The COVID-19 pandemic has had a diverse range of both direct and indirect impacts on health (both physical and mental), the economy, and the environment. The relevant data sources used to inform pandemic-related decisions have been similarly diverse, though decision-makers have primarily relied upon data sets from non-satellite sources such as traditional public health data. As we move from initial crisis response to more long-term management, there is both an interest and a need for considering a wider diversity of data sources and impacts. It is difficult for any person to absorb and respond strategically to the broad sets of data that are relevant to the issues regarding COVID management. To address this, the authors propose a five part, integrated data visualization and modeling framework entitled the Vida Decision Support System. The goal of Vida is to create an accessible and openly available online platform that can be customized by the leadership team for a city or region and bring together knowledge from several areas of expertise. The five components of Vida, each of which serve to model a specific domain, include Public Health, Environment, Socio-economic Impacts, Public Policy, and Technology. This framework is currently being designed and evaluated with collaborators in Angola, Brazil, Chile, Indonesia, Mexico and the United States. The environmental data comes from sources such as in-situ sensors and both civil and commercial earth observation instruments (Landsat, VIIRS, Planet Labs’ PlanetScope, etc.) to track factors such as water quality, forest extent and health, air quality, human mobility, and nighttime urban lighting. Similarly, socioeconomic data derives from both in-situ sources, such as local statistical agencies, and from satellite products, such as those hosted by NASA’s Socioeconomic Data and Applications Center. The authors discuss the value provided by this framework to each of the collaborators, the process used to apply the framework to each local context, and future possibilities for Vida. Even though Vida was first developed and applied in response to COVID-19, it has applications in other public health contexts where policy, environment, and socio-economic impacts are closely tied.

Gold mining has played a significant role in Ghana’s economy for centuries. Regulation of this industry has varied over time and while large-scale mining is prevalent in the country, prevalence of artisanal mining, or Galamsey has escalated throughout Ghana in recent years. These mines are not only harmful to human health due to the use of Mercury in the amalgamation process, but also leave a significant footprint on terrestrial ecosystems, degrading and destroying forested ecosystems in the region. This study used machine learning and Google Earth Engine to quantify the footprint of artisanal gold mines in Ghana and understand how conversion of forested regions to mining has changed from 2002-2019. We used Landsat imagery and a random forest classification to classify areas of anomalous NDVI loss during this time period and used WorldView image collections to assess the accuracy of the model. We then used a 3-year moving average to calculate the year of maximum derivative NDVI values. We used this calculation to identify the year of conversion to mining. Within the study area of Southwestern Ghana, our analysis showed that approximately 35,000 ha of vegetation were converted to mining. The majority of this mining occurred between 2014 and 2017. Additionally, around 700 ha ha of mining occurred within protected areas defined by the World Database on Protected Areas. Often, artisanal mining appears to be co-located with rivers such as the Orin and Ankobra Rivers, demonstrating the potential of these mines to affect access to clean drinking water. Through the process of gold extraction, these mines leave a distinct footprint with a series of ponds following these major rivers. However, while the footprints of these ponds are spatially distinct, our model does not distinguish between active and inactive ponds if no remediation actions are taken following inactivity. Future research should work towards distinguishing between active and inactive mining sites to better understand current levels of mining activity in Ghana.