Development of highly potent non-covalent inhibitors of SARS-CoV-2 3CLpro (preprint)

The SARS-CoV-2 virus is the causal agent of the ongoing pandemic of coronavirus disease 2019 (COVID-19). There is an urgent need for potent, specific antiviral compounds against SARS-CoV-2. The 3C-like protease (3CLpro) is an essential enzyme for the replication of SARS-CoV-2 and other coronaviruses, and thus is a target for coronavirus drug discovery. Nearly all inhibitors of coronavirus 3CLpro reported so far are covalent inhibitors. Here, we report the development of specific, non-covalent inhibitors of 3CLpro. The most potent one, WU-04, effectively blocks SARS-CoV-2 replications in human cells with EC 50 values in the 10-nM range. WU-04 also inhibits the 3CLpro of SARS-CoV and MERS-CoV with high potency, indicating that it is a pan-inhibitor of coronavirus 3CLpro. WU-04 showed anti-SARS-CoV-2 activity similar to that of PF-07321332 (Nirmatrelvir) in K18-hACE2 mice when the same dose was administered orally. Thus, WU-04 is a promising drug candidate for coronavirus treatment. One-Sentence Summary A oral non-covalent inhibitor of 3C-like protease effectively inhibits SARS-CoV-2 replication.

COVID-19 in China: Risk Factors and R0 Revisited (preprint)

Background: The COVID-19 epidemic had spread rapidly through China and subsequently has proliferated globally leading to a pandemic situation around the globe. Human-to-human transmissions, as well as asymptomatic transmissions of the infection, have been confirmed. As of April 3rd, public health crisis in China due to COVID-19 is potentially under control. Methods: We compiled a daily dataset of case counts, mortality, recovery, temperature, population density, and demographic information for each prefecture during the period of January 11 to April 07, 2020 (excluding Wuhan from our analysis due to missing data). Understanding the characteristics of spatiotemporal clustering of the COVID-19 epidemic and R0 is critical in effectively preventing and controlling the ongoing global pandemic. The prefectures were grouped based on several relevant features using unsupervised machine learning techniques. We performed a computational analysis utilizing the reported cases in China to estimate the revised R0 among different regions for prevention planning in an ongoing global pandemic. Results: Finally, our results indicate that the impact of temperature and demographic (different age group percentage compared to the total population) factors on virus transmission may be characterized using a stochastic transmission model. Conclusions: Such predictions will help prioritize segments of a given community/ region for action and provide a visual aid in designing prevention strategies for a specific geographic region. Furthermore, revised estimation and our methodology will aid in improving the human health consequences of COVID-19 elsewhere.

COVID-19 in China: Risk Factors and R0 Revisited (preprint)

The COVID-19 epidemic had spread rapidly through China and subsequently has proliferated globally leading to a pandemic situation around the globe. Human-to-human transmissions, as well as asymptomatic transmissions of the infection, have been confirmed. As of April 3rd, public health crisis in China due to COVID-19 is potentially under control. We compiled a daily dataset of case counts, mortality, recovery, temperature, population density, and demographic information for each prefecture during the period of January 11 to April 07, 2020 (excluding Wuhan from our analysis due to missing data). Understanding the characteristics of spatiotemporal clustering of the COVID-19 epidemic and R0 is critical in effectively preventing and controlling the ongoing global pandemic. The prefectures were grouped based on several relevant features using unsupervised machine learning techniques. We performed a computational analysis utilizing the reported cases in China to estimate the revised R0 among different regions for prevention planning in an ongoing global pandemic. Finally, our results indicate that the impact of temperature and demographic (different age group percentage compared to the total population) factors on virus transmission may be characterized using a stochastic transmission model. Such predictions will help prioritize segments of a given community/ region for action and provide a visual aid in designing prevention strategies for a specific geographic region. Furthermore, revised estimation and our methodology will aid in improving the human health consequences of COVID-19 elsewhere.

COVID-19 Propagation Prediction and Assessment Method with Imported Cases and Infection Generations: Shanxi Province as a Case (preprint)

When everyone focuses on 2019 coronavirus disease (COVID-19) in Hubei province, the epidemic in other province cannot be ignored, which also has an impact on the epidemic in the whole country. The most distinctive epidemic characteristic in all regions except Wuhan is that the most of confirmed cases are imported cases from Wuhan, and the propagation chain is relatively clear. Based on detailed contact tracing information of confirmed cases, combined with first-order outbreak response measures, we establish a disease transmission dynamical model to describe the infection propagation chain among the human population. Using Shanxi province as a case, modeling results indicate that the epidemic peak in Shanxi province occurred in February 2. In addition, our model suggests that according to the current development trend, COVID-19 will disappear in February with the final epidemic number of approximately 175 cases. It is verified that the most effective outbreak control measures in Shanxi include home isolation of people, surveillance and isolation of second-generation cases, contact tracing and management of contacts. With the end of the holiday, if the average number of contacts per person per day is less than 6 , it has little impact on the incidence of COVID-19, and even if third- and fourth-generation cases occur, the epidemic will be under control, no later than late March with a finial outbreak size of 220 cases. However, if the average number of contacts per person per day is greater than 6, the number of COVID-19 cases will continue to be reported resulting in another epidemic peak. Through the forecast and evaluation of COVID-19 in Shanxi, it is verified that the model with infection generations is more accurate to describe the spread mode and can be extended to regions with imported cases.