Highlighting the Compound Risk of COVID-19 and Environmental Pollutants Using Geospatial Technology

The new COVID-19 coronavirus disease has emerged as a global threat and not just to human health but also the global economy. Due to the pandemic, most countries affected have therefore imposed periods of full or partial lockdowns to restrict community transmission. This has had the welcome but unexpected side effect that existing levels of atmospheric pollutants, particularly in cities, have temporarily declined. As found by several authors, air quality can inherently exacerbate the risks linked to respiratory diseases, including COVID-19. In this study, we explore patterns of air pollution for ten of the most affected countries in the world, in the context of the 2020 development of the COVID-19 pandemic. We find that the concentrations of some of the principal atmospheric pollutants were temporarily reduced during the extensive lockdowns in the spring. Secondly, we show that the seasonality of the atmospheric pollutants is not significantly affected by these temporary changes, indicating that observed variations in COVID-19 conditions are likely to be linked to air quality. On this background, we confirm that air pollution may be a good predictor for the local and national severity of COVID-19 infections.The authors acknowledge financial support from the Spanish Government, Grant RTI2018-354 094336-B-I00 (MCIU/AEI/FEDER, UE), the Spanish Carlos III Health Institute, COV 20/01213, and the Basque Government, Grant IT1207-19

Short-Term Statistical Forecasts of COVID-19 Infections in India

COVID-19 cases in India have been steadily increasing since January 30, 2020 and have led to a government-imposed lockdown across the country to curtail community transmission with significant impacts on societal systems. Forecasts using mathematical-epidemiological models have played and continue to play an important role in assessing the probability of COVID-19 infection under specific conditions and are urgently needed to prepare health systems for coping with this pandemic. In many instances, however, access to dedicated and updated information, in particular at regional administrative levels, is surprisingly scarce considering its evident importance and provides a hindrance for the implementation of sustainable coping strategies. Here we demonstrate the performance of an easily transferable statistical model based on the classic Holt-Winters method as means of providing COVID-19 forecasts for India at different administrative levels. Based on daily time series of accumulated infections, active infections and deaths, we use our statistical model to provide 48-days forecasts (28 September to 15 November 2020) of these quantities in India, assuming little or no change in national coping strategies. Using these results alongside a complementary SIR model, we find that one-third of the Indian population could eventually be infected by COVID-19, and that a complete recovery from COVID-19 will happen only after an estimated 450 days from January 2020. Further, our SIR model suggests that the pandemic is likely to peak in India during the first week of November 2020

Development and Standardization of an Innovative Scale for Measuring the Socio-Economic Status of Agroforestry Farmers in South Gujarat, India

Although trees in the agroforestry system play diverse roles in meeting the food, fodder, wood, and energy requirements of the agrarian community, their multifunctional contribution often remains understudied, especially while assessing the socio-economic status (SES) of farmers. Therefore, we conceptualized, developed, and standardized an instrument to appraise the SES of the farmers who practice agroforestry in semi-arid and sub-humid regions of India. We also determined the consistent performance of the scale by testing the reliability using the test–retest method. We found that the validity of the scale was accepted with a high correlation, confirming the validity and reliability of the new scale. We also prepared certain norms to identify different socioeconomic levels of agroforestry farmers. The scale used 9 major, 14 moderate, and 115 relevant minor indicators to address the dynamism of the SES and the diversification of farming systems. The proposed scale was specially designed and elastic in nature so that it has a wide scope regarding local applicability and utility, such as in multi-farming systems. Hence, this scale might be considered for measuring the SES of farmers who practice agroforestry at cross-regional and national levels