Suppression of the alpha, delta, and omicron variants of SARS-Cov-2 in Taiwan.

BACKGROUND: Taiwan was a coronavirus disease 2019 (COVID-19) outlier, with an extraordinarily long transmission-free record: 253 days without locally transmitted infections while the rest of the world battled wave after wave of infection. The appearance of the alpha variant in May 2021, closely followed by the delta variant, disrupted this transmission-free streak. However, despite low vaccination coverage (<1%), outbreaks were well-controlled. METHODS: This study analyzed the time to border closure and conducted one-sample t test to compare between Taiwan and Non-Taiwan countries prior to vaccine introduction. The study also collected case data to observe the dynamics of omicron transmission. Time-varying reproduction number,Rt, was calculated and was used to reflect infection impact at specified time points and model trends of future incidence. RESULTS: The study analyzed and compare the time to border closure in Taiwan and non-Taiwan countries. The mean times to any border closure from the first domestic case within each country were -21 and 5.98 days, respectively (P < .0001). The Taiwanese government invested in quick and effective contact tracing with a precise quarantine strategy in lieu of a strict lockdown. Residents followed recommendations based on self-discipline and unity. The self-discipline in action is evidenced in Google mobility reports. The central and local governments worked together to enact non-pharmaceutical interventions (NPIs), including universal masking, social distancing, limited unnecessary gatherings, systematic contact tracing, and enhanced quarantine measures. The people cooperated actively with pandemic-prevention regulations, including vaccination and preventive NPIs. CONCLUSIONS: This article describes four key factors underlying Taiwan's success in controlling COVID-19 transmission: quick responses; effective control measures with new technologies and rolling knowledge updates; unity and cooperation among Taiwanese government agencies, private companies and organizations, and individual citizens; and Taiwanese self-discipline.

Beyond the waves: Unraveling pandemic outcomes with genomic insights and immunity analysis - Evidence from 14 countries.

OBJECTIVE: Although the World Health Organization and many governments have recategorized COVID-19 as a generally mild to moderately severe disease, consecutive pandemic waves driven by immune escape variants have underscored the need for timely and accurate prediction of the next outbreak. Nevertheless, little attention has been paid to translating genomic data and infection- and vaccine-induced immunity into direct estimates. METHODS: We retrieved epidemiologic and genomic data shortly before pandemic waves across 14 developed countries from late 2021 to mid-2022 and examined associations between early-stage variant competition, infection- and vaccine-induced immunity, and the time intervals between wave peaks. We applied regression analysis and the generalized estimating equation method to construct an inferential model. RESULTS: Each per cent increase in the proportion of a new variant was associated with a 1.0% reduction in interpeak intervals on average. Curvilinear associations between vaccine-induced immunity and outcome variables were observed, suggesting that reaching a critical vaccine distribution rate may decrease the caseload of the upcoming wave. CONCLUSIONS: By leveraging readily accessible pre-outbreak genomic and epidemiologic data, our results not only substantiate the predictive potential of early variant fractions but also propose that immunity acquired through infection alone may not sufficiently mitigate transmission. Conversely, a rapid and widespread vaccination initiative appears to be correlated with a decrease in disease incidence.

Real-time forecasting of COVID-19 spread according to protective behavior and vaccination: autoregressive integrated moving average models.

BACKGROUND: Mathematical and statistical models are used to predict trends in epidemic spread and determine the effectiveness of control measures. Automatic regressive integrated moving average (ARIMA) models are used for time-series forecasting, but only few models of the 2019 coronavirus disease (COVID-19) pandemic have incorporated protective behaviors or vaccination, known to be effective for pandemic control. METHODS: To improve the accuracy of prediction, we applied newly developed ARIMA models with predictors (mask wearing, avoiding going out, and vaccination) to forecast weekly COVID-19 case growth rates in Canada, France, Italy, and Israel between January 2021 and March 2022. The open-source data was sourced from the YouGov survey and Our World in Data. Prediction performance was evaluated using the root mean square error (RMSE) and the corrected Akaike information criterion (AICc). RESULTS: A model with mask wearing and vaccination variables performed best for the pandemic period in which the Alpha and Delta viral variants were predominant (before November 2021). A model using only past case growth rates as autoregressive predictors performed best for the Omicron period (after December 2021). The models suggested that protective behaviors and vaccination are associated with the reduction of COVID-19 case growth rates, with booster vaccine coverage playing a particularly vital role during the Omicron period. For example, each unit increase in mask wearing and avoiding going out significantly reduced the case growth rate during the Alpha/Delta period in Canada (-0.81 and -0.54, respectively; both p < 0.05). In the Omicron period, each unit increase in the number of booster doses resulted in a significant reduction of the case growth rate in Canada (-0.03), Israel (-0.12), Italy (-0.02), and France (-0.03); all p < 0.05. CONCLUSIONS: The key findings of this study are incorporating behavior and vaccination as predictors led to accurate predictions and highlighted their significant role in controlling the pandemic. These models are easily interpretable and can be embedded in a "real-time" schedule with weekly data updates. They can support timely decision making about policies to control dynamically changing epidemics.

Contribution of Testing Strategies and Contact Tracing towards COVID-19 Outbreaks Control: A Mathematical Modeling Study.

This modeling study considers different screening strategies, contact tracing, and the severity of novel epidemic outbreaks for various population sizes, providing insight into multinational containment effectiveness of emerging infectious diseases, prior to vaccines development. During the period of the ancestral SARS-Cov-2 virus, contact tracing alone is insufficient to achieve outbreak control. Although universal testing is proposed in multiple nations, its effectiveness accompanied by other measures is rarely examined. Our research investigates the necessity of universal testing when contact tracing and symptomatic screening measures are implemented. We used a stochastic transmission model to simulate COVID-19 transmission, evaluating containment strategies via contact tracing, one-time high risk symptomatic testing, and universal testing. Despite universal testing having the potential to identify subclinical cases, which is crucial for non-pharmaceutical interventions, our model suggests that universal testing only reduces the total number of cases by 0.0009% for countries with low COVID-19 prevalence and 0.025% for countries with high COVID-19 prevalence when rigorous contact tracing and symptomatic screening are also implemented. These findings highlight the effectiveness of testing strategies and contact tracing in reducing COVID-19 cases by identifying subclinical cases.

A comprehensive evaluation of COVID-19 policies and outcomes in 50 countries and territories.

The COVID-19 pandemic struck the world unguarded, some places outperformed others in COVID-19 containment. This longitudinal study considered a comparative evaluation of COVID-19 containment across 50 distinctly governed regions between March 2020 and November 2021. Our analysis distinguishes between a pre-vaccine phase (March-November 2020) and a vaccinating phase (December 2020-November 2021). In the first phase, we develop an indicator, termed lockdown efficiency (LE), to estimate the efficacy of measures against monthly case numbers. Nine other indicators were considered, including vaccine-related indicators in the second phase. Linear mixed models are used to explore the relationship between each government policy & hygiene education (GP&HE) indicator and each vital health & socioeconomic (VH&SE) measure. Our ranking shows that surveyed countries in Oceania and Asian outperformed countries in other regions for pandemic containment prior to vaccine development. Their success appears to be associated with non-pharmaceutical interventions, acting early, and adjusting policies as needed. After vaccines have been distributed, maintaining non-pharmacological intervention is the best way to achieve protection from variant viral strains, breakthrough infections, waning vaccine efficacy, and vaccine hesitancy limiting of herd immunity. The findings of the study provide insights into the effectiveness of emerging infectious disease containment policies worldwide.

Nonpolio Enterovirus Activity during the COVID-19 Pandemic, Taiwan, 2020.

In Taiwan, lower nonpolio enterovirus activity during the coronavirus disease pandemic in 2020 compared with 2014-2019 might be attributable to adherence to nonpharmaceutical interventions. The preventable fraction among unexposed persons indicated that 90% of nonpolio enterovirus activity might have been prevented during 2014-2019 by adopting the same measures enforced in 2020.

The effect of preventing subclinical transmission on the containment of COVID-19: Mathematical modeling and experience in Taiwan.

The control strategies preventing subclinical transmission differed among countries. A stochastic transmission model was used to assess the potential effectiveness of control strategies at controlling the COVID-19 outbreak. Three strategies included lack of prevention of subclinical transmission (Strategy A), partial prevention using testing with different accuracy (Strategy B) and complete prevention by isolating all at-risk people (Strategy C, Taiwan policy). The high probability of containing COVID-19 in Strategy C is observed in different scenario, had varied in the number of initial cases (5, 20, and 40), the reproduction number (1.5, 2, 2.5, and 3.5), the proportion of at-risk people being investigated (40%, 60%, 80%, to 90%), the delay from symptom onset to isolation (long and short), and the proportion of transmission that occurred before symptom onset (<1%, 15%, and 30%). Strategy C achieved probability of 80% under advantageous scenario, such as low number of initial cases and high coverage of epidemiological investigation but Strategy B and C rarely achieved that of 60%. Considering the unsatisfactory accuracy of current testing and insufficient resources, isolation of all at-risk people, as adopted in Taiwan, could be an effective alternative.

Real-time dengue forecast for outbreak alerts in Southern Taiwan.

Dengue fever is a viral disease transmitted by mosquitoes. In recent decades, dengue fever has spread throughout the world. In 2014 and 2015, southern Taiwan experienced its most serious dengue outbreak in recent years. Some statistical models have been established in the past, however, these models may not be suitable for predicting huge outbreaks in 2014 and 2015. The control of dengue fever has become the primary task of local health agencies. This study attempts to predict the occurrence of dengue fever in order to achieve the purpose of timely warning. We applied a newly developed autoregressive model (AR model) to assess the association between daily weather variability and daily dengue case number in 2014 and 2015 in Kaohsiung, the largest city in southern Taiwan. This model also contained additional lagged weather predictors, and developed 5-day-ahead and 15-day-ahead predictive models. Our results indicate that numbers of dengue cases in Kaohsiung are associated with humidity and the biting rate (BR). Our model is simple, intuitive and easy to use. The developed model can be embedded in a "real-time" schedule, and the data (at present) can be updated daily or weekly based on the needs of public health workers. In this study, a simple model using only meteorological factors performed well. The proposed real-time forecast model can help health agencies take public health actions to mitigate the influences of the epidemic.

Impact of cooking oil fume exposure and fume extractor use on lung cancer risk in non-smoking Han Chinese women.

Smoking tobacco is the major risk factor for developing lung cancer. However, most Han Chinese women with lung cancer are nonsmokers. Chinese cooking methods usually generate various carcinogens in fumes that may inevitably be inhaled by those who cook the food, most of whom are female. We investigated the associations of cooking habits and exposure to cooking fumes with lung cancer among non-smoking Han Chinese women. This study was conducted on 1,302 lung cancer cases and 1,302 matched healthy controls in Taiwan during 2002-2010. Two indices, "cooking time-years" and "fume extractor use ratio," were developed. The former was used to explore the relationship between cumulative exposure to cooking oil fumes and lung cancer; the latter was used to assess the impact of fume extractor use for different ratio-of-use groups. Using logistic models, we found a dose-response association between cooking fume exposure and lung cancer (odds ratios of 1, 1.63, 1.67, 2.14, and 3.17 across increasing levels of cooking time-years). However, long-term use of a fume extractor in cooking can reduce the risk of lung cancer by about 50%. Furthermore, we provide evidence that cooking habits, involving cooking methods and oil use, are associated with risk of lung cancer.

Cost-effectiveness of preventing hepatitis B virus reactivation in patients with lymphoma and resolved HBV infection.

BACKGROUND/PURPOSE: Hepatitis B virus (HBV) reactivation may occur in >10% of patients with lymphoma and resolved HBV infection who undergo rituximab-containing chemotherapy. Preventive strategies may have marked impact on resource allocation in HBV endemic areas. This study aims to compare the cost-effectiveness between prophylactic antiviral therapy and HBV DNA monitoring for the prevention of HBV-related complications. METHODS: Data sources are studies of HBV-related events and survival for patients with lymphoma and resolved HBV infection published since 2006. Decision tree analysis was used to compare the incremental cost-effectiveness ratio (ICER) of preventing HBV-related death or liver decompensation for patients who undergo first-line rituximab-containing chemotherapy. Sensitivity analysis was performed to examine the impact of the preventive efficacy, the duration of prophylactic antiviral therapy, and the cost of different interventions. The direct medical cost was derived from the database of the NHI Administration, Taiwan. The time frame of our analysis was set to 3 years after the completion of chemotherapy. RESULTS: The median ICER of prophylactic antiviral therapy, according to current practice guidelines, ranged between USD 150,000 and 250,000 if we apply the guidelines generally. When a long-course (12 months after completion of chemotherapy according to clinical guidelines) prophylactic therapy was assumed, Option A was cheaper and more effective only in the anti-HBs-negative subgroup (median ICER US$149,932 vs. US$161,526, p = 0.013). CONCLUSION: Identification of anti-HBs-negative subgroups is critical to improve the cost-effectiveness of prophylactic antiviral therapy in lymphoma patients with resolved HBV infection.

The association of genetic polymorphisms in the κ-opioid receptor 1 gene with body weight, alcohol use, and withdrawal symptoms in patients with methadone maintenance.

Methadone is a synthetic opioid that binds to the κ-opioid receptor with a low affinity. This study tested the hypotheses that the genetic polymorphisms in the κ-opioid receptor 1 (OPRK1) gene region are associated with methadone treatment responses in a Taiwan methadone maintenance treatment (MMT) cohort. Seventeen single nucleotide polymorphisms (SNPs) in OPRK1 were selected and genotyped on DNA of 366 MMT patients. Six SNPs from rs7843965 to rs1051660 (intron 2 to exon 2) were significantly associated with body weight (P < 0.007). A haplotype of 4 SNPs rs7832417-rs16918853-rs702764-rs7817710 (exon 4 to intron 3) was associated with bone or joint aches (P ≤ 0.004) and with the amount of alcohol use (standard drinks per day; global P < 0.0001). The haplotype rs10958350-rs7016778-rs12675595 was associated with gooseflesh skin (global P < 0.0001), yawning (global P = 0.0001), and restlessness (global P < 0.0001) withdrawal symptoms. The findings suggest that genetic polymorphisms in OPRK1 were associated with the body weight, alcohol use, and opioid withdrawal symptoms in MMT patients.

Functional genetic polymorphisms in CYP2C19 gene in relation to cardiac side effects and treatment dose in a methadone maintenance cohort.

Abstract Methadone maintenance therapy is an established treatment for heroin dependence. This study tested the influence of functional genetic polymorphisms in CYP2C19 gene encoding a CYP450 enzyme that contributes to methadone metabolism on treatment dose, plasma concentration, and side effects of methadone. Two single nucleotide polymorphisms (SNPs), rs4986893 (exon 4) and rs4244285 (exon 5), were selected and genotyped in 366 patients receiving methadone maintenance therapy in Taiwan. The steady-state plasma concentrations of both methadone and its EDDP metabolite enantiomers were measured. SNP rs4244285 allele was significantly associated with the corrected QT interval (QTc) change in the electrocardiogram (p=0.021), and the Treatment Emergent Symptom Scale (TESS) total score (p=0.021) in patients who continued using heroin, as demonstrated with a positive urine opiate test. Using the gene dose (GD) models where the CYP2C19 SNPs were clustered into poor (0 GD) versus intermediate (1 GD) and extensive (2 GD) metabolizers, we found that the extensive metabolizers required a higher dose of methadone (p=0.035), and showed a lower plasma R-methadone/methadone dose ratio (p=0.007) in urine opiate test negative patients, as well as a greater QTc change (p=0.008) and higher total scores of TESS (p=0.018) in urine opiate test positive patients, than poor metabolizers. These results in a large study sample from Taiwan suggest that the gene dose of CYP2C19 may potentially serve as an indicator for the plasma R-methadone/methadone dose ratio and cardiac side effect in patients receiving methadone maintenance therapy. Further studies of pharmacogenetic variation in methadone pharmacokinetics and pharmacodynamics are warranted in different world populations.

CYP1A2 genetic polymorphisms are associated with early antidepressant escitalopram metabolism and adverse reactions.

AIM: The liver CYP1A2 enzyme may metabolize antidepressant escitalopram (S-CIT) to S-desmethylcitalopram (S-DCIT) and S-didesmethylcitalopram (S-DDCIT). This study tested whether genetic polymorphisms in the CYP1A2 gene are associated with the treatment responses to S-CIT. MATERIALS & METHODS: Ten SNPs in CYP1A2 were selected and genotyped in 158 patients under S-CIT treatment. The serum levels of S-CIT and its metabolites were measured by HPLC. RESULTS: CYP1A2 SNPs rs2069521, rs2069526, rs4646425 and rs4646427 are significantly associated with the metabolic ratios of S-DDCIT/S-DCIT (p = 0.002, 0.018, 0.008 and 0.004, respectively) at week 2 of treatment. Carriers of the allele types associated with higher S-DDCIT/S-DCIT ratios had more severe side effects. CONCLUSION: These results suggest that genetic variants in CYP1A2 may be indicators for S-CIT metabolism and that the fast metabolizers may experience more severe adverse reactions in the early stages of S-CIT treatment. Original submitted 27 December 2012; Revision submitted 15 May 2013.

OPRM1 genetic polymorphisms are associated with the plasma nicotine metabolite cotinine concentration in methadone maintenance patients: a cross sectional study.

Majority of the heroin-dependent patients smoke cigarettes. Although it has been reported that the OPRM1 genetic polymorphism is associated with the brain mu-opioid receptor binding potential in cigarette smokers, there is no direct evidence showing the impact of plasma cotinine, a nicotine metabolite, on treatment responses to methadone maintenance treatment (MMT). In this study, we aimed to test the hypothesis that the genetic polymorphisms in the OPRM1 are associated with the methadone treatment responses and the severity of cigarette smoking directly measured by the plasma concentration of cotinine in a Taiwanese MMT cohort. Fifteen OPRM1 single-nucleotide polymorphisms (SNPs) were selected and genotyped on DNA samples of 366 MMT patients. Plasma concentrations of cotinine were measured by cotinine enzyme-linked immunosorbent assay. The plasma cotinine concentration had positive correlation with concentrations of methadone (P = 0.042) and its metabolite 2-ethylidene-1,5-dimethyl-3,3-diphenyl-pyrrolidine (P = 0.037). Methadone treatment non-responders, defined by a positive urine morphine test, had a higher plasma concentration of cotinine (P = 0.005), but a lower plasma concentration-to-dose ratio of both R- and S-methadone (P = 0.001 and 0.012, respectively) than the responders. OPRM1 genetic variants, rs1074287, rs6912029, rs1799971, rs12209447, rs510769, rs3798676, rs553202, rs7748401, rs495491, rs10457090, rs589046, rs3778152 and rs563649, were significantly associated with the plasma concentration of cotinine when using recessive model for genotypes (general linear model (GLM), P<0.038; false discovery rate (FDR)<0.035) and additive model for allele types (GLM, P<0.03; FDR<0.049) in association analyses. The G allele carriers of SNP rs1799971 (A118G) on exon 1 of OPRM1 gene had a lower plasma cotinine concentration than the A allele carriers (GLM, P = 0.029). OPRM1 genetic polymorphisms are associated with the plasma concentration of cotinine in a Taiwanese MMT cohort. Carriers with the major allele of SNP rs1799971 had a higher plasma cotinine concentration.

Genetic polymorphisms in the opioid receptor mu1 gene are associated with changes in libido and insomnia in methadone maintenance patients.

Methadone, a synthetic racemic opioid that primarily works as a µ-opioid receptor (OPRM1) agonist, is commonly used for the treatment of heroin addiction. Genetic association studies have reported that the OPRM1 gene is involved in the physiology of heroin and alcohol addiction. Our current study is designed to test the hypothesis that genetic polymorphisms in the OPRM1 gene region are associated with methadone dosage, plasma concentrations, treatment responses, adverse reactions and withdrawal symptoms in a methadone maintenance treatment (MMT) cohort from Taiwan. Fifteen OPRM1 single nucleotide polymorphisms (SNPs) were selected and genotyped using DNA samples from 366 MMT patients. The plasma concentrations of methadone and its metabolite were measured by high performance liquid chromatography. The results obtained using dominant model analysis indicate that the OPRM1 SNPs rs1074287, rs6912029, rs12209447, rs510769, rs3798676, rs7748401, rs495491, rs10457090, rs589046, rs3778152, rs563649, and rs2075572 are significantly associated with change-in-libido side effects (adjusted p<0.042). Using recessive model analysis, these SNPs were also found to be significantly associated with insomnia side effects in this cohort (p<0.009). The significance of the insomnia findings was mainly contributed by a subgroup of patients who had a positive urine morphine test (p<0.022), and by individuals who did not use benzodiazepine hypnotics (p<0.034). Our current data thus suggest that genetic polymorphisms in OPRM1 may influence the change-in-libido and insomnia side effects sometimes found in MMT patients.

CYP2B6 polymorphisms influence the plasma concentration and clearance of the methadone S-enantiomer.

Methadone is a racemic compound composed of the R-form and S-form enantiomers. The drug is usually used in maintenance therapy for the heroin-addicted patients. In our previous study, we found that the cytochrome P-450 (CYP) isozyme 2B6 preferentially metabolizes the S-methadone enantiomer. We thus tested whether CYP2B6 gene polymorphisms had any influence on the concentration or clearance of methadone. Ten single nucleotide polymorphisms within this gene region were evaluated in 366 patients undergoing methadone maintenance for at least 3 months. The plasma steady-state levels of racemic methadone and its metabolite 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine were then measured in these individuals. The rs10403955 (T allele in intron 1), rs3745274 (G allele in exon 4), rs2279345 (T allele in intron 5), and rs707265 (A allele in exon 9) CYP2B6 allele types were found to be significantly associated with a higher clearance, a lower plasma concentration, and a lower concentration-to-dosage (C/D) ratio of (S)-methadone (P < 0.0017). Two haplotype blocks of a trinucleotide haplotype (rs8100458-rs10500282-rs10403955 in intron 1) and a hexanucleotide haplotype (rs2279342-rs3745274-rs2279343-rs2279345-rs1038376-rs707265 from intron 2 to exon 9) were constructed within CYP2B6. The major combinations of T-T-T and A-G-A-T-A-A of these particular haplotypes showed significant associations with the plasma concentrations of S-methadone and its C/D ratio (P < 0.0001, respectively). We conclude that genetic polymorphisms in the CYP2B6 gene may therefore be indicators of the clearance, plasma concentration and C/D ratio of S-methadone.