Abstract This project is funded by the US National Science Foundation (NSF) through their NSF RAPID program under the title “Modeling Corona Spread Using Big Data Analytics.” The project is a joint effort between the Department of Computer & Electrical Engineering and Computer Science at FAU and a research group from LexisNexis Risk Solutions. The novel coronavirus Covid-19 originated in China in early December 2019 and has rapidly spread to many countries around the globe, with the number of confirmed cases increasing every day. Covid-19 is officially a pandemic. It is a novel infection with serious clinical manifestations, including death, and it has reached at least 124 countries and territories. Although the ultimate course and impact of Covid-19 are uncertain, it is not merely possible but likely that the disease will produce enough severe illness to overwhelm the worldwide health care infrastructure. Emerging viral pandemics can place extraordinary and sustained demands on public health and health systems and on providers of essential community services. Modeling the Covid-19 pandemic spread is challenging. But there are data that can be used to project resource demands. Estimates of the reproductive number (R) of SARS-CoV-2 show that at the beginning of the epidemic, each infected person spreads the virus to at least two others, on average (Emanuel et al. in N Engl J Med. 2020, Livingston and Bucher in JAMA 323(14):1335, 2020). A conservatively low estimate is that 5 % of the population could become infected within 3 months. Preliminary data from China and Italy regarding the distribution of case severity and fatality vary widely (Wu and McGoogan in JAMA 323(13):1239–42, 2020). A recent large-scale analysis from China suggests that 80 % of those infected either are asymptomatic or have mild symptoms; a finding that implies that demand for advanced medical services might apply to only 20 % of the total infected. Of patients infected with Covid-19, about 15 % have severe illness and 5 % have critical illness (Emanuel et al. in N Engl J Med. 2020). Overall, mortality ranges from 0.25 % to as high as 3.0 % (Emanuel et al. in N Engl J Med. 2020, Wilson et al. in Emerg Infect Dis 26(6):1339, 2020). Case fatality rates are much higher for vulnerable populations, such as persons over the age of 80 years (> 14 %) and those with coexisting conditions (10 % for those with cardiovascular disease and 7 % for those with diabetes) (Emanuel et al. in N Engl J Med. 2020). Overall, Covid-19 is substantially deadlier than seasonal influenza, which has a mortality of roughly 0.1 %. Public health efforts depend heavily on predicting how diseases such as those caused by Covid-19 spread across the globe. During the early days of a new outbreak, when reliable data are still scarce, researchers turn to mathematical models that can predict where people who could be infected are going and how likely they are to bring the disease with them. These computational methods use known statistical equations that calculate the probability of individuals transmitting the illness. Modern computational power allows these models to quickly incorporate multiple inputs, such as a given disease’s ability to pass from person to person and the movement patterns of potentially infected people traveling by air and land. This process sometimes involves making assumptions about unknown factors, such as an individual’s exact travel pattern. By plugging in different possible versions of each input, however, researchers can update the models as new information becomes available and compare their results to observed patterns for the illness. In this paper we describe the development a model of Corona spread by using innovative big data analytics techniques and tools. We leveraged our experience from research in modeling Ebola spread (Shaw et al. Modeling Ebola Spread and Using HPCC/KEL System. In: Big Data Technologies and Applications 2016 (pp. 347-385). Springer, Cham) to successfully model Corona spread, we will obtain new results, and help in reducing the number of Corona patients. We closely collaborated with LexisNexis, which is a leading US data analytics company and a member of our NSF I/UCRC for Advanced Knowledge Enablement. The lack of a comprehensive view and informative analysis of the status of the pandemic can also cause panic and instability within society. Our work proposes the HPCC Systems Covid-19 tracker, which provides a multi-level view of the pandemic with the informative virus spreading indicators in a timely manner. The system embeds a classical epidemiological model known as SIR and spreading indicators based on causal model. The data solution of the tracker is built on top of the Big Data processing platform HPCC Systems, from ingesting and tracking of various data sources to fast delivery of the data to the public. The HPCC Systems Covid-19 tracker presents the Covid-19 data on a daily, weekly, and cumulative basis up to global-level and down to the county-level. It also provides statistical analysis for each level such as new cases per 100,000 population. The primary analysis such as Contagion Risk and Infection State is based on causal model with a seven-day sliding window. Our work has been released as a publicly available website to the world and attracted a great volume of traffic. The project is open-sourced and available on GitHub. The system was developed on the LexisNexis HPCC Systems, which is briefly described in the paper.
more »
« less
Satellite mapping of urban built-up heights reveals extreme infrastructure gaps and inequalities in the Global South
Information on urban built-up infrastructure is essential to understand the role of cities in shaping environmental, economic, and social outcomes. The lack of data on built-up heights over large areas has limited our ability to characterize urban infrastructure and its spatial variations across the world. Here, we developed a global atlas of urban built-up heights circa 2015 at 500-m resolution from the Sentinel-1 Ground Range Detected satellite data. Results show extreme gaps in per capita urban built-up infrastructure in the Global South compared with the global average, and even larger gaps compared with the average levels in the Global North. Per capita urban built-up infrastructures in some countries in the Global North are more than 30 times higher than those in the Global South. The results also show that the built-up infrastructure in 45 countries in the Global North combined, with ∼16% of the global population, is roughly equivalent to that of 114 countries in the Global South, with ∼74% of the global population. The inequality in urban built-up infrastructure, as measured by an inequality index, is large in most countries, but the largest in the Global South compared with the Global North. Our analysis reveals the scale of infrastructure demand in the Global South that is required in order to meet sustainable development goals.
more »
« less
- Award ID(s):
- 2041859
- NSF-PAR ID:
- 10399501
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 119
- Issue:
- 46
- ISSN:
- 0027-8424
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
more » « less
Abstract This study investigates municipal water use patterns across the Contiguous United States (CONUS). The objectives of this study are to explore temporal trends in water use, improve characterization of indoor and outdoor uses, and improve characterization of commercial, industrial, and institutional (CII) water use in cities across the CONUS. A comprehensive survey was conducted to compile monthly water use data from 126 municipalities for the period 2005–2017 with specific information about residential and CII water use categories. Changes in liters per capita per day and the CII to Residential water use ratio were related to climatic, urban‐geologic and socio‐economic variables. Results indicate an overall decreasing trend in municipal water uses with higher reductions achieved in residential sector. Both residential and CII water use exhibit high seasonality over an average year. The CII to Residential ratio increases with city population and is highest in cities in the Northeast Census region. Cities in South and West Census regions have high municipal water uses and highest reductions in annual per capita‐day water use. Cities in arid climate regions have the highest water uses, compared to other cities, due to landscape irrigation. April precipitation, annual vapor pressure deficit, number of employees in the manufacturing (or, other services except public administration) sector, total percentage of houses built before 1950 and total percentage of single‐family houses explain much of the variation in CII to Residential water use ratio across the CONUS. This study guides improved characterization of municipal water uses and water demand management strategies.
-
more » « less
Abstract Population concentration and built‐up land expansion are two prominent features of contemporary urbanization. Existing literature on the population aspect of urbanization has mostly focused on national and regional aggregates, and literature on the land development aspect has often relied on spatial case studies of individual cities or their meta‐analyses. Using newly‐available data, here we conduct the first global‐coverage, spatial analysis of the relationship between (changes in) population and built‐up land at multiple spatial scales, and compare to existing common beliefs about urbanization based on individual city studies. We find that population and built‐up land show distinctly different spatial and temporal patterns (with a global correlation coefficient around 0.6). Contrary to common impressions, our results show that during recent decades, developed and developing regions across the world experienced comparable amounts of built‐up land expansion. While meta‐analyses have reported that built‐up land in urban areas expands globally on average twice as fast as population grows, our results show the global change rates of built‐up land and population are similar. Also, most global population, including what national statistics agencies call urban population, reside in areas with low land development levels (which are frequently less than 5% built up). These changes in perspective suggest that urbanization's potential large‐scale impacts may need to be re‐evaluated, and lead to best‐practice recommendations for urbanization modeling and analysis. Especially, the common practice in large‐scale earth system modeling of assuming demographically‐defined urban population resides in areas with medium to high built‐up land development levels should change.
-
Inadequate water access is central to the experience of urban inequality across low- and middle-income countries and leads to adverse health and social outcomes. Previous literature on water inequality in Mumbai, India’s second-largest city, offers diverse explanations for water disparities between and within slums. This study provides new insights on water disparities in Mumbai’s slums by evaluating the influence of legal status on water access. We analysed data from 593 households in Mandala, a slum with legally recognized (notified) and unrecognized (non-notified) neighbourhoods. Households in a non-notified neighbourhood suffered relative disadvantages in water infrastructure, accessibility, reliability and spending. Non-notified households also used significantly fewer litres per capita per day of water, even after controlling for religion and socioeconomic status. Findings suggest that legal exclusion may be a central driver of water inequality. Extending legal recognition to excluded slum settlements, neighbourhoods and households could be a powerful intervention for reducing urban water inequality.more » « less
-
null (Ed.)Green spaces have recently received wide acknowledgement for urban sustainability benefits and are mentioned in the Sustainable Development Goals SDGs Target 11.7. The article aims to address the knowledge gap on indicators used for assessment of green spaces for urban sustainability in the Arctic using an example of Nadym, Russia which is illustrative of compact cities built during the Soviet time using a system of microrayons. Different indicators implemented by international organizations for assessment of green spaces are compared with indicators used in Russia. Utilizing very high-resolution WorldView-3 satellite image and open source data, the quantity and quality of green spaces are estimated with high accuracy. In addition to traditionally used indicators of share of green space per capita, share of public spaces for common use within walking distance to assess availability and accessibility of green spaces, the paper suggests importance of taking into account governance, distribution, and composition using analysis of historic legacies, municipal budget allocation for green space maintenance, and Normalized Difference Vegetation Index values. Such detailed view can enrich discussions about green spaces as sources for resilience both at the local and global levels, in comparison with other cities and across countries.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1073/pnas.2214813119
