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CiiS Lab
Johns Hopkins University
112 Hackerman Hall
3400 N. Charles Street
Baltimore, MD 21218
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Lab Director
Russell Taylor
127 Hackerman Hall
rht@jhu.edu
Last updated: 04/12/2020
Note: This project was changed in mid-semester, in response to the COVID-19 outbreak. The old project was “Improved Generalization of Pelvis Landmark Detection”, and its web page can be found here.
At present, this page is based on the original paper, available here.
As the coronavirus disease 2019 (COVID-19) becomes a global pandemic, policy makers must enact interventions to stop its spread. Data driven approaches might supply information to support the implementation of mitigation and suppression strategies. To facilitate research in this direction, we present a machine-readable dataset that aggregates relevant data from governmental, journalistic, and academic sources on the county level. In addition to county-level time-series data from the JHU CSSE COVID-19 Dashboard [1], our dataset contains more than 300 variables that summarize population estimates, demographics, ethnicity, housing, education, employment and income, climate, transit scores, and healthcare system-related metrics. Furthermore, we present aggregated out-of-home activity information for various points of interest for each county, including grocery stores and hospitals, summarizing data from SafeGraph [2]. By collecting these data, as well as providing tools to read them, we hope to aid researchers investigating how the disease spreads and which communities are best able to accommodate stay-at-home mitigation efforts.
COVID-19 has had a devastating impact on the United States’ health care system, economy, and social wellbeing. Despite early promises of an ”American Resurrection” by April 12, 2020 [4], social distancing measures remain in effect through the month of April, and many scientists and public health experts speculate they may last much longer. As of the time of writing, restrictions in Hubei province, China, where the disease originated in December, 2019, are only now gradually being lifted [5]. Confirmed COVID-19 cases, hospitalizations, and—unfortunately—deaths are increasing exponentially. Drastic interventions like social distancing are necessary to slow the spread of the disease, giving more time to
* provide treatment within our healthcare system’s capacity, * develop effective testing capability, * establish sophisticated tracing mechanisms, and * discover novel treatments for the virus.
At the same time, the current mitigation strategies have had severe effects on society and the economy. Widespread closures of schools and daycares have left working parents with limited childcare options [6]; shuttered bars, restaurants, and entertainment venues have forced owners to lay off employees, predominantly in the service industry [7]; and a plummeting stock market has fueled fears of a recession which may far outlast the current crisis [8]. To combat these effects, representatives recently passed the largest economic stimulus package in U.S. history [9]. However, no stimulus can offset the effects of an indefinite quarantine. Determining when and how to roll back non-pharmaceutical interventions in a manner which is safe and responsible is of the utmost importance.
The initial quarantine period is necessary to avoid overwhelming our hospital systems. After this, we must balance reducing the risk of spread with the adverse economic consequences of millions of furloughed and unemployed people. To inform this process, we have curated a machine-readable dataset that aggregates data from governmental, journalistic, and academic sources on the county level. While most of these sources are freely available, there is significant work to align them and put them in a standard format that enables analysis. In addition to time-series data from [1], which details COVID19 per-county infections and deaths, our dataset contains more than 300 variables that summarize population estimates, demographics, ethnicity, housing, education, employment and income, climate, transit scores, and healthcare system-related metrics. Further, we source a significant number of journal articles detailing implementation dates of interventions, including stay-at-home orders, school closures, and restaurant and entertainment venue closures [10]–[48]. Finally, we aggregate out-of-home activity data from [2] in each county, possibly measuring compliance with the aforementioned restrictions. We hope that this dataset proves to be a useful resource to the community, facilitating important research on epidemiological forecasting. In particular, a machine learning approach to identify highly relevant factors may inform a graduated rollback of isolation measures and travel restrictions.
Thus far, this work has focused on the aggregation and visualization of a county-level dataset containing static and dynamic data related to epidemiological modeling. Going forward, I am involved in efforts to predict how rolling back interventions will affect a possibly flattening curve, using clusters of counties to extrapolate previous spread.
Deliverables:
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Dataset Structured county-level dataset including COVID-19 cases, out-of-home activity, and healthcare capacity, available on GitHub and Kaggle.
Implementation Formatting tools using Python, available on GitHub.
Minimum Analysis Exponential model illustrating rapid spread.
Publication [Medium article](https://medium.com/@mathias.unberath/facilitating-machine-learning-research-to-inform-coronavirus-response-a93a55808462) describing the dataset in a general overview.
---------- ---------------- --------------------------------------------------------------------------------------------------
Dataset Structured county-level dataset including COVID-19 cases, out-of-home activity, and healthcare capacity, available on GitHub and Kaggle.
Implementation Formatting tools using Python, available on GitHub.
Expected Analysis Exponential model illustrating rapid spread.
Publication [Medium article](https://medium.com/@mathias.unberath/facilitating-machine-learning-research-to-inform-coronavirus-response-a93a55808462) describing the dataset in a general overview.
---------- ---------------- --------------------------------------------------------------------------------------------------
Dataset Structured county-level dataset including COVID-19 cases, out-of-home activity, and healthcare capacity, available on GitHub and Kaggle.
Implementation Formatting tools using Python, available on GitHub.
Maximum Analysis Exponential model illustrating rapid spread.
Publication [Medium article](https://medium.com/@mathias.unberath/facilitating-machine-learning-research-to-inform-coronavirus-response-a93a55808462) describing the dataset in a general overview.
---------- ---------------- --------------------------------------------------------------------------------------------------
We describe the structure of our dataset, which includes each component in its raw form as well as a narroweddown, machine-readable form conducive to a machine-learning approach. Table I summarizes the sources and availability for each type of data, and a full description of each variable can be found in our repository.
We populate a CSV file with 348 variables for 3220 county-equivalent areas (as well as the fifty states, District of Columbia, and the whole United States) with numerous types of data, including population, education, economic, climate, housing, health care capacity, public transit, and crime statistics. Each area is uniquely identified by its Federal Information Processing Standard (FIPS) code, a five digit number where they first two digits designate the state, and the last three digits describe the county-equivalent. Our sources include the United States Census Bureau [49], [50], [55], [56], the United States Department of Agriculture (USDA) Economic Research Service [51], [52], the National Oceanic and Atmosphere Administration (NOAA) [53], the Association of American Medical Colleges (AAMC) [57], the Henry J. Kaiser Family Foundation (KFF) [3], [58], [59], the Center for Neighborhood Technology (CNT) [61], and the Bureau of Justice Statistics, Department of Justice (DOJ) [62]. Perhaps most relevant to the ongoing effort to mitigate the effects of COVID-19 in the U.S. is county-level healthcare system capacity. The dataset includes detailed counts for each type of medical practitioner as well as the number of Intensive Care Unit beds in each county.
For the most part, these basic descriptive variables are unaltered from their original state. Where appropriate, missing values have been imputed with the state-wide average. See original paper.
Our dataset describes mitigation efforts taken at the state level, including stay-at-home advisories, banning large gatherings, public school closures, and restaurant and entertainment venue closures. For machine readability, we provide each date of implementation as a Gregorian ordinal, i.e. the integer number of days starting at January 1, Year 1 CE, consistent with standard software libraries. Moreover, these data are provided according to the same county-level row ordering as our county descriptor data (see Sec. III-A). Interventions made at the state level have been assigned to each county in that state, and we include county-level interventions wherever possible An intervention is designated NA if the county or state has not yet enacted it.
We have aggregated point-of-interest location data gathered from user’s smartphones to show out-of-home activity, using raw data from [2]. For privacy and IP reasons, our dataset does not include user location data in its raw form but rather in several time-series files summarizing county-level activity. Fig. 1 shows the time-series for selected counties which have a high incidence of COVID-19 cases. The decline in overall activity on May 12 corresponds to an increased media attention and stay-at-home advisories in those areas. At the same time, a spike in grocery store visits points to a panic-buying spree which has since subsided.
Finally, we provide time-series data for the cumulative number of COVID-19 confirmed cases and related deaths, from [1]. This data begins on January 22, 2020. It should be noted that epidemiological modeling efforts may want to consider the uncertainty surrounding U.S. testing [71], on which these data are based. At the time of this writing, efforts to improve the availability of COVID-19 tests are ongoing, but the current strategies prioritize patients with severe symptoms. Thus, modeling efforts may wish to take into account random subsampling of the true population, where untested individuals still spread the virus. This is especially true given that nearly half of all COVID-19 infections may be asymptomatic [72]. Fig. 3 shows the infections in King County, WA collected by [1]. King County had an early confirmed cases of the virus, and the exponential curve illustrates the rapid growth currently taking place there as a result.
Dependency Solution Alternative Status -------------------------------------------------- ------------------------------------------------------------ --------------------------------------------- ----------------------------------------------------- Demographic, Socioeconomic Data United States Census Bureau NA Solved Climate Data National Oceanic and Atmosphere Administration NA Solved Economic Indicators United States Dept. of Agriculture NA Solved Healthcare Capacity Kaiser Family Foundation NA Solved Out-of-home activity SafeGraph NA Solved Public Transit Scores Center for Neighborhood Technology NA Solved COVID-19 Infections and Deaths Time Series JHU CSSE COVID-19 Dashboard NA Solved Compute Resources Personal workstation + laptop Contact Mathias Unberath Solved -------------------------------------------------- ------------------------------------------------------------ --------------------------------------------- -----------------------------------------------------
(in development, see checkpoint presentation for full list)
Milestone Date Status ------------------------------------------- ------- -------- Brainstorm Contributions 03/15 Done Gather Data 03/23 Done Format and Unify Initial Data 03/24 Done ------------------------------------------- ------- --------
Due to the COVID-19 pandemic, this project has shifted from “Improved Generalization in Pelvis X-ray Landmark Detection.” Some of the materials below are from that project.
[1] E. Dong, H. Du, and L. Gardner, “An interactive web-based dashboard to track COVID-19 in real time,” The Lancet Infectious Diseases, vol. 0, no. 0, Feb. 2020.
[2] SafeGraph, “Footprint data,” safeGraph, a data company that aggregates anonymized location data from numerous applications in order to provide insights about physical places. To enhance privacy, SafeGraph excludes census block group information if fewer than five devices visited an establishment in a month from a given census block group.
[3] “Millions of older americans live in counties with no ICU beds as pandemic intensifies,” Kaiser Health News, Mar. 2020.
[4] M. Vazquez, N. Valencia, J. Acosta, and K. Liptak, “Trump says he wants the country ’opened up and just raring to go by Easter,’ despite health experts’ warnings,” https://www.cnn.com/2020/03/24/politics/trump-easter-economycoronavirus/index.html.
[5] V. Wang and S.-L. Wee, “China to ease coronavirus lockdown on hubei 2 months after imposing it,” The New York Times, Mar. 2020.
[6] “Map: Coronavirus and School Closures - Education Week,” Education Week, Mar. 2020.
[7] B. Casselman, S. Maheshwari, and D. Yaffe-Bellany, “Layoffs Are Just Starting, and the Forecasts Are Bleak,” The New York Times, Mar. 2020.
[8] S. Clement and D. Balz, “Poll finds recession fears high amid layoffs and pay cuts from coronavirus fallout,” https://www.washingtonpost.com/politics/poll-finds-recessionfears-high-amid-layoffs-and-pay-cuts-from-coronavirusfallout/2020/03/26/00c412ba-6f5e-11ea-b148-e4ce3fbd85b5 story.html.
[9] “House gives final passage to $2 trillion coronavirus stimulus bill,” https://www.nbcnews.com/politics/congress/house-gives-finalpassage-2-trillion-coronavirus-stimulus-bill-n1170281.
[10] Carter, “‘It’s over.’ NC bars, restaurants close to the public, leaving employees with uncertainty,” https://www.newsobserver.com/news/coronavirus/article241284781.html, library Catalog: www.newsobserver.com.
[11] S. Carroll, “62 coronavirus cases in Arkansas; governor extends school closure, bans restaurant dine-in,” https://katv.com/news/local/coronavirus-cases-rise-to-46-in-arkansas, Mar. 2020, library Catalog: katv.com.
[12] C. Hansen, “Alabama Governor Closes Nonessential Businesses as Coronavirus Spreads,” https://www.usnews.com/news/nationalnews/articles/2020-03-27/alabama-gov-kay-ivey-closes-nonessentialbusinesses-as-coronavirus-spreads, library Catalog: www.usnews.com.
[13] R. Rettner, “Arkansas: Latest updates on coronavirus,” https://www.livescience.com/coronavirus-arkansas.html, library Catalog: www.livescience.com.
[14] S. Mook, “Burgum closes bars, restaurants amid coronavirus concerns; schools to stay closed indefinitely,” /news/education/5007393-Burgumcloses-bars-restaurants-amid-coronavirus-concerns-schools-to-stayclosed-indefinitely, library Catalog: www.grandforksherald.com.
[15] Z. Anderson, “Coronavirus Florida: Governor closes all restaurants and gyms,” https://www.heraldtribune.com/news/20200320/coronavirusflorida-governor-closes-all-restaurants-and-gyms, library Catalog: www.heraldtribune.com.
[16] K. Staff, “Coronavirus in Nebraska, Iowa: The latest headlines and resources to keep you informed,” https://www.ketv.com/article/coronavirus-covid19-nebraska-omahalatest/31213658, Mar. 2020, library Catalog: www.ketv.com.
[17] A. Soga, “Coronavirus in Vermont: Governor orders bars, restaurants closed,” https://www.burlingtonfreepress.com/story/news/2020/03/16/coronavirusvermont-burlington-mayor-orders-24-hour-restaurant-barclosure/5062491002/, library Catalog: www.burlingtonfreepress.com.
[18] A. Kite, K. Hardy, J. Smith, and S. Vockrodt, “Coronavirus shuts Kansas City restaurants, leaving staff unemployed, yearning for work,” https://www.kansascity.com/news/coronavirus/article241494536.html, library Catalog: www.kansascity.com.
[19] W. B. L. J. Feuer, Noah Higgins-Dunn, “Coronavirus: NY, NJ, CT coordinate restrictions on restaurants, limit events to fewer than 50 people,” https://www.cnbc.com/2020/03/16/new-york-new-jerseyand-connecticut-agree-to-close-restaurants-limit-events-to-less-than-50- people.html, Mar. 2020, library Catalog: www.cnbc.com.
[20] G. Hiatt, “D.C. Adds ‘Stay-at-Home’ Order on Same Day as Maryland and Virginia,” https://dc.eater.com/2020/3/15/21180673/dc-mayormuriel-bowser-coronavirus-response-elminate-bar-seats-limit-table-size, Mar. 2020, library Catalog: dc.eater.com.
[21] P. Svitek, “Gov. Greg Abbott closes bars, restaurants and schools as he anticipates tens of thousands could test positive for coronavirus,” https://www.texastribune.org/2020/03/19/texas-restaurants-barsclosed-greg-abbott/, Mar. 2020, library Catalog: www.texastribune.org.
[22] KCCI, “Gov. Reynolds issues state of public health disaster emergency, closing Iowa businesses,” https://www.kcci.com/article/gov-reynoldsissues-state-of-public-health-disaster/31700874, Mar. 2020, library Catalog: www.kcci.com.
[23] A. J. Capuano, “In Missouri, no dining-in at restaurants, groups of 10 or more banned amid coronavirus,” https://ktvo.com/news/local/inmissouri-no-dining-in-at-restaurants-groups-of-10-or-more-bannedamid-coronavirus, Mar. 2020, library Catalog: ktvo.com.
[24] B. Tobin, D. Ghabour, D. Costello, and M. Glowicki, “Kentucky Derby postponed, restaurants restricted as state tries to control spread of virus,” https://www.courier-journal.com/story/news/2020/03/16/coronaviruskentucky-beshear-orders-restaurants-bars-close/5057062002/, library Catalog: www.courier-journal.com.
[25] HNN, “LIST: Here’s how the state and each island is responding to coronavirus,” https://www.hawaiinewsnow.com/2020/03/18/list-bar-closurescruise-ship-screening-here-are-all-iges-covid-directives/, library Catalog: www.hawaiinewsnow.com.
[26] “LIVE UPDATES: Here’s the latest on the coronavirus in Forsyth County and Georgia,” https://www.forsythnews.com/news/healthcare/heres-latest-coronavirus-georgia/, library Catalog: www.forsythnews.com.
[27] J. Helminiak, “Local bars, resaurants, gyms, theaters react to coronavirus,” https://www.peninsulaclarion.com/news/local-barsresaurants-gyms-theaters-react-to-coronavirus/, Mar. 2020, library Catalog: www.peninsulaclarion.com.
[28] WGME, “Maine bars, restaurants ordered to close to dine-in customers, coronavirus cases increase,” https://wgme.com/news/coronavirus/govmills-mandates-maine-bars-restaurants-close-to-dine-in-customers, Mar. 2020, library Catalog: wgme.com.
[29] “Map: Coronavirus and School Closures - Education Week,” Education Week, Mar. 2020.
[30] A. Ganucheau, “Mayors scramble to know: Does Gov. Reeves’ coronavirus declaration clash with local orders?” https://mississippitoday.org/2020/03/25/mayors-scramble-to-knowdoes-gov-reeves-coronavirus-declaration-clash-with-local-orders/, Mar. 2020, library Catalog: mississippitoday.org.
[31] A. Press, “Montana Extends School, Restaurant Closures 2 More Weeks,” https://www.usnews.com/news/beststates/montana/articles/2020-03-22/evidence-of-community-spreadin-montanas-gallatin-county, library Catalog: www.usnews.com.
[32] M. Etehad and L. K. Peterson, “Nevada orders all casinos, bars, restaurants closed as U.S. coronavirus cases surge,” https://www.latimes.com/world-nation/story/2020-03-17/las-vegasto-close-all-casinos-at-midnight, Mar. 2020, library Catalog: www.latimes.com.
[33] WHDH, “New Hampshire bans dine-in restaurant meals until April 7,” library Catalog: whdh.com.
[34] K. Media, “New restrictions for New Mexico restaurants and bars to begin Monday,” Mar. 2020, library Catalog: www.krqe.com.
[35] B. Webb, “Phoenix, Tucson order closures of bars, restaurants,” https://www.fox10phoenix.com/news/phoenix-tucson-order-closures-ofbars-restaurants1, Mar. 2020, library Catalog: www.fox10phoenix.com.
[36] A. Kludt and B. Houck, “Restaurants and Bars Shuttered Across the U.S. in Light of Coronavirus Pandemic,” https://www.eater.com/2020/3/15/21180761/coronavirus-restaurantsbars-closed-new-york-la-chicago, Mar. 2020, library Catalog: www.eater.com.
[37] R. Nunes, “RI Restaurants Closed Amid Community Spread Of Coronavirus,” https://patch.com/rhode-island/newport/coronavirus-ridine-restaurants-closed-2-weeks, Mar. 2020, library Catalog: patch.com.
[38] S. Mervosh, D. Lu, and V. Swales, “See Which States and Cities Have Told Residents to Stay at Home,” The New York Times, Mar. 2020.
[39] L. Ruskin, “State bans restaurant dining as Alaska’s confirmed coronavirus cases grow to 6,” Mar. 2020, library Catalog: www.alaskapublic.org.
[40] C. Gross, “State to Restrict Bars and Restaurants Statewide Starting at 8PM,” https://www.ny1.com/nyc/allboroughs/coronavirus/2020/03/16/bars-restaurants-gyms-movietheaters-casinos-new-york-state, library Catalog: www.ny1.com.
[41] Axios, “States order bars and restaurants to close due to coronavirus,” https://www.axios.com/ohio-governor-bars-restaurants-coronavirus26e4b6e3-7f65-4f6a-abf9-f3940220cc6f.html, library Catalog: www.axios.com.
[42] B. Kelman and J. McGee, “Tennessee governor orders restaurants, bars closed except for takeout and delivery; gyms closed over coronavirus,” https://www.tennessean.com/story/news/health/2020/03/22/tennesseegovernor-restaurants-bars-closed-takeout-and-delivery/2892481001/, library Catalog: www.tennessean.com.
[43] A. Lee, “These states have implemented stay-athome orders. Here’s what that means for you,” https://www.cnn.com/2020/03/23/us/coronavirus-which-states-stayat-home-order-trnd/index.html, library Catalog: www.cnn.com.
[44] T. Semerad, “Utah Orders Restaurants,Bars to Close All Dining to Curb Coronavirus,” https://www.sltrib.com/news/2020/03/18/utahorders-restaurants/, library Catalog: www.sltrib.com.
[45] A. Spiegel, “Virginia Restaurants and Bars Close for Dine-In Service to Help Curb Coronavirus,” Mar. 2020, library Catalog: www.washingtonian.com.
[46] E. C. Wida, “Which states have closed restaurants and bars due to coronavirus?” https://www.today.com/food/which-states-haveclosed-restaurants-bars-due-coronavirus-t176039, library Catalog: www.today.com.
[47] Star-Tribune, “Wyoming cancellations and closures caused by coronavirus,” https://trib.com/news/state-and-regional/health/wyomingcancellations-and-closures-caused-by-coronavirus/article 228b1e3a56b6-5e09-a351-3bcb8825e7f3.html, library Catalog: trib.com.
[48] M. Specia, “What You Need to Know About Trump’s European Travel Ban,” The New York Times, Mar. 2020.
[49] United States Census Bureau, “2018 Population Estimates.”
[Online]. Available: https://factfinder.census.gov/faces/tableservices/jsf/ pages/productview.xhtml?src=bkmk
[50] ——, “Selected social characteristics in the united states: 2018 acs 1 year data estimate profiles.” [Online]. Available: https://data.census.gov/ cedsci/table?q=dp02&hidePreview=true&tid=ACSDP1Y2018.DP02& vintage=2018&g=0400000US36.050000&tp=true&y=2018
[51] United States Department of Agriculture Economic Research Service, “Poverty estimates for the U.S., States and counties, 2018.” [Online]. Available: https://www.ers.usda.gov/data-products/ county-level-data-sets/download-data/
[52] ——, “Unemployment and median household income for the U.S., states and counties, 2007-18.” [Online]. Available: https://www.ers. usda.gov/data-products/county-level-data-sets/download-data/
[53] National Oceanic and Atmospheric Administration, “NOAA’s Climate Divisional Database.” [Online]. Available: ftp://ftp.ncdc.noaa.gov/pub/ data/cirs/climdiv/
[54] United States Census Bureau, “Population and housing unit counts: 2010.” [Online]. Available: https://www.census.gov/library/publications/ 2012/dec/cph-2.html
[55] ——, “Selected Social Characteristics in the United States: Households By Type.” [Online]. Available: https://data.census.gov/cedsci/table?q= dp02&hidePreview=true&tid=ACSDP1Y2018.DP02&vintage=2018& g=0400000US36.050000&tp=true&y=2018
[56] ——, “Annual County Resident Population Estimates by Age, Sex, Race, and Hispanic Origin: April 1, 2010 to July 1, 2018 (CC-EST2018-ALLDATA).” [Online]. Available: https://www.census.gov/data/tables/time-series/demo/popest/ 2010s-counties-detail.html#par textimage 1383669527
[57] American Association of Medical Colleges, “State physician workforce data report,” 2019.
[58] “Professionally active primary care physicians by field,” KFF’s State Health Facts, Mar. 2019.
[59] “Professionally active specialist physicians by field,” KFF’s State Health Facts, Mar. 2019.
[60] Henry J. Kaiser Family Foundation, “Sepcial data request,” KFF’s State Health Facts, Mar. 2019.
[61] Center for Neighborhood Technology, “Alltransit performance score.”
[Online]. Available: https://alltransit.cnt.org/data-download/
[62] Bureau of Justice Statistics, Department of Justice, “United states crime rates by county.” [Online]. Available: https://www.icpsr.umich. edu/icpsrweb/
[63] The New York Times, “We’re Sharing Coronavirus Case Data for Every U.S. County,” The New York Times, Mar. 2020.
[64] A. Madrigal, J. Hammerbacher, E. Kissane, and COVID Tracking Project Team, “The covid tracking project.” [Online]. Available: https://covidtracking.com/
[65] E. Chen, K. Lerman, and E. Ferrara, “COVID-19: The First Public Coronavirus Twitter Dataset,” arXiv:2003.07372 [cs, q-bio], Mar. 2020.
[66] [Online]. Available: http://www.socialmediaforpublichealth.org/ covid-19/
[67] S. Zhang, M. Diao, W. Yu, L. Pei, Z. Lin, and D. Chen, “Estimation of the reproductive number of novel coronavirus (COVID-19) and the probable outbreak size on the Diamond Princess cruise ship: A datadriven analysis,” International Journal of Infectious Diseases, vol. 93, pp. 201–204, Apr. 2020.
[68] K. C. Santosh, “AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data,” Journal of Medical Systems, 2020.
[69] S. J. Fong, G. Li, N. Dey, R. G. Crespo, and E. Herrera-Viedma, “Composite Monte Carlo Decision Making under High Uncertainty of Novel Coronavirus Epidemic Using Hybridized Deep Learning and Fuzzy Rule Induction,” ArXiv, 2020.
[70] S. Fong, G. Li, N. Dey, R. G. Crespo, and E. Herrera-Viedma, “Finding an Accurate Early Forecasting Model from Small Dataset: A Case of 2019-nCoV Novel Coronavirus Outbreak,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 6, no. 1, p. 132, 2020.
[71] I. Scher, “The US is severely under-testing for coronavirus as death toll and new cases rise,” https://www.businessinsider.com/theus-is-not-testing-enough-people-for-covid-19-2020-3, library Catalog: www.businessinsider.com.
[72] J. Gale, “Coronavirus Cases Without Symptoms Spur Call for Wider Tests,” Bloomberg, Mar. 2020. [Online]. Available: https://www.bloomberg.com/news/articles/2020-03-22/ one-third-of-coronavirus-cases-may-show-no-symptom-scmp-reports
Here give list of other project files (e.g., source code) associated with the project. If these are online give a link to an appropriate external repository or to uploaded media files under this name space (2020-03).
