Metaverse in Dynamic Viral-shedding Surveillance for Emerging Infectious Disease: A Digital Twin Approach to COVID-19 Epidemic (preprint)

Metaverse in effective surveillance of outbreaks of emerging infectious diseases such as COVID-19 opens a new avenue for precision and efficient contact tracing, quarantine, and isolation. We adopted a digital twin model to generate digital threads for tracing and tracking virtual data on the cycle threshold (Ct) values of the repeated RT-PCR with parameters learned from real-world (physical) data fitted with Markov machine learning algorithms. Such a digital twin method is demonstrated with COVID-19 community-acquired outbreaks of the Alpha and Omicron Variants of Concern (VOCs) in Taiwan. The personalized dynamics of Ct-defined transitions were derived from the digital threads of the two community-acquired outbreaks to guide precision contact tracing, quarantine, and isolation of both Alpha and Omicron VOCs outbreaks. Metaverse surveillance with such a Ct-guided digital twin model is supposed to be useful for timely containing the spread of emerging infectious diseases in the future.

Quantifying the Effects of Viral Load on Multistate COVID-19 Infection and Progression of Alpha and Omicron VOCs: A Bayesian Competing Markov Exponential Regression Model (preprint)

We used a Bayesian competing four-state Markov model to explore how viral shedding in terms of cycle threshold (Ct) value makes relative contribution between persistent and non-persistent asymptomatic mode, and whether it affects the subsequent progression to show symptoms. The proposed model was applied to data from two large outbreaks on Alpha and Omicron variants of concern (VOCs) in Changhua, Taiwan. A multistate Markov exponential regression model was proposed for quantifying the odds ratio (OR) of viral shedding measured by cycle threshold (Ct). A Bayesian Markov Chain Monte Carlo (MCMC) method was used for estimating the parameters of the posterior distribution. The estimated results show that developing non-persistent asymptomatic mode relative to persistent asymptomatic mode was reduced by 14% (adjusted OR = 0.86, 95% CI: 0.81–0.92) per one increasing unit of Ct for Alpha VOC, whereas these figures were shrunk to 5% (aOR = 0.95, 95% CI: 0.93–0.98) for Omicron VOC. Similar significant gradient relationships were also observed between three viral load levels. Similar, but not statistically significant, dose-response effects of viral load on the progression to symptoms for non-persistent asymptomatic mode were observed. The application of statistical model helps elucidate the pathways of SARS-CoV-2 infectious process associated with viral shedding that demonstrate viral shedding plays a crucial role in determining the path of either non-persistent or persistent asymptomatic mode in a dose-response manner, which was more pronounced for the Alpha than the Omicron. Modelling such a multistate infectious process with two competing pathways would provide a new insight into the transmissibility and the duration of insidious infection before onset of symptom and the deployment of precision containment measures with a better use of the Ct value as virologic surveillance for projecting the individual epidemic course.

Can ship travel contain COVID-19 outbreak after re-opening: a Bayesian meta-analysis.

Large gatherings of people on cruise ships and warships are often at high risk of COVID-19 infections. To assess the transmissibility of SARS-CoV-2 on warships and cruise ships and to quantify the effectiveness of the containment measures, the transmission coefficient (ß), basic reproductive number (R0), and time to deploy containment measures were estimated by the Bayesian Susceptible-Exposed-Infected-Recovered model. A meta-analysis was conducted to predict vaccine protection with or without non-pharmaceutical interventions (NPIs). The analysis showed that implementing NPIs during voyages could reduce the transmission coefficients of SARS-CoV-2 by 50%. Two weeks into the voyage of a cruise that begins with 1 infected passenger out of a total of 3,711 passengers, we estimate there would be 45 (95% CI:25-71), 33 (95% CI:20-52), 18 (95% CI:11-26), 9 (95% CI:6-12), 4 (95% CI:3-5), and 2 (95% CI:2-2) final cases under 0%, 10%, 30%, 50%, 70%, and 90% vaccine protection, respectively, without NPIs. The timeliness of strict NPIs along with implementing strict quarantine and isolation measures is imperative to contain COVID-19 cases in cruise ships. The spread of COVID-19 on ships was predicted to be limited in scenarios corresponding to at least 70% protection from prior vaccination, across all passengers and crew.

A new approach to modeling pre-symptomatic incidence and transmission time of imported COVID-19 cases evolving with SARS-CoV-2 variants.

There is paucity of the statistical model that is specified for data on imported COVID-19 cases with the unique global information on infectious properties of SARS-CoV-2 variant different from local outbreak data used for estimating transmission and infectiousness parameters via the established epidemic models. To this end, a new approach with a four-state stochastic model was proposed to formulate these well-established infectious parameters with three new parameters, including the pre-symptomatic incidence rate, the median of pre-symptomatic transmission time (MPTT) to symptomatic state, and the incidence (proportion) of asymptomatic cases using imported COVID-19 data. We fitted the proposed stochastic model to empirical data on imported COVID-19 cases from D614G to Omicron with the corresponding calendar periods according to the classification GISAID information on the evolution of SARS-CoV-2 variant between March 2020 and Jan 2022 in Taiwan. The pre-symptomatic incidence rate was the highest for Omicron followed by Alpha, Delta, and D614G. The MPTT (in days) increased from 3.45 (first period) ~ 4.02 (second period) of D614G until 3.94-4.65 of VOC Alpha but dropped to 3.93-3.49 of Delta and 2 days (only first period) of Omicron. The proportion of asymptomatic cases increased from 29% of D-614G period to 59.2% of Omicron. Modeling data on imported cases across strains of SARS-CoV-2 not only bridges the link between the underlying natural infectious properties elucidated in the previous epidemic models and different disease phenotypes of COVID-19 but also provides precision quarantine and isolation policy for border control in the face of various emerging SRAS-CoV-2 variants globally.

New Surveillance Metrics for Alerting Community-Acquired Outbreaks of Emerging SARS-CoV-2 Variants Using Imported Case Data: Bayesian Markov Chain Monte Carlo Approach.

BACKGROUND: Global transmission from imported cases to domestic cluster infections is often the origin of local community-acquired outbreaks when facing emerging SARS-CoV-2 variants. OBJECTIVE: We aimed to develop new surveillance metrics for alerting emerging community-acquired outbreaks arising from new strains by monitoring the risk of small domestic cluster infections originating from few imported cases of emerging variants. METHODS: We used Taiwanese COVID-19 weekly data on imported cases, domestic cluster infections, and community-acquired outbreaks. The study period included the D614G strain in February 2020, the Alpha and Delta variants of concern (VOCs) in 2021, and the Omicron BA.1 and BA.2 VOCs in April 2022. The number of cases arising from domestic cluster infection caused by imported cases (Dci/Imc) per week was used as the SARS-CoV-2 strain-dependent surveillance metric for alerting local community-acquired outbreaks. Its upper 95% credible interval was used as the alert threshold for guiding the rapid preparedness of containment measures, including nonpharmaceutical interventions (NPIs), testing, and vaccination. The 2 metrics were estimated by using the Bayesian Monte Carlo Markov Chain method underpinning the directed acyclic graphic diagram constructed by the extra-Poisson (random-effect) regression model. The proposed model was also used to assess the most likely week lag of imported cases prior to the current week of domestic cluster infections. RESULTS: A 1-week lag of imported cases prior to the current week of domestic cluster infections was considered optimal. Both metrics of Dci/Imc and the alert threshold varied with SARS-CoV-2 variants and available containment measures. The estimates were 9.54% and 12.59%, respectively, for D614G and increased to 14.14% and 25.10%, respectively, for the Alpha VOC when only NPIs and testing were available. The corresponding figures were 10.01% and 13.32% for the Delta VOC, but reduced to 4.29% and 5.19% for the Omicron VOC when NPIs, testing, and vaccination were available. The rapid preparedness of containment measures guided by the estimated metrics accounted for the lack of community-acquired outbreaks during the D614G period, the early Alpha VOC period, the Delta VOC period, and the Omicron VOC period between BA.1 and BA.2. In contrast, community-acquired outbreaks of the Alpha VOC in mid-May 2021, Omicron BA.1 VOC in January 2022, and Omicron BA.2 VOC from April 2022 onwards, were indicative of the failure to prepare containment measures guided by the alert threshold. CONCLUSIONS: We developed new surveillance metrics for estimating the risk of domestic cluster infections with increasing imported cases and its alert threshold for community-acquired infections varying with emerging SARS-CoV-2 strains and the availability of containment measures. The use of new surveillance metrics is important in the rapid preparedness of containment measures for averting large-scale community-acquired outbreaks arising from emerging imported SARS-CoV-2 variants.

The Preventive Role of mRNA Vaccines in Reducing Death among Moderate Omicron-Infected Patients: A Follow-Up Study.

Very few studies have been conducted to assess the potential preventive role of vaccines, particularly mRNA vaccines, in the improvement of survival among moderate and severe hospitalized patients with COVID-19. After community-acquired outbreaks of the Omicron variant from 18 March until 31 May 2022, occurred in Taiwan, this retrospective cohort of 4090 moderate and 1378 severe patients admitted to hospital was classified according to whether they were administered an mRNA-based vaccine, and followed up to ascertain rates of death in both the vaccinated (≥2 doses) and unvaccinated (no or 1 dose) groups. The age-adjusted hazard ratio (aHR) of less than 1 was used to assess the preventive role of mRNA vaccines in reducing deaths among moderate and severe Omicron-infected patients. Survival was statistically significantly better for the ≥2 dose jab group (aHR, 0.75, 95% confidence interval [CI], 0.60 to 0.94) and even higher among those who had received a booster jab (aHR, 0.71; 95% CI, 0.55 to 0.91) compared with the unvaccinated group among moderate patients, but not among severe patients. In conclusion, unveiling the role of mRNA vaccines in preventing moderate but not severe COVID-19 patients from death provides new insights into how mRNA vaccines play a role in the pathway leading to a severe outcome due to Omicron COVID-19.

Evaluating correlates of protection for mix-match vaccine against COVID-19 VOCs with potential of evading immunity.

BACKGROUND: In the face of rapid emerging variants of concern (VOCs) with potential of evading immunity from Beta to Omicron and uneven distribution of different vaccine brands, a mix-match strategy has been considered to enhance immunity. However, whether increasing immunogenicity using such a mix-match can lead to high clinical efficacy, particularly when facing Omicron pandemic, still remains elusive without using the traditional phase 3 trial. The aim of this study is to demonstrate how to evaluate correlates of protection (CoP) of the mix-match vaccination. METHODS: Data on neutralizing antibody (NtAb) titers and clinical efficacy against Wuhan or D614G strains of homologous ChAdOx1 nCov-19 or mRNA-1273 and heterologous vaccination were extracted from previous studies for demonstration. The reductions in NtAb titers of homologous vaccination against Beta, Delta, and Omicron variants were obtained from literatures. A Bayesian inversion method was used to derive CoP from homologous to mix-match vaccine. Findings The predicted efficacy of ChAdOx1 nCov-19 and mRNA-1273 for Wuhan or D614G strains was 93 % (89 %-97 %). Given 8 âˆ¼ 11-fold, 2 âˆ¼ 5.5-fold, and 32.5 âˆ¼ 36-fold reduction of NtAb for Beta, Delta, and Omicron variants compared with D614G, the corresponding predictive efficacy of the mix-match ranged from 75.63 % to 73.87 %, 84.87 % to 81.25 %, and 0.067 % to 0.059 %, respectively. Interpretations While ChAdOx1 nCov-19 and mRNA-1273 used for demonstrating how to timely evaluate CoP for the mix-match vaccine still provides clinical efficacy against Beta and Delta VOCs but it appears ineffective for Omicron variants, which highlights the urgent need for next generation vaccine against Omicron variant.

Experiences of COVID-19 patients in a Fangcang shelter hospital in China during the first wave of the COVID-19 pandemic: a qualitative descriptive study.

OBJECTIVES: This study aimed to examine COVID-19 patients' experiences in a Fangcang shelter hospital in China, to provide insights into the effectiveness of this centralised isolation strategy as a novel solution to patient management during emerging infectious disease outbreaks. DESIGN: This study adopted a qualitative descriptive design. Data were collected by individual semistructured interviews and analysed using thematic analysis. SETTING: This study was undertaken in 1 of the 16 Fangcang shelter hospitals in Wuhan, China between 28 February 2020 and 7 March 2020. Fangcang shelter hospitals were temporary healthcare facilities intended for large-scale centralised isolation, treatment and disease monitoring of mild-to-moderate COVID-19 cases. These hospitals were an essential component of China's response to the first wave of the COVID-19 pandemic. PARTICIPANTS: A total of 27 COVID-19 patients were recruited by purposive sampling. Eligible participants were (1) COVID-19 patients; (2) above 18 years of age and (3) able to communicate effectively. Exclusion criteria were (1) being clinically or emotionally unstable and (2) experiencing communication difficulties. RESULTS: Three themes and nine subthemes were identified. First, COVID-19 patients experienced a range of psychological reactions during hospitalisation, including fear, uncertainty, helplessness and concerns. Second, there were positive and negative experiences associated with communal living. While COVID-19 patients' evaluation of essential services in the hospital was overall positive, privacy and hygiene issues were highlighted as stressors during their hospital stay. Third, positive peer support and a trusting patient-healthcare professional relationship served as a birthplace for resilience, trust and gratitude in COVID-19 patients. CONCLUSIONS: Our findings suggest that, while sacrificing privacy, centralised isolation has the potential to mitigate negative psychological impacts of social isolation in COVID-19 patients by promoting meaningful peer connections, companionship and support within the shared living space. To our knowledge, this is the first study bringing patients' perspectives into healthcare service appraisal in emergency shelter hospitals.

Review of epidemic, containment strategies, clinical management, and economic evaluation of COVID-19 pandemic.

The spread of the emerging pathogen, named as SARS-CoV-2, has led to an unprecedented COVID-19 pandemic since 1918 influenza pandemic. This review first sheds light on the similarity on global transmission, surges of pandemics, and the disparity of prevention between two pandemics. Such a brief comparison also provides an insight into the potential sequelae of COVID-19 based on the inference drawn from the fact that a cascade of successive influenza pandemic occurred after 1918 and also the previous experience on the epidemic of SARS and MERS occurring in 2003 and 2015, respectively. We then propose a systematic framework for elucidating emerging infectious disease (EID) such as COVID-19 with a panorama viewpoint from natural infection and disease process, public health interventions (non-pharmaceutical interventions (NPIs) and vaccine), clinical treatments and therapies (antivirals), until global aspects of health and economic loss, and economic evaluation of interventions with emphasis on mass vaccination. This review not only concisely delves for evidence-based scientific literatures from the origin of outbreak, the spread of SARS-CoV-2 to three surges of pandemic, and NPIs and vaccine uptakes but also provides a new insight into how to apply big data analytics to identify unprecedented discoveries through COVID-19 pandemic scenario embracing from biomedical to economic viewpoints.

Economic evaluation for mass vaccination against COVID-19.

BACKGROUND: Vaccine is supposed to be the most effective means to prevent COVID-19 as it may not only save lives but also reduce productivity loss due to resuming pre-pandemic activities. Providing the results of economic evaluation for mass vaccination is of paramount importance for all stakeholders worldwide. METHODS: We developed a Markov decision tree for the economic evaluation of mass vaccination against COVID-19. The effectiveness of reducing outcomes after the administration of three COVID-19 vaccines (BNT162b2 (Pfizer-BioNTech), mRNA-1273 (Moderna), and AZD1222 (Oxford-AstraZeneca)) were modelled with empirical parameters obtained from literatures. The direct cost of vaccine and COVID-19 related medical cost, the indirect cost of productivity loss due to vaccine jabs and hospitalization, and the productivity loss were accumulated given different vaccination scenarios. We reported the incremental cost-utility ratio and benefit/cost (B/C) ratio of three vaccines compared to no vaccination with a probabilistic approach. RESULTS: Moderna and Pfizer vaccines won the greatest effectiveness among the three vaccines under consideration. After taking both direct and indirect costs into account, all of the three vaccines dominated no vaccination strategy. The results of B/C ratio show that one dollar invested in vaccine would have USD $13, USD $23, and USD $28 in return for Moderna, Pfizer, and AstraZeneca, respectively when health and education loss are considered. The corresponding figures taking value of the statistical life into account were USD $176, USD $300, and USD $443. CONCLUSION: Mass vaccination against COVID-19 with three current available vaccines is cost-saving for gaining more lives and less cost incurred.

Assessing efficacy of antiviral therapy for COVID-19 patients: A case study on remdesivir with bayesian synthesis design and multistate analysis.

BACKGROUND/PURPOSE: A synthesis design and multistate analysis is required for assessing the clinical efficacy of antiviral therapy on dynamics of multistate disease progression and in reducing the mortality and enhancing the recovery of patients with COVID-19. A case study on remdesivir was illustrated for the clinical application of such a novel design and analysis. METHODS: A Bayesian synthesis design was applied to integrating the empirical evidence on the one-arm compassion study and the two-arm ACTT-1 trial for COVID-19 patients treated with remdesivir. A multistate model was developed to model the dynamics of hospitalized COVID-19 patients from three transient states of low, medium-, and high-risk until the two outcomes of recovery and death. The outcome measures for clinical efficacy comprised high-risk state, death, and discharge. RESULTS: The efficacy of remdesivir in reducing the risk of death and enhancing the odds of recovery were estimated as 31% (95% CI, 18-44%) and 10% (95% CI, 1-18%), respectively. Remdesivir therapy for patients with low-risk state showed the efficacy in reducing subsequent progression to high-risk state and death by 26% (relative rate (RR), 0.74; 95% CI, 0.55-0.93) and 62% (RR, 0.38; 95% CI, 0.29-0.48), respectively. Less but still statistically significant efficacy in mortality reduction was noted for the medium- and high-risk patients. Remdesivir treated patients had a significantly shorter period of hospitalization (9.9 days) compared with standard care group (12.9 days). CONCLUSION: The clinical efficacy of remdesvir therapy in reducing mortality and accelerating discharge has been proved by the Bayesian synthesis design and multistate analysis.

Clinical features and prognosis of COVID-19 patients with metabolic syndrome: A multicenter, retrospective study Características clínicas y pronóstico de los pacientes con COVID-19 y síndrome metabólico: un estudio multicéntrico y retrospectivo

Background Few studies have investigated the impacts of metabolic syndrome (MS) on coronavirus disease 2019 (COVID-19). We described the clinical features and prognosis of confirmed COVID-19 patients with MS during hospitalization and after discharge. Methods Two hundred and thirty-three COVID-19 patients from the hospitals in 8 cities of Jiangsu, China were retrospectively included. Clinical characteristics of COVID-19 patients were described and risk factors of severe illness were analyzed by logistic regression analysis. Results Forty-five (19.3%) of 233 COVID-19 patients had MS. The median age of COVID-19 patients with MS was significantly higher than non-MS patients (53.0 years vs. 46.0 years, P = 0.004). There were no significant differences of clinical symptoms, abnormal chest CT images, and treatment drugs between two groups. More patients with MS had severe illness (33.3% vs. 6.4%, P < 0.001) and critical illness (4.4% vs. 0.5%, P = 0.037) than non-MS patients. The proportions of respiratory failure and acute respiratory distress syndrome in MS patients were also higher than non-MS patients during hospitalization. Multivariate analysis showed that concurrent MS (odds ratio [OR] 7.668, 95% confidence interval [CI] 3.062–19.201, P < 0.001) and lymphopenia (OR 3.315, 95% CI 1.306–8.411, P = 0.012) were independent risk factors of severe illness of COVID-19. At a median follow-up of 28 days after discharge, bilateral pneumonia was found in 95.2% of MS patients, while only 54.7% of non-MS patients presented bilateral pneumonia. Conclusions 19.3% of COVID-19 patients had MS in our study. COVID-19 patients with MS are more likely to develop severe complications and have worse prognosis. More attention should be paid to COVID-19 patients with MS.

Clinical features and prognosis of COVID-19 patients with metabolic syndrome: A multicenter, retrospective study.

Background: Few studies have investigated the impacts of metabolic syndrome (MS) on coronavirus disease 2019 (COVID-19). We described the clinical features and prognosis of confirmed COVID-19 patients with MS during hospitalization and after discharge. Methods: Two hundred and thirty-three COVID-19 patients from the hospitals in 8 cities of Jiangsu, China were retrospectively included. Clinical characteristics of COVID-19 patients were described and risk factors of severe illness were analyzed by logistic regression analysis. Results: Forty-five (19.3%) of 233 COVID-19 patients had MS. The median age of COVID-19 patients with MS was significantly higher than non-MS patients (53.0 years vs. 46.0 years, P = 0.004). There were no significant differences of clinical symptoms, abnormal chest CT images, and treatment drugs between two groups. More patients with MS had severe illness (33.3% vs. 6.4%, P < 0.001) and critical illness (4.4% vs. 0.5%, P = 0.037) than non-MS patients. The proportions of respiratory failure and acute respiratory distress syndrome in MS patients were also higher than non-MS patients during hospitalization. Multivariate analysis showed that concurrent MS (odds ratio [OR] 7.668, 95% confidence interval [CI] 3.062-19.201, P < 0.001) and lymphopenia (OR 3.315, 95% CI 1.306-8.411, P = 0.012) were independent risk factors of severe illness of COVID-19. At a median follow-up of 28 days after discharge, bilateral pneumonia was found in 95.2% of MS patients, while only 54.7% of non-MS patients presented bilateral pneumonia. Conclusions: 19.3% of COVID-19 patients had MS in our study. COVID-19 patients with MS are more likely to develop severe complications and have worse prognosis. More attention should be paid to COVID-19 patients with MS.

Antecedentes: Pocos estudios han investigado el impacto del síndrome metabólico (SM) en la enfermedad por coronavirus 2019 (COVID-19). Describimos las características clínicas y el pronóstico de los pacientes con COVID-19 confirmados con SM durante la hospitalización y después del alta. Métodos: Se incluyó de forma retrospectiva a 233 pacientes con COVID-19 de los hospitales de 8 ciudades de Jiangsu (China). Se describieron sus características clínicas y se analizaron los factores de riesgo de enfermedad grave mediante un análisis de regresión logística. Resultados: De los 233 pacientes, 45 (19,3%) tenían EM. La mediana de edad de estos pacientes con EM fue significativamente mayor que la de los pacientes sin él (53,0 años frente a 46,0 años; p = 0,004). No hubo diferencias significativas en cuanto a los síntomas clínicos, las imágenes de TC torácica anormales y los fármacos de tratamiento entre los 2 grupos. Hubo más pacientes con EM que tuvieron enfermedades graves (33,3% frente a 6,4%; p < 0,001) y críticas (4,4% frente a 0,5%; p = 0,037) que los pacientes sin EM. Las proporciones de insuficiencia respiratoria y síndrome de dificultad respiratoria aguda en los pacientes con EM también fueron mayores que en los pacientes sin EM durante la hospitalización. El análisis multivariante mostró que la EM concurrente (odds ratio [OR] 7,668; intervalo de confianza [IC] del 95%: 3,062-19,201; p < 0,001) y la linfopenia (OR 3,315; IC del 95%: 1,306-8,411; p = 0,012) eran factores de riesgo independientes de COVID-19 grave. En una mediana de seguimiento de 28 días tras el alta, se encontró neumonía bilateral en el 95,2% de los pacientes con EM, mientras que solo la presentaron el 54,7% de los pacientes sin EM. Conclusiones: El 19,3% de los pacientes con COVID-19 tenían EM en nuestro estudio. Los pacientes con COVID-19 y EM son más propensos a desarrollar complicaciones graves y tienen peor pronóstico. Se debe prestar más atención a los pacientes con COVID-19 y EM.

Promoting the Sustainable Development of Rural EFL Learners’ Email Literacy through a Facebook Project

Promoting the sustainable development of rural EFL students’ English ability is a vital issue in the general curriculum guidelines of Taiwan’s 12-Year Basic Education. This study aimed to investigate the effects of a Facebook project on developing rural EFL learners’ email literacy in English. There were two participant groups: (1) six university English majors and (2) 12 ninth-graders from a rural junior high school. The instruments included a multiple-choice awareness task (MCT), a written discourse completion task (WDCT), a perception questionnaire, interviews, and teaching journals. The university students first received a training session on email literacy, and then they taught the ninth-graders invitation email-writing through Facebook interactions for eight weeks. The results showed that after the project, the ninth-graders made significant improvements when completing the MCT. As for the quality of their emails, the ninth-graders not only scored significantly higher in the post-test but also made qualitative progress in their invitation emails. Furthermore, both participant groups had positive perceptions of this project and indicated the strengths and weaknesses of their participation. This paper concludes with pedagogical implications for English education in Taiwan.

Key factors affecting carbon prices from a time-varying perspective.

For humankind to sustain a livable atmosphere on the planet, many countries have committed to achieving carbon neutralization. Countries mainly reduce carbon emissions by regulations through a carbon tax or by establishing a carbon market using economic stimuli. In this paper, we use the least absolute shrinkage and selection operator (LASSO) method to select the key determinants of a carbon market and then use the Markov switching vector autoregression (MSVAR) model to study the market's driving factors and analyze its time-varying characteristics. The results show that there are perceptible time-varying characteristics and notable differences among markets. During COVID-19, energy factors had a long-term shock on the carbon market, economic factors had a short-term shock on the carbon market, and the economic recession has led to fluctuations in the carbon market. In addition, through MSVAR, the results show that the energy market has a negative effect on the carbon market, and the stock market has a positive effect on the carbon market. In periods of low volatility, compared with the natural gas market and coal market, the oil market has a stronger shock on the carbon market. In periods of high volatility, the coal market has a stronger shock on the carbon market. In terms of emission reduction, countries around the world would be wise to change their energy consumption structure, reduce coal use, and shift to a cleaner energy consumption structure.

Population-based Oral Cancer Service Screening Disrupted by COVID-19 Pandemic: Observational and Simulation Study (preprint)

Background: It is important for understanding the impact of COVID-19 pandemic on the missing opportunity for the early detection of oral cancer. This study aimed to assess the impact of COVID-19 pandemic on the existing population-based oral cancer (OC) service screening program in Taiwan. Methods: Before and after COVID-19 pandemic design was used to assess the impact of COVID-19 on the reduction of screening rate, referral rate, and the effectiveness of this OC service screening. Data and analysis after pandemic covered non-VOC period in 2020 and VOC period in 2021 compared to the historical control before pandemic in 2019. Results: The screening rate decreased substantially from 26.6% before COVID-19 in 2019 to 16.7% in 2020 and 15.3% in 2021 after pandemic. The reduction of screening rate varied with months, being the most remarkable decline in March (RR=0.61, 95% CI (0.60-0.62)) and June (RR=0.09, 95% CI (0.09-0.10)) in 2021 compared with January. The referral rate was stable at 81.5% in 2020 but it was reduced to 73.1% in 2021. The reduction of screening and referral rate led to the attenuation of effectiveness of advance cancer and mortality attenuated by 4% and 5%, respectively. Conclusion: COVID-19 pandemic disrupted the screening and the referral rate and further led to statistically significant reduction in effectiveness for preventing advanced cancer and death. Appropriate prioritized strategies must be adopted to ameliorate malignant transformation and tumor upstaging due to deference from participation in the screening. Funding: This study was financially supported by Health Promotion Administration of the Ministry of Health and Welfare of Taiwan (A1091116).

A pre-symptomatic incubation model for precision strategies of screening, quarantine, and isolation based on imported COVID-19 cases in Taiwan.

Facing the emerging COVID viral variants and the uneven distribution of vaccine worldwide, imported pre-symptomatic COVID-19 cases play a pivotal role in border control strategies. A stochastic disease process and computer simulation experiments with Bayesian underpinning was therefore developed to model pre-symptomatic disease progression during incubation period on which we were based to provide precision strategies for containing the resultant epidemic caused by imported COVID-19 cases. We then applied the proposed model to data on 1051 imported COVID-19 cases among inbound passengers to Taiwan between March 2020 and April 2021. The overall daily rate (per 100,000) of pre-symptomatic COVID-19 cases was estimated as 106 (95% credible interval (CrI): 95-117) in March-June 2020, fell to 37 (95% CrI: 28-47) in July-September 2020 (p < 0.0001), resurged to 141 (95% CrI: 118-164) in October-December 2020 (p < 0.0001), and declined to 90 (95% CrI: 73-108) in January-April 2021 (p = 0.0004). Given the median dwelling time, over 82% cases would progress from pre-symptomatic to symptomatic phase in 5-day quarantine. The time required for quarantine given two real-time polymerase chain reaction (RT-PCR) tests depends on the risk of departing countries, testing and quarantine strategies, and whether the passengers have vaccine jabs. Our proposed four-compartment stochastic process and computer simulation experiments design underpinning Bayesian MCMC algorithm facilitated the development of precision strategies for imported COVID-19 cases.

A New Approach to Modelling Pre-symptomatic Incidence and Transmission Time of SARS-CoV-2 Variants (preprint)

We applied a four-state stochastic process to decipher the natural infectious process of SARS-CoV-2 superimposed with the disease axis of pre-symptomatic, asymptomatic, and symptomatic states. So doing provides new insights into how pre-symptomatic transmission and the proportion of asymptomatic cases have been affected by SARS-CoV-2 variants, NPIs, and vaccination. We fitted the proposed model to empirical data on imported COVID-19 cases from D614G to Omicron between March 2020 and Jan 2022 in Taiwan. The pre-symptomatic incidence rate was the highest for Omicron followed by Alpha, Delta, and D614G. The median pre-symptomatic transmission time (MPTT) (in days) increased from 3.45 (first period) ~ 4.02(second period) of D614G until 3.94 ~ 4.65 of VOC Alpha before vaccination but dropped to 3.93 ~ 3.49 of Delta and 2 days (only first period) of Omicron after vaccination. The MPTT of the second re-surge was longer than the first surge for each variant before vaccination but this phenomenon disappeared for Delta after vaccination. The proportion of asymptomatic cases increased from 29% of D-614G period to 59.2% of Omicron. Modelling pre-symptomatic incidence and transmission time evolving with SARS-CoV-2 variants throws light on the underlying natural infectious properties of variants and also reveals how their properties are affected by vaccination and NPIs.

Identification of RPSA as a Potential Biomarker in Bronchoalveolar Lavage Fluid for Acute Respiratory Distress Syndrome (preprint)

Background: Acute respiratory distress syndrome (ARDS) is a life-threatening condition leading to severe pulmonary injuries, and proteomic analysis of bronchoalveolar lavage fluid (BALF) might elucidate potential biomarkers for diagnosis and targets for treatment of ARDS. Methods: Through iTRAQ analysis, we investigated paired BALF samples from three ARDS patients in the acute and recovery phases. The proteins sharing the same expression patterns between the two ARDS phases among different patients were determined as co-upregulated and co-downregulated proteins (CUDPs), and differentially expressed proteins (DEPs), whose fold change > 1.2 and P value < 0.05, were selected from CUDPs. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were applied to determine the enriched functions and pathways of the CUDPs. Protein-protein interaction (PPI) network was generated at STRING database, and hub genes were identified by the Cytoscape software. A549 cells were treated by lipopolysaccharide (LPS) to simulate alveolar epithelial cells in ARDS. Results: We identified 374 CUDPs and 53 DEPs. The GO analysis indicated that the most significantly enriched function was neutrophil mediated immunity response, and the KEGG analysis revealed that the 374 CUDPs were most significantly enriched in Coronavirus disease COVID-19 interaction. RPSA was discovered as the most top hub gene among DEPs, and was downregulated at protein levels during ARDS recovery. Moreover, we further confirmed that both RNA and protein level of RPSA increased upon inflammatory stimulation in vitro. Conclusion: Our results proposed RPSA as a candidate for biomarker and therapeutic target of ARDS.