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

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

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

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

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.

Impact of COVID-19 pandemic on accessibility of Taiwanese medical care.

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.

Modelling the impacts of COVID-19 pandemic on the quality of population-based colorectal cancer screening.

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.

Mitigating the impact of COVID-19 on colorectal cancer screening: Organized service screening perspectives from the Asia-Pacific region.

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.

Evaluating medical capacity for hospitalization and intensive care unit of COVID-19: A queue model approach.

BACKGROUND: The surge of COVID-19 pandemic has caused severe respiratory conditions and a large number of deaths due to the shortage of intensive care unit (ICU) in many countries. METHODS: We developed a compartment queue model to describe the process from case confirmation, home-based isolation, hospitalization, ICU, recovery, and death. By using public assessed data in Lombardy, Italy, we estimated two congestion indices for isolation wards and ICU. The excess ICU needs were estimated in Lombardy, Italy, and other countries when data were available, including France, Spain, Belgium, New York State in the USA, South Korea, and Japan. RESULTS: In Lombardy, Italy, the congestion of isolation beds had increased from 2.2 to the peak of 6.0 in March and started to decline to 3.9 as of 9th May, whereas the demand for ICU during the same period has not decreased yet with an increasing trend from 2.9 to 8.0. The results showed the unmet ICU need from the second week in March as of 9th May. The same situation was shown in France, Spain, Belgium, and New York State, USA but not for South Korea and Japan. The results with data until December 2020 for Lombardy, Italy were also estimated to reflect the demand for hospitalization and ICU after the occurrence of viral variants. CONCLUSION: Two congestion indices for isolation wards and ICU beds using open assessed tabulated data with a compartment queue model underpinning were developed to monitor the clinical capacity in hospitals in response to the COVID-19 pandemic.