Journal of Data Science

Millions of people travel from Wuhan to other cities from Jan. 1st 2020 to Jan 23rd 2020. Taking advantage of the masked software development kit data from Aurora Mobile Ltd and open epidemic data released by health authorities, we analyze the relationship between number of confirmed COVID-19 cases in a region and the people who traveled from Wuhan to this region in this period. Further, we identify high risk carriers of COVID-19 to improve the control of COVID-19. The key findings are three-folds: (1) in each region the number of high-risk carriers is highly positively correlated with the severity of illness; (2) history of visit to the 62 designated hospitals is the foremost index of risk; (3) the second most important index is the travelers’ duration of stay in Wuhan. Based on our analysis, we estimate that, as of February 4, 2020, (a) among the 8.5 million people held up in Wuhan, there are 425 thousand high risk carriers; and (b) among all the 3.5 million migrant workers held up in Hubei, there are 175 thousand high risk carriers. The disease control authorities should closely minotor these groups.

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.

Abstract: In this study, the data based on nucleic acid amplification tech niques (Polymerase chain reaction) consisting of 23 different transcript vari ables which are involved to investigate genetic mechanism regulating chlamy dial infection disease by measuring two different outcomes of muring C. pneumonia lung infection (disease expressed as lung weight increase and C. pneumonia load in the lung), have been analyzed. A model with fewer reduced transcript variables of interests at early infection stage has been obtained by using some of the traditional (stepwise regression, partial least squares regression (PLS)) and modern variable selection methods (least ab solute shrinkage and selection operator (LASSO), forward stagewise regres sion and least angle regression (LARS)). Through these variable selection methods, the variables of interest are selected to investigate the genetic mechanisms that determine the outcomes of chlamydial lung infection. The transcript variables Tim3, GATA3, Lacf, Arg2 (X4, X5, X8 and X13) are being detected as the main variables of interest to study the C. pneumonia disease (lung weight increase) or C. pneumonia lung load outcomes. Models including these key variables may provide possible answers to the problem of molecular mechanisms of chlamydial pathogenesis.

Subsampling the data is used in this paper as a learning method about the influence of the data points for drawing inference on the parameters of a fitted logistic regression model. The alternative, alternative regularized, alternative regularized lasso, and alternative regularized ridge estimators are proposed for the parameter estimation of logistic regression models and are then compared with the maximum likelihood estimators. The proposed alternative regularized estimators are obtained by using a tuning parameter but the proposed alternative estimators are not regularized. The proposed alternative regularized lasso estimators are the averaged standard lasso estimators and the alternative regularized ridge estimators are also the averaged standard ridge estimators over subsets of groups where the number of subsets could be smaller than the number of parameters. The values of the tuning parameters are obtained to make the alternative regularized estimators very close to the maximum likelihood estimators and the process is explained with two real data as well as a simulated study. The alternative and alternative regularized estimators always have the closed form expressions in terms of observations that the maximum likelihood estimators do not have. When the maximum likelihood estimators do not have the closed form expressions, the alternative regularized estimators thus obtained provide the approximate closed form expressions for them.

The COVID-19 pandemic has created a sudden need for a wider uptake of home-based telework as means of sustaining the production. Generally, teleworking arrangements impact directly worker’s efficiency and motivation. The direction of this impact, however, depends on the balance between positive effects of teleworking (e.g. increased flexibility and autonomy) and its downsides (e.g. blurring boundaries between private and work life). Moreover, these effects of teleworking can be amplified in case of vulnerable groups of workers, such as women. The first step in understanding the implications of teleworking on women is to have timely information on the extent of teleworking by age and gender. In the absence of timely official statistics, in this paper we propose a method for nowcasting the teleworking trends by age and gender for 20 Italian regions using mobile network operators (MNO) data. The method is developed and validated using MNO data together with the Italian quarterly Labour Force Survey. Our results confirm that the MNO data have the potential to be used as a tool for monitoring gender and age differences in teleworking patterns. This tool becomes even more important today as it could support the adequate gender mainstreaming in the ‘Next Generation EU’ recovery plan and help to manage related social impacts of COVID-19 through policymaking.