Geographic information system-based analysis of COVID-19 cases in India during pre-lockdown, lockdown, and unlock phases

Advanced statistical analysis of air quality and its health impacts in India: Quantifying significance by detangling weather-driven effects

Air quality has emerged as a significant concern due to its direct impact on human health. Over recent decades, India has witnessed a marked deterioration in air quality due to rising anthropogenic emissions and climate change. The COVID-19 lockdown offered a unique opportunity to examine air pollutant reductions under restricted human activities. This study conducted a long-term analysis of air quality in five major Indian cities-Delhi, Kolkata, Bengaluru, Hyderabad, and Visakhapatnam-by analysing variations in PM2.5, PM10, NOx, NH3, SO2, CO, and O3, incorporating a de-weathering strategy to isolate meteorological influences. In Delhi, we observed significant reductions in PM10 (92.50-136.70 μg/m³), NOx (62.13-151.91 ppb), and CO (0.53-0.88 mg/m³), which shifted health risks from the 'extreme' to 'low' category. Visakhapatnam also experienced notable declines in NOx levels (7.50-17.13 ppb). Conversely, Hyderabad exhibited no significant reductions, and AQHI increased (+0.97) due to rising NOx concentrations. Ozone concentrations showed a significant increase across cities, attributed to VOC-limited effects. The analysis revealed that meteorological variability and long-range transport of airmass played critical roles in shaping pollutant concentrations. These findings highlight the complexity of urban air quality dynamics and underscore the benefits of emission reductions for public health.

The Geographical Conditioning of Regional Differentiation Characterising the COVID-19 Pandemic in European Countries

The aim of this paper is to assess the influence of selected geographical factors on the diversity of the development of the COVID-19 pandemic in Europe's regions, and on its dynamics across the continent. The work took into account 250 of NUTS-2 regions. The datasets included the course of the COVID-19 pandemic (two dependent variables), intervening actions (four variables of the research background), and potential environmental and socio-economic conditioning (twelve independent variables). The dependent variables' set was composed of two indexes: morbidity and temporal inertia. The temporal scope of the research was 23 March 2020-15 May 2022, with weekly resolution. By means of multiple linear regression model, the influence of the administrative actions and of the selected natural and socio-economic factors was assessed. Finally, a synthetic Regional Epidemic Vulnerability Index (REVI) for each individual region was calculated. It allowed us to classify the regions into three categories: resistant, neutral, or sensitive. REVI's spatial distribution indicates that the zone of above-average vulnerability occurred in the western part of Europe and around the Alps. Therefore, focus ought to extend beyond regional statistics, towards spatial relationships, like contiguous or transit position. This research also validated the strong impact of national borders.

Mapping of space-time patterns of infectious disease using spatial statistical models: a case study of COVID-19 in India

INTRODUCTION

Mapping of infectious diseases like COVID-19 is the foremost importance for diseases control and prevention. This study attempts to identify the spatio-temporal pattern and evolution trend of COVID-19 at the district level in India using spatial statistical models.

MATERIALS AND METHODS

Active cases of eleven time-stamps (30 March-2 December, 2020) with an approximately 20-day interval are considered. The study reveals applications of spatial statistical tools, i.e. optimised hotspot and outlier analysis (which follow Gi* and Moran I statistics) and emerging hotspot with the base of space time cube, are effective for the spatio-temporal evolution of disease clusters.

RESULTS

The result shows the overall increasing trend of COVID-19 infection with a Mann-Kendall trend score of 2.95 (p = 0.0031). The spatial clusters of high infection (hotspots) and low infection (coldspots) change their location over time but are limited to the districts of the south-western states (Kerala, Karnataka, Andhra Pradesh, Maharashtra, Gujarat) and the north-eastern states (West Bengal, Jharkhand, Assam, Tripura, Manipur, etc.) respectively.

CONCLUSIONS

A total of eight types of patterns are identified, but the most concerning types are consecutive (7.24% of districts), intensifying (15.13% districts) and persistent (24.34% of districts) which will help health policy makers and the government to prioritize-based resource allocation and control measures.

The face behind the Covid-19 mask - A comprehensive review

The threat of epidemic outbreaks like SARS-CoV-2 is growing owing to the exponential growth of the global population and the continual increase in human mobility. Personal protection against viral infections was enforced using ambient air filters, face masks, and other respiratory protective equipment. Available facemasks feature considerable variation in efficacy, materials usage and characteristic properties. Despite their widespread use and importance, face masks pose major potential threats due to the uncontrolled manufacture and disposal techniques. Improper solid waste management enables viral propagation and increases the volume of associated biomedical waste at an alarming rate. Polymers used in single-use face masks include a spectrum of chemical constituents: plasticisers and flame retardants leading to health-related issues over time. Despite ample research in this field, the efficacy of personal protective equipment and its impact post-disposal is yet to be explored satisfactorily. The following review assimilates information on the different forms of personal protective equipment currently in use. Proper waste management techniques pertaining to such special wastes have also been discussed. The study features a holistic overview of innovations made in face masks and their corresponding impact on human health and environment. Strategies with SDG3 and SDG12, outlining safe and proper disposal of solid waste, have also been discussed. Furthermore, employing the CFD paradigm, a 3D model of a face mask was created based on fluid flow during breathing techniques. Lastly, the review concludes with possible future advancements and promising research avenues in personal protective equipment.

Characterizing COVID-19 waves in urban and rural districts of India

Understanding spatial determinants, i.e., social, infrastructural, and environmental features of a place, which shape infectious disease is critically important for public health. We present an exploration of the spatial determinants of reported COVID-19 incidence across India's 641 urban and rural districts, comparing two waves (2020-2021). Three key results emerge using three COVID-19 incidence metrics: cumulative incidence proportion (aggregate risk), cumulative temporal incidence rate, and severity ratio. First, in the same district, characteristics of COVID-19 incidences are similar across waves, with the second wave over four times more severe than the first. Second, after controlling for state-level effects, urbanization (urban population share), living standards, and population age emerge as positive determinants of both risk and rates across waves. Third, keeping all else constant, lower shares of workers working from home correlate with greater infection risk during the second wave. While much attention has focused on intra-urban disease spread, our findings suggest that understanding spatial determinants across human settlements is also important for managing current and future pandemics.

Epidemiological investigation of the COVID-19 outbreak in Vellore district in South India using Geographic Information Surveillance (GIS)

OBJECTIVES

Geographical Information Surveillance (GIS) is an advanced digital technology tool that maps location-based data and helps in epidemiological modeling. We applied GIS to analyze patterns of spread and hotspots of COVID-19 cases in the Vellore district in South India.

METHODS

Laboratory-confirmed COVID-19 cases from the Vellore district and neighboring taluks from March 2020 to June 2021 were geocoded and spatial maps were generated. Time trends exploring urban-rural burden with an age-sex distribution of cases and other variables were correlated with outcomes.

RESULTS

A total of 45,401 cases of COVID-19 were detected, with 20,730 cases during the first wave and 24,671 cases during the second wave. The overall incidence rates of COVID-19 were 462.8 and 588.6 per 100,000 population during the first and second waves, respectively. The spread pattern revealed epicenters in densely populated urban areas with radial spread sparing rural areas in the first wave. The case fatality rate was 1.89% and 1.6% during the first and second waves, which increased with advancing age.

CONCLUSIONS

Modern surveillance systems like GIS can accurately predict the trends and spread patterns during future pandemics. In addition, real-time mapping can help design risk mitigation strategies, thereby preventing the spread to rural areas.

A Geospatial Artificial Intelligence and satellite-based earth observation cognitive system in response to COVID-19

The pandemic emergency caused by the spread of COVID-19 has stressed the importance of promptly identifying new epidemic clusters and patterns, to ensure the implementation of local risk containment measures and provide the needed healthcare to the population. In this framework, artificial intelligence, GIS, geospatial analysis and space assets can play a crucial role. Social media analytics can be used to trigger Earth Observation (EO) satellite acquisitions over potential new areas of human aggregation. Similarly, EO satellites can be used jointly with social media analytics to systematically monitor well-known areas of aggregation (green urban areas, public markets, etc.). The information that can be obtained from the Earth Cognitive System 4 COVID-19 (ECO4CO) are both predictive, aiming to identify possible new clusters of outbreaks, and at the same time supervisorial, by monitoring infrastructures (i.e. traffic jams, parking lots) or specific categories (i.e. teenagers, doctors, teachers, etc.). In this perspective, the technologies described in this paper will allow us to detect critical areas where individuals can be involved in risky aggregation clusters. The ECO4CO data lake will be integrated with ad hoc data obtained by health care structures to understand trends and dynamics, to assess criticalities with respect to medical response and supplies, and to test possibilities useful to tackle potential future emergencies. The System will also provide geographical information on the spread of the infection which will allow an appropriate context-specific public health response to the epidemic. This project has been co-funded by the European Space Agency under its Business Applications programme.

Air pollution in five Indian megacities during the Christmas and New Year celebration amidst COVID-19 pandemic

Urban air quality and COVID-19 have been considered significant issues worldwide in the last few years. The current study highlighted the variation in air pollutants (i.e., PM2.5, PM10, NO2, and SO2) profile between Christmas and new year celebrations in 2019, 2020, and 2021. It can be seen that the concentration of selected air pollutants shows a substantially higher concentration in celebration periods in all reported years. The results indicate that air pollutants values are always higher than permissible limits. This observation indicates that people gather and reunite during Christmas and new year celebrations than the preceding years (2020 and 2021) amidst the pandemic. In the pandemic year, a higher margin enhanced the transportation and firework-induced air pollutant load in urban city Jodhpur, Rajasthan. In all states, a significant tendency was observed to retain the concentration profile of air pollutants in baseline concentration for almost more than one week after the celebration. This study addresses the pandemic situation, but it also dealt with the air pollutant parameter that brings down the sustainable quality of the environment due to the high usage of private vehicles, and crackers. In addition, a study on COVID-19 (cases and death rate) indicates a clear picture of the increasing trend after the event in probably all states. Thus, this approach suggested that stringent law enforcement is needed to ameliorate gatherings/reunions and pollution levels due to such events.

Risk mapping and estimation of COVID-19 transmission in South Sulawesi, Indonesia by a self-identification survey

The rapid transmission rate of coronavirus disease 2019 (COVID-19) is multi-factorial but primarily due to population mobility and aggregation. This research aimed at estimating the rate based on risk mapping and investigation of geospatial distribution. It was divided into different phases that included data collection through a self-identification form available online; data validation of the data collected; application of spatial statistics; comparison with official numbers of positive COVID-19; and mapping of the results. The results show that self-identification based on procurement of independent personal data online had an accuracy of 89%.

Biomedical waste generation and management during COVID-19 pandemic in India: challenges and possible management strategies

The COVID-19 pandemic has resulted in the massive generation of biomedical waste (BMW) and plastic waste (PW). This sudden spike in BMW and PW has created challenges to the existing waste management infrastructure, especially in developing countries. Safe disposal of PW and BMW is essential; otherwise, this virus will lead to a waste pandemic. This paper reviews the generation of BMW and PW before and during the COVID-19 pandemic, the regulatory framework for BMW management, policy interventions for COVID-19-based BMW (C-BMW), the capacity of BMW treatment and disposal facilities to cope with the challenges, possible management strategies, and perspectives in the Indian context. This study indicated that policy intervention helped minimize the general waste treated as C-BMW, especially during the second pandemic. Inadequacy of common BMW treatment facilities' (CBMWTFs) capacity to cope with the BMW daily generation was observed in some states resulting in compromised treatment conditions. Suggestions for better management of BMW and PW include decontamination of used personal protective equipment (PPEs) and recycling, alternate materials for PPEs, segregation strategies, and use of BMW for co-processing in cement kilns. All upcoming CBMWTFs should be equipped with higher capacity and efficient incinerators for the sound management of BMW. Post-pandemic monitoring of environmental compartments is imperative to assess the possible impacts of pandemic waste.

Poverty in India in the face of Covid-19: Diagnosis and prospects

India has been hard-hit by the Covid-19 pandemic. The virus has exacted a heavy toll in terms of lives lost and deteriorating health outcomes. The economic consequences of the pandemic have been similarly grim. In this paper we attempt an initial, interim, assessment of the impacts of the crisis on poverty. We review the growing literature that considers emerging poverty impacts, noting that there remain significant knowledge gaps due to limited evidence on current welfare outcomes. We analyze pre-Covid survey data to examine the incidence of chronic poverty and downward mobility during a period of rapid economic growth and declining poverty. A profile of poverty during such a period might offer a plausible, partial, window on population groups currently at risk. We suggest that, notwithstanding the severe initial impacts of the crisis on poverty, there are grounds for expecting further consequences going forward. As the virus has spread out of the relatively affluent cities, and as economic stagnation persists, rural areas, with historically higher rates of chronic poverty and vulnerability, may see particularly sharp increases in poverty. While recent vaccination developments offer some grounds for optimism, there remains an urgent need to identify, implement and amplify effective policy alleviation measures.