The timing and effectiveness of implementing mild interventions of COVID-19 in large industrial cities

The outbreak of novel coronavirus disease (COVID-19) has spread around the world since it was detected in December 2019. The Chinese government executed a series of interventions to curb the pandemic. The "battle" against COVID-19 in Shenzhen, China is valuable because populated industrial cities are the epic centres of COVID-19 in many regions. We made use of synthetic control methods to create a reference population matching specific characteristics of Shenzhen. With both the synthetic and observed data, we introduced an epidemic compartmental model to compare the spread of COVID-19 between Shenzhen and its counterpart regions in the United States that didnt implement interventions for policy evaluation. Once the effects of policy interventions adopted in Shenzhen were estimated, the delay effects of those interventions were referred to provide the further control degree of interventions. Thus, the hypothetical epidemic situations in Shenzhen were inferred by using time-varying reproduction numbers in the proposed SIHR (Susceptible, Infectious, Hospitalized, Removed) model and considering if the interventions were delayed by 0 day to 5 days. The expected cumulative confirmed cases would be 1546, which is 5.75 times of the observed cumulative confirmed cases of 269 in Shenzhen on February 3, 2020, based on the data from the counterpart counties (mainly from Broward, New York, Santa Clara, Pinellas, and Westchester) in the United States. If the interventions were delayed by 5 days from the day when the interventions started, the expected cumulative confirmed cases of COVID-19 in Shenzhen on February 3, 2020 would be 676 with 95% credible interval (303,1959). Early implementation of mild interventions can subdue the epidemic of COVID-19. The later the interventions were implemented, the more severe the epidemic was in the hard-hit areas. Mild interventions are less damaging to the society but can be effective when implemented early. AMS 2000 O_SCPLOWSUBJECTC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWCLASSIFICATIONSC_SCPLOW: Primary 00K00, 00K01; secondary 00K02.

COVID-Net: A deep learning based and interpretable predication model for the county-wise trajectories of COVID-19 in the United States

The cases of COVID-19 have been reported in the United States since January 2020. We propose a COVINet by combining the architecture of both Long Short-Term Memory and Gated Recurrent Unit. First, we use the 10-fold cross-validation to train and assess different prediction models for which all counties serve alternatively as the training and test counties. Then, we focus on the prediction for the 10 severest counties. We employ the Mean Relative Errors (MREs) to measure the performance of the COVINet in predicting confirmed cases and deaths. Two COVINet models with 26 and 19 input variables, respectively, are trained. We estimate their respective MREs in the last 30 days before January 23, 2021, by the 10-fold CV, which are 0.0898 and 0.1068 for the number of confirmed cases, and 0.0694 and 0.0724 for the number of deaths. The MREs are also small for all predictions of the events in the last 7 or 30 days before January 23, 2021. The COVINet uses features including workforce driving alone to work, traffic volume, income inequality, and longitude and latitude of infected counties to predict the trajectories of COVID-19 in counties of the United States. The increasing awareness of how predictors affect the pandemic helps policymakers develop plans to mitigate the spread of COVID-19.

Risk factors associated with mortality of COVID-19 in 2692 counties of the United States

BackgroundThe number of cumulative confirmed cases of COVID-19 in the United States has risen sharply since March 2020. A county health ranking and roadmaps program has been established to identify factors associated with disparity in mobility and mortality of COVID-19 in all counties in the United States. MethodsTo find out the risk factors associated with county-level mortality of COVID-19 with various levels of prevalence, a negative binomial design was applied to the county-level mortality counts of COVID-19 as of August 27, 2020 in the United States. In this design, the infected counties were categorized into three levels of infections using clustering analysis based on time-varying cumulative confirmed cases from March 1 to August 27, 2020. COVID-19 patients were not analyzed individually but were aggregated at the county-level, where the county-level deaths of COVID-19 confirmed by the local health agencies. Results3125 infected counties were assigned into three classes corresponding to low, median, and high prevalence levels of infection. Several risk factors were significantly associated with the mortality counts of COVID-19, where higher level of air pollution (0.153, P<0.001) increased the mortality in the low prevalence counties and elder individuals were more vulnerable in both the median and high prevalence counties. The segregation between non-Whites and Whites and higher Hispanic population had higher likelihood of risk of the deaths in all infected counties. ConclusionsThe mortality of COVID-19 depended on sex, race/ethnicity, and outdoor environment. The increasing awareness of the impact of these significant factors may lead to the reduction in the mortality of COVID-19.

Evaluate the timing of resumption of business for the states of New York, New Jersey and California via a pre-symptomatic and asymptomatic transmission model of COVID-19

The United States has the highest numbers of confirmed cases of COVID-19 in the world. The early hot spot states were New York, New Jersey, and Connecticut. The workforce in these states was required to work from home except for essential services. It was necessary to evaluate an appropriate date for resumption of business since the premature reopening of the economy would lead to a broader spread of COVID-19, while the opposite situation would cause greater loss of economy. To reflect the real-time risk of the spread of COVID-19, it was crucial to evaluate the population of infected individuals before or never being confirmed due to the pre-symptomatic and asymptomatic transmissions of COVID-19. To this end, we proposed an epidemic model and applied it to evaluate the real-time risk of epidemic for the states of New York, New Jersey, and Connecticut. We used California as the benchmark state because California began a phased reopening on May 8, 2020. The dates on which the estimated numbers of unidentified infectious individuals per 100,000 for states of New York, New Jersey, and Connecticut were close to those in California on May 8, 2020, were June 1, 22, and 22, 2020, respectively. By the practice in California, New York, New Jersey, and Connecticut might consider reopening their business. Meanwhile, according to our simulation models, to prevent resurgence of infections after reopening the economy, it would be crucial to maintain sufficient measures to limit the social distance after the resumption of businesses. This precaution turned out to be critical as the situation in California quickly deteriorated after our analysis was completed and its interventions after the reopening of business were not as effective as those in New York, New Jersey, and Connecticut.

The Effects of Stringent Interventions for Coronavirus Pandemic

The pandemic of COVID-19 has caused severe public health consequences around the world. Many interventions of COVID-19 have been implemented. It is of great public health and societal importance to evaluate the effects of interventions in the pandemic of COVID-19. In this paper, with help of synthetic control method, regression discontinuity and a Susceptible-Infected and infectious without isolation-Hospitalized in isolation-Removed (SIHR) model, we evaluate the horizontal and longitudinal effects of stringent interventions implemented in Wenzhou, a representative urban city of China, where stringent interventions were enforced to curb its own epidemic situation with rapidly increasing newly confirmed cases. We found that there were statistically significant treatment effects of those stringent interventions which reduced the cumulative confirmed cases of COVID-19. Those reduction effects would increase over time. Also, if the stringent interventions were delayed by 2 days or mild interventions were implemented instead, the expected number of cumulative confirmed cases would have been nearly 2 times or 5 times of the actual number. The effects of stringent interventions are significant in mitigating the epidemic situation of COVID-19. The slower the interventions were implemented, the more severe the epidemic would have been, and the stronger the interventions would have been required.

Data-driven analysis on the simulations of the spread of COVID-19 under different interventions of China

Since February 2020, COVID-19 has spread rapidly to more than 200 countries in the world. During the pandemic, local governments in China have implemented different interventions to efficiently control the spread of the epidemic. Characterizing transmission of COVID-19 under some typical interventions is essential to help countries develop appropriate interventions. Based on the pre-symptomatic transmission patterns of COVID-19, we established a novel compartmental model: Baysian SIHR model with latent Markov structure, which treated the numbers of infected and infectious individuals without isolation to be the latent variables and allowed the effective reproduction number to change over time, thus the effects of policies could be reasonably estimated. By using the epidemic data of Wuhan, Wenzhou and Shenzhen, we migrated the corresponding estimated policy modes to South Korea, Italy, and the United States and simulated the potential outcomes for these countries when they adopted similar policy strategies of three cities in China. We found that the mild interventions implemented in Shenzhen were effective to control the epidemic in the early stage, while more stringent policies which were issued in Wuhan and Wenzhou were necessary if the epidemic was more severe and needed to be controlled in a short time.