020). Climate affects
global patterns of COVID-19 early outbreak
dynamics. Science of the total Environment,
761, 1-24.
Beaubian, J. (2020, 12-March). Singapore Wins
Praise For Its COVID-19 Strategy. The U.S.
Does Not. Retrieved 2020, 12-July from The
Coronavirus Crisis: https://npr.tumblr.com/
post/612394953510961152/singapore-winspraise-for-its-covid-19-strategy
Finch, W. H., & Finch, M. E. (2020). Poverty and
Covid-19: Rates of Incidence and Deaths in
the United States during the First 10 Weeks
of the Pandemic. Frontiers in Sociology,
5(47), 1-10.
BMJ. (2020, 1-June). Underlying illness risk
factors for severe COVID-19 or death.
Frey, W. H. (2020, 2-May). Coronavirus is
making some people rethink where they
want to live. (CNN, Interviewer) Retrieved
238 | Transactions | Vol. 43, No. 2, 2021
2020, 9-July from CNN: https://www.
wyff4.com/article/coronavirus-is-makingsome-people-rethink-where-they-want-tolive-1588513869/32355488#
Frieden, J. (2020, 19-June). Lack of Health
Literacy a Barrier to Grasping COVID-19.
Retrieved 2020, 12-July from MedPage
Today:
https://www.medpagetoday.com/
infectiousdisease/covid19/87002
Hamidi, S., Sabouri, S., & Ewing, R. (2020).
Does Density Aggravate the COVID-19
Pandemic? Journal of the American Planning
Association, 86(4), 495-509.
Henderson, E. (2020, 3-July). High population
density in India associated with spread of
COVID-19. Retrieved 2020, 7-July from
News Medical: https://www.news-medical.
net/news/20200703/High-populationdensity-in-India-associated-with-spread-ofCOVID-19.aspx
Hughes, N. (2020, 18-June). Urban Density
not linked to higher coronavirus infection
rates. Retrieved 2020, 9-July from Science
Daily:
https://www.sciencedaily.com/
releases/2020/06/200618110953.htm
Lopes, H., & McKay, V. (2020). Adult learning
and education as a tool to contain pandemics:
The COVID-19 experience. International
Review of Education, 66, 575–602.
Maor, M., & Howlett, M. (2020). Explaining
variations in state COVID-19 responses:
psychological, institutional, and strategic
factors in governance and public policymaking. Policy Design and Practice, 3(3),
228-241.
Meinsenzahi, M. (2020, 27-February). Photos
show what it’s like to travel around the
world by train, bus, boat, and plane in the
age of coronavirus. Retrieved 2020, 11July from Business Insider: https://www.
businessinsider.in/slideshows/miscellaneous/
photos-show-what-its-like-to-travel-aroundthe-world-by-train-bus-boat-and-plane-in-
the-age-of-coronavirus/slidelist/74326701.
cms
Monnat, S. M., & Cheng, K. J. (2020, 1-April).
COVID-19 Testing Rates are Lower in
States with More Black and Poor Residents.
Syracuse University. Lerner Center for Public
Health Promotion.
Office of the Registrar General & Census
Commissioner, Ministry of Home Affairs,
Government of India. (2011). District Census
Handbook.
Paakkari, L., & Okan, O. (2020). COVID-19:
Health literacy is an underestimated problem.
The Lancet Public Health, 5(5), 249-250.
Papandreou, T. (2020, 27-March). Is the
Coronavirus The Transportation Industry’s
Opportunity? Retrieved 2020, 16-July
from
https://www.forbes.com/sites/
timothypapandreou/2020/03/27/is-thecoronavirus-the-transportation-industrysopportunity/?sh=5af228c4752b
Pirouz, B., Golmohammadi, A., Masouleh, H.
S., Delazzari, C., & Violini, G. (2020, 23July). Relationship between Average Daily
Temperature and Average Cumulative
Daily Rate of Confirmed Cases of
COVID-19. Retrieved 2020, 1-October
from MedRxiv: https://www.medrxiv.org/
content/10.1101/2020.04.10.20059337v3
Rocklöv, J., & Sjödin, H. (2020). High Population
Densities Catalyse the Spread of COVID-19.
Journal of Travel Medicine, 27(23), 186-192.
Rodrigue, J. P. (2016). The geography of transport
systems (5th ed.). Oxon: Routledge.
Sebastian, M. (2020, 19-May). India Confirms
Over 1 Lakh Covid-19 Cases: How Many
ICU Beds And Ventilators Does The Country
Have? Retrieved 2020, 12-July from HuffPost:
https://www.huffpost.com/archive/in/entry/
india-covid-19-cases-hospital-beds-icuventilators_in_5ec375adc5b6e607c1990187
Transactions | Vol. 43, No. 2, 2021 | 239
Setti, L., Passarini, F., Gennaro, G. D., Baribieri,
P., Perrone, M. G., Borelli, M., . . . Miani,
A. (2020). SARS-Cov-2 RNA Found
on Particulate Matter of Bergamo in
Northern Italy: First Preliminary Evidence.
Environmental Research, 188, 216-233.
Singh, P., Ravi, S., & Chakraborty, S. (2020,
24-March). Is India’s health infrastructure
equipped to handle an epidemic? Retrieved
2020, 12-July from Brookings: https://www.
brookings.edu/blog/up-front/2020/03/24/
is-indias-health-infrastructure-equipped-tohandle-an-epidemic/
Siri, J. G., Newell, B., & Proust, K. (2015).
Urbanization, extreme events, and health: the
case for systems approaches in mitigation,
management and response. Asia Pacific
Journal of Public Health, 28(2), 15-27.
World Health Organisation. (2020). Strengthening
Preparedness for COVID-19 in Cities and
Urban Settings. London, UK.
Yadav, P. (2020). Globalization and India’s
international trade: does distance still matter?
GeoJournal, 86(1), 1927-1941.
240 | Transactions | Vol. 43, No. 2, 2021
Yadav, P., & Bhattacharjee, A. (2020). Impact
of COVID-19 on mobility in India: A
spatial approach. Radical Statistics(Special
Coronavirus Issue 126), 56-66.
Zhang, Y., Tian, H., Zhang, Y., & Chen, Y.
(2020, 9-April). Is the epidemic spread
related to GDP? Visualizing the distribution
of COVID-19 in Chinese Mainland.
arXiv. Retrieved 2020, 11-July from
arXiv:2004.04387 (q-bio): https://arxiv.org/
abs/2004.04387
Purva Yadav*
Assistant Professor
and
Aisharya Bhattacharjee
Research Scholar,
Centre for the Study of
Regional Development,
School of Social Sciences,
Jawaharlal Nehru University,
New Delhi, India
*Author for correspondence
E-mail: purvayadavjnu@gmail.com