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1. The Twitter Social Mobility Index: Measuring Social Distancing Practices With Geolocated Tweets

   https://doi.org/10.2196/21499  

   Xu, Paiheng; Dredze, Mark; Broniatowski, David A (January 2020, Journal of Medical Internet Research)

   null (Ed.)

   Background Social distancing is an important component of the response to the COVID-19 pandemic. Minimizing social interactions and travel reduces the rate at which the infection spreads and “flattens the curve” so that the medical system is better equipped to treat infected individuals. However, it remains unclear how the public will respond to these policies as the pandemic continues. Objective The aim of this study is to present the Twitter Social Mobility Index, a measure of social distancing and travel derived from Twitter data. We used public geolocated Twitter data to measure how much users travel in a given week. Methods We collected 469,669,925 tweets geotagged in the United States from January 1, 2019, to April 27, 2020. We analyzed the aggregated mobility variance of a total of 3,768,959 Twitter users at the city and state level from the start of the COVID-19 pandemic. Results We found a large reduction (61.83%) in travel in the United States after the implementation of social distancing policies. However, the variance by state was high, ranging from 38.54% to 76.80%. The eight states that had not issued statewide social distancing orders as of the start of April ranked poorly in terms of travel reduction: Arkansas (45), Iowa (37), Nebraska (35), North Dakota (22), South Carolina (38), South Dakota (46), Oklahoma (50), Utah (14), and Wyoming (53). We are presenting our findings on the internet and will continue to update our analysis during the pandemic. Conclusions We observed larger travel reductions in states that were early adopters of social distancing policies and smaller changes in states without such policies. The results were also consistent with those based on other mobility data to a certain extent. Therefore, geolocated tweets are an effective way to track social distancing practices using a public resource, and this tracking may be useful as part of ongoing pandemic response planning. 

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2. Video-Based Social Distancing: Evaluation in the COSMOS Testbed

   https://doi.org/10.1109/JIOT.2023.3305587  

   Ghasemi, Mahshid; Yang, Zhengye; Sun, Mingfei; Ye, Hongzhe; Xiong, Zihao; Ghaderi, Javad; Kostic, Zoran; Zussman, Gil (January 2023, IEEE Internet of Things Journal)

   Social distancing is an effective public health tool to reduce the spread of respiratory pandemics such as COVID-19. To analyze compliance with social distancing policies, we design two video-based pipelines for social distancing analysis, namely, Auto-SDA and B-SDA. Auto-SDA (Automated video-based Social Distancing Analyzer) is designed to measure social distancing using street-level cameras. To avoid privacy concerns of using street-level cameras, we further develop B-SDA (Bird’s eye view Social Distancing Analyzer), which uses bird’s eye view cameras, thereby preserving pedestrian’s privacy. We used the COSMOS testbed deployed in West Harlem, New York City, to evaluate both pipelines. In particular, Auto-SDA and B-SDA are applied on videos recorded by two of COSMOS cameras deployed on the 2nd floor (street-level) and 12th floor (bird’s eye view) of Columbia University’s Mudd building, looking at 120th St. and Amsterdam Ave. intersection, New York City. Videos are recorded before and during the peak of the pandemic, as well as after the vaccines became broadly available. The results represent the impact of social distancing policies on pedestrians’ social behavior. For example, the analysis shows that after the lockdown, less than 55% of the pedestrians failed to adhere to the social distancing policies, whereas this percentage increased to 65% after the vaccines’ availability. Moreover, after the lockdown, 0-20% of the pedestrians were affiliated with a social group, compared to 10-45% once the vaccines became available. The results also show that the percentage of face-to-face failures has decreased from 42.3% (pre-pandemic) to 20.7%(after the lockdown). 

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3. Measuring sensitivity to social distancing behavior during the COVID-19 pandemic

   https://doi.org/10.1038/s41598-022-20198-4  

   Kontokosta, Constantine E.; Hong, Boyeong; Bonczak, Bartosz J. (September 2022, Scientific Reports)

   Abstract Social distancing remains an effective nonpharmaceutical behavioral interventions to limit the spread of COVID-19 and other airborne diseases, but monitoring and enforcement create nontrivial challenges. Several jurisdictions have turned to “311” resident complaint platforms to engage the public in reporting social distancing non-compliance, but differences in sensitivity to social distancing behaviors can lead to a mis-allocation of resources and increased health risks for vulnerable communities. Using hourly visit data to designated establishments and more than 71,000 social distancing complaints in New York City during the first wave of the pandemic, we develop a method, derived from the Weber-Fechner law, to quantify neighborhood sensitivity and assess how tolerance to social distancing infractions and complaint reporting behaviors vary with neighborhood characteristics. We find that sensitivity to non-compliance is lower in minority and low-income neighborhoods, as well as in lower density areas, resulting in fewer reported complaints than expected given measured levels of overcrowding. 

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4. Gang‐related crime in Los Angeles remained stable following COVID‐19 social distancing orders

   https://doi.org/10.1111/1745-9133.12541  

   Jeffrey_Brantingham, Paul; Tita, George_E; Mohler, George (April 2021, Criminology & Public Policy)

   Research SummaryThe onset of extreme social distancing measures is expected to have a dramatic impact on crime. Here, we examine the impact of mandated, city‐wide social distancing orders aimed at limiting the spread of COVID‐19 on gang‐related crime in Los Angeles. We hypothesize that the unique subcultural processes surrounding gangs may supersede calls to shelter in place and allow gang‐related crime to persist. If the normal guardianship of people and property is also disrupted by social distancing, then we expect gang violence to increase. Using autoregressive time series models, we show that gang‐related crime remained stable and crime hot spots largely stationary following the onset of shelter in place. Policy ImplicationsIn responding to disruptions to social and economic life on the scale of the present pandemic, both police and civilian organizations need to anticipate continued demand, all while managing potential reductions to workforce. Police are faced with this challenge across a wide array of crime types. Civilian interventionists tasked with responding to gang‐related crime need to be prepared for continued peacekeeping and violence interruption activities, but also an expansion of responsibilities to deal with “frontline” or “street‐level” management of public health needs. 

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5. Predicting Public Transportation Load to Estimate the Probability of Social Distancing Violations

   Martinez, Juan; Mukhopadhyay, Ayan; Ayman, Afiya; Wilbur, Michael; Pugliese, Philip; Freudberg, Dan; Laszka, Aron; Dubey, Abhishek (January 2021, Proceedings of the Workshop on AI for Urban Mobility at the 35th AAAI Conference on Artificial Intelligence)

   Public transit agencies struggle to maintain transit accessibility with reduced resources, unreliable ridership data, reduced vehicle capacities due to social distancing, and reduced services due to driver unavailability. In collaboration with transit agencies from two large metropolitan areas in the USA, we are designing novel approaches for addressing the afore-mentioned challenges by collecting accurate real-time ridership data, providing guidance to commuters, and performing operational optimization for public transit. We estimate rider-ship data using historical automated passenger counting data, conditional on a set of relevant determinants. Accurate ridership forecasting is essential to optimize the public transit schedule, which is necessary to improve current fixed lines with on-demand transit. Also, passenger crowding has been a problem for public transportation since it deteriorates passengers’ wellbeing and satisfaction. During the COVID-19 pandemic, passenger crowding has gained importance since it represents a risk for social distancing violations. Therefore, we are creating optimization models to ensure that social distancing norms can be adequately followed while ensuring that the total demand for transit is met. We will then use accurate forecasts for operational optimization that includes (a) proactive fixed-line schedule optimization based on predicted demand, (b) dispatch of on-demand micro-transit, prioritizing at-risk populations, and (c) allocation of vehicles to transit and cargo trips, considering exigent vehicle maintenance requirements (i.e., disinfection). Finally, this paper presents some initial results from our project regarding the estimation of ridership in public transit. 

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