Infectious diseases and social distancing under state-dependent probabilities.

We analyze the implications of infectious diseases and social distancing in an extended SIS framework to allow for the presence of stochastic shocks with state dependent probabilities. Random shocks give rise to the diffusion of a new strain of the disease which affects both the number of infectives and the average biological characteristics of the pathogen causing the disease. The probability of such shock realizations changes with the level of disease prevalence and we analyze how the properties of the state-dependent probability function affect the long run epidemiological outcome which is characterized by an invariant probability distribution supported on a range of positive prevalence levels. We show that social distancing reduces the size of the support of the steady state distribution decreasing thus the variability of disease prevalence, but in so doing it also shifts the support rightward allowing eventually for more infectives than in an uncontrolled framework. Nevertheless, social distancing is an effective control measure since it concentrates most of the mass of the distribution toward the lower extreme of its support.

Literature Review of Statistical Methods Comparisons through Simulations When Using External Control Arm for Regulatory or HTA Submissions

Objectives: Using a historical control or external control arm (ECA) to augment or replace a concurrent control arm in a randomized trial is a hot topic given the challenge of patient recruitment in rare diseases or during COVID-19 pandemic. The FDA released draft guidance in 2021 on effectiveness and safety submissions using real-world evidence. While the guidance focuses mainly on elements of study design and data source selection, there is a lack of consensus in the selection of appropriate statistical methods when constructing an ECA. This study discusses rigorous statistical methodology for ECA-supported trials in regulatory or HTA submissions. Method(s): Targeted literature reviews of statistical simulations comparing methods for ECA in statistical journals were performed. The articles compared commonly used ECA-construction and analysis methods were selected and summarized, including but not limited to propensity score (PS)-based matching, weighting, and stratification, and PS plus Bayesian integrated approaches. Result(s): Type I error, power, bias, and coverage probability are common criteria used to compare different methods. When imbalances only exist in known baseline covariates and the outcome distributions are the same between the trial concurrent control and ECA, the PS method alone or paired with commensurate prior yield almost unbiased estimates, good Type I errors, and coverage probability. PS plus Bayesian approaches have wider interval width and lower power compared with PS-only methods. When there is a change in the outcome distribution over time, the PS (matching or IPTW) and commensurate prior integrated methods yield the smallest biases among all methods. Conclusion(s): PS and Bayesian integrated methods outperformed the PS-only methods in terms of bias and Type I error when outcome distribution changed with current trial control. A "sweet spot" that balances all criteria through trial-specific simulations could provide the ideal setting of trial analyses plan based on specific trial design and scenarios.Copyright © 2023

Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review

With the advent of the fourth industrial revolution, data-driven decision making has also become an integral part of decision making. At the same time, deep learning is one of the core technologies of the fourth industrial revolution that have become vital in decision making. However, in the era of epidemics and big data, the volume of data has increased dramatically while the sources have become progressively more complex, making data distribution highly susceptible to change. These situations can easily lead to concept drift, which directly affects the effectiveness of prediction models. How to cope with such complex situations and make timely and accurate decisions from multiple perspectives is a challenging research issue. To address this challenge, we summarize concept drift adaptation methods under the deep learning framework, which is beneficial to help decision makers make better decisions and analyze the causes of concept drift. First, we provide an overall introduction to concept drift, including the definition, causes, types, and process of concept drift adaptation methods under the deep learning framework. Second, we summarize concept drift adaptation methods in terms of discriminative learning, generative learning, hybrid learning, and others. For each aspect, we elaborate on the update modes, detection modes, and adaptation drift types of concept drift adaptation methods. In addition, we briefly describe the characteristics and application fields of deep learning algorithms using concept drift adaptation methods. Finally, we summarize common datasets and evaluation metrics and present future directions.

Perceptions of COVID-19 Vaccines Side Effects

BACKGROUND: Vaccines are one of the best interventions developed for eradicating COVID-19. In Albania, COVID-19 vaccination uses different types of vaccines: Pfizer, AstraZeneca, CoronaVac, and Sputnik V. Like any other vaccine, these have side effects too. AIM: This study was carried out to identify the perception of the side effects of vaccines. METHOD(S): A quantitative study using a cross-sectional survey was conducted between April and September 2021 to collect data on the effects of the COVID-19 vaccine among individuals in Shkodra region. Data were collected online through a self-administered survey created on Google Forms which had been randomly delivered to individuals (aged >=18 years) using social media sites (Email and WhatsApp). All data collected were analyzed with Microsoft Office Excel 2010, using the exact Fisher's test and x2 test. RESULT(S): This study included 292 citizens, out of which 200 were female and 92 were male;62% were from urban areas and 38% from rural areas of Shkodra region. The random sample of the citizens who took part in this study is 44.5% (18-30 years old). A massive percentage of the participants, 66.4%, had received the second dose of the vaccine. Our study shows that 55.8% of these citizens have had side effects after the first vaccination dose, and only 43.8% have had side effects after the second dose. About 80.6% of the participants were well informed about the type of vaccine they got. CONCLUSION(S): Side effects from vaccines were reported. Injection site pain and fatigue were the most common first dose side effects (55.8%). The same side effects were reported for the second dose. The side effects were presented during the first 12 h after the vaccination in most cases. Side effects were more prevalent in people >50 years old. Older people have a higher probability to have more side effects from the COVID vaccine. There is no statistically significant relationship between gender and the presence of the side effect from the COVID vaccine. People living in urban areas have a higher probability to have side effect from COVID vaccine comparing with people living in rural areas. People being vaccinated with Pfizer vaccine have a higher probability to admit the presence of side effects.Copyright: © 2022 Zamira Shabani, Arketa Guli, Julian Kraja, Arlinda Ramaj, Nertila Podgorica.

A User-Friendly Tool to Compute Infection Probability of Sars-Cov-2 Indoor: The User Guide and Its Application in Medical Practice

After some initial hesitancy at the beginning of the COVID-19 pandemic, the academic community agreed that the infection process is mostly airborne and generally associated with closed environments. Therefore, assessing the indoor infection probability is mandatory to contain the spread of the disease, especially in those environments, like school classrooms, hospital wards or public transportation, with higher risk of overcrowding. For this reason, we developed a software tool in Python to compute infection probability and determine those mechanisms that contribute to reduce its diffusion in closed settings. In this paper, we will briefly illustrate the model we used and focus our attention on the description of the main features of the software and give some examples of how it can be used in clinical practice to predict the spread of the disease in the rooms of a generic ward, optimize room occupancy or drive healthcare workers activity schedule. Finally, some limitations and further implementations of our work will be reported.

Case Report of Succesful Multidisiplinary Treatment for Patient with Metastatic Lung Cancer and Relapsed/Refractory Chronic Lymphatic Leukemia/Small Lymphocytic Lymphoma

Introduction: Croatian National Cancer Registry of Croatian Institute for Public Health reported that in year 2020 lung cancer was the second most common cancer site diagnosed in men with 16% and the third most common in women with 10% incidence among all cancer sites. Unfortunatelly lung cancer has the highest mortality in both men and women. Haematological malignancies had 7% share in all malignancies in both male and female cances cases. In 2020 190 newly diagnosed cases of lymphatic leukemia in men and 128 cases in women were reporeted, meaning 1.5 and 1.2% of all malignancies, respectively. Chronic lymphatic leukemia (CLL) is an advanced age disease and incidence increases with age. Impaired immunity, T and B cell dysfunction in CLL, chromosomal aberations, long-term immunosuppressive therapy and genetic factors can all cause secondary malignancies. Co- occurence of solid tumors and CLL is very rare. Although patiens with CLL have an increased risk of developing second primary malignancies including lung carcinoma, the data about their clinical outcomes are lacking. Parekh et al. retrospectively analyzed patients with simultaneous CLL and lung carcinoma over a 20-year period, and they found that ~2% of patients with CLL actually developed lung carcinoma. The authors claimed that up to 38% of patients will also develop a third neoplasm more likely of the skin (melanoma and basal cell carcinoma), larynx (laryngeal carcinoma) or colon. Currently there are no specific guidelines for concurrent CLL and non-small cell lung carcinoma (NSCLC) treatment. Usually, when the tumors are diagnosed simultaneously, treatment is based to target the most aggressive malignancy, as the clinical outcomes depend on the response of the tumor with the poorest prognosis. For this reason, a multidisciplinary approach is mandatory. Case report: A patient with history of coronary heart disease, myocardial infarction and paroxysmal atrial fibrillation was diagnosed in 2019 (at the age of 71) with B chronic lymphocytic leukemia with bulky tumor (inguinal lymph nodes 8x5 cm), stage B according to Binet, intermediate risk. He was treated with 6 cycles of chemoimmunotherapy (rituximab/cyclofosfamid/fludarabine). In 10/2019 remission was confirmed, but MSCT described tumor in the posterior segment of upper right lung lobe measuring 20x17 mm and bilateral metastases up to 11 mm. Bronchoscopy and biopsy were performed, and EGFR neg, ALK neg, ROS 1 neg, PD-L1>50% adenocarcinoma was confirmed. He was referred to Clinical Hospital Center Osijek where monotherapy with pembrolizumab in a standard dose of 200 mg intravenously was started in 01/2020. Partial remission was confirmed in October 2020. Immunotherapy was discontinued due to development of pneumonitis, dysphagia and severe weight loss (20kg), but without radiologically confirmed disease progression. At that time he was referred to our hospital for further treatment. Gastroscopy has shown erosive gastritis with active duodenal ulcus, Forrest III. Supportive therapy and proton pump inhibitor were introduced. After complete regression of pneumonitis, improvement of general condition and resolution of dysphagia, no signs of lung cancer progression were found and pembrolizumab was reintroduced in 12/2021. Hypothyroidism was diagnosed in 01/2021 and levothyroxine replacement ther apy was started. In 03/2021 he underwent surgical removal of basal cell carcinoma of skin on the right temporal region with lobe reconstruction. From 02/2021, when pembrolizumab was reintroduced, regression in tumor size was continously confirmed with complete recovery of general condition. He was hospitalized for COVID 19 infection in 09/2021, and due to complications pembrolizumab was discontinued till 11/2021. Lung cancer immunotherapy proceeded till 11/2022, when Multidisciplinary team decided to finish pembrolizumab because of CLL relapse. CLL was in remission till August 2022 when due to B symptoms, lymphcytosis, anemia and generalized lymphadenopathy, hematological workup including biopsy of cervical lymph node was performed and CLL/SLL relapse was confirmed. Initially chlorambucil was introduced, but disease was refractory. Based on cytogenetic test results (IGHV unmutated, negative TP53) and due to cardiovascular comorbidity (contraindication for BTK inhibitors) venetoclax and rituximab were started in 01/2023. After just 1 cycle of treatment normal blood count as well as regression of B symptoms and peripheral lymphadenopathy occured, indicating the probability of complete disease remission. In our patient with metastatic lung adenocarcinoma excellent disease control is achieved during 41 month of treatment in first line setting. Furthermore, relapsed/refractory CLL/SLL is currently in confirmed remission. Conclusion(s): Successful treatment of patients with multiple primary malignancies is based on multidisciplinarity, early recognition and management of side effects, treatment of comorbidities with the aim of prolonging life, controlling symptoms of disease and preserving quality of life.

A Literature Review Assessing the Most Common Attributes Used in Stated-Preference Studies Assessing Influenza Vaccination: Do We Know What Health Care Professionals Value?

Objectives: Several attributes may be important in flu vaccine and since Covid-19, the role of health care professionals (HCPs) may have become more important in increasing flu vaccine uptake. We conducted a literature review to assess if previous preference research could inform future flu vaccination policies. Method(s): We conducted a literature review to assess the most common attributes used in stated-preference studies to determine seasonal flu vaccination preferences. PubMed with key terms such as "discrete choice", "stated preference" and "flu/vaccin*" was used to retrieve relevant research. Result(s): In total, twelve studies investigating consumer and HCP preferences for flu vaccines using a discrete-choice experiment were included. Six studies were conducted in vaccine-eligible populations, three were conducted with parents (specifically, two focused on older adults and one elicited preferences directly from HCPs in Hong Kong). Three studies were conducted in the Netherlands, two in Japan and four in China. Vaccine efficacy was most often framed in terms of percentage (n=7). Out of pocket cost and duration of immunity were common attributes. Source of recommendation for vaccination (i.e., regulatory or public health body) was assessed in 25% of the studies. In studies assessing parental preferences for their children influenza vaccination, risks of fewer side effects were, unsurprisingly, preferred. Finally, among HCPs, vaccine effectiveness and vaccination location (staff clinic/mobile vaccination center) were most important and could increase the probability of vaccination. Conclusion(s): Information incompleteness and asymmetry could play a role in vaccine hesitation and/or aversion. To increase vaccination rates, evidence on the attributes perceived to be important to both HCPs and the general population may help the design and delivery of vaccines that match consumers' preferences. Currently, there is a critical need for more stated-preference studies among HCPs to better understand the attributes likely to increase vaccination rates against seasonal influenza.Copyright © 2023

Visualising the Optimistic, Realistic, and Pessimistic Financial Distress Outlooks for Airport Operations in Malaysia

This paper aims to visualise three financial distress outlooks using computer simulations. The financial distress exposure for airport operations in Malaysia between 1991 and 2021 is given by Altman Z”-score and modelled by the multivariate generalized linear model (MGLM). Seven determinants contributing to the financial distress from literature are examined. The determinant series are fitted individually by using linear model with time series components and autoregressive integrated moving average models to forecast values for the next 10 financial years. Future short- to long-term memory effects following COVID-19 are apparent in time series plots. In the simulations, the MGLM procedure utilised Gaussian, gamma, and Cauchy probability distributions associated with expectations and challenges of doing business as well as uncertainties in the economy. The underlying trends of realistic, optimistic, and pessimistic financial distress outlooks insinuate that the increasing risk of financial distress of airport operations in Malaysia is expected to continue for the next decade.

Can We Detect Urban‐Scale CO2 Emission Changes Within Medium‐Sized Cities?

The COVID‐19 pandemic resulted in a widespread lockdown during the spring of 2020. Measurements collected on a light rail system in the Salt Lake Valley (SLV), combined with observations from the Utah Urban Carbon Dioxide Network observed a notable decrease in urban CO2 concentrations during the spring of 2020 relative to previous years. These decreases coincided with a ∼30% reduction in average traffic volume. CO2 measurements across the SLV were used within a Bayesian inverse model to spatially allocate anthropogenic emission reductions for the first COVID‐19 lockdown. The inverse model was first used to constrain anthropogenic emissions for the previous year (2019) to provide the best possible estimate of emissions for 2020, before accounting for emission reductions observed during the COVID‐19 lockdown. The posterior emissions for 2019 were then used as the prior emission estimate for the 2020 COVID‐19 lockdown analysis. Results from the inverse analysis suggest that the SLV observed a 20% decrease in afternoon CO2 emissions from March to April 2020 (−90.5 tC hr−1). The largest reductions in CO2 emissions were centered over the northern part of the valley (downtown Salt Lake City), near major roadways, and potentially at industrial point sources. These results demonstrate that CO2 monitoring networks can track reductions in CO2 emissions even in medium‐sized cities like Salt Lake City.Alternate :Plain Language SummaryHigh‐density measurements of CO2 were combined with a statistical model to estimate emission reductions across Salt Lake City during the COVID‐19 lockdown. Reduced traffic throughout the COVID‐19 lockdown was likely the primary driver behind lower CO2 emissions in Salt Lake City. There was also evidence that industrial‐based emission sources may of had an observable decrease in CO2 emissions during the lockdown. Finally, this analysis suggests that high‐density CO2 monitoring networks could be used to track progress toward decarbonization in the future.

FaST-Net: Face Style Self-Transfer Network for Masked Face Inpainting

During the COVID-19 coronavirus epidemic, people usually wear masks to prevent the spread of the virus, which has become a major obstacle when we use face-based computer vision techniques such as face recognition and face detection. So masked face inpainting technique is desired. Actually, the distribution of face features is strongly correlated with each other, but existing inpainting methods typically ignore the relationship between face feature distributions. To address this issue, in this paper, we first show that the face image inpainting task can be seen as a distribution alignment between face features in damaged and valid regions, and style transfer is a distribution alignment process. Based on this theory, we propose a novel face inpainting model considering the probability distribution between face features, namely Face Style Self-Transfer Network (FaST-Net). Through the proposed style self-transfer mechanism, FaST-Net can align the style distribution of features in the inpainting region with the style distribution of features in the valid region of a face. Ablation studies have validated the effectiveness of FaST-Net, and experimental results on two popular human face datasets (CelebA and VGGFace) exhibit its superior performance compared with existing state-of-the-art methods. © 2023 SPIE.

Carnosic acid as potential anti-SARS-CoV-2 therapeutics: a mechanistic insight

Background/Objectives: Rosmarinus Officinalis L.(Rosemary) extract Carnosic acid(CA) has been investigated for its antimicrobial and antioxidative properties(1). Only limited number of publications reported the utilization of this extract in SARSCoV-2 infection. Also, the mechanistic understanding of CA remains to be determined. Our goal was to elucidate the potential role of CA in COVID19. To obtain mechanistic insight of pharmacogenomic action of CA, comprehensive in silico analyses were performed. Further in vitro experiments were done to illustrate the cytotoxicity of CA and confirm in silico findings. Method(s): CA was extracted from Rosmarinus Officinalis L. by HPLC. Stimulation assays were performed using the COVID19 samples. In silico pharmacogenomic properties of CA were performed by using SwissADME. SwissTargetPrediction tool was utilized to define the possible targets. SARS-CoV-2-interacting proteins were evaluated using STRING(2). To verify in silico findings, gene expression levels were analyzed using qPCR. Result(s): Among the top 15 SwissTargetPrediction target molecules(out of 100), Prostaglandin E synthase(PTGES) had the highest probability for CA. Among 332 proteins identified using the STRING, PGES2 was found to be interacting with the nsp7, important molecule for viral replication. The stimulation assays and gene expression analyses confirmed the viral inhibitory role of CA through PTGES pathway. Conclusion(s): To our knowledge, our work is the first to reveal the inhibitory role of CA in COVID19 through PTGES pathway. Given the crucial role of PTGES in inflammation, it is noteworthy to examine CA as potential anti-SARS-CoV2 therapeutics.

A Computational Approach for the Characterization of Airborne Pathogen Transmission in Turbulent Molecular Communication Channels

Airborne pathogen transmission mechanisms play a key role in the spread of infectious diseases such as COVID-19. In this work, we propose a computational fluid dynamics (CFD) approach to model and statistically characterize airborne pathogen transmission via pathogen-laden particles in turbulent channels from a molecular communication viewpoint. To this end, turbulent flows induced by coughing and the turbulent dispersion of droplets and aerosols are modeled by using the Reynolds-averaged Navier-Stokes equations coupled with the realizable k-model and the discrete random walk model, respectively. Via simulations realized by a CFD simulator, statistical data for the number of received particles are obtained. These data are post-processed to obtain the statistical characterization of the turbulent effect in the reception and to derive the probability of infection. Our results reveal that the turbulence has an irregular effect on the probability of infection, which shows itself by the multi-modal distribution as a weighted sum of normal and Weibull distributions. Furthermore, it is shown that the turbulent MC channel is characterized via multi-modal, i.e., sum of weighted normal distributions, or stable distributions, depending on the air velocity. Crown

Cost-Effectiveness Analysis of Nonpharmaceutical Interventions Combined with Inactivated Vaccination and Oral Medicine in China

Objectives: Various interventions were used to control the COVID-19 pandemic and protect population health, including vaccination, medication and nonpharmaceutical interventions (NPIs). This study aims to examine the cost-effectiveness of different combinations of NPIs (including social distancing, mask wearing, tracing-testing-isolation, mass testing, and lockdown), oral medicine (Paxlovid), and vaccination (including two-dose and three-dose vaccination) under the Delta and Omicron pandemic in China. Method(s): We constructed a Markov model using a SIRI structure with a one-week cycle length over one-year time horizon to estimate the cost-effectiveness of different combinations in China from societal perspective. Effectiveness of interventions, disease transition probabilities and costs were from published data, quality-adjusted life years (QALYs) gained and incremental cost-effectiveness ratios (ICER) and net monetary benefits were calculated for one-year time horizon. One-way and probabilistic sensitivity analyses were performed to test the robustness of the model. Scenario analysis was developed to examine different situations under the Omicron pandemic. Result(s): Under the Delta pandemic, implementing the combination of social distancing, mask wearing, mass testing and three-dose vaccination was the optimal strategy, with cost at $11165635.33 and utility of 94309.94 QALYs, and had 60% probability of being cost-effective compared with other strategies. Three-dose vaccination combinations were better than two-dose combinations. Under the Omicron pandemic, antigen testing was better than nucleic testing by avoiding cross infections;second, adding Paxlovid or lockdown to the combined intervention strategies could increase limited health outcomes at huge cost and thus were not cost-effective;last, encouraging patients to stay at home can save societal costs compared with concentrated quarantine at hospitals. Conclusion(s): Three-dose vaccination and self-quarantine of asymptomatic and mild cases can save total costs. Under the Omicron pandemic outbreak, antigen testing is a better way to control the pandemic, and adding Paxlovid or lockdown to intervention combinations is not cost-effective.Copyright © 2023

Match Performance Statistics Related to Winning of Chinese Super League Under Different Match Stages

This paper aims to determine relationships between 160 matches statistics and the match results in two match stages of 2020 CSL under the COVID-19 pandemic prevention and control. A team's winning probability was evaluated by a two-standard-deviation increase in the value of each variable. The smallest worthwhile change was used to evaluate nonclinical magnitude-based inferences. The results showed that for group round robin stage, nine match statistics had clearly positive effects on the probability of winning (Shot, Shot on Target, Shot from Set Piece, Cross Accuracy, Counterattack, Won Challenge, Tackle Gaining, HIR Distance in BP, Sprinting Distance in BP), two had obviously negative effects (Distance Covered in Penalty Area, Sprinting Distance Out of BP), other twenty-three statistics had either trivial or unclear effects. While for the knockout stage, the effects of nine match statistics (Pass Accuracy, Forward Pass Accuracy, Delivery into Attacking Third, Delivery into Penalty Area, Dribble into Attacking Third, Corner, Foul Committed, Yellow Card, Distance Covered in Attacking Third) turned to clearly positive, the effects of Won Challenge, Cross Accuracy turned to trivial and clearly negative, respectively. Coaches and players should take these different aspects into account when planning practices and competitions for their teams. © 2023 SPIE.

Pandemics and the Individual Life Insurance Market: Evidence of the Impact of COVID-19

COVID-19 has led to significant loss of life and has adversely impacted the worldwide economy. While anecdotal evidence indicates a growing interest in life insurance among U.S. consumers during the pandemic, little is known about how the pandemic may have affected the life insurance market. We utilize insurer-state data to create a measure that captures an insurer's exposure to COVID in each state in which it conducts business. Using this measure to examine the impact of the pandemic on the market for individual life insurance, we find that greater insurer-state COVID exposure is associated with smaller changes in issuances and surrenders in the U.S. We also find that observations with the greatest COVID exposure are more likely to experience declines in issuance and surrender activity. These results indicate that insurers were deliberate with respect to their policy issuance decisions while policyholders kept their policies in force during a period of significant uncertainty. [Key words: COVID-19;life insurance;pandemics;policy issuances;policy surrenders.] JEL Classifications: D12, D22, G22

Studying COVID-19 in light of critical approaches to risk and uncertainty: research pathways, conceptual tools, and some magic from Mary Douglas

This editorial is a response to the recent COVID-19 pandemic and underlines the valuable role that critical social science approaches to risk and uncertainty can play in helping us understand how risk is being understood and mitigated. Drawing on Heyman's approach to understanding risk as a configuration of probabilistic knowledge, time-framing, categories and values, I explore COVID-19 risk in relation to each of these features while also emphasising how different features stabilise one another. I suggest lines of inquiry into each of these features and their interrelatedness. I then move to present some important insights from the work of Mary Douglas which are especially germane to studying the risk of COVID-19 and, again, I raise possibilities for future research. Emphasising the centrality of ritual to Douglas's theory, I develop these considerations to encourage an exploration of magic and magical thinking, alongside rational approaches to COVID-19 risk.

Pragmatic cluster randomized controlled trial evaluating the effectiveness and cost effectiveness of the early youth engagement intervention in early intervention in psychosis services

Background: Early Intervention in Psychosis services improve outcomes for young people with psychosis but a significant proportion disengage with potential costs to their mental health. Method(s): This study evaluated effectiveness and cost-effectiveness of the EYE-2 intervention, a motivational engagement intervention, delivered by EIP clinicians, compared to standardized EIP (sEIP) in a cluster RCT in 20 EIP teams in 5 sites across England. Participants were 1027 young people with first episode psychosis. The primary outcome was time to disengagement. Economic outcomes were NHS mental health and wider societal costs, clinical and social outcomes and cost-effectiveness. Result(s): The adjusted hazard ratio for EYE-2 + sEIP versus sEIP alone was 1.07, (95% CI 0.76 to 1.49;p = .713). Disengagement was 16% with no observed differences between arms for any secondary outcomes. The health economic evaluation indicated lower average mental health costs [-543 (95% CI -2715 to 1628)] and marginally improved mental health states, with a 63% probability of the EYE-2 intervention being dominant in cost-effectiveness compared to usual care. There were very tentative indications of lower societal costs and better social outcomes with 30 more days per year spent in education and training (95% CI 1.52 to 53.68;probability positive outcome for the intervention: 99%) in a sub-sample of 22% of participants. Conclusion(s): Cost-effectiveness analyses revealed estimates in the direction of dominance of EYE-2, but 95% confidence limits ruled out a reduction of more than 24% in the risk of disengagement. Implementation, fidelity and COVID-19 impacts are discussed.

Cost-Effectiveness Analysis of Liposomal Amphotericin B Versus Isavuconazole for Treating Mucormycosis in the Consolidation Phase from the Perspective of the Brazilian Unified Health System

Objectives: Mucormycosis is a rare invasive fungal infection with high lethality, affecting mainly patients with hematological neoplasia, decompensated diabetes, and covid-19 infection. The aim was to perform a cost-effectiveness analysis of liposomal Amphotericin B (standard treatment) versus isavuconazole for treating mucormycosis in the consolidation phase from the perspective of the Brazilian Unified Health System. Method(s): A decision tree model was built. The analysis considered the costs of the treatment over a six-month time horizon. This included hospitalization during the entire course of treatment and the expenditures related to dialysis, complication occurring in 5% (3%-6%) of cases treated with the Amphotericin B. Appointments with specialists were included in the isavuconazole arm, and amphotericin B was used if the patient failed to respond to isavuconazole. The utility of the patient with mucormycosis, cured and with renal failure was estimated. Uncertainties were assessed through probabilistic and deterministic sensitivity analyses. Result(s): The average cost of amphotericin B and isavuconazole arm was R$1.054.874,39 and R$522.344,05, respectively. The utility was 0.479 with amphotericin B and 0.480 with isavuconazole. The ICER was R$ -684,494,237 (dominant). In deterministic sensitivity analysis, the probability of dialysis was the variable with the greatest impact. In probabilistic analysis, the ICER is distributed in the right and left lower quadrant, the acceptability curve for all the scenarios analyzed is favorable for isavuconazole. The budget impact suggests a potential savings of between R$ 350 million and R$ 415 million over five years. Conclusion(s): The treatment of mucormycosis during the consolidation phase with isavuconazole represents a lower cost, besides the convenience of oral treatment and reduced incidence of severe adverse events, with mortality similar to the Amphotericin B arm. In Brazil, the formulation of posaconazole approved is inadequate for treating mucormycosis during the consolidation phase, therefore isavuconazole is the single oral drug available.Copyright © 2023

Real-World Effectiveness of Sotrovimab for the Early Treatment of COVID-19: Evidence from the National COVID Cohort Collaborative (N3C)

Objectives: To describe and compare real-world outcomes for patients with mild-to-moderate COVID-19 at high risk for progression to severe COVID-19, treated with sotrovimab versus untreated. Method(s): Electronic health records from the National COVID Cohort Collaborative were used to identify US patients (aged >=12 years) diagnosed with COVID-19 (positive test or ICD-10: U07.1) in an ambulatory setting (26 May 2021-30 April 2022) who met Emergency Use Authorization high-risk criteria. Patients receiving the monoclonal antibody (mAb) sotrovimab within 10 days of diagnosis were assigned to the sotrovimab cohort with an index date on the day of infusion. Untreated patients (no evidence of early mAb treatment or prophylaxis mAb or oral antiviral treatment) were assigned to the untreated cohort with an imputed index date based on the time distribution between diagnosis and sotrovimab infusion for the sotrovimab cohort. The primary endpoint was hospitalization or death (both all-cause) within 29 days of index, reported as descriptive rates and adjusted (via inverse-probability-of-treatment weighting [IPTW]) odds ratios (OR) and 95% confidence intervals (CI). Result(s): Of nearly 2.9 million patients diagnosed with COVID-19 during the analysis time period, 4,992 met the criteria for the sotrovimab cohort and 541,325 were included in the untreated cohort. Patients in the sotrovimab cohort were older (60 versus 54 years), more likely to be male (40% versus 38%) and White (85% versus 75%), and met more EUA criteria (3 versus 2) versus the untreated cohort. The 29-day hospitalization or mortality rates were 3.5% (176/4,992) and 4.5% (24,163/541,325) in the sotrovimab and untreated cohorts respectively (unadjusted OR [95% CI]: 0.77 [0.67,0.90];p=0.001;IPTW-adjusted OR [95% CI]: 0.74 [0.61,0.91];p=0.004). Conclusion(s): Sotrovimab demonstrated clinical effectiveness in preventing severe outcomes (hospitalization, mortality) between 26 May 2021-30 April 2022, which included the Delta variant and early surge of Omicron BA.1/BA.2. Funding(s): GSK (Study 219020)Copyright © 2023

Tropical Conditions and Outbreak of COVID-19

Background: The new novel Coronavirus 2019 (nCOV-19 or COVID-19) has caused an unprecedented pandemic in humans. All nations have heightened their surveillances after the quick diagnosis of potential cases of the COVID-19. Objective(s): Recent statistics have mentioned that virus outbreak in tropical countries is relatively low compared to cold nations. To support this conclusion, we considered the six main tropical regions to investigate the pandemic distribution at the initial phase. Method(s): Chi-square test was applied to understand the correlation between outbreak and temperature changes. Significant probability P-value was set to P<0.01. P-values were calculated to both positive and death cases. Result(s): Out of 1211562 infected cases, 41776 cases (3.45%) were registered at hightemperature countries (P<0.0001) and 1161786 cases (96.55%) at other countries like European countries or the USA. Moreover, only 1433 mortality cases (2.2%) happened, and the remaining 97.8% of mortality happened among other nations. Conclusion(s): Similar to other respiratory viruses like flu and influenza, there is a low outbreak of COVID-19 in tropical nations compared to the other countries. Apart from weather conditions, it is also recommended to follow the serious preventive measures imposed by governments to survive this novel epidemic.Copyright © 2020