An assessment of government measures in combatting COVID-19 using ISM and DEMATEL modelling

PurposeThe purpose of this paper is to evaluate the interplay of various measures used by different governments around the world in combatting COVID-19.Design/methodology/approachThe research uses the interpretative structural modelling (ISM) for assessing the powerful measures amongst the recognized ones, whereas to establish the cause-and-effect relations amongst the variables, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used. Both approaches utilized in the study aid in the comprehension of the relationship amongst the assessed measures.FindingsAccording to the ISM model, international support measures have the most important role in reducing the risk of COVID-19. There has also been a suggestion of a relationship between economic and risk measures. Surprisingly, no linkage factor (unstable one) was reported in the research. The study indicates social welfare measures, R&D measures, centralized power and decentralized governance measures and universal healthcare measures as independent factors. The DEMATEL analysis reveals that the net causes are social welfare measures, centralized power and decentralized government, universal health coverage measure and R&D measures, while the net effects are economic measures, green recovery measures, risk measures and international support measures.Originality/valueThe study includes a list of numerous government measures deployed throughout the world to mitigate the risk of COVID-19, as well as the structural links amongst the identified government measures. The Matrice d'Impacts croises-multiplication applique and classment analysis can help the policymakers in understanding measures used in combatting COVID-19 based on their driving and dependence power. These insights may assist them in employing these measures for mitigating the risks associated with COVID-19 or any other similar pandemic situation in the future.

MODELING LEMMA FREQUENCY BANDS FOR LEXICAL COMPLEXITY ASSESSMENT OF RUSSIAN TEXTS

The paper is devoted to the problem of modeling general-language frequency using data of large Russian corpora. Our goal is to develop a methodology for forming a consolidated frequency list which in the future can be used for assessing lexical complexity of Russian texts. We compared 4 frequency lists developed from 4 corpora (Russian National Corpus, ruTenTen11, Araneum Russicum III Maximum, Taiga). Firstly, we applied rank correlation analysis. Secondly, we used the measures “coverage” and “enrichment”. Thirdly, we applied the measure “sum of minimal frequencies”. We found that there are significant differences between the compared frequency lists both in ranking and in relative frequencies. The application of the “coverage” measure showed that frequency lists are by no means substitutable. Therefore, none of the corpora in question can be excluded when compiling a consolidated frequency list. For a more detailed comparison of frequency lists for different frequency bands, the ranked frequency list, based on RNC data, was divided into 4 equal parts. Then 4 random samples (containing 20 lemmas from each quartile) were formed. Due to the wide range of values, accepted by ipm measure, relative frequency values are difficult to interpret. In addition, there are no reliable thresholds separating high-frequency, mid-frequency, and low-frequency lemmas. Meanwhile, to assess the lexical complexity of texts, it is useful to have a convenient way of distributing lemmas with certain frequencies over the bands of the frequency list. Therefore, we decided to assign lemmas “Zipf-values”, which made the frequency data interpretable because the range of measure values is small. The result of our work will be a publicly accessible reference resource called “Frequentator”, which will allow to obtain interpretable information about the frequency of Russian words.

1308. The Association Between Refill History and Viral Load Suppression in HIV-infected Patients at a University-Based HIV Clinic in the Midwest

Background Patients with HIV (PWH) with sustained virologic suppression (VS) on antiretroviral therapy (ART) achieve better health outcomes and pose effectively no risk of transmitting HIV to their sexual contacts. Adherence to ART is the main predictor of VS in PWH, yet no adherence benchmark has been identified. The clinical utility of ART pharmacy refill history collection is unknown. We hypothesize that pharmacy refill histories of ART represented as a percentage of days covered (PDC) will correlate with VS in PWH. Methods We conducted a single-center, retrospective cohort study of PWH ( ≥19 years) receiving care at a Midwestern HIV clinic between January 1, 2018 and December 31, 2018, with at least 1 HIV RNA reading during the study period. Refill histories were collected for each eligible study patient and a PDC was calculated as the “number of tablets dispensed / number of days within study period” to provide an ART coverage measure. ART regimen, sociodemographic, and clinical characteristics were abstracted from the HIV registry. An HIV RNA ≤ 50 copies/mL and a PDC of ≥80% were used as measures of VS and sufficient adherence, respectively. Pearson’s chi-square tests and binary logistic regression were used to determine the effect of PDC on VS. Results A total of 1019 patients were included in the study. 705 (69%) patients had a PDC ≥80% and 314 (31%) had a PDC <80%. VS between groups was 96% (PDC ≥80%) vs. 74% (PDC < 80%). A significant association was observed between VS and PDC (P < 0.0001) [HJP1]. Patients with a PDC ≥80% were 9.5 times more likely to attain VS as compared with patients with PDC < 80% (95% CI, 5.89–15.17). After adjusting for ART regimen, sociodemographic, and other clinical characteristics, the likelihood of VS remained higher for patients with a PDC ≥ 80% (aOR: 6.3; 95% CI, 3.7–11.0). Factors found to be negatively associated with VS were single marital status (aOR: 0.49; 95% CI, 0.24–0.95), current or historical opportunistic infection (aOR: 0.51; 95% CI, 0.26–0.99), and usage of a multiclass or dual ART regimen (aOR: 0.40; 95% CI, 0.16–0.98). Conclusion The utilization of PDC as an ART adherence benchmark was significantly associated with VS. PDC is an easy measure to calculate and could be useful in the clinical care of PWH. Future prospective studies are needed to confirm these findings. Disclosures All authors: No reported disclosures.

Hierarchical Semantic Relational Coverage Measure Based Web Document Clustering Using Semantic Ontology

The problem of web document clustering has been well studied. Web documents has been grouped based various features like textual, topical and semantic features. Number of approaches has been discussed earlier for the clustering of web documents. However the method does not produce promising results towards web document clustering. To overcome this, an efficient hierarchical semantic relational coverage based approach is presented in this paper. The method extracts the features of web document by preprocessing the document. The features extracted have been used to measure the semantic relational coverage measure in different levels. As the documents are grouped in a hierarchical manner, the method estimates the relational coverage measure in each level of the cluster. Based on the semantic relational measure at different level, the method estimates the topical semantic support measure. Using these two, the method computes the class weight. The estimated class weight has been used to perform document clustering. The proposed method improves the performance of document clustering and reduces the false classification ratio.