Journal of Data Science

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronvirus, which was declared as a global pandemic by the World Health Organization on March 11, 2020. In this work, we conduct a cross-sectional study to investigate how the infection fatality rate (IFR) of COVID-19 may be associated with possible geographical or demographical features of the infected population. We employ a multiple index model in combination with sliced inverse regression to facilitate the relationship between the IFR and possible risk factors. To select associated features for the infection fatality rate, we utilize an adaptive Lasso penalized sliced inverse regression method, which achieves variable selection and sufficient dimension reduction simultaneously with unimportant features removed automatically. We apply the proposed method to conduct a cross-sectional study for the COVID-19 data obtained from two time points of the outbreak.

COVID-19 is quickly spreading around the world and carries along with it a significant threat to public health. This study sought to apply meta-analysis to more accurately estimate the basic reproduction number (R0) because prior estimates of R0 have a broad range from 1.95 to 6.47 in the existing literature. Utilizing meta-analysis techniques, we can determine a more robust estimation of R0, which is substantially larger than that provided by the World Health Organization (WHO). A susceptible-Infectious-removed (SIR) model for the new infection cases based on R0 from meta analysis is proposed to estimate the effective reproduction number Rt. The curves of estimated Rt values over time can illustrate that the isolation measures enforced in China and South Korea were substantially more effective in controlling COVID-19 compared to the measures enacted early in both Italy and the United States. Finally, we present the daily standardized infection cases per million population over time across countries, which is a good index to indicate the effectiveness of isolation measures on the prevention of COVID-19. This standardized infection case determines whether the current infection severity status is out of range of the national health capacity to care for patients.

Proteins play a key role in facilitating the infectiousness of the 2019 novel coronavirus. A specific spike protein enables this virus to bind to human cells, and a thorough understanding of its 3-dimensional structure is therefore critical for developing effective therapeutic interventions. However, its structure may continue to evolve over time as a result of mutations. In this paper, we use a data science perspective to study the potential structural impacts due to ongoing mutations in its amino acid sequence. To do so, we identify a key segment of the protein and apply a sequential Monte Carlo sampling method to detect possible changes to the space of lowenergy conformations for different amino acid sequences. Such computational approaches can further our understanding of this protein structure and complement laboratory efforts.

COVID-19 is a disease caused by the severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) that was reported to spread in people in December 2019. Understanding epidemiological

As the COVID-19 pandemic has strongly disrupted people’s daily work and life, a great amount of scientific research has been conducted to understand the key characteristics of this new epidemic. In this manuscript, we focus on four crucial epidemic metrics with regard to the COVID-19, namely the basic reproduction number, the incubation period, the serial interval and the epidemic doubling time. We collect relevant studies based on the COVID-19 data in China and conduct a meta-analysis to obtain pooled estimates on the four metrics. From the summary results, we conclude that the COVID-19 has stronger transmissibility than SARS, implying that stringent public health strategies are necessary.

The COVID-19 pandemic has triggered explosive activities in searching for cures, including vaccines against the SARS-CoV-2 infection. As of April 30, 2020, there are at least 102 COVID-19 vaccine development programs worldwide, the majority of which are in preclinical development phases, five are in phase I trial, and three are in phase I/II trial. Experts caution against rushing COVID-19 vaccine development, not only because the knowledge about SARS-CoV-2 is lacking (albeit rapidly accumulating), but also because vaccine development is a complex, lengthy process with its own rules and timelines. Clinical trials are critically important in vaccine development, usually starting from small-scale phase I trials and gradually moving to the next phases (II and III) after the primary objectives are met. This paper is intended to provide an overview on design considerations for vaccine clinical trials, with a special focus on COVID-19 vaccine development. Given the current pandemic paradigm and unique features of vaccine development, our recommendations from statistical design perspective for COVID-19 vaccine trials include: (1) novel trial design (e.g., master protocol) to expedite the simultaneous evaluation of multiple candidate vaccines or vaccine doses, (2) human challenge studies to accelerate clinical development, (3) adaptive design strategies (e.g., group sequential designs) for early termination due to futility, efficacy, and/or safety, (4) extensive modeling and simulation to characterize and establish long-term efficacy based on early-phase or short-term follow-up data, (5) safety evaluation as one of the primary focuses throughout all phases of clinical trials, (6) leveraging real-world data and evidence in vaccine trial design and analysis to establish vaccine effectiveness, and (7) global collaboration to form a joint development effort for more efficient use of resource and expertise and data sharing.