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Background: Previous studies have highlighted the importance of viral shedding using cycle threshold (Ct) values obtained via reverse transcription polymerase chain reaction to understand the epidemic trajectories of SARS-CoV-2 infections. However, it is rare to elucidate the transition kinetics of Ct values from the asymptomatic or presymptomatic phase to the symptomatic phase before recovery using individual repeated Ct values., Objective: This study proposes a novel Ct-enshrined compartment model to provide a series of quantitative measures for delineating the full trajectories of the dynamics of viral load from infection until recovery., Methods: This Ct-enshrined compartment model was constructed by leveraging Ct-classified states within and between presymptomatic and symptomatic compartments before recovery or death among people with infections. A series of recovery indices were developed to assess the net kinetic movement of Ct-up toward and Ct-down off recovery. The model was applied to (1) a small-scale community-acquired Alpha variant outbreak under the "zero-COVID-19" policy without vaccines in May 2021 and (2) a large-scale community-acquired Omicron variant outbreak with high booster vaccination rates following the lifting of the "zero-COVID-19" policy in April 2022 in Taiwan. The model used Bayesian Markov chain Monte Carlo methods with the Metropolis-Hastings algorithm for parameter estimation. Sensitivity analyses were conducted by varying Ct cutoff values to assess the robustness of the model., Results: The kinetic indicators revealed a marked difference in viral shedding dynamics between the Alpha and Omicron variants. The Alpha variant exhibited slower viral shedding and lower recovery rates, but the Omicron variant demonstrated swifter viral shedding and higher recovery rates. Specifically, the Alpha variant showed gradual Ct-up transitions and moderate recovery rates, yielding a presymptomatic recovery index slightly higher than 1 (1.10), whereas the Omicron variant had remarkable Ct-up transitions and significantly higher asymptomatic recovery rates, resulting in a presymptomatic recovery index much higher than 1 (152.5). Sensitivity analysis confirmed the robustness of the chosen Ct values of 18 and 25 across different recovery phases. Regarding the impact of vaccination, individuals without booster vaccination had a 19% higher presymptomatic incidence rate compared to those with booster vaccination. Breakthrough infections in boosted individuals initially showed similar Ct-up transition rates but higher rates in later stages compared to nonboosted individuals. Overall, booster vaccination improved recovery rates, particularly during the symptomatic phase, although recovery rates for persistent asymptomatic infection were similar regardless of vaccination status once the Ct level exceeded 25., Conclusions: The study provides new insights into dynamic Ct transitions, with the notable finding that Ct-up transitions toward recovery outpaced Ct-down and symptom-surfacing transitions during the presymptomatic phase. The Ct-up against Ct-down transition varies with variants and vaccination status. The proposed Ct-enshrined compartment model is useful for the surveillance of emerging infectious diseases in the future to prevent community-acquired outbreaks., (©Ting-Yu Lin, Amy Ming-Fang Yen, Sam Li-Sheng Chen, Chen-Yang Hsu, Chao-Chih Lai, Dih-Ling Luh, Yen-Po Yeh, Tony Hsiu-Hsi Chen. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 19.09.2024.)

Background: The trajectories of all-cause deaths linked to omicron infections are rarely studied, especially in relation to the efficacy of booster shots. For assessing three epidemiological death trajectories, including dying from COVID-19, dying with COVID-19, and non-COVID-19 death, we offer a new COVID-19-and-death competing risk model that deals with the primary pathway (e.g., dying from COVID-19) competing with two other pathways., Methods: We applied this model to track three trajectories: deaths directly from COVID-19, deaths with COVID-19 as a contributing factor, and indirect non-COVID-19 deaths. The study used data from a Taiwanese cohort, covering periods of Omicron subvariants BA.2, BA.5, and BA.2.75. It focused on the effectiveness of monovalent and bivalent booster vaccines against these death trajectories., Results: The highest mortality was observed during the BA.2 phase, which decreased in the BA.5 period and increased again in the BA.2.75 period. Analyzing each trajectory, we noted similar trends in deaths directly from and with COVID-19, while non-COVID-19 deaths remained stable across subvariants. Booster vaccines reduced all-cause mortality by 58% (52%-62%) for BA.2, 70% (65%-75%) for BA.5%, and 75% (70%-80%) for BA.2.75, compared to incomplete vaccination. The reduction in deaths directly from COVID-19 was 66% (61%-72%) for BA.2, 78% (72%-84%) for BA.5%, and 85% (76%-93%) for BA.2.75. For deaths with COVID-19, the figures were 46% (36%-55%), 76% (68%-84%), and 90% (86%-95%). Additionally, the booster shots decreased non-COVID-19 deaths by 64% (63%-66%) for BA.2, 38% (36%-40%) for BA.5, and 19% (17%-21%) for BA.2.75., Conclusion: Our competing risk analysis is effective for monitoring all-cause death trajectories amidst various Omicron infections. It provides insights into the impact of booster vaccines, especially bivalent ones, and highlights the consequences of inadequate healthcare for vulnerable groups., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Contact tracing for containing emerging infectious diseases such as COVID-19 is resource intensive and requires digital transformation to enable timely decision-making., Objective: This study demonstrates the design and implementation of digital contact tracing using multimodal health informatics to efficiently collect personal information and contain community outbreaks. The implementation of digital contact tracing was further illustrated by 3 empirical SARS-CoV-2 infection clusters., Methods: The implementation in Changhua, Taiwan, served as a demonstration of the multisectoral informatics and connectivity between electronic health systems needed for digital contact tracing. The framework incorporates traditional travel, occupation, contact, and cluster approaches and a dynamic contact process enabled by digital technology. A centralized registry system, accessible only to authorized health personnel, ensures privacy and data security. The efficiency of the digital contact tracing system was evaluated through a field study in Changhua., Results: The digital contact tracing system integrates the immigration registry, communicable disease report system, and national health records to provide real-time information about travel, occupation, contact, and clusters for potential contacts and to facilitate a timely assessment of the risk of COVID-19 transmission. The digitalized system allows for informed decision-making regarding quarantine, isolation, and treatment, with a focus on personal privacy. In the first cluster infection, the system monitored 665 contacts and isolated 4 (0.6%) cases; none of the contacts (0/665, 0%) were infected during quarantine. The estimated reproduction number of 0.92 suggests an effective containment strategy for preventing community-acquired outbreak. The system was also used in a cluster investigation involving foreign workers, where none of the 462 contacts (0/462, 0%) tested positive for SARS-CoV-2., Conclusions: By integrating the multisectoral database, the contact tracing process can be digitalized to provide the information required for risk assessment and decision-making in a timely manner to contain a community-acquired outbreak when facing the outbreak of emerging infectious disease., (©Hsiao-Chi Wang, Ting-Yu Lin, Yu-Chin Yao, Chen-Yang Hsu, Chang-Jung Yang, Tony Hsiu-Hsi Chen, Yen-Po Yeh. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 08.11.2023.)

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 (β), basic reproductive number (R 0 ), 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.

Background: Little is known about long-term effectiveness of COVID-19 vaccine in reducing severity and deaths associated with Omicron VOC not perturbed by prior infection and independent of oral anti-viral therapy and non-pharmaceutical (NPI)., Methods: A retrospective observational cohort study was applied to Taiwan community during the unprecedent large-scale outbreaks of Omicron BA.2 between April and August, 2022. Primary vaccination since March, 2021 and booster vaccination since January, 2022 were offered on population level. Oral Anti-viral therapy was also offered as of mid-May 2022. The population-based effectiveness of vaccination in reducing the risk of moderate and severe cases of and death from Omicron BA.2 with the consideration of NPI and oral anti-viral therapy were assessed by using Bayesian hierarchical models., Results: The risks of three clinical outcomes associated with Omicron VOC infection were lowest for booster vaccination, followed by primary vaccination, and highest for incomplete vaccination with the consistent trends of being at increased risk for three outcomes from the young people aged 12 years or below until the elderly people aged 75 years or older with 7 age groups. Before the period using oral anti-viral therapy, complete primary vaccination with the duration more than 9 months before outbreaks conferred the statistically significant 47 % (23-64 %) reduction of death, 48 % (30-61 %) of severe disease, and 46 % (95 % CI: 37-54 %) of moderate disease after adjusting for 10-20 % independent effect of NPI. The benefits of booster vaccination within three months were further enhanced to 76 % (95 % CI: 67-86 %), 74 % (95 % CI: 67-80 %), and 61 % (95 % CI: 56-65 %) for three corresponding outcomes. The additional effectiveness of oral anti-viral therapy in reducing moderate disease was 13 % for the booster group and 5.8 % for primary vaccination., Conclusions: We corroborated population effectiveness of primary vaccination and its booster vaccination, independent of oral anti-viral therapy and NPI, in reducing severe clinical outcomes associated with Omicron BA.2 naïve infection population., Competing Interests: Competing interests Authors declare no competing interests., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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., (©Amy Ming-Fang Yen, Tony Hsiu-Hsi Chen, Wei-Jung Chang, Ting-Yu Lin, Grace Hsiao-Hsuan Jen, Chen-Yang Hsu, Sen-Te Wang, Huong Dang, Sam Li-Sheng Chen. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 25.11.2022.)

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.

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 ∼ 11-fold, 2 ∼ 5.5-fold, and 32.5 ∼ 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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

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., (© 2022. The Author(s).)

COVID-19 pandemic has severely affected regular public health interventions including population-based cancer screening. Impacts of such screening delays on the changes in structure and screening process and the resultant long-term outcomes are unknown. It is therefore necessary to develop a systematic framework to assess theses impacts related to these components of quality. Using population-based cancer screening with fecal immunochemical test (FIT) as an illustration, the main analysis was to assess how various scenarios of screening delays were associated with the capacity for primary screening and full time equivalent (FTE) for colonoscopy and impact long-term outcomes based on a Markov decision tree model on population level. The second analysis was to quantify how the extent of COVID-19 epidemic measured by social distancing index affected capacity and FTE that were translated to delays with an exponential relationship. COVID-19 epidemic led to 25%, 29%, 34%, and 39% statistically significantly incremental risks of late cancer for the delays of 0.5-year, 1-year,1.5-year, and 2-year, respectively compared with regular biennial FIT screening. The corresponding statistically findings of four delayed schedules for death from colorectal cancer (CRC) were 26%, 28%, 29%, and 30%, respectively. The higher social distancing index led to a lower capacity of uptake screening and a larger reduction of FTE, resulting in longer screening delay and longer waiting time, which further impacted long-term outcomes as above. In summary, a systematic modelling approach was developed for demonstrating the strong impact of screening delays caused by COVID-19 epidemic on long-term outcomes illustrated with a Taiwan population-based FIT screening of CRC., (Copyright © 2021. Published by Elsevier Inc.)

Colorectal cancer(CRC) is one of the most prevalent malignancies in the Asia-Pacific region, and many countries in this region have launched population CRC service screening. In this study, CRC screening key indicators, including the FIT(fecal immunochemical test) screening rate (or participation rate) and the rate of undergoing colonoscopy after positive FIT in 2019 and 2020, were surveyed in individual countries in the Asia-Pacific region. The impact of the pandemic on the effectiveness of CRC screening was simulated given different screening rates and colonoscopy rates and assuming the pandemic would persist or remain poorly controlled for a long period of time, using the empirical data from the Taiwanese program and the CRC natural history model. During the COVID-19 pandemic, most of the programs in this region were affected, but to different extents, which was largely influenced by the severity of the local pandemic. Most of the programs continued screening services in 2020, although a temporary pause occurred in some countries. The modeling study revealed that prolonged pauses of screening led to 6% lower effectiveness in reducing CRC mortality. Screening organizers should coordinate with health authorities to elaborate on addressing screening backlogs, setting priorities for screening, and applying modern technologies to overcome potential obstacles. Many novel approaches that were developed and applied during the COVID-19 pandemic, such as the risk-stratified approach that takes into account personal CRC risk and the local epidemic status, as well as new digital technologies, are expected to play important roles in CRC screening in the future., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Objectives: Whether and how the COVID-19 pandemic affected utilization of routine medical care in areas with low infection risk, such as Taiwan, has not been widely addressed. We aimed to evaluate the impact of the COVID-19 pandemic on access to medical care., Study Design: Before and after exposure (COVID-19 pandemic) design with a historical control group for comparison of clinical visits based on a retrospective cohort of 6722 customary patients of a community hospital in Zhunan, Taiwan., Methods: Repeated measurements of medical utilization in 4-month periods (January to April) of 2019 and 2020 in light of the emerging COVID-19 pandemic were collected. Access to medical care was defined as the mean frequencies of clinical visits. The impacts of the COVID-19 pandemic on access in the overall and specific groups were quantified with a multivariable Poisson regression model., Results: The overall outpatient visits per month declined by 39% (rate ratio [RR], 0.61; P < .0001) after adjusting for demographics. A notable reduction in visits was observed in foreign patients (RR, 0.50; P < .0001). The visits of the elderly (≥ 80 years) were the most frequent before the COVID-19 pandemic but were reduced by 44% (RR, 0.56; P < .0001) after it began. Most disease categories revealed a declining trend, but the size of reduction varied by International Classification of Diseases codes., Conclusions: The COVID-19 pandemic prevented some individuals from keeping regular medical appointments even in an area with a low infection risk. Our findings imply that more research is required to mitigate the effects of delayed medical care for patients who infrequently utilized medical care during and after the long-lasting pandemic period.

Background: The COVID-19 outbreaks associated with mass religious gatherings which have the potential of invoking epidemics at large scale have been a great concern. This study aimed to evaluate the risk of outbreak in mass religious gathering and further to assess the preparedness of non-pharmaceutical interventions (NPIs) for preventing COVID-19 outbreak in this context., Methods: The risk of COVID-19 outbreak in mass religious gathering was evaluated by using secondary COVID-19 cases and reproductive numbers. The preparedness of a series of NPIs for preventing COVID-19 outbreak in mass religious gathering was then assessed by using a density-dependent model. This approach was first illustrated by the Mazu Pilgrimage in Taiwan and validated by using the COVID-19 outbreak in the Shincheonji Church of Jesus (SCJ) religious gathering in South Korea., Results: Through the strict implementation of 80% NPIs in the Mazu Pilgrimage, the number of secondary cases can be substantially reduced from 1508 (95% CI: 900-2176) to 294 (95% CI: 169-420) with the reproductive number (R) significantly below one (0.54, 95% CI: 0.31-0.78), indicating an effective containment of outbreak. The expected number of secondary COVID-19 cases in the SCJ gathering was estimated as 232 (basic reproductive number (R 0 ) = 6.02) and 579 (R 0  = 2.50) for the first and second outbreak, respectively, with a total expected cases (833) close to the observed data on high infection of COVID-19 cases (887, R 0  = 3.00)., Conclusion: We provided the evidence on the preparedness of NPIs for preventing COVID-19 outbreak in the context of mass religious gathering by using a density-dependent model., Competing Interests: Declaration of competing interest Authors declare no competing interests., (Copyright © 2021. Published by Elsevier B.V.)

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., Competing Interests: Declaration of competing interest The authors have no conflicts of interest relevant to this article., (Copyright © 2021 Formosan Medical Association. Published by Elsevier B.V. All rights reserved.)

Background: Household transmission is responsible for the subsequent outbreak of community-acquired COVID-19. The aim of this study was to elucidate the household transmission mode and to further estimate effective and basic reproductive number with and without non-pharmaceutical interventions (NPIs)., Methods: A total of 26 households with 39 family clusters between January, 2020 and February, 2021 in Taiwan were enrolled for analysis. The Becker's chain binomial model was used to analyze the probabilities of being infected and escaping from SARS-COV-2 before and after January 1st, 2021, which were further converted to estimating basic reproductive numbers in the absence of NPIs. The likelihood of leading to the subsequent community-acquired outbreak given NPIs was further assessed., Results: The secondary attack rate was 46.2%. Given the saturated Greenwood model selected as the best fitted model, the probability of being infected and escaping from COVID-19 within household was estimated as 44.4% (95% CI: 5.0%-53.7%) and 55.7% (95% CI: 46.3%-65.0%), respectively. In the second period of early 2021, the infected probability was increased to 58.3% (95% CI: 12.7%-90.0%) and the escape probability was lowered to 41.7% (95% CI: 0.0%-86.9%). The corresponding basic reproductive numbers (R 0 ) increased from 4.29 in the first period to 6.73 in the second period without NPIs. However, none of subsequent community-acquired outbreak was noted in Taiwan given very effective NPIs in both periods., Conclusion: The proposed method and results are useful for designing household-specific containment measures and NPIs to stamp out a large-scale community-acquired outbreak as demonstrated in Taiwan., Competing Interests: Declaration of competing interest The authors have no conflicts of interest relevant to this article., (Copyright © 2021. Published by Elsevier B.V.)

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., Competing Interests: Declaration of competing interest The authors have no conflicts of interest relevant to this article., (Copyright © 2021. Published by Elsevier B.V.)

There is a serious concern over the variation of case fatality of COVID-19 patients that reflects the preparedness of the medical care system in response to the surge of pneumonia patients. We aimed to quantify the disease spectrum of COVID-19 on which we are based to develop a key indicator on the probability of progression from pneumonia to acute respiratory disease syndrome (ARDS) for fatal COVID-19. The retrospective cohort on 12 countries that have already experienced the epidemic of COVID-19 with available open data on the conformed cases with detailed information on mild respiratory disease (MRD), pneumonia, ARDS, and deaths were used. The pooled estimates from three countries with detailed information were 73% from MRD to pneumonia and 27% from MRD to recovery and the case-fatality rate of ARDS was 43%. The progression from pneumonia to ARDS varied from 3% to 63%. These key estimates were highly associated with the case fatality rates reported for each country with a statistically significant positive relationship (adjusted R 2 = 95%). Such a quantitative model provides key messages for the optimal medical resources allocation to a spectrum of patients requiring quarantine and isolation at home, isolation wards, and intensive care unit in order to reduce deaths from COVID-19., (Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

We developed a Bayesian competing four-state Markov exponential regression model to explore how viral shedding in terms of cycle threshold (Ct) values makes a relative contribution between persistent and nonpersistent asymptomatic phenotypes and whether viral shedding affects the subsequent progression to symptoms. The proposed model was applied to data from two large community-acquired outbreaks involving both the Alpha and Omicron variants of concern (VOCs) in Changhua, Taiwan. A multistate Markov exponential regression model was proposed for quantifying the adjusted odds ratio (aOR) of viral shedding measured by cycle threshold (Ct) values. A Bayesian Markov Chain Monte Carlo (MCMC) method was used to estimate the parameters of the posterior distribution. For Alpha VOCs, the estimated results showed that the odds of developing a nonpersistent asymptomatic phenotype, as opposed to a persistent asymptomatic phenotype, were reduced by 14% (aOR = 0.86, 95% CI: 0.81–0.92) for each unit increase in the Ct value, whereas this figure shrunk to 5% (aOR = 0.95, 95% CI: 0.93–0.98) for Omicron VOCs. Similar significant gradient relationships were also observed from low to high viral load levels. Similar, but not statistically significant, dose–response effects of viral load on the progression to symptoms for the nonpersistent asymptomatic phenotype were observed. The proposed model elucidates the dose–response effects of viral shedding in conjunction with state-specific covariates on the two pathways of the SARS-CoV-2 infectious process via a nonpersistent or asymptomatic phenotype for both the Alpha and Omicron VOCs. Modeling the infectious process and disease progression superimposed with dynamic viral load and other state-specific covariates following two competing pathways of nonpersistent and persistent asymptomatic cases provides new insight into the development of personalized virological surveillance and the deployment of precision containment measures for responding to emerging or re-emerging infectious diseases. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 on the Diamond Princess Cruise Ship provides an unprecedented opportunity to estimate its original transmissibility with basic reproductive number (R0) and the effectiveness of containment measures. We developed an ordinary differential equation-based Susceptible-Exposed-Infected-Recovery (SEIR) model with Bayesian underpinning to estimate the main parameter of R0 determined by transmission coefficients, incubation period, and the recovery rate. Bayesian Markov Chain Monte Carlo (MCMC) estimation method was used to tackle the parameters of uncertainty resulting from the outbreak of COVID-19 given a small cohort of the cruise ship. The extended stratified SEIR model was also proposed to elucidate the heterogeneity of transmission route by the level of deck with passengers and crews. With the application of the overall model, R0 was estimated as high as 5.70 (95% credible interval: 4.23–7.79). The entire epidemic period without containment measurements was approximately 47 days and reached the peak one month later after the index case. The partial containment measure reduced 63% (95% credible interval: 60–66%) infected passengers. With the deck-specific SEIR model, the heterogeneity of R0 estimates by each deck was noted. The estimated R0 figures were 5.18 for passengers (5–14 deck), mainly from the within-deck transmission, and 2.46 for crews (2–4 deck), mainly from the between-deck transmission. Modelling the dynamic of COVID-19 on the cruise ship not only provides an insight into timely evacuation and early isolation and quarantine but also elucidates the relative contributions of different transmission modes on the cruise ship though the deck-stratified SEIR model. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Implementing and lifting social distancing (LSD) is an urgent global issue during the COVID-19 pandemic, particularly when the travel ban is lifted to revive international businesses and economies. However, when and whether LSD can be considered is subject to the spread of SARS-CoV-2, the recovery rate, and the case-fatality rate. It is imperative to provide real-time assessment of three factors to guide LSD.Objective: A simple LSD index was developed for health decision makers to do real-time assessment of COVID-19 at the global, country, region, and community level.Methods: Data on the retrospective cohort of 186 countries with three factors were retrieved from a publicly available repository from January to early July. A simple index for guiding LSD was measured by the cumulative number of COVID-19 cases and recoveries, and the case-fatality rate was envisaged. If the LSD index was less than 1, LSD can be considered. The dynamic changes of the COVID-19 pandemic were evaluated to assess whether and when health decision makers allowed for LSD and when to reimplement social distancing after resurgences of the epidemic.Results: After large-scale outbreaks in a few countries before mid-March (prepandemic phase), the global weekly LSD index peaked at 4.27 in March and lasted until mid-June (pandemic phase), during which most countries were affected and needed to take various social distancing measures. Since, the value of LSD has gradually declined to 0.99 on July 5 (postpandemic phase), at which 64.7% (120/186) of countries and regions had an LSD<1 with the decile between 0 and 1 to refine risk stratification by countries. The LSD index decreased to 1 in about 115 days. In addition, we present the results of dynamic changes of the LSD index for the world and for each country and region with different time windows from January to July 5. The results of the LSD index on the resurgence of the COVID-19 epidemic in certain regions and validation by other emerging infectious diseases are presented.Conclusions: This simple LSD index provides a quantitative assessment of whether and when to ease or implement social distancing to provide advice for health decision makers and travelers. [ABSTRACT FROM AUTHOR]

Background: Low infection and case-fatality rates have been thus far observed in Taiwan. One of the reasons for this major success is better use of big data analytics in efficient contact tracing and management and surveillance of those who require quarantine and isolation.Objective: We present here a unique application of big data analytics among Taiwanese people who had contact with more than 3000 passengers that disembarked at Keelung harbor in Taiwan for a 1-day tour on January 31, 2020, 5 days before the outbreak of coronavirus disease (COVID-19) on the Diamond Princess cruise ship on February 5, 2020, after an index case was identified on January 20, 2020.Methods: The smart contact tracing-based mobile sensor data, cross-validated by other big sensor surveillance data, were analyzed by the mobile geopositioning method and rapid analysis to identify 627,386 potential contact-persons. Information on self-monitoring and self-quarantine was provided via SMS, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) tests were offered for symptomatic contacts. National Health Insurance claims big data were linked, to follow-up on the outcome related to COVID-19 among those who were hospitalized due to pneumonia and advised to undergo screening for SARS-CoV-2.Results: As of February 29, a total of 67 contacts who were tested by reverse transcription-polymerase chain reaction were all negative and no confirmed COVID-19 cases were found. Less cases of respiratory syndrome and pneumonia were found after the follow-up of the contact population compared with the general population until March 10, 2020.Conclusions: Big data analytics with smart contact tracing, automated alert messaging for self-restriction, and follow-up of the outcome related to COVID-19 using health insurance data could curtail the resources required for conventional epidemiological contact tracing. [ABSTRACT FROM AUTHOR]