The COVID-19 global pandemic is an erratic phenomenon and has raged for about six months, affecting 212 countries and territories globally and two international conveyances (https://www.worldometers.info/coronavirus/), in all afflicting over 6.4 million people and causing nearly 380,000 deaths till date. In India, number of covid cases crossed the 2,00,000-mark on 3rd June 3, 2020. As per the Ministry of Health and Family Welfare (MoHFW, GoI), nearly half of this number (1,01,497) are active cases and 1,00,303 have been cured or discharged. India has so far recorded 5,815 deaths. Maharashtra continued to lead the tally of the states with highest caseload with more than 72,000 positive covid cases. Nonetheless, this deadly corona virus and the stringent lockdown measures implemented to contain it have exposed the marked inequalities that put certain populations more at risk of adverse outcomes than others. As the lockdown gets lifted, state/district or local government need to keep in mind which districts are most vulnerable, and may consequently require effective surveillance, monitoring, and adequate protective measures. What does vulnerability mean in the context of the outbreak? Suggested precautionary measures such as physical distancing, self-quarantine, frequent hand washing with sanitizer or face masking, among others are neither accessible nor affordable especially for those residing in concentrated poverty, congested and derelict residential environments, with meagre access to essential goods and services. While there are certain common and well recognized risk factors for COVID-19 infection such as aging and co-morbidity which apply to individuals, we are really unaware about risk factors and resultant vulnerability at the levels of a community and dis-aggregated local geographies. Yet these vulnerabilities are as likely as individual-level factors to result in higher risk of infection and unwanted outcomes.
With no vaccine yet available, the prospect of a viral return in this coronavirus’ original or mutated form casts further threat, more so on populations and areas that are most at risk from such an outbreak. Therefore, tracing the geographic trajectories of COVID-19 outbreaks and measuring the local level vulnerability using a multidimensional approach would certainly provide a nuanced understanding regarding the identification of regions that are at the greatest risk from its further spread, if the rising trend of the COVID-19 outbreak in India continues unabated over the coming days. Such attempts are extremely crucial towards gaining a comprehensive understanding of the pandemic’s likely impact on a region (in health, economic and societal terms), not only in light of the current ongoing situation, but to also gauge possible scenarios and ascertain target areas for pre-emptive resource allocation in the near future.
Geographic trajectories of the COVID-19 outbreak
If district wise numbers of COVID-19 cases, as reported by the MoHFW (GoI), are mapped for different dates, one can understand how this deadly virus is spreading across space and time and feasibly demonstrate each stage of the pre and ongoing lockdown phases. These dates are- a) Pre-lockdown Phase (on 23rd March); b) Early lockdown Phase (29th March); c) Mid-lockdown Phase (10th April); d) Late lockdown Phase (18th April), and e) Unlock Phase-1 (1st June).
COVID-19 cases were first diagnosed in India in late January, 2020 and crossed 60,000-mark by the end of the second week of May, 2020. With this highly contagious disease transmitting primarily through international tourists and returning travellers from aboard, the initially affected areas were mostly large cities that have international airports (or are regions adjoining such places) or are major tourist destinations. This is made apparent by the district-wise spread pattern of the COVID-19 virus (Figure 1). In the first phase (Figure 1A), cases were reported from western India (around Mumbai and Ahmadabad- two of the main commercial hubs of the country), from around New Delhi (the national capital) and Ladakh (popular tourist destination as well as a prominent base of the Indian Army) and from the southern states of Kerala (which has a high number residents who migrate/travel for work to the Gulf region), Tamil Nadu, Andhra Pradesh and Karnataka (all of which have major metropolitan centres that are important commercial hubs- Chennai, Hyderabad and Bengaluru). Further intensification of the above pattern was observed in the next time step on 29th March, 2020, with the adjoining regions of the above areas reporting substantial numbers of COVID-19 cases (Figure 1B). Broad swathes of the eastern part of the country still remained mostly unaffected, except for Kolkata (the major regional metropolitan centre) and its adjoining areas.
In the next phase on 10th April, 2020, two large contiguous zones in northern India and western-central-south India reported high numbers of COVID-19 cases (Figure 1C). The northern entity manifested in a broad swath from western Rajasthan, through Haryana, Delhi, western Uttar Pradesh and Punjab, up to Ladakh and parts of Jammu and Kashmir. The west-central-southern entity, while contiguous in nature had two broad trends- one portion of it stretched into the interior of the country from west to east across Maharashtra and Madhya Pradesh, the other followed the alignment of the eastern and western coastal plains, with one arm trending from Gujarat down to Kerala via Maharashtra, Goa and Karnataka’s littoral districts and the other incorporating the coastal tracts of Andhra Pradesh and Tamil Nadu (with the adjoining parts of Telangana and Karnataka). Even at this comparatively advanced stage of the pandemic in India, the eastern portion of the country reported markedly lower numbers of cases, with only a few centres in Gangetic West Bengal, coastal Orissa and along the Ganga valley in eastern Uttar Pradesh and Bihar. The pandemic’s footprint in the north-eastern states of India was by and large negligible. The next time step map of 18th April, 2020 (Figure 1D), showed the merging of the two entities described above, with infilling of their intervening districts (i.e. reporting of cases from areas that did not have any before) and a rise in the numbers of cases of those districts already afflicted previously. This resulted in a near-continuous stretch of the nation, from Kashmir to Kanyakumari, along the central and western corridors and down the eastern and western coastal belts, reporting COVID-19 cases. By now the country’s eastern part had noted a rise in cases (with its epicenter at Kolkata). This manifested as a narrow line of districts along the densely populated Ganga plains in Uttar Pradesh and Bihar, which merged into the larger/more contiguous zone further west. The final phase unlock-1 map of 1st June, 2020 (Figure 1E) glaringly discerned that about 92% districts (i.e. 590 of 640) reported at least one positive covid cases and in addition to districts from north and south-western Indian states (e.g. Delhi, Rajasthan, Western MP, Gujarat, Maharashtra, Karnataka, Telengana, Andhra Pradesh and Tamil Nadu), the entire eastern India are now dotted with increasing caseload and seems to be emerged as a COVID-19 hotspot in coming weeks. Other factors aside, huge influx of reverse migration from north, and south-western states coming back to their original homes carrying infection with them are largely responsible for further spreading of coronavirus in these socioeconomically vulnerable regions.
Which districts are more vulnerable to virus outbreak than others?
The COVID-19 vulnerability index is developed from a PCA of 15 interlinked indicators, identified based on existing research measuring the socio-economic, demographic, health, hygiene, and environmental dimensions of COVID-19 vulnerability. For instance, in general, densely populated areas may raise the risk of physical contacting with the people. As the COVID-19 is a highly contagious disease, population density is an unavoidable measure (Rocklöv & Sjödin, 2020). Cities or urban areas are the early centre for COVID-19 spreading, therefore percent urbanization is taken. Overcrowded households (percent HH with HH size 5+) are more vulnerable to COVID-19 than others. Percent women with below 10 years of schooling are taken as proxy for the lack of awareness about COVID-19 spreading (Zhong et al., 2020). Lack of basic hygiene practice leads more infection and smoking in any form may also elevate the risk of infection of COVID-19 (WHO, 2020). Percent of under-5 children or adult women lacking nutrition support- undernourishment or micro-nutrient deficiency in the body raises the risk of infection from any communicable diseases (França et al. 2009). Both lower temperature and humidity increases the risk of COVID-19 spreading (Sajadi et al., 2020; Wang et al., 2020). The economically well-off section can make better arrangements for rigid interventions such as lockdown, while the poorest and poor may go out for their food. Accessing the food from community kitchens, receiving food items from donations, buying essential commodities from the public distribution systems (PDS), among others, may exacerbate the probability of infections. Prevalence of NCDs and percent elderly– comorbidities and aging are also highly positively linked with coronavirus infection (Dowd et al., 2020).The higher value of the index indicates higher vulnerability of the district to COVID-19 and vice-versa. This disaggregated level vulnerability risk mapping would facilitate policy makers with some indication on which districts are likely to be most vulnerable to a COVID-19 outbreak and specifically where should the Government target its resources and accordingly plan a data driven intervention strategy.
A closer look at the spatial visualization of district vulnerability score reveals an interesting pattern (Figure 2). Most of the districts in Bihar, Jharkhand, West Bengal, Odisha, Madhya Pradesh, Chhattisgarh and Gujarat, and adjoining districts in Rajasthan and Maharashtra show high vulnerability scores. Moderate vulnerability is seen in northern districts of Karnataka, Eastern Maharashtra, Telangana, Andhra Pradesh and Eastern districts of Tamil Nadu. Finally, the districts of Kerala, Himachal Pradesh, Haryana, Uttarakhand, Punjab, Jammu & Kashmir, and most districts of the Northeastern states show relatively low vulnerability scores. The vulnerability index scores independent of the reported number of cases clearly suggest that a large number of districts are already in a precarious condition. The districts with higher vulnerability scores are typically characterized by poor socio-economic conditions, chronic poverty, and weak health systems. High-vulnerability districts are those where COVID-19 is likely to spread rapidly, once it is introduced, while also remaining undetected for longer periods. There is an 84% overlap of the places most at risk in these states with those demarcated under the Union Government’s ‘Transformation of Aspirational Districts’ programme, i.e. 104 of the discerned 125 highly to very highly vulnerable districts is also aspirational districts.
The relatively well-off states and districts have so far reported more cases, and imposed stringent lockdown that have caused huge job loss and catastrophic financial impact on poor people, particularly informal migrant workers. On examining the distribution of vulnerabilities together with that of reported cases, it is suggested that the outbreak will rapidly spread to districts with higher vulnerability scores (Figure 3). This may be attributed to the effect of reverse migration, as people from high vulnerable districts usually migrate to the more urbanized, well-off and industrialized districts or large cities for livelihood, and now are returning home because of limited or no work opportunities and higher cost of living.
Conclusion
This short analysis highlighted the initial centres of the COVID-19 pandemic in India, how its incidence occurred, its spread over time and concentrations. By tracing the geographic trajectories of the virus’ outbreak, it pinpointed the areas where possible community transmission has occurred and which need targeted measures to control the situation. Geo-spatial visualization was based on the data garnered about the cases occurring in the initial, middle and last stages of the five-phase lockdown underway in India. However, subsequent reports of the pandemic’s spread largely validate the estimation of the areas that were most likely to be affected in the forthcoming days (i.e. the case of the virus gaining a foothold and then spreading in various clusters of Eastern India, around Kolkata). Vulnerability index based risk mapping can be a useful tool to gauge where it is most critical to be cautious and to protect and priorities strengthening health system capacity. The most vulnerable districts are also where the epidemic will have the most devastating impact. The districts emerging as most vulnerable are frequently those that are poorest, with the weakest health systems and which are home to the most marginalized populations. Aspirational districts have a higher magnitude of vulnerability to COVID-19. Discerning such locations can allow targeted resource allocation by the Governments to combat the next phase of this pandemic in India. By and large, vulnerability provides a lens to anticipate the fallout of the epidemic which we cannot afford to ignore.
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