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SSRN; 2021.
Preprint Dans Anglais | SSRN | ID: ppcovidwho-291806


The speed of recovery from supply chain disruption has been identified as the predominant factor in building a resilient supply chain. However, COVID-19, as a rapidly evolving crisis, may challenge this assumption. Infection risk concerns would increase if managers decided to resume production immediately after the shutdown caused by the pandemic. Any incidents of infection may lead to further shutdowns of production lines and undermine firms’ long-term cash flows. Sampling 244 production resumption announcements by Chinese manufacturers in the early COVID-19 crisis (February to March 2020), our analysis shows that investors perceived the earlier production resumptions are with higher risk (indicated by declined stock price). Such concerns were exacerbated by more locally confirmed cases of COVID-19, while less salient for manufacturers with high debts (liquidity pressure). In addition, concerns about risks raised in response to early manufacturers’ resumption of production were found to proliferate overseas, as reflected by the more negative investor reactions seen in 252 US customers of the Chinese manufacturers. This study calls for a reassessment of the current disruption management mindset in response to new disasters and rapidly evolving crises (e.g., COVID-19), and provides theoretical, practical, and policy implications for building resilient supply chains.

Preprint Dans Anglais | medRxiv | ID: ppmedrxiv-21254918


SARS-CoV-2 vaccines are powerful tools to combat the COVID-19 pandemic, but vaccine hesitancy threatens these vaccines effectiveness. To address COVID-19 vaccine hesitancy and ensure equitable distribution, understanding the extent of and factors associated with vaccine acceptance and uptake is critical. We report the results of a large nationwide study conducted December 2020-May 2021 of 34,470 users from COVID-19-focused smartphone-based app How We Feel on their willingness to receive a COVID-19 vaccine. Nineteen percent of respondents expressed vaccine hesitancy, the majority being undecided. Of those who were undecided or unlikely to get a COVID-19 vaccine, 86% reported they ultimately did receive a COVID-19 vaccine. We identified sociodemographic and behavioral factors that were associated with COVID-19 vaccine hesitancy and uptake, and we found several vulnerable groups at increased risk of COVID-19 burden, morbidity, and mortality were more likely to be vaccine hesitant and had lower rates of vaccination. Our findings highlight specific populations in which targeted efforts to develop education and outreach programs are needed to overcome vaccine hesitancy and improve equitable access, diversity, and inclusion in the national response to COVID-19.

Preprint Dans Anglais | medRxiv | ID: ppmedrxiv-21253496


Amidst the continuing spread of COVID-19, real-time data analysis and visualization remain critical to track the pandemics impact and inform policy making. Multiple metrics have been considered to evaluate the spread, infection, and mortality of infectious diseases. For example, numbers of new cases and deaths provide measures of absolute impact within a given population and time frame, while the effective reproduction rate provides a measure of the rate of spread. It is critical to evaluate multiple metrics concurrently, as they provide complementary insights into the impact and current state of the pandemic. We describe a unified framework for estimating and quantifying the uncertainty in the smoothed daily effective reproduction number, case rate, and death rate in a region using log-linear models. We apply this framework to characterize COVID-19 impact at multiple geographic resolutions, including by US county and state as well as by country, demonstrating the variation across resolutions and the need for harmonized efforts to control the pandemic. We provide an open-source online dashboard for real-time analysis and visualization of multiple key metrics, which are critical to evaluate the impact of COVID-19 and make informed policy decisions.

Preprint Dans Anglais | medRxiv | ID: ppmedrxiv-20126813


Summary ParagraphDespite social distancing and shelter-in-place policies, COVID-19 continues to spread in the United States. A lack of timely information about factors influencing COVID-19 spread and testing has hampered agile responses to the pandemic. We developed How We Feel, an extensible web and mobile application that aggregates self-reported survey responses, to fill gaps in the collection of COVID-19-related data. How We Feel collects longitudinal and geographically localized information on users health, behavior, and demographics. Here we report results from over 500,000 users in the United States from April 2, 2020 to May 12, 2020. We show that self-reported surveys can be used to build predictive models of COVID-19 test results, which may aid in identification of likely COVID-19 positive individuals. We find evidence among our users for asymptomatic or presymptomatic presentation, as well as for household and community exposure, occupation, and demographics being strong risk factors for COVID-19. We further reveal factors for which users have been SARS-CoV-2 PCR tested, as well as the temporal dynamics of self-reported symptoms and self-isolation behavior in positive and negative users. These results highlight the utility of collecting a diverse set of symptomatic, demographic, and behavioral self-reported data to fight the COVID-19 pandemic.

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