Geographic trajectories

Objectives: Our research adopts computational techniques to analyze disease outbreaks weekly over a large geographic area
while maintaining local-level analysis by incorporating relevant high-spatial resolution cultural and environmental datasets.
The abundance of data about disease outbreaks gives scientists an excellent opportunity to uncover patterns in disease
spread and make future predictions. However, data over a sizeable geographic area quickly outpace human cognition.
Our study area covers a significant portion of the African continent (about 17,885,000 km2). The data size makes computational
analysis vital to assist human decision-makers.
Methods: We first applied global and local spatial autocorrelation for malaria, cholera, meningitis, and yellow fever case
counts. We then used machine learning to predict the weekly presence of these diseases in the second-level administrative
district. Lastly, we used machine learning feature importance methods on the variables that affect spread.
Results: Our spatial autocorrelation results show that geographic nearness is critical but varies in effect and space.
Moreover, we identified many interesting hot and cold spots and spatial outliers. The machine learning model infers a binary
class of cases or none with the best F1 score of 0.96 for malaria. Machine learning feature importance uncovered critical
cultural and environmental factors affecting outbreaks and variations between diseases.
Conclusions: Our study shows that data analytics and machine learning are vital to understanding and monitoring disease
outbreaks locally across vast areas. The speed at which these methods produce insights can be critical during epidemics and
emergencies.

SARS-CoV-2 has been transmitted and outbreak took place in India during the last week before nationwide 1st lockdown took place. Urban areas found more vulnerable and reported nearly 65% of cases during every phase of lockdown. Mumbai, among four metropolitan cities found huge number of containment zones with nearly 30% of SARS-CoV-2 cases indicating clustering of cases. Most of the containment zones of SARS-CoV-2 cases in Kolkata Municipal Corporation found a significant relation with slum areas. The study primarily tries considering the nature of SARS-CoV-2 cases in different urban centres with the help of cartographic techniques. AHP method has been used to determine the factors responsible for such concentration of SARS-CoV-2 cases with vulnerability assessment (exposure, sensitivity and resilience) and risks. Before nationwide lockdown starts, the share of urban centres found 25% which has been transformed into nearly 60% at the end of 3 rd phase of lockdown. Growth rate of SARS-CoV-2 cases found very high for Chennai and Thane with less number of doubling time to nation. Slum concentration and containment density shows a higher degree of correlation in Kolkata Municipal Corporation. Risk map also shows the concentration of cases in central and north Kolkata with higher degree of diseases exposure and sensitivity. Control measures must be taken by the central and state Government to minimise the transmission rate of SARS-CoV-2 mainly urban areas. As urban area contributing a higher share of SARS-CoV-2 cases, a proper management plan must be enforce.

In this nationwide study, we trace the COVID-19 global pandemic's footprint across India's districts. We identify its primary epicentres, which are the major international airports of Mumbai and Delhi. We then track the outbreak into India's hinterlands in four separate time-steps that encapsulate the different lockdown stages implemented. Using a detailed district-level database that encompasses climatic, demographic and socioeconomic parameters, we identify hotspots and significant clusters of COVID-19 cases, which are examined to discern temporal changes and predict areas where the pandemic can next spread into. Of prime concern are the significant clusters in the country's western and northern parts and the threat of rising numbers in the east. Encouraging insights emerge from Kerala in South India, where virus hotspots have been eradicated through effective contact-tracing, mass testing and accessible treatment. Allied with this, we perform epidemiological and socioeconomic susceptibility and vulnerability analyses. The former elicits areas whose resident populations are likely to be physiologically weaker in combating the virus and therein we expect a high incidence of cases. The latter shows regions that can report high fatalities due to ambient poor demographic and health-related factors. Correlations derived from the generalised additive model show that a high share of urban population and high population density (1500-2500 people/km 2), particularly in slum areas, elevate the COVID-19 risk. Aspirational districts have a higher magnitude of transmission (susceptibility) as well as fatality (vulnerability). Discerning such locations can allow targeted resource allocation by governments to combat the next phase of this pandemic in India.

Coronavirus disease (COVID-19), caused by acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a worldwide challenge effecting millions of people in more than 210 countries, including the Sultanate of Oman (Oman). Spatiotemporal analysis was adopted to explore the spatial patterns of the spread of COVID-19 during the period from 29th April to 30th June 2020. Our assessment was made using five geospatial techniques within a Geographical Information System (GIS) context, including a weighted mean centre (WMC), standard deviational ellipses, Moran’s I autocorrelation coefficient, Getis-Ord General-G high/low clustering, and Getis-Ord $$G_{i}^{*}$$ G i ∗ statistic. The Moran’s I-/G- statistics proved that COVID-19 cases in datasets (numbers of cases) were clustered throughout the study period. The Moran’s I and Z scores were above the 2.25 threshold (a confidence level above 95%), ranging from 2274 cases on 29th April to 40,070 cases on 30th June 2020. The results of $$G_{i}^{*}$$ ...

Modelling of pandemic vulnerability in a development context can be improved through combining disciplines, combining data, and recognising the many nested levels of the epidemic. Models of transmission have been constructed at national level or for multiple nations. We instead construct a model allowing for social-group differentials in risk, along with conditioning regional factors and lifestyle factors. Severe COVID-19 disease is our innovative key outcome. We use three data sources at once: National Family and Health Survey for India, Indian Census 2011, and COVID-19 deaths. We provide results for 11 states of India, enabling best-yet targeting of policy actions. The future uses of such models are many. COVID-19 deaths in north and central India were higher in areas with older populations and overweight populations, and was more common among those with pre-existing health conditions, or who smoke or live in urban areas. Policy experts may both want to ‘follow World Health Organi...

As cases of COVID-19 were rising in India and the country's political leadership instituted a nationwide lockdown, one of the authors of this article received a timely invitation from a frienda government official to make rounds with him and his team to various neighbourhoods within the metropolitan city of Bangalore. The team consisted of members working in healthcare, municipal corporation, and local police, and was tasked with ensuring that the government enacted measures of social distancing were being observed by local residents in public spaces. The author witnessed, in real time, the ways in which residents were engaging with the containment measures that were instituted as part of the political leadership's attempt to flatten the curve of COVID-19. What was observed in an urban slum was particularly poignant and illuminating. The observations captured how, for residents of the urban slum, social distancing is more an aspiration than an attainable reality. Indeed, social distancing is impossible if such a protocol does not come with concomitant economic support targeted to the most socially vulnerable in society.

On an auspicious day of Eid-Ul Fitr the 25th May 2020 India got Jersey no-10, an extremely popular number in the sports arena. India holds the 9th position among the countries affected by the COVID-19 Pandemic as on 28th May 2020. Government of India confirmed a biggest 24 hrs spike of 7713 cases on 29th May 2020 to reach a total tally of 168,780. The death total crossed the number of 4978. Thus the case count quadrupled and deaths tripled in the first 25 days since 1 May 2020 when special trains started ferrying labourers from the badly effected cities to their native villages, followed within a week bringing back by flights Indians and expatriates from abroad. The overall recovery rates have reached around 48.5% of the affected cases to a total of 82,000 on date. While the public health professionals in the country do not feel we are fighting against any enemy but highly infectious virus infection that we don't know much about and neither there is established treatment or prevention except simple epidemiological ways of minimizing the transmission through social distancing, wearing masks when mingling with others, hand hygiene and cough etiquette. On the other hand, our political and bureaucratic seem to understand a war metaphor when it comes to confronting great challenges. But this COVID-19 pandemic is neither a war nor, a known enemy to strategize the victory. Today we are experimenting many options of treating serious patients in dedicated hospitals protecting the health personnel attending them using personal protective equipment (PPE). While global and national efforts are being made aggressively to make an effective vaccine available or over next 6-12 months the community spread of infection will lead to herd immunity as against many other viruses like SARS, NIPHA, MERS etc. that we have learnt to live with. Basically, I would drive a message home that we do not have to fight or win any war as much as we need to learn to make peace with its existence in our world for long.

India was the second highest COVID-19 affected country in the world with 2.1 million cases by 11 th August. This study focused on the spatial transmission of the pandemic among the 640 districts in India over time, and aimed to understand the urban-centric nature of the infection. The connectivity context was emphasized that possibly had in icted the outbreak. Using the modes of transmission data for the available cases, the diffusion of this disease was explained. Metropolitans contributed three-fourths of total cases from the beginning. The transport networks attributed signi cantly in transmitting the virus from the urban containment zones. Later, there was a gradual shift of infections from urban to rural areas; however, the numbers kept increasing in the former. The massive reverse migration after lockdown spiked the infected cases further. Districts with airports reported more with in ux of international passengers. A profound east-west division in April with higher infections in the southern and western districts existed. By mid-May eastern India saw a steep rise in active cases. Moran's I analysis showed a low autocorrelation initially which increased over time. Hotspot clustering was observed in western Maharashtra, eastern Tamil Nadu, Gujarat and around Kolkata by the second week of August. The diffusion was due to travel, exposure to infected individuals and among the frontline workers. Spatial regression models con rmed that urbanization was positively correlated with higher incidences of infections. Transit mediums, especially rail and aviation were positively associated. These models validated the crucial role of spatial proximity in diffusion of the pandemic. and water scarcity, increased natural disasters, displaced populations, and increased infectious diseases (Myers and Patz 2009). A rapid increase in the urban population in the lower latitudes imposed newer risks from microbes and resistant genes to new diseases (Mack et al. 2010). Encroachment into natural habitats and closer proximity to wild animals might have led to the origin of diseases like SARS and COVID-19 (Lee et al. 2020). Intensi ed and unscienti c animal keeping methods have raised the import of earlier unknown pathogens, increasing the exposure to new hosts (Lindahl and Grace 2015). Diseases have restructured the urban process over centuries, redistributing population over space. Improvements in connectivity posed catalyst for outbreaks like Chagas disease in Latin America, Seoul Hantavirus, SARS, MERS, AIDS, Ebola, and COVID-19. Traditional rural infectious diseases like lymphatic Filariasis, Dengue, Leishmaniasis, West Nile Virus (WNV) infection, etc. adapted to the urban environment, and surveillance of these are di cult (Neiderud 2015). The emergence of COVID-19 caused by the novel coronavirus, SARS-CoV-2 in the Wuhan city of Hubei, China in early December 2019, took a form of the global pandemic in the next few months. As declared by the World Health Organization (WHO), an international public health emergency, it affected over 213 countries and over 26 million people globally (5 September 2020). The rst report in India was on 30 January 2020, a student who returned from Wuhan to Thrissur, Kerala. The rst fatality in the country was in Karnataka on 11March with a travel history from Saudi Arabia. As an early measure, the government restricted international ights' entry and restricted movements by imposing a nationwide lockdown from 24 March, preceded by the Janta Curfew on 22 March 2020. About 21 million cases were counted in India by 11 August. Transmission of pandemics in urban settings McMichael & McMichael (1993) used the term "crowd infections" to denote them by the outbreak of bacteria and viruses among a densely non-immune population in the urban settings. The consequences of 'imported' and 'exported' infected cases are massive in the travel industry and a country's medical sector (ibid). City life is more associated with mobility, inter-mixing, and risk-taking behavior, which eventually makes it vulnerable to transmissions compared to rural counterparts, especially in developing countries (Saker et al. 2004). SARS outbreak (2003) was transmitted to Hong Kong by a physician traveling from Guangdong, China, unchecked for symptoms due to an absence of border health controls (Abdullah et al. 2004). After the infection spread to his fellow lodgers, they carried the disease to their respective countries, thus starting the global SARS epidemic (ibid). The rapid spread of COVID-19 shows the potential threat that an infectious disease poses in a closely-knit world in the 21 st century. Dr. Michael Ryan, Executive Director, WHO Health Emergencies program, addressed the incidence of higher outbreaks in urban centres with improved travel networks of Europe and North America, compared to Africa at a virtual press conference (13 April 2020). He explained disease penetration from urban-centric core to the periphery with the movement of people, the distance, and social contact between individuals and the environment. He further added, there remained a possibility of weaker surveillance in rural areas, that might have under-detected the prevalence. An article published early March (2020) in Forbes reported rapid rise of community-level infections in all the larger metropolitans in the top ten globally most COVID-19 affected countries; China,

India was the second highest COVID-19 affected country in the world with 2.1 million cases by 11 th August. This study focused on the spatial transmission of the pandemic among the 640 districts in India over time, and aimed to understand the urban-centric nature of the infection. The connectivity context was emphasized that possibly had in icted the outbreak. Using the modes of transmission data for the available cases, the diffusion of this disease was explained. Metropolitans contributed three-fourths of total cases from the beginning. The transport networks attributed signi cantly in transmitting the virus from the urban containment zones. Later, there was a gradual shift of infections from urban to rural areas; however, the numbers kept increasing in the former. The massive reverse migration after lockdown spiked the infected cases further. Districts with airports reported more with in ux of international passengers. A profound east-west division in April with higher infections in the southern and western districts existed. By mid-May eastern India saw a steep rise in active cases. Moran's I analysis showed a low autocorrelation initially which increased over time. Hotspot clustering was observed in western Maharashtra, eastern Tamil Nadu, Gujarat and around Kolkata by the second week of August. The diffusion was due to travel, exposure to infected individuals and among the frontline workers. Spatial regression models con rmed that urbanization was positively correlated with higher incidences of infections. Transit mediums, especially rail and aviation were positively associated. These models validated the crucial role of spatial proximity in diffusion of the pandemic. and water scarcity, increased natural disasters, displaced populations, and increased infectious diseases (Myers and Patz 2009). A rapid increase in the urban population in the lower latitudes imposed newer risks from microbes and resistant genes to new diseases (Mack et al. 2010). Encroachment into natural habitats and closer proximity to wild animals might have led to the origin of diseases like SARS and COVID-19 (Lee et al. 2020). Intensi ed and unscienti c animal keeping methods have raised the import of earlier unknown pathogens, increasing the exposure to new hosts (Lindahl and Grace 2015). Diseases have restructured the urban process over centuries, redistributing population over space. Improvements in connectivity posed catalyst for outbreaks like Chagas disease in Latin America, Seoul Hantavirus, SARS, MERS, AIDS, Ebola, and COVID-19. Traditional rural infectious diseases like lymphatic Filariasis, Dengue, Leishmaniasis, West Nile Virus (WNV) infection, etc. adapted to the urban environment, and surveillance of these are di cult (Neiderud 2015). The emergence of COVID-19 caused by the novel coronavirus, SARS-CoV-2 in the Wuhan city of Hubei, China in early December 2019, took a form of the global pandemic in the next few months. As declared by the World Health Organization (WHO), an international public health emergency, it affected over 213 countries and over 26 million people globally (5 September 2020). The rst report in India was on 30 January 2020, a student who returned from Wuhan to Thrissur, Kerala. The rst fatality in the country was in Karnataka on 11March with a travel history from Saudi Arabia. As an early measure, the government restricted international ights' entry and restricted movements by imposing a nationwide lockdown from 24 March, preceded by the Janta Curfew on 22 March 2020. About 21 million cases were counted in India by 11 August. Transmission of pandemics in urban settings McMichael & McMichael (1993) used the term "crowd infections" to denote them by the outbreak of bacteria and viruses among a densely non-immune population in the urban settings. The consequences of 'imported' and 'exported' infected cases are massive in the travel industry and a country's medical sector (ibid). City life is more associated with mobility, inter-mixing, and risk-taking behavior, which eventually makes it vulnerable to transmissions compared to rural counterparts, especially in developing countries (Saker et al. 2004). SARS outbreak (2003) was transmitted to Hong Kong by a physician traveling from Guangdong, China, unchecked for symptoms due to an absence of border health controls (Abdullah et al. 2004). After the infection spread to his fellow lodgers, they carried the disease to their respective countries, thus starting the global SARS epidemic (ibid). The rapid spread of COVID-19 shows the potential threat that an infectious disease poses in a closely-knit world in the 21 st century. Dr. Michael Ryan, Executive Director, WHO Health Emergencies program, addressed the incidence of higher outbreaks in urban centres with improved travel networks of Europe and North America, compared to Africa at a virtual press conference (13 April 2020). He explained disease penetration from urban-centric core to the periphery with the movement of people, the distance, and social contact between individuals and the environment. He further added, there remained a possibility of weaker surveillance in rural areas, that might have under-detected the prevalence. An article published early March (2020) in Forbes reported rapid rise of community-level infections in all the larger metropolitans in the top ten globally most COVID-19 affected countries; China,

In this paper, an attempt has been made to assess the impact of complete lockdown on COVID-19 infection in India. This study obtained COVID-19 data on daily confirmed, recovered, and deaths for 21 days and have implemented the exponential growth model to predict future cases and study the role of lockdown in reducing the number of confirmed COVID 19 cases and deaths. The mathematical model was used to calculate the average reproduction number and herd immunity. Initially, the curve of confirmed cases and deaths grows exponentially; but later, the government-enforced lockdown has helped in curtailing the growth of the curve. The preventive measures may have averted around 4,31,915 confirmed cases and 32,856 deaths till May 3, 2020. The reproduction rate was estimated at 2.56 during the pre-lockdown period and is reduced to 1.56 during lockdown period. It is also observed that the number of COVID-19 patients in a government hospital in India as on May 3 would have been on an average of 15 cases per hospital if it grows exponentially. However, due to lockdown, it has reduced to the level of 2 cases per hospital. The number of confirmed cases grew around 19 percent during the pre-lockdown period of March 14-24, 2020, and stabilizes at the same pace till April 5. Post-April 5, the decline in the growth rate of confirmed cases reached as low as 10 percent. Results indicate that the preventive measures taken at the early stages have successfully helped in preventing a large number of deaths and infected cases in India.

The incidence of known cases of novel coronavirus originated from Wuhan City of China has now been decreased by using prevention measures including strict social distancing; however, the pandemic SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is continued transmitting in the developing countries including India. In news media and documents, Government of India and Wikipedia reported the information of first affected human by SARS-CoV-2 in Kerala, India, which was recognized in late January 2020, originating from China. As expected, the reported case returned from China and transmitted the infection to all surrounding people with unprotected contact. As the proportion of asymptomatic SARS-CoV-2 cases to be reported is large, it is quite impossible that some asymptomatic persons went undetected. In this regard, Kagaya et al. (2020) estimated the asymptomatic proportion of the outbreak of about 18% from Diamond Princess Cruise Ship. In India, as of April 07, 2020, 4421 cases of, including 114 deaths attributed to, novel Covid-19 (coronavirus disease 2019) were reported (Aajtak.intoday.in/: 7 April, 2020). The variation of number of affected persons can easily observed in India, where the highest and lowest cases have been reported from Maharashtra and Mizoram, respectively (Fig. 1). According to Fig. 1, the highest number of cases have been reported in Maharashtra (15%), Tamil Nadu (14%), Delhi (13%), and Kerala (10%); however, it will be increased with time as the pandemic is still unravelling as the testing facilities are not broadly available across the country. On the other hand, few states of India fare better like Mizoram, Arunachal Pradesh, Manipur, etc. In contrast, Zhou et al. (2020) reported that the infection rate and risk of death are higher in older age persons. As per population census 2011, the states of India like Kerala (12.6%), Goa (11.2%) and Tamil Nadu (10.4%) have maximum * Sneha Gautam

SARS-CoV-2 has been transmitted and outbreak took place in India during the last week before nationwide 1st lockdown took place. Urban areas found more vulnerable and reported nearly 65% of cases during every phase of lockdown. Mumbai, among four metropolitan cities found huge number of containment zones with nearly 30% of SARS-CoV-2 cases indicating clustering of cases. Most of the containment zones of SARS-CoV-2 cases in Kolkata Municipal Corporation found a significant relation with slum areas. The study primarily tries considering the nature of SARS-CoV-2 cases in different urban centres with the help of cartographic techniques. AHP method has been used to determine the factors responsible for such concentration of SARS-CoV-2 cases with vulnerability assessment (exposure, sensitivity and resilience) and risks. Before nationwide lockdown starts, the share of urban centres found 25% which has been transformed into nearly 60% at the end of 3 rd phase of lockdown. Growth rate of SARS-CoV-2 cases found very high for Chennai and Thane with less number of doubling time to nation. Slum concentration and containment density shows a higher degree of correlation in Kolkata Municipal Corporation. Risk map also shows the concentration of cases in central and north Kolkata with higher degree of diseases exposure and sensitivity. Control measures must be taken by the central and state Government to minimise the transmission rate of SARS-CoV-2 mainly urban areas. As urban area contributing a higher share of SARS-CoV-2 cases, a proper management plan must be enforce.

Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre-including this research content-immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

From time to time, until the crisis has passed, the HEPL blog series authors will be given the opportunity to provide short updates on their country/region's continuing response to this worldwide catastrophe and their further reflections on those responses. Each update will be labelled accordingly with the original response at the bottom of each post.

The present ongoing global pandemic caused by SARS-CoV-2 virus is creating havoc across the world. The absence of any vaccine as well as any definitive drug to cure, has made the situation very grave. Therefore only few effective tools are available to contain the rapid pace of spread of this disease, named as COVID-19. On 24th March, 2020, the the Union Government of India made an announcement of unprecedented complete lockdown of the entire country effective from the next day. No exercise of similar scale and magnitude has been ever undertaken anywhere on the globe in the history of entire mankind. This study aims to scientifically analyze the implications of this decision using a kinetic model covering more than 96% of Indian territory. This model was further constrained by large sets of realistic parameters pertinent to India in order to capture the ground realities prevailing in India, such as: (i) true state wise population density distribution, (ii) accurate state wise infect...

The outbreak of 2019 novel coronavirus disease (COVID-19) is a public health emergency of international concern. • The ability to detect disease in their early stages is a key component of efficient disease control and prevention. • The research focuses to develop a transparent user-friendly method to simulate spatial-temporal disease outbreak data. • GIS techniques, resources, and methods can be used in Pakistan for more effective investigation of disease. • It is quite easy for authorities to locate the highly effected area and take appropriate actions in that particular areas.

Objectives: To map the alcohol hot spots and understand the Sociodemographic Indices (SDI) affecting alcohol consumption in Indian men and women.Methods: Data from National Family Health Survey-4 carried out from 2015 to 2016 with a sample size of 103,411 men and 699,686 women were used for Geographic Information System mapping, and hot spot identification by spatial statistics (Getis-Ord Gi*). Bivariate analyses and multiple logistic regressions were used to analyze SDI.Results: India has three major alcohol hot spots: (1) North-East (NE) states, (2) Eastern Peninsular states formed by Chhattisgarh, Odisha, Jharkhand, and Telangana, and (3) Southern states of Tamil Nadu and Kerala. Hot spot analysis strongly correlated with region-wise analysis of SDI. Respondents who consumed tobacco have higher odds (men adjusted odds ratio [aOR]: 5.42; women aOR: 4.30) of consuming alcohol. Except for religion and social category, other socioeconomic factors have a low to moderate effect on alco...

India was the second highest COVID-19 affected country in the world with 2.1 million cases by 11th August. This study focused on the spatial transmission of the pandemic among the 640 districts in India over time, and aimed to understand the urbancentric nature of the infection. The connectivity context was emphasized that possibly had inflicted the outbreak. Using the modes of transmission data for the available cases, the diffusion of this disease was explained. Metropolitans contributed three-fourths of total cases from the beginning. The transport networks attributed significantly in transmitting the virus from the urban containment zones. Later, there was a gradual shift of infections from urban to rural areas; however, the numbers kept increasing in the former. The massive reverse migration after lockdown spiked the infected cases further. Districts with airports reported more with influx of international passengers. A profound east-west division in April with higher infections in the southern and western districts existed. By mid-May eastern India saw a steep rise in active cases. Moran's I analysis showed a low autocorrelation initially which increased over time. Hotspot clustering was observed in western Maharashtra, eastern Tamil Nadu, Gujarat and around Kolkata by the second week of August. The diffusion was due to travel, exposure to infected individuals and among the frontline workers. Spatial regression models confirmed that urbanization was positively correlated with higher incidences of infections. Transit mediums, especially rail and aviation were positively associated. These models validated the crucial role of spatial proximity in diffusion of the pandemic. Keywords COVID-19 Á Urban-centric Á Cities Á Modes of transmission Á Travel networks Á Spatial regression models

In this nationwide study, we trace the COVID-19 global pandemic's footprint across India's districts. We identify its primary epicentres, which are the major international airports of Mumbai and Delhi. We then track the outbreak into India's hinterlands in four separate time-steps that encapsulate the different lockdown stages implemented. Using a detailed district-level database that encompasses climatic, demographic and socioeconomic parameters, we identify hotspots and significant clusters of COVID-19 cases, which are examined to discern temporal changes and predict areas where the pandemic can next spread into. Of prime concern are the significant clusters in the country's western and northern parts and the threat of rising numbers in the east. Encouraging insights emerge from Kerala in South India, where virus hotspots have been eradicated through effective contact-tracing, mass testing and accessible treatment. Allied with this, we perform epidemiological and socioeconomic susceptibility and vulnerability analyses. The former elicits areas whose resident populations are likely to be physiologically weaker in combating the virus and therein we expect a high incidence of cases. The latter shows regions that can report high fatalities due to ambient poor demographic and health-related factors. Correlations derived from the generalised additive model show that a high share of urban population and high population density (1500-2500 people/km 2), particularly in slum areas, elevate the COVID-19 risk. Aspirational districts have a higher magnitude of transmission (susceptibility) as well as fatality (vulnerability). Discerning such locations can allow targeted resource allocation by governments to combat the next phase of this pandemic in India.

COVID-19 pandemic is a global concern, due to its high spreading and alarming fatality rate. Mathematical models can play a decisive role in mitigating the spread and predicting the growth of the epidemic. India is a large country, with a highly variable inter-state mobility, and dynamically varying infection cases in different locations; thus, the existing models, based solely on the aspects of growth rates, or generalized network concepts, may not provide desired predictions. The internal mobility of a country must be considered, for accurate prediction. Herein, we propose a framework for predicting the geographical spread of COVID-19, using reported COVID-19 cases, census migration data, and monthly airline data of passengers. We discover that spreading depends on the spatial distribution of existing cases, human mobility patterns, and administrative decisions. In India, the mobility towards professional sites can surge incoming cases at Maharastra and Karnataka, while migration ...

The outbreak of Covid-19 disease started in the late 2019 precisely in December, and it blowout rapidly with several confirmed cases across the globe. For instance, in a study conducted by Gao, Tian, & Yang (2020) disclosed that in February 16, 2020 Covid-19 has affected 70,548 people and 1770 were recorded death in Mainland China. In a related development Japan along have recorded about 413 infection cases in December 2019. Similarly, in December in Wuhan, China had a first covid-19 outbreak which was affected serious respiratory problem Corona Virus 2 (Covid-19). In December 2019, Wuhan, in China, first experienced the outbreak of the coronavirus disease 2019 (COVID-19). Physical futures of the affected individual of the covid-19 disease is include pathological flaking (Zhou et al., 2020).

In India, the Covid-19 pandemic has thrown open challenges on multiple fronts: (a) the reconfiguration of care in hospitals, in response to Covid-19, has led to many patients suffering non-Covid conditions having to delay their treatment, and (b) the lockdown which though necessary has affected people unequally, some being much worse-off than others. This article unpacks the impact of Covid-19 on healthcare systems in India by raising moral and ethical questions about the plight of patients with other medical conditions while accessing care. This article also proposes a set of actions by which healthcare systems can address Covid and non-Covid related healthcare needs.

Human travel fed the worldwide spread of Covid-19, but it remains unclear whether the volume of incoming air passengers and the centrality of airports in the global airline network made some regions more vulnerable to earlier and higher mortality. We assess whether the precocity and severity of Covid-19 deaths were contingent on these measures of air travel intensity, controlling for differences in local non-pharmaceutical interventions and pre-pandemic structural characteristics of 503 sub-national areas on five continents in April-July 2020. OLS models of precocity (i.e., the timing of the 1st and 10th death outbreaks) reveal that the volume of incoming passengers and the centrality of airports were not impactful once we controlled for local characteristics. We model severity (i.e., the weekly death incidence of Covid-19) with both GLMM and OLS regressions. Results suggest that death incidence was insensitive to the number of passengers and airport centrality, with no substantial ...

This research proposes a Spatiotemporal Modeling approach to understand the role of architecture, specifically the built environment, in the COVID-19 pandemic. The model integrates spatial and temporal parameters to calculate the probability of spread of and exposure to SARS-CoV-2 virus (responsible of COVID-19 disease) due to the combination of four aspects: Spatial configuration, organizational schedules, people's behavior, and virus characteristics. Spatiotemporal Modeling builds upon the current models of building analytics for architecture combined with predictive models of COVID-19 spread. While most of the current research on COVID-19 spread focuses on mathematical models at regional scales and the CDC guidelines emphasizing on human behavior, our research focuses on the role of buildings in this pandemic, as the intermediate mechanism where human and social activities occur. The goal is to understand the most significant parameters that influence the virus spread within built environments, including human-to-human, fomite (surface-to-human), and airborne ways of transmission, with the purpose of providing a comprehensive parametric model that may help identify the most influential design and organizational decisions for controlling the pandemic. The proof-of-concept study is a healthcare facility.

The paper tries to analyse the spatial distribution of Covid-19 cases in India and the fundamental determinants behind this pattern. Some pre-identified factors that are responsible for rising infection rate are analysed thoroughly through geographically weighted regression to identify the level of the association behind the spatial variation of Covid-19 cases in Indian states. Migration and international mobility are having a strong positive relationship with the rising of Covid-19 cases in north Indian states. In contrast, Chronic morbidity is the most influential factor in shaping the trajectories of infection in southern states of India. The major proportion of cases was confined within the Urban and metropolitan cities in India indicating the correlation between the level of urbanization and population density with raising the cases; which is highly significant for Delhi-NCR region and Mumbai metropolitan region. On the contrary, irrespective of an increased level of immunization it is not positively associated in arresting the rate of infection of Covid-19.

The paper tries to analyse the spatial distribution of Covid-19 cases in India and the fundamental determinants behind this pattern. Some pre-identified factors that are responsible for rising infection rate are analysed thoroughly through geographically weighted regression to identify the level of the association behind the spatial variation of Covid-19 cases in Indian states. Migration and international mobility are having a strong positive relationship with the rising of Covid-19 cases in north Indian states. In contrast, Chronic morbidity is the most influential factor in shaping the trajectories of infection in southern states of India. The major proportion of cases was confined within the Urban and metropolitan cities in India indicating the correlation between the level of urbanization and population density with raising the cases; which is highly significant for Delhi-NCR region and Mumbai metropolitan region. On the contrary, irrespective of an increased level of immunization it is not positively associated in arresting the rate of infection of Covid-19.

SARS-CoV-2 has been transmitted and outbreak took place in India during the last week before nationwide 1st lockdown took place. Urban areas found more vulnerable and reported nearly 65% of cases during every phase of lockdown. Mumbai, among four metropolitan cities found huge number of containment zones with nearly 30% of SARS-CoV-2 cases indicating clustering of cases. Most of the containment zones of SARS-CoV-2 cases in Kolkata Municipal Corporation found a significant relation with slum areas. The study primarily tries considering the nature of SARS-CoV-2 cases in different urban centres with the help of cartographic techniques. AHP method has been used to determine the factors responsible for such concentration of SARS-CoV-2 cases with vulnerability assessment (exposure, sensitivity and resilience) and risks. Before nationwide lockdown starts, the share of urban centres found 25% which has been transformed into nearly 60% at the end of 3 rd phase of lockdown. Growth rate of SARS-CoV-2 cases found very high for Chennai and Thane with less number of doubling time to nation. Slum concentration and containment density shows a higher degree of correlation in Kolkata Municipal Corporation. Risk map also shows the concentration of cases in central and north Kolkata with higher degree of diseases exposure and sensitivity. Control measures must be taken by the central and state Government to minimise the transmission rate of SARS-CoV-2 mainly urban areas. As urban area contributing a higher share of SARS-CoV-2 cases, a proper management plan must be enforce.

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Background and Aims: The COVID-19 pandemic has posed an unprecedented public health crisis globally irrespective of age. There is no doubt that the older population (above the age of 60) is the most vulnerable age category due to multiple co-morbidities, which often is not in their favour. Age category, types of co-morbidities and nutritional levels indeed play a pivotal role in determining mortality rates. This paper focuses on Kerala, a southern Indian state and how it maintained low mortality levels due to COVID-19 during the first wave despite being the state with the highest proportion of the older population. The present study argues although the state possessed a robust health system and had an active engagement of the public health sector with its citizens through local governments, it was the state’s meticulous planning, innovative schemes centred around older persons such as reverse quarantine, the prevailing excellent nutritional status among its population and initiative...

Malaria is the leading cause of morbidity and mortality in Bihar (India), and concomitant with the spatial and temporal discrepancy throughout the region. Present study aims to determine the changes in spatial distribution of Plasmodium falciparum in Bihar from year 2007-2015. We used Moran's I indices and Getis-Ord G i * statistics to determine the spatial clustering pattern of malaria. The highest variability of disease distribution was observed in 2009. The maximum spatial autocorrelation value was calculated in 2011 (Moran's I = 0.62, p \ 0.00001). Results of cluster-outlier analysis showed significant highhigh clustered. Plasmodium falciparum were spread in the western part of the state from 2007 to 2015. Most of the hotspot districts were observed in the southwest corner of the state. Furthermore, temporal disparities in malaria incidence were also observed. There is an intense change in the spatial clustering pattern of P. falciparum malaria within a 9-year period. Malaria hot spots are exhibited as risk maps that are valuable for observing and spatial targeting of deterrence and control actions against the disease.

Globally, COVID-19 is a top level public health concern. This paper is an attempt to identify and assess COVID-19 incidence hotspots in Metropolitan area of Pakistan using geo-statistical approach. The study is based on secondary data. The COVID-19 confirmed cases record (15/03/2020 to 15/04/2020) of entire Metropolitan area is obtained from hospitals and National Institute of Health website. Point-level geo-coding technique was applied on patient's record and the relative location was converted into absolute location. Getis-Ord Gi* statistical model was applied in ArcGIS 10.3 to calculate Z-score and P values for each point location representing the COVID-19 incidence intensity. Then inverse distance weighted technique of spatial interpolation was applied on Z-score and spatial clusters of crime were geo-visualized in the form hotspot and cold spot. Spatially, more than 50% of land area of Allama Iqbal, Samanabad, Gulburg and Cantonment is covered by very high incidence zone which is surrounded by high incidence zone whereas Ravi, Shalimar, and north of Wagha and Aziz Bhatti towns are located in very low incidence zone. This study provides a suitable methodological framework for identification and analysis infectious disease hotspots. The study can also facilitate health and related authorities to fight war against COVID-19. Similarly, it can help policy makers to manage the movement of travelers and restrict social interaction.

Malaria is the leading cause of morbidity and mortality in Bihar (India), and concomitant with the spatial and temporal discrepancy throughout the region. Present study aims to determine the changes in spatial distribution of Plasmodium falciparum in Bihar from year 2007-2015. We used Moran's I indices and Getis-Ord G i * statistics to determine the spatial clustering pattern of malaria. The highest variability of disease distribution was observed in 2009. The maximum spatial autocorrelation value was calculated in 2011 (Moran's I = 0.62, p \ 0.00001). Results of cluster-outlier analysis showed significant highhigh clustered. Plasmodium falciparum were spread in the western part of the state from 2007 to 2015. Most of the hotspot districts were observed in the southwest corner of the state. Furthermore, temporal disparities in malaria incidence were also observed. There is an intense change in the spatial clustering pattern of P. falciparum malaria within a 9-year period. Malaria hot spots are exhibited as risk maps that are valuable for observing and spatial targeting of deterrence and control actions against the disease.

The COVID-19 pandemic poses new challenges to both individuals and societies that impact health behaviour in many ways. This narrative review brings together initial findings for smoking, alcohol use, nutrition, physical activity and obesity. Smoking and obesity are potential direct risk factors for a severe course of COVID-19, and alcohol abuse, physical inactivity and an unbalanced diet can be indirect risk factors. The constraints of public life to contain the COVID-19 pandemic reduced the opportunities for physical activity and sports, although the initial results on physical activity during this period for Germany do not reflect this assumption. While a part of the population reports making healthier diet choices than before the pandemic, others do not. For smoking and risky alcohol use, data at an aggregate level for the general population do not indicate any behaviour changes. However, different trends appear to be emerging for different population groups pointing to the fact that social inequalities in pandemic-related changes to health behaviour must be assumed. Should further studies confirm these results, this would indicate a need for pandemic-specific prevention measures. Furthermore, specifically during the pandemic, prevention and health promotion measures directed at changes to health behaviour should continue to be implemented and adapted to the restrictions due to the pandemic. Equity in health should be promoted in particular.

As the COVID-19 pandemic marches exponentially, epidemiological data is of high importance to analyse the current situation and guide intervention strategies. This study analyses the epidemiological data of COVID-19 from 17 countries, representing 85 per cent of the total cases within first 90 days of lockdown in Wuhan, China. It follows a population-level observational study design and includes countries with 20,000 cases (or higher) as of 21 April 2020. We sourced the data for these 17 countries from worldometers. info, a digital platform being used by several media and reputed academic institutions worldwide. We calculated the prevalence, incidence, case fatality rate and trends in the epidemiology of COVID-19, and its correlation with population density, urbanisation and elderly population. The analysis represents 85 per cent ( N = 2,183,661) of all cases within the first 90 days of the pandemic. Across the analysed period, the burden of the pandemic primarily focused on high- a...

The pandemic of “Corona Virus Disease 2019” or COVID-19 has taken the world by storm. Majority of nations of the world have been challenged by the novel coronavirus, which is supposedly of zoonotic origin and is known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The present work attempts to evaluate the spread of COVID-19 in India. The methodology of assessment uses SEIR (Susceptible-Exposed-Infectious-Removed) model to establish the impact of socio-behavioural aspect, especially social distancing, affecting the numbers of COVID-19 cases per day. The lockdown initiated by Government of India (GoI) scenario is weighed against a scenario with a possible initiation of community spread due to crowded gatherings in India. The resultant changes, as against the lockdown scenario, has been reported in terms of the increase in the number of cases and stretch of the timeline to mitigate the COVID-19 spread. Impact of environmental factors like temperature and relative humi...

The Corona Virus Disease (COVID-19) pandemic has been created due to the infection by novel coronavirus. Globally, countries have taken measures to reduce social contact to slow down the spread of the virus. Social (physical) distancing via lockdown and awareness on personal hygiene are mitigating measures to prevent transmission of disease. Indian government has implemented the first phase of national lockdown for 21 days (March 24 to April 14, 2020) to reduce the transmission of COVID-19. This study is performed in two stages. First, review of Indian scenario till the end of the first phase of national lockdown and second the impact assessment of the first phase of social lockdown on doubling rate of COVID-19 cases in different Indian states and overall India. The timeline of activities, health care facilities, schemes and services by government and private organizations in combating COVID-19 in India are presented. Analysis is performed using the 3-day moving average daily growth...

To combat the coronavirus disease 2019 (COVID-19) pandemic, the world has vaccination, plasma therapy, herd immunity, and epidemiological interventions as few possible options. The COVID-19 vaccine development is underway and it may take a significant amount of time to develop the vaccine and after development, it will take time to vaccinate the entire population, and plasma therapy has some limitations. Herd immunity can be a plausible option to fight COVID-19 for small countries. But for a country with huge population like India, herd immunity is not a plausible option, because to acquire herd immunity approximately 67% of the population has to be recovered from COVID-19 infection, which will put an extra burden on medical system of the country and will result in a huge loss of human life. Thus epidemiological interventions (complete lockdown, partial lockdown, quarantine, isolation, social distancing, etc.) are some suitable strategies in India to slow down the COVID-19 spread un...

The pandemic state of COVID-19 caused by the SARS CoV-2 put the world in quarantine, led to hundreds of thousands of deaths and is causing an unprecedented economic crisis. However, COVID-19 is spreading in different rates at different countries. Here, we tested the effect of three classes of predictors, i.e., socioeconomic, climatic and transport, on the rate of daily increase of COVID-19 on its exponential phase. We found that population size and global connections, represented by countries’ importance in the global air transportation network, are the main explanations for the early growth rate of COVID-19 in different countries. Climate and socioeconomics had no significant effect in this big picture analysis. Our results indicate that the current claims that the growth rate of COVID-19 may be lower in warmer and humid countries should be taken very carefully, risking to disturb well-established and effective policy of social isolation that may help to avoid higher mortality rate...

In this nationwide study, we trace the COVID-19 global pandemic's footprint across India's districts. We identify its primary epicentres, which are the major international airports of Mumbai and Delhi. We then track the outbreak into India's hinterlands in four separate time-steps that encapsulate the different lockdown stages implemented. Using a detailed district-level database that encompasses climatic, demographic and socioeconomic parameters, we identify hotspots and significant clusters of COVID-19 cases, which are examined to discern temporal changes and predict areas where the pandemic can next spread into. Of prime concern are the significant clusters in the country's western and northern parts and the threat of rising numbers in the east. Encouraging insights emerge from Kerala in South India, where virus hotspots have been eradicated through effective contact-tracing, mass testing and accessible treatment. Allied with this, we perform epidemiological and socioeconomic susceptibility and vulnerability analyses. The former elicits areas whose resident populations are likely to be physiologically weaker in combating the virus and therein we expect a high incidence of cases. The latter shows regions that can report high fatalities due to ambient poor demographic and health-related factors. Correlations derived from the generalised additive model show that a high share of urban population and high population density (1500-2500 people/km 2), particularly in slum areas, elevate the COVID-19 risk. Aspirational districts have a higher magnitude of transmission (susceptibility) as well as fatality (vulnerability). Discerning such locations can allow targeted resource allocation by governments to combat the next phase of this pandemic in India.

The outbreak of 2019 novel coronavirus disease (COVID-19) is a public health emergency of international concern. • The ability to detect disease in their early stages is a key component of efficient disease control and prevention. • The research focuses to develop a transparent user-friendly method to simulate spatial-temporal disease outbreak data. • GIS techniques, resources, and methods can be used in Pakistan for more effective investigation of disease. • It is quite easy for authorities to locate the highly effected area and take appropriate actions in that particular areas.

Understanding dynamics of an outbreak like that of COVID-19 is important in designing effective control measures. This study aims to develop an agent based model that compares changes in infection progression by manipulating different parameters in a synthetic population. Model input includes population characteristics like age, sex, working status etc. of each individual and other factors influencing disease dynamics. Depending on number of epicentres of infection, location of primary cases, sensitivity, proportion of asymptomatic and frequency or duration of lockdown, our simulator tracks every individual and hence infection progression through community over time. In a closed community of 10000 people, it is seen that without any lockdown, number of cases peak around 6th week and wanes off around 15th week. If primary case is located inside dense population cluster like slums, cases peak early and wane off slowly. With introduction of lockdown, cases peak at slower rate. If sensi...

Malaria is one of the leading causes of illness and death in developing countries. Despite growing international concern and efforts to provide effective treatment through the development and improvement of vector control mechanisms, malaria infection continues to remain a leading health problem in Africa, particularly south of the Sahara. At present, however, very few studies have been undertaken that investigate the spatial distribution of malaria cases. This study, therefore, aims to employ geospatial tools to map out areas of high and low malaria prevalence among children under the age of ve years. Annual malaria prevalence was computed using con rmed cases from 2014 to 2019 in Jigawa state. Spatial autocorrelation techniques using global Moran's I and Getis-Ord Gi* statistics were applied. Overall malaria prevalence ranged between 2,743 and 12,916 per 100,000 populations. Results of the overall global Moran's I indicate a statistically signi cant degree of positive autocorrelation (I = 0.358122, Z = 3.721018, P = 0.000198) and all the years under study showed clustered patterns. Hotspot analysis was further explored to show the location of clusters. The results of the analysis detected high prevalence clusters in central and northwestern parts of the state. The study recommends targeting hotspot areas in the design and implementation of malaria control activities.

The development of contagious disease has a significant impact on world health, and a thorough assessment of the literature on the subject has already been completed. Some scientific papers on contagious diseases in humans have been published, as well as an investigation into how to cure a virus. A dataset of serious coronavirus infectious disease from five different states in India has been considered for clustering in the Pandemic. Based on the silhouette index and Dunn's index of cluster validity, we've been using this technique to investigate for ways to produce a high-quality cluster. Using its dual index formed cluster, the considered dataset was used to examine the cluster compactness. The compression of the cluster aids in limiting virus transmission and limiting the virus's influence on the community to cluster individuals. In this post, we explain the importance of encouraging researchers in this field, as well as some recommendations for the future infectious disease research.

The breakout of the Covid-19 global pandemic in India since March 2020 devastated the national economy, disrupted the federal balance of power between the Centre and the State governments, and led to a migrant crisis. In this article, data-derived lessons are drawn from the management of the pandemic at the State government levels. The article yields an answer to whether the availability of health infrastructure singularly affects the effectiveness of the control of the pandemic. This article compares the management of the Covid-19 pandemic by the health systems of the three Indian States of Madhya Pradesh, Delhi and Kerala in the year 2020. Although the response to the outbreak in Kerala was hailed for its more effective arrest of the disease, the response of the Kerala health system was also distinguished by the prior experience in handling viral outbreaks, active community participation in controlling the disease and a proactive mitigation strategy.

India was the second highest COVID-19 affected country in the world with 2.1 million cases by 11th August. This study focused on the spatial transmission of the pandemic among the 640 districts in India over time, and aimed to understand the urbancentric nature of the infection. The connectivity context was emphasized that possibly had inflicted the outbreak. Using the modes of transmission data for the available cases, the diffusion of this disease was explained. Metropolitans contributed three-fourths of total cases from the beginning. The transport networks attributed significantly in transmitting the virus from the urban containment zones. Later, there was a gradual shift of infections from urban to rural areas; however, the numbers kept increasing in the former. The massive reverse migration after lockdown spiked the infected cases further. Districts with airports reported more with influx of international passengers. A profound east-west division in April with higher infections in the southern and western districts existed. By mid-May eastern India saw a steep rise in active cases. Moran's I analysis showed a low autocorrelation initially which increased over time. Hotspot clustering was observed in western Maharashtra, eastern Tamil Nadu, Gujarat and around Kolkata by the second week of August. The diffusion was due to travel, exposure to infected individuals and among the frontline workers. Spatial regression models confirmed that urbanization was positively correlated with higher incidences of infections. Transit mediums, especially rail and aviation were positively associated. These models validated the crucial role of spatial proximity in diffusion of the pandemic. Keywords COVID-19 Á Urban-centric Á Cities Á Modes of transmission Á Travel networks Á Spatial regression models

The coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is an ongoing global pandemic that has devastated our globalized economic system. Apart from infecting 12,102,328 human beings and taking a toll of over 551,046 lives globally by the 11th of July 2020, it has fundamentally affected the way we live, work, travel, and relate to each other. Despite imposing early preventive measures, India has crossed a total caseload of 820,916 with a total death of 22,123 till-date, ranking third in the descending list of worst-affected nations. Although comparatively lower, the case fatality rate in India is 2.72% against the global fatality of 4.53%. It is an empirical fact that every process and institution must evolve to meet the needs of its time. A model for categorization of fatality among real-time reverse transcriptase-polymerase chain reaction (RT-PCR) positive COVID-19 patients has been developed at Uttar Pradesh University of M...

The world has now facing a health crisis due to outbreak of novel coronavirus 2019 (COVID-19). The numbers of infection and death have been rapidly increasing which result in a serious threat to the social and economic crisis. India as the second most populous nation of the world has also running with a serious health crisis, where more than 8,300,500 people have been infected and 123,500 deaths due to this deadly pandemic. Therefore, it is urgent to highlight the spatial vulnerability to identify the area under risk. Taking India as a study area, a geospatial analysis was conducted to identify the hotspot areas of the COVID-19. In the present study, four factors naming total population, population density, foreign tourist arrivals to India and reported confirmed cases of the COVID-19 were taken as responsible factors for detecting hotspot of the novel coronavirus. The result of spatial autocorrelation showed that all four factors considered for hotspot analysis were clustered and the results were statistically significant (p value \\ 0.01). The result of Getis-Ord Gi* statistics revealed that the total population and reported COVID-19 cases have got high priority for considering hotspot with greater z-score ([ 3 and [ 0.7295 respectively). The present analysis reveals that the reported cases of COVID-19 are higher in Maharashtra, followed by Tamil Nadu, Gujarat, Delhi, Uttar Pradesh, and West Bengal. The spatial result and geospatial methodology adopted for detecting COVID-19 hotspot in the Indian subcontinent can help implement strategies both at the macro and micro level. In this regard, social distancing, avoiding social meet, staying at home, avoiding public transport, self-quarantine and isolation are suggested in hotspot zones; together with, the international support is also required in the country to work jointly for mitigating the spread of COVID-19.

As cases of COVID-19 were rising in India and the country's political leadership instituted a nationwide lockdown, one of the authors of this article received a timely invitation from a frienda government official to make rounds with him and his team to various neighbourhoods within the metropolitan city of Bangalore. The team consisted of members working in healthcare, municipal corporation, and local police, and was tasked with ensuring that the government enacted measures of social distancing were being observed by local residents in public spaces. The author witnessed, in real time, the ways in which residents were engaging with the containment measures that were instituted as part of the political leadership's attempt to flatten the curve of COVID-19. What was observed in an urban slum was particularly poignant and illuminating. The observations captured how, for residents of the urban slum, social distancing is more an aspiration than an attainable reality. Indeed, social distancing is impossible if such a protocol does not come with concomitant economic support targeted to the most socially vulnerable in society.