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.

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.

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.

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 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.

Epidemic Surveillance Models for Containing the Spread of SARS-CoV-2 Variants: Taiwan Experience (preprint)

Objectives: Two kinds of epidemic surveillance models are presented for containing the spread of SARS-CoV-2 variants so as to avert and stamp out a community-acquired outbreak (CAO) with non-pharmaceutical interventions (NPIs), tests, and vaccination. Design: The surveillance of domestic cluster infections transmitted from imported cases with one-week time lag assessed by the Poisson model and the surveillance of whether, how and when NPIs and test contained the CAO with the SEIR model. Settings: Border and Community of Taiwan. Main Outcome Measurements: The expected number and the upper bound of the 95% credible interval (CrI) of weekly covid-19 cases compared with the observed number for assessing the threshold of a CAO; effective reproductive number (Rt) and the effectiveness of NPIs for containing a CAO. Results: For the period of January-September 2020 when the wild type and the D614G period were prevailing, an increase in one imported case prior to one week would lead to 9.54% (95% CrI 6.44% to 12.59%) higher risk of domestic cluster infection that provides a one-week prior alert signal for more stringent NPIs and active testing locally. Accordingly, there was an absence of CAO until the Alpha VOC period of February 2021. However, given level one of NPI alert the risk of domestic cluster infections was gradually elevated to 14.14% (95% CrI 5.41% to 25.10%), leading to the Alpha VOC CAOs of six hotspots around mid-May 2021. It took two-and-half months for containing this CAO mainly with level three of NPI alert and rapid test and partially by the rolling out of vaccination. By applying the SEIR model, the Rt decreased from 4.0 at beginning to 0.7 on 31 July 2021 in parallel with the escalating NPIs from 30% to 90%. Containing a small outbreak of Delta VOC during this CAO period was also evaluated and demonstrated. After controlling the CAO, it again returned to imported-domestic transmission for Delta VOC from July until September 2021, giving an estimate of 10.16% (95% CrI: 7.01% to 13.59%) for the risk of several small cluster infections. However, there was an absence of CAO that resulted from the effectiveness of NPIs and tests, and the rapid expansion of vaccination. Conclusions: Averting and containing CAOs of SARS-CoV-2 variants are demonstrated by two kinds of epidemic surveillance models that have been applied to Taiwan scenario. These two models can be accommodated to monitor the epidemic of forthcoming emerging SARS-CoV-2 VOCs with various circumstances of vaccine coverage, NPIs, and tests in countries worldwide.

Probabilistic forecasts of COVID-19 deaths with the progression rate from pneumonia to ARDS: An open-data-based global study.

BACKGROUND: Cumulative data of case-fatality rates (CFR) of COVID-19 varied across countries. A forecasting model generated based on detailed information from three countries during the initial phase of pandemic showed that progression rates from pneumonia to ARDS (PRPA) varied by country and were highly associated with CFR. We aim to elucidate the impact of the PRPA on COVID-19 deaths in different periods of pandemic. METHODS: We used the country-based, real-time global COVID-19 data through GitHub repository to estimate PRPA on the first period (January to June), second period (July to September), and third period (October to December) in 2020. PRPA was used for predicting COVID-19 deaths and assessing the reduction in deaths in subsequent two periods. RESULTS: The estimated PRPA varied widely from 0.38% to 51.36%, with an average of 15.99% in the first period. The PRPA declined to 8.44% and 6.35% in the second and third period. The CFR declined stepwise and was 4.94%, 2.61%, and 1.96%, respectively. Some countries exhibited a decrease in the PRPA from the second to the third period whereas others showed the opposite, particularly where selected viral mutants were prevalent. Overall, the number of observed deaths was lower than that of the predicted deaths in the second and third periods, suggesting an improvement in management of COVID-19 patients. Besides, the degree of improvement depends on the extent of change in PRPA. CONCLUSION: PRPA is a useful indicator to facilitate decision making and assess the improvement of clinical management and medical capacity by forecasting deaths.

Easing social distancing index after COVID-19 pandemic (preprint)

Abstract Context Easing social distancing (ESD) is a global public health issue in post-pandemic period of COVID-19 and requires a simple index for real time assessment. Objective We aimed to develop a simple index for ESD to quantify the impacts of social distancing for reducing confirmed infected cases, optimal triage and care of patients for recovery, and critical care capacity for reducing death from COVID-19. Design, Setting, and Participants Data on the retrospective cohort of 185 countries with reported numbers on confirmed cases, recovery, and death from COVID-19 were retrieved from publicity available repository. Up to May 31, a total of 5,844,136 confirmed cases, 2,639,961 recovered, and 327,487 deaths were reported globally. Main Outcome Measures The ESD index measured by cumulative number of COVID-19 cases and recovery and case-fatality rate. Results We developed a simple index for the guidance of easing social distancing (ESD). If the ESD index is less than 1, ESD would be considered. The global ESD index declined from 3.87 at peak in March to 1.35 by the end of May, consisting of 56.76% countries/regions (105/185) with the ESD lower than one. Conclusion and Relevance This simple ESD index provides a quantitative assessment on whether and when to ease social distancing from local to global community.

Impacts of remdesivir on dynamics and efficacy stratified by the severity of COVID-19: a simulated two-arm controlled study (preprint)

Background: The impact of remdesivir on length of stay of hospitalization, high-risk state, and death stratified by the severity of COVID-19 at enrollment is controversial. Methods: We applied a simulated two-arm controlled study design to the data on compassionate use of remdesivir as a secondary analysis. Dynamics of risk states and death from COVID-19 patients defined by the six-point disease severity recommended by the WHO R&D and the time to discharge from hospital were used to evaluate the efficacy of remdesivir treatment compared with standard care. Results: Stratified by the risk state at enrollment, low-risk patients exhibited the highest efficacy of remdesivir in reducing subsequent progression to high-risk state by 67% (relative risk (RR)=0.33,95% CI: 0.30-0.35) and further to death by 55% (RR=0.45, 95%CI: 0.39-0.50). For the medium-risk patients, less but still statistically significant efficacy results were noted in reducing progression to high-risk state by 52% (RR=0.48, 95% CI: 0.45-0.51) and further to death by 40% (RR=0.60, 95% CI:0.54-0.66). High-risk state patients treated with remdesivir led to a 25% statistically significant reduction in death (RR=0.75, 95% CI: 0.69-0.82). Regarding the outcome of discharge, remdesivir treatment was most effective for medium-risk patients at enrollment (RR: 1.41, 95% CI: 1.35-1.47) followed by high- (RR=1.34, 95% CI: 1.27-1.42) and low-risk patients (RR=1.28, 95% CI: 1.25-1.31). Conclusion: Our results with a simulated two-arm controlled study have provided a new insight into the precision treatment of remdesivir for COVID-19 patients based on risk-stratified efficacy.