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1. Small businesses and government assistance during COVID-19: Evidence from the paycheck protection program in the U.S.

   https://doi.org/10.1177/0308518X231166407  

   Wang, Qingfang; Kang, Wei (April 2023, Environment and Planning A: Economy and Space)

   The COVID-19 pandemic has caused unprecedented losses for small businesses in cities across the globe. Policymakers have relied on a wide range of measures to support firms and sustain business continuity. However, significant concerns have been expressed about the degree of equity in the distribution and efficiency of government assistance during the pandemic disruption. Drawing on the case of the Paycheck Protection Program (PPP) and its implementation in inland Southern California, this study examines the spatial distribution of PPP loans at the neighborhood level. Based on spatial regressions and in-depth interviews with small businesses, banks, government agencies, and nonprofit organizations, the study finds that, in terms of their total number and value, the PPP loans have roughly succeeded in reaching their small business targets. However, communities with higher shares of pandemic-vulnerable businesses or higher levels of socioeconomic vulnerability are less likely to have received PPP loans. There have also been spatial spillover effects of community vulnerability when it comes to receiving PPP loans at the neighborhood level. The correlation between fewer PPP loans and community vulnerability also reflects both short-term needs and longstanding challenges facing entrepreneurship and business development in socioeconomically disadvantaged communities. Moreover, small business resilience and community resilience are inseparable, and thus government business assistance must be considered in the context of local communities. 

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2. Community-Connect: COVID-19 Small Business Marketplace with Automated Regulation Matching and Company Lead Retrieval

   https://doi.org/10.1109/IRI51335.2021.00014  

   Coltey, Erik; Vassigh, Shahin; Chen, Shu-Ching (August 2021, IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI))

   Abstract—Periods of unique economic distress such as the COVID-19 pandemic can be quite difficult for small businesses. Challenges acquiring the supplies necessary to adhere to safety regulations created in the wake of such events can introduce stress on these businesses. This is further exacerbated when supply chains have slowed down, leading to global shortages from most large suppliers. This paper proposes a platform to aid such businesses in procuring COVID-19 related supplies such as Personal Protective Equipment (PPE) from one another, leveraging advanced data acquisition, integration, and Natural Language Processing (NLP) methods. With the pandemic end in sight, the platform described in this paper can be reused for other emergencies such as hurricanes, floods, among others. The proposed platform supports business transactions within a Buyer’s Club (BC), keyword-based sourcing of new businesses to join the platform, and matching products to relevant regulations using greater-than-word length encoding, helping businesses comply with the ever-changing regulatory landscape. Index Terms—COVID-19, Disaster, Natural Language Processing, Data Acquisition, Data Retrieval, User Interfaces 

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3. Post-lockdown infection rates of COVID-19 following the reopening of public businesses

   https://doi.org/10.1093/pubmed/fdab325  

   Bruckhaus, Alexander; Martinez, Aubrey; Garner, Rachael; La Rocca, Marianna; Duncan, Dominique (August 2021, Journal of Public Health)

   Abstract Background The Coronavirus Disease 2019 (COVID-19) pandemic warranted a myriad of government-ordered business closures across the USA in efforts to mitigate the spread of the virus. This study aims to discover the implications of government-enforced health policies of reopening public businesses amidst the pandemic and its effect on county-level infection rates. Methods Eighty-three US counties (n = 83) that reported at least 20 000 cases as of 4 November 2020 were selected for this study. The dates when businesses (restaurants, bars, retail, gyms, salons/barbers and public schools) partially and fully reopened, as well as infection rates on the 1st and 14th days following each businesses’ reopening, were recorded. Regression analysis was conducted to deduce potential associations between the 14-day change in infection rate and mask usage frequency, median household income, population density and social distancing. Results On average, infection rates rose significantly as businesses reopened. The average 14-day change in infection rate was higher for fully reopened businesses (infection rate = +0.100) compared to partially reopened businesses (infection rate = +0.0454). The P-value of the two distributions was 0.001692, indicating statistical significance (P < 0.01). Conclusion This research provides insight into the transmission of COVID-19 and promotes evidence-driven policymaking for disease prevention and community health. 

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4. Modeling food-related business closure in select New York City communities using multi-scale and spatial features

   https://doi.org/10.1177/23998083241254573  

   Wang, Shu; Laefer, Debra_F (June 2024, Environment and Planning B: Urban Analytics and City Science)

   This paper introduces an extensible framework to predict small-business closures to inform urban planners, lenders, and business owners as to factors to improve business resilience. This paper couples machine learning with two point of interest (POI) datasets and infrastructure data and uses New York State’s COVID-19 PAUSE as a stressor for investigating small-business resiliency. The study included 2537 food-related, non-chain, retail businesses across select New York City zip codes, of which 17.7% closed permanently. Macro-, meso-, and micro-levels of features included the neighborhood profile, street dynamics, and venue-specific, location-related characteristics. A Gaussian Mixture Neural Network model achieved 74.1% precision, 92.5% recall, and an 82.3% F1-score without use of financial data. High-end restaurants located further than average from public transit were most at risk for closure, while non-restaurant, food businesses in commercially diverse areas having higher-than-average social media ratings were least at risk. This paper introduces a model for timely prediction of pandemic-induced, food-related, small-business closures without reliance on private or protected financial data, and provides insights into urban design to promote small, food business survivability. 

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5. COVID-GAN: Estimating Human Mobility Responses to COVID-19 Pandemic through Spatio-Temporal Conditional Generative Adversarial Networks

   https://doi.org/10.1145/3397536.3422261  

   Bao, Han; Zhou, Xun; Zhang, Yingxue; Li, Yanhua; Xie, Yiqun (November 2020, Proceedings of the 28th International Conference on Advances in Geographic Information Systems)

   null (Ed.)

   The COVID-19 pandemic has posed grand challenges to policy makers, raising major social conflicts between public health and economic resilience. Policies such as closure or reopen of businesses are made based on scientific projections of infection risks obtained from infection dynamics models. While most parameters in infection dynamics models can be set using domain knowledge of COVID-19, a key parameter - human mobility - is often challenging to estimate due to complex social contexts and limited training data under escalating COVID-19 conditions. To address these challenges, we formulate the problem as a spatio-temporal data generation problem and propose COVID-GAN, a spatio-temporal Conditional Generative Adversarial Network, to estimate mobility (e.g., changes in POI visits) under various real-world conditions (e.g., COVID-19 severity, local policy interventions) integrated from multiple data sources. We also introduce a domain-constraint correction layer in the generator of COVID-GAN to reduce the difficulty of learning. Experiments using urban mobility data derived from cell phone records and census data show that COVID-GAN can well approximate real-world human mobility responses, and that the proposed domain-constraint based correction can greatly improve solution quality. 

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