key: cord-0734051-ldvfa4nu authors: Ran, Jinjun; Zhao, Shi; Han, Lefei; Chong, Marc K.C.; Qiu, Yulan; Yang, Yiwei; Wang, Jiayi; Wu, Yushan; Javanbakht, Mohammad; Wang, Maggie H.; He, Daihai title: The changing patterns of COVID-19 transmissibility during the social unrest in the United States: A nationwide ecological study with a before-and-after comparison date: 2020-12-01 journal: One Health DOI: 10.1016/j.onehlt.2020.100201 sha: 14f78003f87ad81ab54c8b3bb5850e3b4b09da61 doc_id: 734051 cord_uid: ldvfa4nu nan The pandemic of the coronavirus disease 2019 (COVID-19) caused tremendous impacts on public health and the global economy, comparable to the 1918 influenza pandemic 1 . As of November 23, 2020, the United States (US) has over 12.2 million COVID-19 cases and approximately 0.25 million deaths 2 . The surge in the growth of the epidemic curve seems to slow down in the US after a series of social distancing restrictions 3 . However, nationwide mass demonstrations have emerged since May 25, which raises broad concerns about whether the protests would affect the COVID-19 pandemic control. In this study, we explore the temporal changes of the COVID-19 transmissibility associated with the mass demonstrations in the US. The COVID-19 cases data were collected via public domain https://guangchuangyu.github.io/nCov2019/, where the US part was collected from the New York Times (link of data: https://github.com/nytimes/covid-19-data/blob/master/us-states.csv). The time-varying reproduction numbers (R t ) are constructed to quantify the instantaneous COVID-19 transmissibility of each state (n = 47, excluding Connecticut, Hawaii, and North Dakota due to missing data) in the US. The daily percentage change (η) in the R t series is estimated, which quantifies the changing rate of the COVID-19 transmissibility. A negative η is desired when the outbreak control measures are effective, under which the COVID-19 transmissibility decreases steadily. By using the generalized linear discontinuity design, we examined the structural break in the trends of R t and then estimated ηs before and after the time window of social unrest. We adopt the R t to quantify the instantaneous COVID-19 transmissibility. Referring to previous studies 4-7 , the epidemic growth is modeled as a branching process, and thus the R t can be expressed as a ratio of C(t) over ∫ ( ) ( ) . Here, the function w(•) is the distribution of the generation time (GT) of COVID-19. The 'dk' means differentiation, which is a commonly used and widely accepted mathematical notation. The C(t) was the numbers of COVID-19 cases at the t-th date, which is modeled to obey a Poisson process with the rate determined by the R t . As such, the likelihood-based parameter We suspect the R t series pattern may be different between May 19 -May 25 and May 29 -June 4, since numerous protests were outbroken in a time window from May 26 to May 28. By using the generalized linear discontinuity design, we examine a structural break in the trends of R t . We fit the following discontinuity model to the R t series against the time index t. Here There is no denying that COVID-19 also indirectly promotes the series of protests. With uncertainty and unpredictability, long-time physical distancing or lockdown result in unemployment, social isolation, increased access to alcohol and online gambling, as well as decreased social support 16 . These known risk factors for mental health problems may exaggerate personal emission to public events, weaken resistance to inflammatory remarks, and accelerate aggressive behaviors during protests. Therefore, accessible mental health services and remote community supports, provided by local health-care workers, may contribute to both the protest alleviation and the pandemic control 17 . Caution should be exercised when interpreting. First, reported cases could not be representative of the whole population, because sampling has not been random, and most of asymptomatic are missed. Testing protocols might differ between countries and even within countries, especially at different points in time. However, since we only restricted in a short period (May 19 -May 25 vs. May 29 -June 4), the mission of asymptomatic patients was assumed to be balanced across the period. And since we preferred to observing the R t variation between the two different periods for each state of the US, the influence of testing protocols across states could be less significant. Second, our data were collected from the New Comparing COVID-19 and the 1918-19 influenza pandemics in the United Kingdom An interactive web-based dashboard to track COVID-19 in real time Early Evidence on Social Distancing in Response to COVID-19 in the United States Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures A new framework and software to estimate timevarying reproduction numbers during epidemics Transmission dynamics of the 2009 influenza A (H1N1) pandemic in India: The impact of holiday-related school closure Improved inference of time-varying reproduction numbers during infectious disease outbreaks Temporal dynamics in viral shedding and transmissibility of COVID-19 Estimating the time interval between transmission generations when negative values occur in the serial interval data: using COVID-19 as an example Estimating the generation interval for coronavirus disease (COVID-19) based on symptom onset data nationwide sample of patients on dialysis in the USA: a cross-sectional study COVID-19 and African Americans COVID-19 and Racial/Ethnic Disparities Political interference in public health science during covid-19 How mental health care should change as a consequence of the COVID-19 pandemic Online mental health services in China during the COVID-19 outbreak