Understanding the habitat vulnerability of Slums to COVID 19: Case of two megacities of India

Sudha Panda; Soumyendu Shankar Ray

doi:10.12688/f1000research.153809.1

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Research Article

Understanding the habitat vulnerability of Slums to COVID 19: Case of two megacities of India

[version 1; peer review: 1 approved, 2 approved with reservations]

Sudha Panda ¹, Soumyendu Shankar Ray²

PUBLISHED 30 Jul 2024

Author details Author details

¹ School of Architecture and Planning, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, 751024, India
² School of Architecture and Planning, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, 751024, India

Sudha Panda
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Soumyendu Shankar Ray
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Kalinga Institute of Industrial Technology (KIIT) collection.

Abstract

Background

Urban slums are hotspots of infectious diseases like COVID-19 as was seen in the waves of 2020 and 2021. One of the primary reasons why slums are disproportionately affected is their location in inaccessible and uninhabitable zones, crowded and poorly ventilated living spaces, unsanitary conditions and common facilities (water taps, common toilets, etc.). Staying at home during pandemics is hardly an option for slum dwellers as it often means giving up work and even basic necessities.

Methodology

This paper aims to understand the habitat vulnerabilities of slums in the two Indian megacities of Pune and Surat which were the worst hit during both waves. The study is done at the level of wards, which is the smallest administrative boundary, taking the habitat vulnerability (congestion and access to basic services). To identify the explanatory variables which increase the vulnerability of slums to infectious diseases, literature study is done on the triggering factors which affect habitat vulnerability derived from common characteristics and definitions of slum.

Results

The aim of the research is to categorize the slums into 3 levels of risk zones and map them subsequently.

Conclusion

This study will help in formulating a model to prioritize the allocation of sparse resources in developing countries to tackle the habitat vulnerabilities of the slum dwellers especially during health emergencies of contagious diseases like COVID-19.

Keywords

Habitat vulnerability; COVID-19; urban slums; congestion; access to basic services; risk exposure;

Corresponding author: Sudha Panda

Competing interests: No competing interests were disclosed.

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2024 Panda S and Ray SS. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Panda S and Ray SS. Understanding the habitat vulnerability of Slums to COVID 19: Case of two megacities of India [version 1; peer review: 1 approved, 2 approved with reservations]. F1000Research 2024, 13:849 (https://doi.org/10.12688/f1000research.153809.1) First published: 30 Jul 2024, 13:849 (https://doi.org/10.12688/f1000research.153809.1) Latest published: 30 Jul 2024, 13:849 (https://doi.org/10.12688/f1000research.153809.1)

Introduction

India is among the foremost countries which were severely impacted by COVID-19 in the 2021 wave. It managed to dodge the severity of the impact of the 2020 wave by imposing a very rigid lockdown which just about managed to flatten the curve giving the government the time to scale up the public health system. But in future if we are to be better equipped to contain the transmission of a pandemic we have to primarily identify the risk factors that catalyze the disease spread in our urban realm and take appropriate steps to counter the same. It was observed that states with greater urban population, relatively higher population density, greater slum population, and a comparitively higher crowding in dwellings had a higher incidence of COVID-19. This was further intensified by comparatively poor medical facilities and lower expenditure on health infrastructure (Pathak et al., 2020). Social distancing which reduce exposure to COVID 19 is difficult to practice in slums which have crowded and congested living (Corburn et al., 2020; Friesen and Pelz, 2020). Besides space restriction and congestion, the lack of basic amenities like water supply, toilets, sewerage system, drainage facilities, solid waste collection, secure and adequate housing and existing poor medical conditions of the slum dwellers further reinforces their vulnerability to COVID-19 (Khatua, 2020).

The objective of this research is to obtain a critical understanding of the relationship between habitat vulnerability, health and slums so that risk exposure of slum dwellers can be quantified. The study is done at the smallest administrative level i.e. at ward level so that it is easy to identify the susceptible wards in a pandemic, enabling greater resources to be diverted to these places and taking preventive steps to counter the risk factors.

Background

Spread of the pandemic in urban centers was a global phenomenon. Cities with a larger population had a larger infection base and India was no exception with almost 40% of the cases in the 2020 wave being found in the 4 major metro cities of Delhi, Mumbai, Kolkata and Chennai which see a lot of international visitors. But the rapid spread of the contagion there onwards mainly happened where the urban concentration was more. The Census 2011 report states that one-third of India’s population lives in slums. Considering that the highly congested slums are breeding grounds for COVID 19 which thrives on proximity, it is imperative to understand the inherent habitat vulnerability of these slums to be able to propose an effective and long term policy planning to reduce these risk factors. Research has shown that in Mumbai, settlements have been built around industrial areas like garment factories where people stay in cramped spaces with eight people sharing a room surrounded by hazardous chemicals and machines (Parpiani M., 2020).

Daily wage laborers and migrant labor constitute a major chunk of the slum dwellers (Ghosh et al., 2020). Social distancing is a safeguard against the spread of COVID-19 but it is a luxury which cannot be afforded by daily wage laborers living in slums (Courtemanche et al., 2020; Weill et al., 2020). For most slum dwellers it is important to continue livelihood activities which will ensure an income and subsequently food security than the possibility of contracting COVID-19.

Literature study

To have a better understanding of the relationship between ‘Slums’, ‘habitat vulnerability’ and ‘health’ it is important to delve deeper into their definitions given by noted organizations.

Slums

The term “slum” refers to squalid housing within urban areas that are overcrowded and have inadequate provision of basic services. UN-HABITAT definition for a slum household needs to satisfy one or more of the five conditions given below:

- Temporary housing that does not give protection against extreme climate conditions;
- Inadequate dwelling space;
- Availability of affordable, sufficient and safe water;
- Provision of a private or public toilet and
- Security of tenure (UN-Habitat 2003a; 2003b; 2006/7).

World Health Organization (WHO) recommends 2m physical distancing as a preventive against COVID 19 spread which is next to impossible in a slum household where 5-6 persons are packed in a 25-30 square meters dwelling with poor ventilation which is further aggravated due to cooking methods which rely on coal or wood as fuel. Hygiene and access to clean water is a critical parameter for precaution against COVID-19 but with large number of people dependent on municipality tankers for water and very low ratio of private toilets it is very difficult to practice the hand washing and hygiene norms suggested by WHO.

We take a comparative look in Table 1 on various definitions and parameters (Legality, Density, Housing, Water and Sanitation) by various noted national and international organizations to understand their commonality.

Table 1. Parameters of Slum definition by various organizations.

Organisations	Legality	Density	Housing	Water	Sanitation
Census	Recognized as Slum by State/Local Govt under any Act including Slum Act	Should have a population of around 300 persons	Temporary overcrowded settlements in usually unsanitary conditions with lack of infrastructure	Inadequate potable water supply	Lacking proper sanitation facilities
National Family Health Survey	NA	Should have a population of around 300 persons	Temporary overcrowded settlements in usually unsanitary conditions with lack of infrastructure	Inadequate potable water supply	Lacking proper sanitation facilities
UN Habitat	NA	Inadequate dwelling space	Temporary housing that does not give protection against extreme climate conditions	Availability of affordable, sufficient and safe water	Lack of access to improved sanitation facilities

Vulnerability and risk exposure

The term “Vulnerability” refers to certain inherent and inbuilt set of conditions that maintain people in a perilous and hazardous conditions. In the context of susceptibility to health, it implies situations leading to risk to diseases. It can also be defined as “the degree to which a system, subsystem or system component is likely to experience harm due to exposure to a hazard, either to a perturbation or stress or stressors” (Turner et al., 2003). Risk is the chance (or possibility) that the hazard will reoccur. Vulnerability is a function of exposure, sensitivity and adaptive capacity of the system (Cutter, 1996).

Health vulnerability of Slums to COVID 19 due to habitat conditions

The definition of vulnerability in the context of health leads us to understand the inherent structural conditions in slums that makes the slum dwellers prone to contagious diseases like COVID-19. It mainly spreads through contact, hence the health vulnerability in slums is mainly due to the inability to practice physical distancing and other conditions which aid the disease transmission once the disease has been contracted. The literature study revealed that the health vulnerability parameters which exist in slums can be clubbed into two types of factors

1. Socio Economic Conditions
2. Habitat Condition

Table 2 (below), clubs all the factors derived from literature study into the above headings to later filter out the factors can be controlled or improved.

Table 2. Parameters impacting health vulnerability of slums to COVID-19.

	Factors	Themes	Parameters	Sources
HEALTH VULNERABILITY FACTORS IN SLUMS	Factors preventing Social Isolation	Socio Economic Conditions	Working for survival-daily wage labor	1. Ghosh et al., 2020. 2. Khatua, 2020. 3. Courtemanche et al., 2020. 4. Weill et al., 2020.
			Mostly migrant labor	1. Ghosh et al., 2020. 2. Khatua, 2020 3. Courtemanche et al., 2020.
			Low level of education so less disease awareness	1. Pathak et al., 2020.
			Joint and extended families with 7-8 people in cramped quarters	1. Pathak et al., 2020.
			Asymptomatic nature of disease	1. George et al., 2021.
			Fear and stigma associated with disease	1. George et al., 2021.
			Mistrust between slum dwellers and health officers	1. George et al., 2021.
		Habitat conditions	High Population	1. Sclar et al., 2005. 2. Gibson and Rush, 2020. 3. Wasdani and Prasad, 2020.
			High Density	1. Corburn et al., 2020 2. Gibson and Rush, 2020. 3. Wasdani and Prasad, 2020.
			Illegality of settlements	1. Ayeb-Karlsson, 2020. 2. Ayeb-Karlsson et al., 2020. 3. Ezeh et al., 2017.
			Very less ventilation and congestion	1. Corburn et al., 2020.
			Erratic electric supply coupled with low ventilation	1. Raju, 2020.
			Multipurpose business related use of household	1. Pathak et al., 2020.
			Most products hand related so isolation difficult	1. Pathak et al., 2020.
			Narrow slum pathways making medical help difficult	1. Raju and Ayeb-Karlsson, 2020.
	Factors facilitating disease transmission	Socio Economic Conditions	Inadequate data availability on number, living condition, and health comorbidity	1. Friesen and Pelz, 2020. 2. Pathak et al., 2020
		Socio Economic Conditions	Lower health expenditure by family	1. Pathak et al., 2020.
		Habitat conditions	Inadequate access to sanitation, waste disposal and healthcare facilities	1. Corburn et al., 2020. 2. Ghosh et al., 2020. 3. Sclar, Garau and Carolini, 2005. 4. Subbaraman et al., 2012.
			Most slums are not notified, hence excluded from basic amenities	1. Nolan et al., 2017.
			Drinking water at a distance from homes	1. Raju et al., 2021. 2. Raju, 2020.
			Poor health infrastructure	1. Raju et al., 2021. 2. Raju, 2020.
			Continuous handwashing not possible (lack of water and soap)	Raju and Ayeb-Karlsson, 2020.

Methods

The study was conducted with an objective to understand the existing habitat vulnerability which is present in slums which aggravates the susceptibility of the slum dwellers to contagious diseases like COVID-19. In order to identify these explanatory variables, the characteristics which define a slum given by various organizations are studied and superimposed on the habitat and socio-economic factors identified by previous literature which are considered responsible for increasing the vulnerability of slum dwellers. Data is collected about the common parameters (congestion and access to basic services) from sample cities of Surat and Pune wherein a ward (smallest administrative border in the city) is the unit of comparison.

To derive the risk intensity, the raw data is transformed into relative values to normalize all values so that they range from 0 to 1. After giving weights to the indicators a linear summation is done to obtain a composite value for the risk exposure and depending on the range classified into high, medium or low. When this risk exposure value is referenced on the ward map of the city, a lucid and graphic picture emerges for policy planners while prioritizing the allocation of scarce resources to areas which demand urgent attention and to be fully prepared in the event of a disaster like COVID-19. Figure 1 (below) shows the complete research steps followed to achieve this end.

Figure 1. Steps in the research process.

Sampling

Pune and Surat have been selected for testing this model because they are contrasting cases of cities in the way they have dealt with slum issue. Pune has 40.56% of its population living in slums, whereas Surat has only 19.24% of its population living in the slums. The low slum population in Surat is due to the aggressive implementation of slum re-location and development programs after the plague in 1992.

Pune

Pune municipal corporation (PMC) is responsible for the provision of basic services in slums under the Maharashtra Slum Improvement and Clearance Act of 1971. As of 2011, Ghole Road, Dhole Patil Road and Sangamwadi ward shows the maximum percentage of slum population (between 40-50%). The least number of slums population is found in Bibvewadi, Kasba and Dhankawadi wards (less then 5%). Tilak Road ward and Karve Road have maximum number of declared slum i.e. about 13% and 12% respectively.

A study of the trend between slum population percentage and COVID-19 infection rate during both waves of 2020 and 2021(as seen in Figures 2 and 3) shows that there’s no particular relationship that would imply a higher population in slums having a higher infection rate.

Figure 2. Wardwise relation in Pune between % Slum Population and Covid 19 infection rate in 2020.

(Source: Pune Municipal Corporation, 2020).

Figure 3. Wardwise relation in Pune between % Slum Population and Covid 19 infection rate in 2021.

(Source: Mohol, 2021).

Surat

Slums in Surat constituted 27.5 percent of the city’s population in 1992. However, after 1992, the slum growth rate has decreased from an annual average of 14.6 percent in 1992 to an annual average of 1.46 percent in 2001.

Figure 4. Wardwise relation in Surat between % Slum Population and Covid 19 infection rate in 2020.

(Source: Surat Municipal Corporation, 2020).

In Surat too, a study of the trend between slum population percentage and COVID-19 infection rate during both waves of 2020 and 2021(as seen in Figures 4 and 5) shows the same results as in Pune. There’s no particular relationship that would imply a higher population in slums having a higher infection rate.

Figure 5. Wardwise relation in Surat between % Slum Population and Covid 19 infection rate in 2021.

(Source: Surat Municipal Corporation, 2021).

Empirical Model

Based on the habitat vulnerability parameters obtained from the literature study and superimposing on the parameters which define a slum (given by noted organizations) it was seen that ‘Congestion’ and ‘Lack of access to Basic Services’ come out as the most significant parameters responsible which create vulnerability of slums to COVID-19. The two indicators selected under ‘Congestion’ were ‘Population’ and ‘Density’ (persons per unit square km in slums). The four indicators selected under ‘Lack of access to Basic Services’ were ‘Percentage population with access to water supply’, ‘Percentage population using private toilet’, ‘Percentage population using common toilet’ and ‘Percentage population with near accessibility to health facility’. Expert opinion on the weightage which should be given to the final six indicators were 0.25 to each of the two ‘Congestion indicators’ and 0.125 to each of the four ‘Lack of access to Basic Services’ indicators.

Hence the linear model obtained was

Risk Exposure of a slum = α1 + β1 Congestion factors (f(‘Population’, ‘Density’)) + Lack of access to Basic services (f(‘Percentage population with access to water supply’, ‘Percentage population using private toilet’, ‘Percentage population using common toilet’ and ‘Percentage population with near accessibility to health facility’)) + εi

Data collection

True values of indicators for sample cities of Pune and Surat are seen in Tables 3 and 4 (below)

Table 3. True values for indicators of Pune slums.

Ward	Congestion		Access to basic services
Ward	Slum population	Population density/sqkm of slum	% population with access to water supply	% population using private toilet	% population using common toilet	% population with near accessibility to health facility
Aundh	41475	151923	61.6	12	33.3	73
Kothrud	81045	170621	67.9	45	21.7	77
Ghole road	84405	187984	72.8	25	32.3	77
Warje karvenagar	52245	120658	62.3	23	20.4	66
Dhole Patil Road	72040	140703	64.1	18	17.9	76
Hadapsar	84465	117476	62.9	18	7.5	73
NagarRoad	29775	145956	80.3	31	19.6	84
Sangam Wadi	116390	135495	66	52	29.4	74
Bhawani Peth	60615	206174	74.9	14	34.5	75
Kasba	8880	17976	73.9	1	16.9	76
Sahakar Nagar	70900	212275	64.7	22	15.6	77
Tilak road	83595	154177	62.1	22	16.7	74
Bibvewadi	15725	201603	65.1	10	12.3	73
Dhankawadi	5260	228696	69.4	14	20.8	69

Table 4. True values for indicators of Surat slums.

Ward area	Congestion		Access to basic services
Ward area	Population	Population density/sqkm of slum	% population with access to water supply	% population using private toilet	% population using common toilet	% population with near accessibility to health facility
East	711516	252405	42	4	34	56
West	449943	112888	97	37	75	20
North	716110	293287	59	7	62	8
South	881070	229663	68	10	60	26
Central	408760	77213	68	58	38	100
South-West	348423	150036	70	64	57	70
South-East	754128	172494	77	62	49	51

Results and discussion

As can be seen in the linear model above measuring risk exposure of the slums the indicators for “congestion” and “access to basic services” have different measurement units (persons, persons/sqkm and percentages etc). However, to make these indicators comparable they need to be transformed into a standard form. Hence, each of the indicators included in the analysis, is normalized using the actual, minimum and maximum risk threshold values. The value of the normalized relative indicator varies between 0 to 1 and is calculated thus:

(1)

Relative indicator = \frac{Actual value - Minimum value}{Maximum value - Minimum value}

For some indicators, a higher score is equivalent to a higher risk (as in the congestion factors), whereas for other indicators, a higher score might imply lower risk (as in percentage access to water and individual toilet facilities). For the scores to be formulated according to higher the value the lower the risk (as in percentage access to water and individual toilet facilities), the values are transformed into negative values.

To obtain a composite value of the risk

(2)

Ri = \sum_{k = 1}^{l} w_{k} x_{ik} = w_{1} s_{i1} + w_{2} s_{i2} + + \dots \dots . w_{m} s_{il}

Where Ri the overall score of Risk i and S_il the Relative indicator value i for criterion j of which wj is the weight.

Calculating the composite values of the Risk exposure across the different wards in Pune and Surat, the following values are obtained as seen in Tables 5 and 6.

Table 5. Risk exposure of slums in different wards of Pune.

Ward	Relative value of population	Relative value of population density	Relative value of %population with access to water supply	Relative value of %population using private toilet	Relative value of %population using common toilet	Relative value of %population with near accessibility to health facility	Composite risk value	Risk range
Aundh	0.325	0.635	0	0.25	1	0.389	0.285	High
Kothrud	0.681	0.724	0.336	1	0.55	0.611	0.176	Medium
Ghole road	0.712	0.806	0.598	0.545	0.961	0.611	0.280	High
Warje karvenagar	0.422	0.487	0.037	0.5	0.5	0	0.222	High
Dhole Patil Road	0.600	0.582	0.133	0.386	0.403	0.556	0.211	High
Hadapsar	0.712	0.472	0.069	0.386	0	0.389	0.190	Medium
Nagar Road	0.220	0.607	1	0.682	0.469	1	-0.069	Low
Sangam Wadi	1	0.557	0.235	1.159	0.849	0.444	0.265	High
Bhawani Peth	0.498	0.893	0.711	0.295	1.047	0.5	0.290	High
Kasba	0.032	0	0.657	0	0.364	0.556	-0.098	Low
Sahakar Nagar	0.590	0.922	0.165	0.477	0.314	0.611	0.260	High
Tilak road	0.704	0.646	0.026	0.477	0.357	0.444	0.264	High
Bibvewadi	0.094	0.871	0.187	0.205	0.186	0.389	0.167	Medium
Dhankawadi	0	1	0.417	0.295	0.516	0.167	0.204	High

Table 6. Risk exposure of slums in different wards of Surat.

Ward area	Relative value of population	Relative value of population density	Relative value of %population with access to water supply	Relative value of %population using private toilet	Relative value of %population using common toilet	Relative value of %population with near accessibility to health facility	Composite risk value	Risk range
East	0.682	0.811	0	0	0	0.522	0.308	High
West	0.191	0.165	1	0.55	1	0.13	0.004	Medium
North	0.69	1	0.309	0.05	0.683	0	0.463	High
South	1	0.706	0.473	0.1	0.634	0.196	0.410	High
Central	0.113	0	0.473	0.9	0.098	1	-0.256	Low
South-West	0	0.337	0.509	1	0.561	0.674	-0.119	Low
South-East	0.762	0.441	0.636	0.967	0.366	0.467	0.088	Medium

Transforming these numeric values into a more comprehensive picture, a range of > 0.20 is taken as high, 0 to 0.20 is taken as medium and less than 0 is taken as low. Finally, to represent the values graphically, it is graphically represented on the ward map of the cities as seen in Figures 6 (Pune) and 7 (Surat).

Figure 6. Risk exposure of slums in Pune to COVID 19.

(Source: Authors, 2023).

Figure 7. Risk exposure of slums in Surat to COVID 19.

(Source: Authors, 2023).

A major part (9 out 15 wards) of the Pune slums fall in the high risk zone. Only 3 are in the medium risk zone and 2 are in the low risk zone. With almost 40.56% of the Pune population living in slums, this spells trouble for the entire city as slums are the hotspots from where the contagion starts spreading.

The picture is much brighter in Surat, in spite of the rapid influx of migrants, as only 3 wards are in high risk zone. This is due to the proactive measures taken by Surat Municipal Corporation (SMC) in 1994 when they took aggressive and rigorous measures after the 1994 plague. However, in spite of the concentrated efforts of the Surat municipality there were open areas in the south and south eastern parts of the city where due to rural migration slums developed again.

Conclusion

The aim of the paper was to find a method to quantify the risk exposure to pandemics in slums through a critical understanding of the relationship between habitat vulnerability, health and slums which was done by taking a case study of 2 cities of Pune and Surat during the COVID-19 waves of 2020 and 2021. By concentrating on the parameters which define a slum and are simultaneously the triggering factors for the spread of a pandemic (as revealed by the literature study), a method is devised to quantify the risk exposure.

It is important to find both immediate and long term solutions to the problem. COVID 19 has cemented the fact this will not be the first or last contagious disease which the world will witness in a long time but we need to be better prepared to contain such pandemics and the first step lies in curtailing its spread in the hotbeds i.e. the slums of urban centers. By identifying the critical slums the study has found a method for quantifying the risk exposure due to habitat conditions which will help to prioritize the allocation of scarce resources in emergency times and also for long term slum redevelopment planning.

As an immediate measure, access to provision of basic amenities should be prioritized in the times of pandemics by water tanks, mobile sanitizing centers/hand washing stations, portable toilets at critical points which can be done under Swachh Bharat Abhiyaan or Clean India Mission. To incentivize the collection of solid waste, youth in the slum can be encouraged by giving a bag of groceries in exchange for a bag of solid waste. The bags of solid waste obtained can be recycled by the slum dwellers and be a source of employment generation. This can be managed by slum emergency planning committees which will also provide mobile health clinics to carry out testing, diagnosis and immediate treatment. These mobile clinics should be able to maneuver through the existing narrow streets in the slums and be well equipped with equipment for the treatment of COVID-19 and common ailments in urban slums, which are usually diarrhea and respiratory infections. The long term measures should be relocation or redevelopment of slums in a phased manner providing well ventilated housing having all the basic amenities like water, sanitation, drainage, toilets, access to health facilities.

Informed consent statement

The University has granted consent to participate and publish.

Author’s contribution

Panda S: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Resources, Supervision, Validation, Visualization, writing – Original Draft Preparation, writing – Review & Editing; Ray SS: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Resources, Supervision, Validation, Visualization, writing – Original Draft Preparation, Writing – Review & Editing;

Ethical approval and consent for third party data

The Ethics Committee of the School of Architecture and Planning, KIIT University on 6th June,2024 waived the need for ethics approval and the need to obtain consent for the collection, analysis and publication of the data as it has been obtained from freely available online content which has been properly referenced and cited.

Consent to participate

All authors have given consent to participate.

Consent to publish

All matter in paper is original work and not published elsewhere. There is consent to publish.

Data availability statement

1. Figshare: Ward wise Covid 19 Positive Cases in Pune City on May 29, 2021. https://doi.org/10.6084/m9.figshare.26196443 (Panda & Ray, 2024a).

The project contains the following underlying data:

• Figure 1.jpeg (Covid 19 positive cases in Pune on May 29, 2021).

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

2. Figshare: Ward wise Covid 19 Positive Cases in Pune City on 11 May, 2020. https://doi.org/10.6084/m9.figshare.26196449 (Panda & Ray, 2024b).

The project contains the following underlying data:

• Figure 2.jpeg (Covid 19 positive cases in Pune on May 11, 2020).

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

3. Figshare: Ward wise Covid 19 Positive Cases in Surat City on 1 May, 2021. https://doi.org/10.6084/m9.figshare.26196455 (Panda & Ray, 2024c).

The project contains the following underlying data:

• Figure 3.jpeg (Covid 19 positive cases in Surat on May 1, 2021).

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

4. Figshare: Ward wise Covid 19 Positive Cases in Surat City on 31 July, 2020. https://doi.org/10.6084/m9.figshare.26196464 (Panda & Ray, 2024d).

The project contains the following underlying data:

• Figure 3.jpeg (Covid 19 positive cases in Surat on July 31, 2020).

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Acknowledgments

We are thankful to the administrative support provided by our University.

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Ghosh S, Seth P, Tiwary H: How does Covid-19 aggravate the multidimensional vulnerability of slums in India? A Commentary. Soc. Sci. Humanit. Open. 2020; 2(1): 100068. Publisher Full Text
Gibson L, Rush D: Novel coronavirus in Cape Town Informal settlements: Feasibility of using informal dwelling outlines to identify high risk areas for COVID-19 transmission from a social distancing perspective. JMIR Public Health Surveill. 2020; 6(2): e18844. PubMed Abstract | Publisher Full Text | Free Full Text
Gupta K, Arnold F, Lhungdim H: Health and Living Conditions in Eight Indian Cities. Calverton, Maryland, USA: National Family Health Survey (NFHS-3), India, 2005-06. Mumbai: International Institute for Population Sciences; 2009. ICF Macro. Reference Source
Khatua S: Density, distancing, informal settlements and the pandemic. Econ. Polit. Wkly. 2020; 55(20). Accessed October 11, 2022. Reference Source
Mahajan S: Housing Study for Pune Municipal Corporation (2009-2010). Maharashtra State Housing and Action League; 2010. Reference Source
Ministry of Housing and Urban Poverty Alleviation: Report of the Committee on Slum Statistics/Census. New Delhi, India: Government of India, Ministry of Housing and Urban Poverty Alleviation; 2008. Reference Source
Nolan LB, Bloom DE, Subbaraman R: Legal status and deprivation in India’s urban slums: an analysis of two decades of national sample survey data, PGDA Working Paper No. 135 Harvard University Program on the Global Demography of Aging Working Paper Series. 2017.2017. Reference Source
Panda S, Ray SS: Ward wise Covid 19 Positive Cases in Pune City on May 29, 2021. figshare. Figure. 2024a. Publisher Full Text
Panda S, Ray SS: Ward wise Covid 19 Positive Cases in Pune City on 11 May, 2020. figshare. Figure. 2024b. Publisher Full Text
Panda S, Ray SS: Ward wise Covid 19 Positive Cases in Surat City on 1 May, 2021. figshare. Figure. 2024c. Publisher Full Text
Panda S, Ray SS: Ward wise Covid 19 Positive Cases in Surat City on 31 July, 2020. figshare. Figure. 2024d. Publisher Full Text
Parpiani M: Fighting fires: migrant workers in Mumbai. Econ. Polit. Wkly. 2020; 55(14). Reference Source
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Pune Municipal Corporation: Check area wise covid patients in Pune city. Tally reaches 3105 in Pune district. Punekar News. 2020, May 11. Reference Source
Mohol MK: [@mohol_murlidhar]. Ward wise Covid-19 Positive Cases in Pune City. [Tweet]. Twitter.2021, May 29. Reference Source
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Sclar ED, Garau P, Carolini G: The 21st century health challenge of slums and cities. Lancet. 2005; 365(9462): 901–903. PubMed Abstract | Publisher Full Text
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Surat Municipal Corporation: Zonewise confirmed cases. Surat Smart City: 2021, May, 01. Reference Source
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¹ School of Architecture and Planning, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, 751024, India
² School of Architecture and Planning, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, 751024, India

Sudha Panda
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Soumyendu Shankar Ray
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

The author(s) declared that no grants were involved in supporting this work.

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https://doi.org/10.12688/f1000research.153809.1

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Panda S and Ray SS. Understanding the habitat vulnerability of Slums to COVID 19: Case of two megacities of India [version 1; peer review: 1 approved, 2 approved with reservations]. F1000Research 2024, 13:849 (https://doi.org/10.12688/f1000research.153809.1)

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ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

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Version 1

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PUBLISHED 30 Jul 2024

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Reviewer Report 23 Oct 2024

Deepashree Choudhury, Apeejay Institute of Technology, Greater Noida, Uttar Pradesh, India

Approved

https://doi.org/10.5256/f1000research.168749.r326369

The article investigates the vulnerability of slum habitats to COVID-19 in two Indian megacities. It seeks to understand how the unique living conditions in these informal settlements increase exposure to the virus and influence the overall pandemic response. The study ... Continue reading

The article investigates the vulnerability of slum habitats to COVID-19 in two Indian megacities. It seeks to understand how the unique living conditions in these informal settlements increase exposure to the virus and influence the overall pandemic response. The study addresses an important topic, given the disproportionate impact of the pandemic on marginalized urban populations. However, while the research is insightful, there are a few areas where the methodology, data presentation, and discussion could be strengthened to ensure clarity and replicability.
1. Presentation and Literature Citations

The paper is well-written, but certain sections, particularly in the introduction and conclusion, could be enhanced by integrating more recent studies on the impact of COVID-19 on slum populations globally and within India. The discussion of the slum environment in relation to pandemic spread is critical.
Suggestions:
(i)Strengthen the introduction by adding more recent studies on slum vulnerability to infectious diseases and specifically COVID-19.
(ii)Provide proper citations for claims related to the broader impacts of COVID-19 on slums beyond the two cities studied.
(iii)Clarify how this study builds upon or diverges from other research in this domain.

2. Study Design and Technical Soundness
The study provides valuable insights, but the rationale for choosing these two specific megacities (Pune and Surat) needs to be expanded. The authors only say’ Pune and Surat have been selected for testing this model because they are contrasting cases of cities in the way they have dealt with slum issues. Pune has 40.56% of its population living in slums, whereas Surat has only 19.24% of its population living in the slums. The low slum population in Surat is due to the aggressive implementation of slum re-location and development programs after the plague in 1992.‘ The authors do not fully explain whether the findings can be generalized to slums across India or if they are unique to these particular cities.

Suggestions:
(i) Add more robust justification as to why Pune and Surat were chosen as case studies over other cities that also have significant slum populations, such as Kolkata or Chennai.
(ii)Discuss whether these findings are representative of slum vulnerability across India or only applicable to the megacities studied.

3. Details of Methods and Analysis

Providing a clear breakdown of how data was collected and Specifying the criteria used to select slums in these cities for the analysis will help enrich this research. A few questions come to the mind of the reader like, whether any specific sampling method was used, and whether the research involved any first-hand data collection(through interviews with slum dwellers or government officials) ? Were the selected case studies representative of slums across the megacities?

4. Statistical Analysis and Interpretation

(i)The authors state that expert opinions were used to assign weightage to the final six indicators—0.25 for each of the two ‘Congestion indicators’ and 0.125 for each of the four ‘Lack of Access to Basic Services’ indicators. However,an elaboration on how this expert opinion was collected (the process and criteria used to select experts, as well as the methods employed (e.g., surveys, interviews, consensus-building techniques) will highly aid in making the article more comprehensible and in validating the weightage assigned to the indicators.
(ii) The authors mention that numeric values were transformed into categories (high, medium, low) based on specific ranges. Detailing out how the risk exposure benchmarks were derived, for the ranges designated as high, medium, and low will help in establishing the validity of the conclusions drawn.

(iii) In Tables 5 and 6, it is suggested that indicators be assigned nomenclature (e.g., Si1, Si2, etc.) to facilitate clearer comprehension and reference throughout the text. This labeling would enhance the readability of the tables and make it easier for readers to track the indicators discussed.

5. Conclusions
While the conclusions align with the overall findings, they are somewhat overstated, particularly regarding the implications for policy. The authors suggest wide-ranging solutions to slum vulnerability, but these are not fully supported by the data presented. The discussion would benefit from a more cautious interpretation of the findings, especially given the limitations of the study.
Suggestions:
(i)Acknowledge the limitations of the study more explicitly in the conclusions, especially in terms of data limitations and the representativeness of the findings. Provide more grounded, evidence-based policy recommendations that are supported by the specific data from the study.
(ii)Consider offering suggestions for future research that could address gaps identified in this study, particularly in terms of broader applicability to other cities and long-term vulnerability assessment.
(iii)separate out recommendations from the conclusion section and present them under a different head.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

References

1. Seawright J, Gerring J: Case Selection Techniques in Case Study Research. Political Research Quarterly. 2008; 61 (2): 294-308 Publisher Full Text
2. Greco S, Ishizaka A, Tasiou M, Torrisi G: On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness. Social Indicators Research. 2019; 141 (1): 61-94 Publisher Full Text

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Built Environment,Architecture,Urban Design,Urban Studies,Research methodology

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Reviewer Report 28 Sep 2024

Partha Sarathi Mishra, Sri Sri University, Cuttack, Odisha, India

Approved with Reservations

https://doi.org/10.5256/f1000research.168749.r326376

Summary:
This paper makes a valuable contribution by examining the habitat vulnerability of slums to COVID-19 in two major Indian cities, Pune, and Surat. The authors develop a quantitative risk assessment model based on congestion and access to basic ... Continue reading

Summary:
This paper makes a valuable contribution by examining the habitat vulnerability of slums to COVID-19 in two major Indian cities, Pune, and Surat. The authors develop a quantitative risk assessment model based on congestion and access to basic services to categorize slum areas into high, medium, and low risk zones. The visual mapping of these risk zones provides an intuitive understanding of vulnerability patterns, which can inform targeted interventions.
Major strengths:
- Addresses a critical and timely topic of slum vulnerability to pandemics
- Develops a quantitative risk assessment model that can be applied to other urban areas
- Provides clear visual representation of risk zones through ward-level mapping
- Offers both immediate and long-term recommendations for reducing vulnerability
- Uses data from two different cities to demonstrate broader applicability of the approach
Minor revisions suggested:
1. Consider including a brief overview of Indian definitions of slums to complement the international definitions provided. This would strengthen the contextual relevance of the study.
2. Provide some additional background on why Pune and Surat were selected as case studies, perhaps with a sentence or two of data on their COVID-19 impacts.
3. Add a few more details on data collection methods and sources in the methodology section to enhance reproducibility.
4. Ensure consistency in how ward data is presented across all tables and figures for clarity.
5. Simple Additive Weighting Method is mentioned in this paper. However, the weighting method and their implications are not available in this paper. Please clarify on this issue
6. Briefly explain the rationale for the risk score thresholds used (high >0.20, medium 0-0.20, low <0) to help readers interpret the results.
7. Consider adding a short limitations section to acknowledge any constraints of the study.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Architectural Design; Heritage Assessment; Statistical Analysis in Architecture and Planning, Multi-Criteria Decision- Making Analysis

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Reviewer Report 29 Aug 2024

Natraj Kranthi, SPA Bhopal, Bhopal, Madhya Pradesh, India

Approved with Reservations

https://doi.org/10.5256/f1000research.168749.r314475

Summary of the Paper - The paper titled ‘Understanding the habitat vulnerability of Slums to Covid 19: Case of two megacities of India’ aims to categorize the slums into three levels of risk zones for mapping. For

Summary of the Paper - The paper titled ‘Understanding the habitat vulnerability of Slums to Covid 19: Case of two megacities of India’ aims to categorize the slums into three levels of risk zones for mapping. For this purpose, ward wise habitat vulnerabilities of slums in the two Indian megacities of Pune and Surat were analysed. Definitions given by UN-Habitat and WHO were reviewed and vulnerability parameters ‘congestion’ with two indicators and ‘lack of access to basic services’ with four indicators were identified. Risk exposure was measured based on the normalized true values. Risk was calculated and categorized as high (>0.2), medium (0-0.2) and low (<0). Accordingly these ward wise risks maps were done. The paper concludes by suggesting the importance for immediate and long term solutions
Observations

i. Definitions - The definitions of slums given by the international organizations like UN-HABITAT and WHO were reviewed. However, the Indian definitions of slums such as (i). Notified slums (as per the Slum Act 1956), (ii). Identified slums (as per the Census of India) and (iii). Recognized slums (that are not formally notified but recognized by the authorities) were not reviewed. As the study is conducted in the Indian cities of Pune and Surat, it becomes imperative to review these definitions as well besides the international definitions.
ii. Identification of vulnerability parameters – Based on the review of both the international and national definitions, the habitat vulnerability parameters and indicators are to be identified. The number of such parameters may increase accordingly. Therefore, there is a need to augment the literature review in this regard.
iii. Identification of indicators - Under ‘congestion’, two indicators ‘Population’ and ‘Density’ were identified. However the current literature such as Namperumal et al (2022) reveals a direct link between builtup density and positive Covid cases. Therefore, it is suggested to cite the current literature and consider ‘Builtup density’ also as one of the indicators under ‘congestion’.
iv. Cities as study area – In the abstract, it is mentioned that two Indian megacities Pune and Surat were considered as they were the worst hit during both the waves. However no adequate data or reference is cited to substantiate this. In the background, it is mentioned as ‘40% of the cases in the 2020 wave being found in the 4 major metro cities of Delhi, Mumbai, Kolkata and Chennai’. If it is so then why these cities were not considered for the study?
v. Study at the ward level – The study was carried out at the ward level and not at the slum level, while the title mentions as vulnerability of slums. The paper does not specify the number of slums considered in each of the wards.
vi. True values – It is not clear as to how the true values were collected/ calculated (see Tables 3 and 4). The following questions need to be answered: How many slums were considered and based on what criteria? What is the sampling type? What is the sample size? Therefore, there is a need to improve the study design to make it more technically sound.
vii. Data –Open source data for covid 19 risk zones (such as covid dashboard of sociometrik website) are available. Official data are available from the covid dashboard of the Ministry of Health and Family Welfare, Government of India. Therefore, there is a need to refer to such source data to ensure reproducibility.
viii. Data presentation - Figures 4 and 5 show the ward wise relation between % Slum Population and Covid 19 infection rate. However the graphs shown are not ward wise but zone wise (south, west, north, etc.). Figures 2 & 3 and Table 3 has the ward names only but not the ward numbers, while the maps show only the ward numbers (Figure 6) and not the ward names. Therefore, there is a need to improve the data presentation and analysis.
ix. Methods – Need to clarify the criteria for using/ adopting the linear model. Relevant literature can be reviewed for adopting such models for the purpose of calculation/ analysis. Therefore, there is a need to improve the method.
x. Basis for ranges– Need to clarify the basis for considering the ranges (> 0.20 is taken as high, 0 to 0.20 is taken as medium and less than 0 is taken as low). Is there any literature to substantiate these ranges? Therefore, there is a need improve the method and statistical analysis.
xi. Conclusion – In the abstract, the aim is to categorize the slums into 3 levels of risk zones. However in the conclusion, the aim is mentioned as to find a method to quantify the risk exposure to pandemics in slums. Also, It is not clear on what basis the immediate and long term solutions are suggested. Therefore, the conclusions do not fully reflect the results.
Refer https://sociometrik.shinyapps.io/covid19riskzones/ https://covid19dashboard.mohfw.gov.in/ and [1]

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Partly

References

1. Namperumal S, Kranthi: Pandemic Spread in Metropolitan Cities of India—Spatial Planning Factors. Reference Source

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Geospatial modeling for urban planning applications, tenure security for the urban poor, and inclusive planning

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Natraj Kranthi, SPA Bhopal, Bhopal, India
Partha Sarathi Mishra, Sri Sri University, Cuttack, India
Deepashree Choudhury, Apeejay Institute of Technology, Greater Noida, India

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23 Oct 2024 | for Version 1

Deepashree Choudhury, Apeejay Institute of Technology, Greater Noida, Uttar Pradesh, India

7 Views Cite this report Responses(0)

Approved

The article investigates the vulnerability of slum habitats to COVID-19 in two Indian megacities. It seeks to understand how the unique living conditions in these informal settlements increase exposure to the virus and influence the overall pandemic response. The study addresses an important topic, given the disproportionate impact of the pandemic on marginalized urban populations. However, while the research is insightful, there are a few areas where the methodology, data presentation, and discussion could be strengthened to ensure clarity and replicability.
1. Presentation and Literature Citations

The paper is well-written, but certain sections, particularly in the introduction and conclusion, could be enhanced by integrating more recent studies on the impact of COVID-19 on slum populations globally and within India. The discussion of the slum environment in relation to pandemic spread is critical.
Suggestions:
(i)Strengthen the introduction by adding more recent studies on slum vulnerability to infectious diseases and specifically COVID-19.
(ii)Provide proper citations for claims related to the broader impacts of COVID-19 on slums beyond the two cities studied.
(iii)Clarify how this study builds upon or diverges from other research in this domain.

2. Study Design and Technical Soundness
The study provides valuable insights, but the rationale for choosing these two specific megacities (Pune and Surat) needs to be expanded. The authors only say’ Pune and Surat have been selected for testing this model because they are contrasting cases of cities in the way they have dealt with slum issues. Pune has 40.56% of its population living in slums, whereas Surat has only 19.24% of its population living in the slums. The low slum population in Surat is due to the aggressive implementation of slum re-location and development programs after the plague in 1992.‘ The authors do not fully explain whether the findings can be generalized to slums across India or if they are unique to these particular cities.

Suggestions:
(i) Add more robust justification as to why Pune and Surat were chosen as case studies over other cities that also have significant slum populations, such as Kolkata or Chennai.
(ii)Discuss whether these findings are representative of slum vulnerability across India or only applicable to the megacities studied.

3. Details of Methods and Analysis

Providing a clear breakdown of how data was collected and Specifying the criteria used to select slums in these cities for the analysis will help enrich this research. A few questions come to the mind of the reader like, whether any specific sampling method was used, and whether the research involved any first-hand data collection(through interviews with slum dwellers or government officials) ? Were the selected case studies representative of slums across the megacities?

4. Statistical Analysis and Interpretation

(i)The authors state that expert opinions were used to assign weightage to the final six indicators—0.25 for each of the two ‘Congestion indicators’ and 0.125 for each of the four ‘Lack of Access to Basic Services’ indicators. However,an elaboration on how this expert opinion was collected (the process and criteria used to select experts, as well as the methods employed (e.g., surveys, interviews, consensus-building techniques) will highly aid in making the article more comprehensible and in validating the weightage assigned to the indicators.
(ii) The authors mention that numeric values were transformed into categories (high, medium, low) based on specific ranges. Detailing out how the risk exposure benchmarks were derived, for the ranges designated as high, medium, and low will help in establishing the validity of the conclusions drawn.

(iii) In Tables 5 and 6, it is suggested that indicators be assigned nomenclature (e.g., Si1, Si2, etc.) to facilitate clearer comprehension and reference throughout the text. This labeling would enhance the readability of the tables and make it easier for readers to track the indicators discussed.

5. Conclusions
While the conclusions align with the overall findings, they are somewhat overstated, particularly regarding the implications for policy. The authors suggest wide-ranging solutions to slum vulnerability, but these are not fully supported by the data presented. The discussion would benefit from a more cautious interpretation of the findings, especially given the limitations of the study.
Suggestions:
(i)Acknowledge the limitations of the study more explicitly in the conclusions, especially in terms of data limitations and the representativeness of the findings. Provide more grounded, evidence-based policy recommendations that are supported by the specific data from the study.
(ii)Consider offering suggestions for future research that could address gaps identified in this study, particularly in terms of broader applicability to other cities and long-term vulnerability assessment.
(iii)separate out recommendations from the conclusion section and present them under a different head.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

References

1. Seawright J, Gerring J: Case Selection Techniques in Case Study Research. Political Research Quarterly. 2008; 61 (2): 294-308 Publisher Full Text
2. Greco S, Ishizaka A, Tasiou M, Torrisi G: On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness. Social Indicators Research. 2019; 141 (1): 61-94 Publisher Full Text

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Built Environment,Architecture,Urban Design,Urban Studies,Research methodology

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Reviewer Report

3 Views

28 Sep 2024 | for Version 1

Partha Sarathi Mishra, Sri Sri University, Cuttack, Odisha, India

3 Views Cite this report Responses(0)

Approved With Reservations

Summary:
This paper makes a valuable contribution by examining the habitat vulnerability of slums to COVID-19 in two major Indian cities, Pune, and Surat. The authors develop a quantitative risk assessment model based on congestion and access to basic services to categorize slum areas into high, medium, and low risk zones. The visual mapping of these risk zones provides an intuitive understanding of vulnerability patterns, which can inform targeted interventions.
Major strengths:
- Addresses a critical and timely topic of slum vulnerability to pandemics
- Develops a quantitative risk assessment model that can be applied to other urban areas
- Provides clear visual representation of risk zones through ward-level mapping
- Offers both immediate and long-term recommendations for reducing vulnerability
- Uses data from two different cities to demonstrate broader applicability of the approach
Minor revisions suggested:
1. Consider including a brief overview of Indian definitions of slums to complement the international definitions provided. This would strengthen the contextual relevance of the study.
2. Provide some additional background on why Pune and Surat were selected as case studies, perhaps with a sentence or two of data on their COVID-19 impacts.
3. Add a few more details on data collection methods and sources in the methodology section to enhance reproducibility.
4. Ensure consistency in how ward data is presented across all tables and figures for clarity.
5. Simple Additive Weighting Method is mentioned in this paper. However, the weighting method and their implications are not available in this paper. Please clarify on this issue
6. Briefly explain the rationale for the risk score thresholds used (high >0.20, medium 0-0.20, low <0) to help readers interpret the results.
7. Consider adding a short limitations section to acknowledge any constraints of the study.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Architectural Design; Heritage Assessment; Statistical Analysis in Architecture and Planning, Multi-Criteria Decision- Making Analysis

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Respond to this report

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Reviewer Report

33 Views

29 Aug 2024 | for Version 1

Natraj Kranthi, SPA Bhopal, Bhopal, Madhya Pradesh, India

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Approved With Reservations

Summary of the Paper - The paper titled ‘Understanding the habitat vulnerability of Slums to Covid 19: Case of two megacities of India’ aims to categorize the slums into three levels of risk zones for mapping. For this purpose, ward wise habitat vulnerabilities of slums in the two Indian megacities of Pune and Surat were analysed. Definitions given by UN-Habitat and WHO were reviewed and vulnerability parameters ‘congestion’ with two indicators and ‘lack of access to basic services’ with four indicators were identified. Risk exposure was measured based on the normalized true values. Risk was calculated and categorized as high (>0.2), medium (0-0.2) and low (<0). Accordingly these ward wise risks maps were done. The paper concludes by suggesting the importance for immediate and long term solutions
Observations

i. Definitions - The definitions of slums given by the international organizations like UN-HABITAT and WHO were reviewed. However, the Indian definitions of slums such as (i). Notified slums (as per the Slum Act 1956), (ii). Identified slums (as per the Census of India) and (iii). Recognized slums (that are not formally notified but recognized by the authorities) were not reviewed. As the study is conducted in the Indian cities of Pune and Surat, it becomes imperative to review these definitions as well besides the international definitions.
ii. Identification of vulnerability parameters – Based on the review of both the international and national definitions, the habitat vulnerability parameters and indicators are to be identified. The number of such parameters may increase accordingly. Therefore, there is a need to augment the literature review in this regard.
iii. Identification of indicators - Under ‘congestion’, two indicators ‘Population’ and ‘Density’ were identified. However the current literature such as Namperumal et al (2022) reveals a direct link between builtup density and positive Covid cases. Therefore, it is suggested to cite the current literature and consider ‘Builtup density’ also as one of the indicators under ‘congestion’.
iv. Cities as study area – In the abstract, it is mentioned that two Indian megacities Pune and Surat were considered as they were the worst hit during both the waves. However no adequate data or reference is cited to substantiate this. In the background, it is mentioned as ‘40% of the cases in the 2020 wave being found in the 4 major metro cities of Delhi, Mumbai, Kolkata and Chennai’. If it is so then why these cities were not considered for the study?
v. Study at the ward level – The study was carried out at the ward level and not at the slum level, while the title mentions as vulnerability of slums. The paper does not specify the number of slums considered in each of the wards.
vi. True values – It is not clear as to how the true values were collected/ calculated (see Tables 3 and 4). The following questions need to be answered: How many slums were considered and based on what criteria? What is the sampling type? What is the sample size? Therefore, there is a need to improve the study design to make it more technically sound.
vii. Data –Open source data for covid 19 risk zones (such as covid dashboard of sociometrik website) are available. Official data are available from the covid dashboard of the Ministry of Health and Family Welfare, Government of India. Therefore, there is a need to refer to such source data to ensure reproducibility.
viii. Data presentation - Figures 4 and 5 show the ward wise relation between % Slum Population and Covid 19 infection rate. However the graphs shown are not ward wise but zone wise (south, west, north, etc.). Figures 2 & 3 and Table 3 has the ward names only but not the ward numbers, while the maps show only the ward numbers (Figure 6) and not the ward names. Therefore, there is a need to improve the data presentation and analysis.
ix. Methods – Need to clarify the criteria for using/ adopting the linear model. Relevant literature can be reviewed for adopting such models for the purpose of calculation/ analysis. Therefore, there is a need to improve the method.
x. Basis for ranges– Need to clarify the basis for considering the ranges (> 0.20 is taken as high, 0 to 0.20 is taken as medium and less than 0 is taken as low). Is there any literature to substantiate these ranges? Therefore, there is a need improve the method and statistical analysis.
xi. Conclusion – In the abstract, the aim is to categorize the slums into 3 levels of risk zones. However in the conclusion, the aim is mentioned as to find a method to quantify the risk exposure to pandemics in slums. Also, It is not clear on what basis the immediate and long term solutions are suggested. Therefore, the conclusions do not fully reflect the results.
Refer https://sociometrik.shinyapps.io/covid19riskzones/ https://covid19dashboard.mohfw.gov.in/ and [1]

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Partly

References

1. Namperumal S, Kranthi: Pandemic Spread in Metropolitan Cities of India—Spatial Planning Factors. Reference Source

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Geospatial modeling for urban planning applications, tenure security for the urban poor, and inclusive planning

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Understanding the habitat vulnerability of Slums to COVID 19: Case of two megacities of India

Abstract

Background

Methodology

Results

Conclusion

Keywords

Introduction

Background

Literature study

Table 1. Parameters of Slum definition by various organizations.

Table 2. Parameters impacting health vulnerability of slums to COVID-19.

Methods

Figure 1. Steps in the research process.

Sampling

Figure 2. Wardwise relation in Pune between % Slum Population and Covid 19 infection rate in 2020.

Figure 3. Wardwise relation in Pune between % Slum Population and Covid 19 infection rate in 2021.

Figure 4. Wardwise relation in Surat between % Slum Population and Covid 19 infection rate in 2020.

Figure 5. Wardwise relation in Surat between % Slum Population and Covid 19 infection rate in 2021.

Data collection

Table 3. True values for indicators of Pune slums.

Table 4. True values for indicators of Surat slums.

Results and discussion

(1)

(2)

Table 5. Risk exposure of slums in different wards of Pune.

Table 6. Risk exposure of slums in different wards of Surat.

Figure 6. Risk exposure of slums in Pune to COVID 19.

Figure 7. Risk exposure of slums in Surat to COVID 19.

Conclusion

Informed consent statement

Author’s contribution

Ethical approval and consent for third party data

Consent to participate

Consent to publish

Data availability statement

Acknowledgments

References

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