India needs to leverage the role of Geographic Information System technology for devising strategies
The ongoing novel coronavirus disease (COVID-19) pandemic has brought in its wake several questions around the issue of housing and cities in particular.
In an almost global lockdown, we are pushed to the corner to think about how we plan our cities, our neighbourhoods, vulnerability mapping, resilience of infrastructure to these risks, predictive analytics, preventive response and its effectiveness to avert a pandemic.
With these imprints and a vision of housing people, not diseases, we explore further on how data science, analytics and spatial visualisations are important for cities.
Preventive or reactive responses
Preparedness becomes a crucial investment — with the increasing disease burden of cities — because its cost is much less than the unmitigated impact of a health emergency.
Ensuring preparedness of health infrastructure against infectious diseases like COVID-19 today and in times to come, becomes crucial.
This becomes even more crucial in urban settings due to the likely emergence and rapid propagation of infectious diseases in dense, unsafe settlements.
A quick check through the National Health Profile, 2019 shows how equipped cities are in terms of health infrastructure to handle the current situation: It is alarming to know that there are only 713,986 government hospital beds available in India.
This amounts to a mere 0.55 beds per 1,000 population, clearly presenting a daunting challenge, in case of any immoderate surge in the number of cases.
The already-weak public sector hospital capacity further suffers from stark inequities between — and within — regions and states. All COVID-19 cases are currently referred to government hospitals.
India, in such a scenario, needs to leverage the role of Geographic Information System (GIS) technology by switching to locational intelligence and map analytics for devising multi-pronged strategies.
Predictive analytics
Integrated command-and-control centres (ICCCs) — functional in 45 cities — were set up under the Smart Cities Mission.
The ICCCs act as 24/7 nerve-centres for real-time surveillance and monitoring of COVID-19 affected districts across the country.
Different states explore ways of applying GIS differently. A broader application of GIS centres around mapping of cases and their spread along with communicating them through interactive maps and city dashboards.
Some states use it for identifying buffer zones and predictive analytics (heat maps) for virus containment across different zones of their respective cities.
Some also employ it for real-time tracking of ambulances and disinfection services.
Gujarat-based non-profit Foundation for Ecological Security (FES) also launched a GIS-enabled portal, under the India Observatory to help relief planning for migrants in India.
But is it enough?
Exit strategy
Cities need to adapt to a spatial planning approach for healthcare infrastructure using locational intelligence.
Response plans for COVID-19 should explore the potential of a comprehensive GIS-based strategy that can be locally executed, state managed and federally supported.
This strategy — along with currently adopted bio-surveillance and monitoring systems — must include mapping vulnerable population, hospital capacities, information management and surge management.
It should also make optimal use of existing government infrastructure, pooling in private healthcare infrastructure if needed and locating alternative care sites such as convention centres, hotels, college or university dormitories for quarantine facilities.
Such analysis models can be easily created using crucial parameters listed in guidelines issued by the Ministry of Health and Family Welfare.
It is suggestive that cities with the highest number of cases in each state employ a risk-based population assessment model and create risk maps, to further assist decision-making in as moves are made towards an exit plan from a nationwide lockdown.
Such risk maps for identifying transmission risk, susceptibility risk, healthcare resource scarcity risk and exposure risk were created for highly vulnerable cities like Hong Kong, China, using gravity, spatial interaction, movement, and centrality modelling tools.
Transmission risk map for Hong Kong
Source: ESRI
The risk surfaces identified for each type, at a ward level, can be weighed and used to generate overall risk-based population demand estimates.
These estimates can then be used for identifying optimal locations for testing, treatment and commodity distribution.
The case of Wuhan in China also presents similar lessons, as GIS and big data technology played a crucial role in their struggle against the disease.
The city employed GIS-based strategies for identifying spatial transmission in spatial prevention and control, allocation of resources, and detection of social sentiment among other things.
ESRI, an international supplier of GIS, the National Centre for Disease Control, National Disaster Management Authority and government departments in Uttar Pradesh, Karnataka, Odisha, Jharkhand, Maharashtra and Manipur, formulated a country-wide task-force, as a part of the global disaster response program.
Mapping vulnerabilities
The target should be to ensure inclusive access to primary healthcare (PHC) infrastructure as the first step of spatial planning for healthcare infrastructure at a city level.
This can be achieved by preparing a city-level location-allocation model for existing and new facilities, factoring in crucial parameters like accessibility, population density, income demographics and distance from existing PHC facilities.
Universal healthcare coverage and its corresponding diagnostic capacity was one of the key tools that empowered South Korea in its battle against the pandemic, a model worthy to emulate.
The next step should entail in-depth spatial analysis to identify vulnerable populations. Spatial locations of secondary / specialised healthcare facilities can then be modelled accordingly.
The actual number of such facilities required can be optimised using this approach. Such a model can also factor in assessments based on a population’s risk of transmission, by exploring human mobility data.
A population’s resources and vulnerabilities factor into susceptibility to any disease, including access to health care and exposure pathways.
One way to ascertain data on vulnerable population in cities with a population of more than 3 lakh could be through geotagged slum data integrated with socio-economic data, that was collected as part GIS, MIS Integration under schemes like Rajiv Awas Yojana (RAY), Pradhan Mantri Awas Yojana (PMAY).
Crisis imprints
The empowered group on COVID-19 and NITI Aayog recently sought technical support for monitoring and surveillance mechanisms from organisations such as the World Health Organisation and the United Nations.
It is time to remain hopeful and expect to rely on some of these technological applications through this pandemic with these initiatives.
There may, however, be severe limitations, with the current state of India’s data infrastructure.
It is, therefore, more important to incorporate these lessons in future planning practices once the crisis is over.
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