Emergency plan selection for epidemic prevention and control based on cumulative prospect theory and hybrid-information MADM

PurposeDecision-making problems in emergency plan selection for epidemic prevention and control (EPAC) are generally characterized by risky and uncertainty due to multiple possible emergency states and vagueness of decision information. In the process of emergency plan selection for EPAC, it is necessary to consider several obvious features, i.e. different states of epidemics, dynamic evolvement process of epidemics and decision-makers' (DMs') psychological factors such as risk preference and loss aversion.Design/methodology/approachIn this paper, a novel decision-making method based on cumulative prospect theory (CPT) is proposed to solve emergency plan selection of an epidemic problem, which is generally regarded as hybrid-information multi-attribute decision-making (HI-MADM) problems in major epidemics. Initially, considering the psychological factors of DMs, the expectations of DMs are chosen as reference points to normalize the expectation vectors and decision information with three different formats. Subsequently, the matrix of gains and losses is established according to the reference points. Furthermore, the prospect value of each alternative is obtained and the comprehensive prospect values of alternatives under different states are calculated. Accordingly, the ranking of alternatives can be obtained.FindingsThe validity and robustness of the proposed method are demonstrated by a case calculation of emergency plan selection. Meanwhile, sensitivity analysis and comparison analysis with fuzzy similarity to ideal solution (FTOPSIS) method and TODIM (an acronym in Portuguese for interactive and MADM) method illustrate the effectiveness and superiority of the proposed method.Originality/valueAn emergency plan selection method is proposed for EPAC based on CPT, taking into account the psychological factors of DMs.HighlightsThis paper proposes a new CPT-based EDM method for EPAC under a major epidemic considering the psychological factorsof DMs, such as risk preference, loss aversion and so on.The authors' work gives approaches of normalization, comparison and distance measurement for dealing with the integration of three hybrid formats of attributes.This article gives some guidance, which contributes to solve the problems of risk-based hybrid multi-attribute EDM.The authors illustrate the advantages of the proposed method by a sensitivity analysis and comparison analysis with existing FTOPSIS method and TODIM method.

Serinc2 deficiency causes susceptibility to sepsis-associated acute lung injury.

BACKGROUND: Severe sepsis and its subsequent complications cause high morbidity and mortality rates worldwide. The lung is one of the most vulnerable organs sensitive to the sepsis-associated inflammatory storm and usually develops into acute respiratory distress syndrome (ARDS)/acute lung injury (ALI). The pathogenesis of sepsis-associated ALI is accompanied by coordinated transmembrane signal transduction and subsequent programmed cell death; however, the underlying mechanism remains largely unclear. RESULTS: Here we find that the expression of serine incorporator 2 (Serinc2), a protein involved in phosphatidylserine synthesis and membrane incorporation, is upregulated in cecal ligation and puncture (CLP)-induced ALI. Furthermore, the Serinc2-knockout (KO) mouse line is generated by the CRISPR-cas9 approach. Compared with wild-type mice, the Serinc2-KO mice exhibit exacerbated ALI-related pathologies after CLP. The expressions of pro-inflammatory factors, including IL1ß, IL6, TNFα, and MCP1, are significantly enhanced by Serinc2 deficiency, concurrent with over-activation of STAT3, p38 and ERK pathways. Conversely, Serinc2 overexpression in RAW264.7 cells significantly suppresses the inflammatory responses induced by lipopolysaccharide (LPS). Serinc2 KO aggravates CLP-induced apoptosis as evidenced by increases in TUNEL-positive staining, Bax expression, and cleaved caspase-3 and decreases in BCL-2 expression and Akt phosphorylation, whereas these changes are suppressed by Serinc2 overexpression in LPS-treated RAW264.7 cells. Moreover, the administration of AKTin, an inhibitor of Akt, abolishes the protective effects of Serinc2 overexpression against inflammation and apoptosis. CONCLUSIONS: Our findings demonstrate a protective role of Serinc2 in the lung through activating the Akt pathway, and provide novel insight into the pathogenesis of sepsis-induced ALI.

A Group Emergency Decision-Making Method for Epidemic Prevention and Control Based on Probabilistic Hesitant Fuzzy Prospect Set Considering Quality of Information

Epidemics can bring huge impacts to economic operation and human health, a practical and effective emergency decision-making (EDM) method is of great significance to reduce all kinds of losses and slow the spread of epidemics. In the process of EDM, decision information is usually uncertain and vague, and the psychological behaviors and various perspectives of decision makers (DMs) should be considered. Hence, this paper develops a group emergency decision-making (GEDM) method under risk based on the probabilistic hesitant fuzzy set (PHFS) and cumulative prospect theory (CPT), in which probabilistic hesitant fuzzy prospect set (PHFPS) that combines PHFS and CPT is developed to portray the vagueness of decision information and psychologies of DMs. Moreover, experts’ creditability in evaluation criteria is generally different because of the differences of their own knowledge structures, practical experience, individual preference and so on. A formula is proposed to measure the quality of decision information provided by experts for revising the expert weights. In addition, the evaluation criteria supporting the GEDM of epidemics are given. Finally, the proposed method is demonstrated by an empirical case study of COVID-19, and the comparison analysis based on the rank-biased overlap model and the sensitivity analysis are conducted to the illustrate the validity of the proposed method.

Video conferencing software selection based on hybrid MCDM and cumulative prospect theory under a major epidemic

The video conferencing software is regarded as a significant tool for social distancing and getting incorporations up and going. Due to the indeterminacy of epidemic evolution and the multiple criteria, this paper proposes a video conferencing software selection method based on hybrid multi-criteria decision making (HMCDM) under risk and cumulative prospect theory (CPT), in which the criteria values are expressed in various mathematical forms (e.g., real numbers, interval numbers, and linguistic terms) and can be changed with natural states of the epidemic. Initially, the detailed description of video conferencing software selection problem under an epidemic are given. Subsequently, a whole procedure for video conferencing software selection is conducted, the approaches for processing and normalizing the multi-format evaluation values are presented. Furthermore, the expectations provided by DMs under different natural states of the epidemic are considered as the corresponding reference points (RP). Based on this, the matrix of gains and losses is constructed. Then, the prospect values of all criteria and the perceived probabilities of natural states are calculated according to the value function and the weighting function in CPT respectively. Finally, the proposed method is illustrated by an empirical case study, and the comparison analysis and the sensitivity analysis for the loss aversion parameter are conducted to prove the effectiveness and robustness. The results show that considering the psychological characteristics of DMs in selection decision is beneficial to avoid the unacceptable and potential loss risks. This study could provide a useful guideline for managers who intend to select appropriate video conferencing software. [ABSTRACT FROM AUTHOR] Copyright of Journal of Intelligent & Fuzzy Systems is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Analysis of thin-section CT in patients with coronavirus disease (COVID-19) after hospital discharge.

PURPOSE: To analyze clinical and thin-section computed tomographic (CT) data from the patients with coronavirus disease (COVID-19) to predict the development of pulmonary fibrosis after hospital discharge. MATERIALS AND METHODS: Fifty-nine patients (31 males and 28 females ranging from 25 to 70 years old) with confirmed COVID-19 infection performed follow-up thin-section thorax CT. After 31.5±7.9 days (range, 24 to 39 days) of hospital admission, the results of CT were analyzed for parenchymal abnormality (ground-glass opacification, interstitial thickening, and consolidation) and evidence of fibrosis (parenchymal band, traction bronchiectasis, and irregular interfaces). Patients were analyzed based on the evidence of fibrosis and divided into two groups namely, groups A and B (with and without CT evidence of fibrosis), respectively. Patient demographics, length of stay (LOS), rate of intensive care unit (ICU) admission, peak C-reactive protein level, and CT score were compared between the two groups. RESULTS: Among the 59 patients, 89.8% (53/59) had a typical transition from early phase to advanced phase and advanced phase to dissipating phase. Also, 39% (23/59) patients developed fibrosis (group A), whereas 61% (36/59) patients did not show definite fibrosis (group B). Patients in group A were older (mean age, 45.4±16.9 vs. 33.8±10.2 years) (P = 0.001), with longer LOS (19.1±5.2 vs. 15.0±2.5 days) (P = 0.001), higher rate of ICU admission (21.7% (5/23) vs. 5.6% (2/36)) (P = 0.061), higher peak C-reactive protein level (30.7±26.4 vs. 18.1±17.9 mg/L) (P = 0.041), and higher maximal CT score (5.2±4.3 vs. 4.0±2.2) (P = 0.06) than those in group B. CONCLUSIONS: Pulmonary fibrosis may develop early in patients with COVID-19 after hospital discharge. Older patients with severe illness during treatment were more prone to develop fibrosis according to thin-section CT results.

Initial chest CT findings in COVID-19: correlation with clinical features.

In December 2019, coronavirus disease 2019 (COVID-19), a new de novo infectious disease, was first identified in Wuhan, China and quickly spread across China and around the world. The etiology was a novel betacoronavirus, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (Lu et al., 2020). On Mar. 11, 2020, World Health Organization (WHO) characterized COVID-19 as a global pandemic. As of Mar. 22, 2020, over 292 000 confirmed COVID-19 cases have been reported globally. To date, COVID-19, with its high infectivity, has killed more people than severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) combined (Wu and McGoogan, 2020).