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.

Rapid and Sensitive Diagnosis of COVID-19 Using an Electricity-Free Self-Testing System.

Rapid and sensitive detection of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential for early diagnosis and effective treatment. Nucleic acid testing has been considered the gold standard method for the diagnosis of COVID-19 for its high sensitivity and specificity. However, the polymerase chain reaction (PCR)-based method in the central lab requires expensive equipment and well-trained personnel, which makes it difficult to be used in resource-limited settings. It highlights the need for a sensitive and simple assay that allows potential patients to detect SARS-CoV-2 by themselves. Here, we developed an electricity-free self-testing system based on reverse transcription loop-mediated isothermal amplification (RT-LAMP) that allows for rapid and accurate detection of SARS-CoV-2. Our system employs a heating bag as the heat source, and a 3D-printed box filled with phase change material (PCM) that successfully regulates the temperature for the RT-LAMP. The colorimetric method could be completed in 40 min and the results could be read out by the naked eye. A ratiometric measurement for exact readout was also incorporated to improve the detection accuracy of the system. This self-testing system is a promising tool for point-of-care testing (POCT) that enables rapid and sensitive diagnosis of SARS-CoV-2 in the real world and will improve the current COVID-19 screening efforts for control and mitigation of the pandemic.

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.

Effects of Dust Event Days on Influenza: Evidence from Arid Environments in Lanzhou

Airborne aerosol is believed to be an important pathway for infectious disease transmissions like COVID-19 and influenza. However, the effects of dust event days on influenza have been rarely explored, particularly in arid environments. This study explores the effects of ambient particulate matter (PM) and dust events on laboratory-confirmed influenza in a semi-arid city. A descriptive analysis of daily laboratory-confirmed influenza (influenza) cases, PM (PM10 and PM2.5), meteorological parameters, and dust events were conducted from 2014 to 2019 in Lanzhou, China. The case-crossover design combined with conditional Poisson regression models was used to estimate the lagging effects of PM and dust events on influenza. In addition, a hierarchical model was used to quantitatively evaluate the interactive effect of PM with ambient temperature and absolute humidity on influenza. We found that PM and dust events had a significant effect on influenza. The effects of PM10 and PM2.5 on influenza became stronger as the cumulative lag days increased. The greatest estimated relative risks (RRs) were 1.018 (1.011,1.024) and 1.061 (1.034,1.087), respectively. Compared with the non-dust days, the effects of dust events with duration ≥ 1 day and with duration ≥ 2 days on influenza were the strongest at lag0 day, with the estimated RRs of 1.245 (95% CI: 1.061–1.463) and 1.483 (95% CI: 1.232–1.784), respectively. Subgroup analysis showed that pre-school children and school-aged children were more sensitive to PM and dust events exposure. Besides, we also found that low humidity and temperature had an interaction with PM to aggravate the risk of influenza. In summary, ambient PM and dust events exposure may increase the risk of influenza, and the risk of influenza increases with the dust events duration. Therefore, more efforts from the government as well as individuals should be strengthened to reduce the effect of PM on influenza, particularly in cold and dry weather.

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.

Psychotherapy and Follow-Up in Health Care Workers After the COVID-19 Epidemic: A Single Center's Experience.

Objective: The present study aims to analysis the mental health of high-risk health care workers (HHCWs) and low-risk HCWs (LHCWs) who were respectively exposed to COVID-19 wards and non-COVID-19 wards by following up on mental disorders in HCWs in China for 6 months. Methods: A multi-psychological assessment questionnaire was used to follow up on the psychological status of HCWs in the Affiliated Hospital of Xuzhou Medical University in Xuzhou City (a non-core epidemic area) at 6 months after the first evaluation conducted during the COVID-19 epidemic. Based on the risk of exposure to COVID-19 patients, the HCWs were divided into two groups: high-risk HCWs, who worked in COVID-19 wards, and low-risk HCWs, who worked in non-COVID-19 wards. Results: A total of 198 HCWs participated in the study, and 168 questionnaires were selected for evaluation. Among them, 93 (55.4%) were in the HHCW group and 75 (44.5%) were in the LHCW group. Significant differences were observed in salary, profession, and altruistic behavior between the two groups (P < 0.05). There were no significant differences in the anxiety, depression, insomnia, or posttraumatic stress disorder (PTSD) scores between the two groups. Logistic regression revealed that work stress was a major joint risk factor for mental disorders in HCWs. Among all the HCWs, a total of 58 voluntarily participated in psychotherapy; the analysis showed a significant decrease in anxiety, depression, PTSD, work stress, and work risk after attending psychotherapy. There were also significant differences in positive and negative coping styles before and after psychotherapy. Conclusion: In the present follow-up, work stress was the major contributing factor to mental disorders in HCWs. Psychotherapy is helpful in terms of stress management and should be provided to first-line COVID-19 HCWs.

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.

Does COVID-19 pandemic event alter the dependence structure breaks between crude oil and stock markets in Europe and America

This article attempts to investigate the influence of novel coronavirus (COVID-19) pandemic on the dependence structure break between crude oil and stock markets in Europe and America using ARMA-GARCH and R-vine copula methods. The empirical results demonstrate that international crude oil and European (American) stock markets have significant asymmetric and symmetric dependence structure, rapid outbreak of COVID-19 pandemic triggers their dependence structure break. The results of Kendall correlation confirms that COVID-19 pandemic amplifies the dependence risks between European Brent crude oil and France (German and Spain) stock markets and reduces the dependence risk between Brent crude oil and UK (Italy) stock markets after February 20, 2020. The COVID-19 pandemic may amplify the dependence risk between West Texas Intermediate (WTI) crude oil and Canada stock markets after March 23, 2020, it first quickly reduces the dependence risks between WTI crude oil and US (Brazil and Mexico) stock markets after March 23, 2020 and then enlarges their dependence risks after June 30, 2020. European and American crude oil and stock markets have induced different ranges of their dependence risks in different time scales and their dependence structure breaks have good robustness.

Extended SARS-CoV-2 RBD booster vaccination induces humoral and cellular immune tolerance in mice.

The repetitive applications of vaccine boosters have been brought up in face of continuous emergence of SARS-CoV-2 variants with neutralization escape mutations, but their protective efficacy and potential adverse effects remain largely unknown. Here, we compared the humoral and cellular immune responses of an extended course of recombinant receptor binding domain (RBD) vaccine boosters with those from conventional immunization strategy in a Balb/c mice model. Multiple vaccine boosters after the conventional vaccination course significantly decreased RBD-specific antibody titers and serum neutralizing efficacy against the Delta and Omicron variants, and profoundly impaired CD4+ and CD8+T cell activation and increased PD-1 and LAG-3 expressions in these T cells. Mechanistically, we confirmed that extended vaccination with RBD boosters overturned the protective immune memories by promoting adaptive immune tolerance. Our findings demonstrate potential risks with the continuous use of SARS-CoV-2 vaccine boosters, providing immediate implications for the global COVID-19 vaccination enhancement strategies.

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.

Dumbbell-type triplex molecular switch-based logic molecular assays of SARS-CoV-2.

Accurate detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is of great importance to control the COVID-19 pandemic. The gold standard assays for COVID-19 diagnostics are mainly based on separately detecting open reading frame 1ab (ORF1ab) and nucleoprotein (N) genes by RT-PCR. However, the current approaches often obtain false positive-misdiagnose caused by cross-contamination or undesired amplification. To address this issue, herein, we proposed a dumbbell-type triplex molecular switch (DTMS)-based, logic-gated strategy for high-fidelity SARS-CoV-2 RNA detection. The DTMS consists of a triple-helical stem region and two-loop regions for recognizing the ORF1ab and N genes of SARS-CoV-2. Only when the ORF1ab and N gene are concurrent, DTMS experiences a structural rearrangement, thus, bringing the two pyrenes into spacer proximity and leading to a new signal readout. This strategy allows detecting SARS-CoV-2 RNA with a detection limit of 1.3 nM, independent of nucleic acid amplification, holding great potential as an indicator probe for screening of COVID-19 and other population-wide epidemics.

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.

Classification of community-acquired outbreaks for the global transmission of COVID-19: Machine learning and statistical model analysis.

BACKGROUND: As Coronavirus disease 2019 (COVID-19) pandemic led to the unprecedent large-scale repeated surges of epidemics worldwide since the end of 2019, data-driven analysis to look into the duration and case load of each episode of outbreak worldwide has been motivated. METHODS: Using open data repository with daily infected, recovered and death cases in the period between March 2020 and April 2021, a descriptive analysis was performed. The susceptible-exposed-infected-recovery model was used to estimate the effective productive number (Rt). The duration taken from Rt > 1 to Rt < 1 and case load were first modelled by using the compound Poisson method. Machine learning analysis using the K-means clustering method was further adopted to classify patterns of community-acquired outbreaks worldwide. RESULTS: The global estimated Rt declined after the first surge of COVID-19 pandemic but there were still two major surges of epidemics occurring in September 2020 and March 2021, respectively, and numerous episodes due to various extents of Nonpharmaceutical Interventions (NPIs). Unsupervised machine learning identified five patterns as "controlled epidemic", "mutant propagated epidemic", "propagated epidemic", "persistent epidemic" and "long persistent epidemic" with the corresponding duration and the logarithm of case load from the lowest (18.6 ± 11.7; 3.4 ± 1.8)) to the highest (258.2 ± 31.9; 11.9 ± 2.4). Countries like Taiwan outside five clusters were classified as no community-acquired outbreak. CONCLUSION: Data-driven models for the new classification of community-acquired outbreaks are useful for global surveillance of uninterrupted COVID-19 pandemic and provide a timely decision support for the distribution of vaccine and the optimal NPIs from global to local community.

Household transmission but without the community-acquired outbreak of COVID-19 in Taiwan.

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 (R0) 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.

Safe reopening of university campuses is possible with COVID-19 vaccination.

We construct an agent-based SEIR model to simulate COVID-19 spread at a 16000-student mostly non-residential urban university during the Fall 2021 Semester. We find that mRNA vaccine coverage at 100% combined with weekly screening testing of 25% of the campus population make it possible to safely reopen to in-person instruction. Our simulations exhibit a right-skew for total infections over the semester that becomes more pronounced with less vaccine coverage, less vaccine effectiveness and no additional preventative measures. This suggests that high levels of infection are not exceedingly rare with campus social connections the main transmission route. Finally, we find that if vaccine coverage is 100% and vaccine effectiveness is above 80%, then a safe reopening is possible even without facemask use. This models possible future scenarios with high coverage of additional "booster" doses of COVID-19 vaccines.

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.