Abstract Human mobility plays an important role in the dynamics of infectious disease spread. Evidence from the initial nationwide lockdowns for COVID− 19 indicates that restricting human mobility is an effective strategy to contain the spread. While a direct correlation was observed early on, it is not known how mobility impacted COVID− 19 infection growth rates once lockdowns are lifted, primarily due to modulation by other factors such as face masks, social distancing, and the non-linear patterns of both mobility and infection growth. This paper introduces a piece-wise approach to better explore the phase-wise association between state-level COVID− 19 incidence data and anonymized mobile phone data for various states in the United States. Prior literature analyzed the linear correlation between mobility and the number of cases during the early stages of the pandemic. However, it is important to capture the non-linear dynamics of case growth and mobility to be usable for both tracking and forecasting COVID− 19 infections, which is accomplished by the piece-wise approach. The associations between mobility and case growth rate varied widely for various phases of the epidemic curve when the stay-at-home orders were lifted. The mobility growth patterns had a strong positive association of 0.7 with the growth in the number of cases, with a lag of 5 to 7 weeks, for the fast-growth phase of the pandemic, for only 20 states that had a peak between July 1st and September 30, 2020. Overall though, mobility cannot be used to predict the rise in the number of cases after initial lockdowns have been lifted. Our analysis explores the gradual diminishing value of mobility associations in the later stage of the outbreak. Our analysis indicates that the relationship between mobility and the increase in the number of cases, once lockdowns have been lifted, is tenuous at best and there is no strong relationship between these signals. But we identify the remnants of the last associations in specific phases of the growth curve.
more »
« less
Association mining based approach to analyze COVID-19 response and case growth in the United States
Containing the COVID-19 pandemic while balancing the economy has proven to be quite a challenge for the world. We still have limited understanding of which combination of policies have been most effective in flattening the curve; given the challenges of the dynamic and evolving nature of the pandemic, lack of quality data etc. This paper introduces a novel data mining-based approach to understand the effects of different non-pharmaceutical interventions in containing the COVID-19 infection rate. We used the association rule mining approach to perform descriptive data mining on publicly available data for 50 states in the United States to understand the similarity and differences among various policies and underlying conditions that led to transitions between different infection growth curve phases. We used a multi-peak logistic growth model to label the different phases of infection growth curve. The common trends in the data were analyzed with respect to lockdowns, face mask mandates, mobility, and infection growth. We observed that face mask mandates combined with mobility reduction through moderate stay-at-home orders were most effective in reducing the number of COVID-19 cases across various states.
more » « less- NSF-PAR ID:
- 10305182
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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.more » « less
-
Working remotely at least some of the time has long been seen as promoting a better integration of work and care obligations, even though prepandemic research is mixed as to the extent to which parents benefit emotionally from remote work. We exploit dual social experiments in schooling and work spawned by the COVID-19 pandemic to understand any stress-reducing effects of working from home under different school-closing state policy contexts. The pandemic led to an unprecedented shift to (and subsequent away from) remote and hybrid work but also to the implementation of various containment policies, most notably school closures driving a shift to remote learning that were put into effect to different degrees across U.S. states. Drawing on parents’ data from a U.S. nationally representative panel survey of workers who spent at least some time working from home since the pandemic onset, we use mixed-effects models to examine whether and in what ways cross-state and over-time variations in school closure policies shape any stress-reducing impacts of remote/hybrid work. Results show that when schools were not mandated to close, remote/hybrid work largely reduces parents’—especially mothers’—stress. However, an opposite pattern emerges in the face of closing mandates. These patterns are especially pronounced among white mothers and are not observed among nonparents.more » « less
-
The US and the rest of the world have suffered from the COVID-19 pandemic for over a year. The high transmissibility and severity of this virus have provoked governments to adopt a variety of mitigation strategies. Some of these previous measures, such as social distancing and mask mandates, were effective in reducing the case growth rate yet became economically and administratively difficult to enforce as the pandemic continued. In late December 2020, COVID-19 vaccines were first approved in the US and states began a phased implementation of COVID-19 vaccination. However, there is limited quantitative evidence regarding the effectiveness of the phased COVID-19 vaccination. This study aims to provide a rapid assessment of the adoption, reach, and effectiveness of the phased implementation of COVID-19 vaccination. We utilize an event-study analysis to evaluate the effect of vaccination on the state-level daily COVID-19 case growth rate. Through this analysis, we assert that vaccination was effective in reducing the spread of COVID-19 shortly after the first shots were given. Specifically, the case growth rate declined by 0.124, 0.347, 0.345, 0.464, 0.490, and 0.756 percentage points corresponding to the 1–5, 6–10, 11–15, 16–20, 21–25, and 26 or more day periods after the initial shots. The findings could be insightful for policymakers as they work to optimize vaccine distribution in later phases, and also for the public as the COVID-19 related health risk is a contentious issue.more » « less
-
The early phases of the COVID-19 pandemic in the United States caused unprecedented disruption to engineering students and society-at-large. Residential students suddenly found themselves forced off-campus and into a new regime of online learning. On top of this, students faced pandemic related uncertainty about their health and the health of their loved ones, restrictions on social life, and the privations of a stuttering economy. It is perhaps unsurprising, then, that during the early days of the COVID-19 pandemic the engineering student population saw large increases in incidences of depression and other psychological conditions. While COVID-19 continues to be a concern, many of the restrictions and precautions associated with the early outbreak have been relaxed. Most engineering programs are back to in person learning, and strict COVID-testing regimes, mask mandates, and limits on large public gatherings have been largely phased out. With academia and society slowly adjusting to this “new normal,” it is important to know whether and to what extent mental health of engineering students has changed over the course of the pandemic. This work explores this question by analyzing longitudinal data collected at four times from 2019-2021. We analyze how the prevalence of different conditions changed with the onset of the COVID-19 pandemic, and whether they have returned to pre-pandemic levels. Among other results, we found statistically significant increases were observed in total depressive disorders between our initial sample (Fall 2019) and a sample taken immediately post-COVID (Spring 2020). While measured rates of depression decreased between Spring 2020 and Fall 2021, it was not statistically significant, potentially indicating that the student population is still recovering on this metric. Conversely our data shows a statistically significant drop in moderate-to-major psychological distress between our Fall 2019 pre-pandemic sampling and our Fall 2021 post-pandemic sampling indicating, potentially indicating an improvement in overall mental health. Breaking the data down by gender, no significant changes were observed across any measure during the four sample periods for women respondents. Men, however, showed a significant increase in depressive disorders from Fall 2019 to Spring 2020; and a significant decrease in depressive disorders from Spring 2020 to Fall 2021.more » « less
