An AI-empowered indoor digital contact tracing system for COVID-19 outbreaks in residential care homes

An AI-empowered indoor digital contact-tracing system was developed using a centralized architecture and advanced low-energy Bluetooth technologies for indoor positioning, with careful preservation of privacy and data security. We analyzed the contact pattern data from two RCHs and investigated a COVID-19 outbreak in one study site. To evaluate the effectiveness of the system in containing outbreaks with minimal contacts under quarantine, a simulation study was conducted to compare the impact of different quarantine strategies on outbreak containment within RCHs. The significant difference in contact hours between weekdays and weekends was observed for some pairs of RCH residents and staff during the two-week data collection period. No significant difference between secondary cases and uninfected contacts was observed in a COVID-19 outbreak in terms of their demographics and contact patterns. Simulation results based on the collected contact data indicated that a threshold of accumulative contact hours one or two days prior to diagnosis of the index case could dramatically increase the efficiency of outbreak containment within RCHs by targeted isolation of the close contacts. This study demonstrated the feasibility and efficiency of employing an AI-empowered system in indoor digital contact tracing of outbreaks in RCHs in the post-pandemic era.

Evaluating the spike in the symptomatic proportion of SARS-CoV-2 in China in 2022 with variolation effects: a modeling analysis

Despite most COVID-19 infections being asymptomatic, mainland China had a high increase in symptomatic cases at the end of 2022. In this study, we examine China's sudden COVID-19 symptomatic surge using a conceptual SIR-based model. Our model considers the epidemiological characteristics of SARS-CoV-2, particularly variolation, from non-pharmaceutical intervention (facial masking and social distance), demography, and disease mortality in mainland China. The increase in symptomatic proportions in China may be attributable to (1) higher sensitivity and vulnerability during winter and (2) enhanced viral inhalation due to spikes in SARS-CoV-2 infections (high transmissibility). These two reasons could explain China's high symptomatic proportion of COVID-19 in December 2022. Our study, therefore, can serve as a decision-support tool to enhance SARS-CoV-2 prevention and control efforts. Thus, we highlight that facemask-induced variolation could potentially reduces transmissibility rather than severity in infected individuals. However, further investigation is required to understand the variolation effect on disease severity.

[Comparative study of percutaneous transfacet screw and pedicle screw in oblique lumbar interbody fusion for the treatment of single-level lumbar spinal stenosis]

Objective: To conduct a comparative study of radiological and clinical outcomes between percutaneous transfacet screw (TFS) and pedicle screw (BPS) in oblique lateral lumbar interbody fusion (OLIF) for single-level lumbar spinal stenosis. Methods: A retrospective cohort study. Patients who underwent OLIF with TFS or BPS for the treatment of single-level lumbar spinal stenosis at Beijing Jishuitan Hospital from January 2019 to June 2022 were retrospectively analyzed. Radiological parameters and clinical indicators were compared between the two groups. Radiological parameters included preoperative, immediate postoperative (within 5 days), and 1-year postoperative measurements of disc height and segmental lordosis angle, as well as interbody fusion status at 1 year postoperatively. Clinical indicators included operative time, blood loss, length of hospital stay, complications, and Oswestry Disability Index (ODI), visual analogue scale (VAS) scores for back pain, and leg pain before and 1 year after surgery. Results: Four male and 10 female patients with an average age of (61.0±11.2) years underwent OLIF with TFS, while 9 male and 12 female patients underwent OLIF with BPS, with a mean age of (60.9±6.7) years. There was no statistically significant difference in preoperative disc height between the TFS and BPS groups (P>0.05). The immediate postoperative disc height was (12.9±2.1) mm and it was (10.4±1.7) mm at 1-year follow-up in the TFS group; in the BPS group, it was (12.9±2.1) mm immediately postoperatively and (11.9±2.1) mm at 1-year follow-up; there was statistically significant difference between the two groups at 1-year follow-up (P=0.037). The segmental lordosis angle showed no significant differences within each group or between the two groups at preoperative, immediate postoperative, or 1-year postoperative follow-up (all P>0.05). At 1-year postoperative follow-up, the fusion rates was 92.9%(13/14) in the TFS group and 95.2%(20/21) in the BPS group, with no statistically significant difference between the two groups (P>0.05). The TFS group had a significantly shorter operative time and less blood loss compared to the BPS group [(164.3±33.9) minutes vs (191.7±31.8) minutes and (74.3±46.9) ml vs (124.8±54.0) ml, respectively] (both P<0.05). Both groups showed significant improvement in ODI and VAS scores at 1 year postoperatively compared to those preoperatively, but with no statistically significant difference was found between the groups (both P>0.05). Conclusions: OLIF with TFS fixation can effectively restore disc height and alleviate back and leg pain in patients with single-level lumbar spinal stenosis. Compared to the OLIF with BPS procedure, OLIF with TFS has shorter operative time and less blood loss.

All cause and cause specific mortality associated with transition to daylight saving time in US: nationwide, time series, observational study

Objectives

To estimate the association between the transition to daylight saving time and the risks of all cause and cause specific mortality in the US.

Design

Nationwide time series observational study based on weekly data.

Setting

US state level mortality data from the National Center for Health Statistics, with death counts from 50 US states and the District of Columbia, from the start of 2015 to the end of 2019.

Population

13 912 837 reported deaths in the US.

Main outcome measures

Weekly counts of mortality for any cause, and for Alzheimer's disease, dementia, circulatory diseases, malignant neoplasms, and respiratory diseases.

Results

During the study period, 13 912 837 deaths were reported. The analysis found no evidence of an association between the transition to spring daylight saving time (when clocks are set forward by one hour on the second Sunday of March) and the risk of all cause mortality during the first eight weeks after the transition (rate ratio 1.003, 95% confidence interval 0.987 to 1.020). Autumn daylight saving time is defined in this study as the time when the clocks are set back by one hour (ie, return to standard time) on the first Sunday of November. Evidence indicating a substantial decrease in the risk of all cause mortality during the first eight weeks after the transition to autumn daylight saving time (0.974, 0.958 to 0.990). Overall, when considering the transition to both spring and autumn daylight saving time, no evidence of any effect of daylight saving time on all cause mortality was found (0.988, 0.972 to 1.005). These patterns of changes in mortality rates associated with transition to daylight saving time were consistent for Alzheimer's disease, dementia, circulatory diseases, malignant neoplasms, and respiratory diseases. The protective effect of the transition to autumn daylight saving time on the risk of mortality was more pronounced in elderly people aged ≥75 years, in the non-Hispanic white population, and in those residing in the eastern time zone.

Conclusions

In this study, transition to daylight saving time was found to affect mortality patterns in the US, but an association with additional deaths overall was not found. These findings might inform the ongoing debate on the policy of shifting daylight saving time.

Association between infarct location and haemorrhagic transformation of acute ischaemic stroke after intravenous thrombolysis

AIM

To evaluate the association between computed tomography (CT)-based imaging variables at the time of admission and haemorrhagic transformation (HT) after intravenous thrombolysis (IVT).

MATERIALS AND METHODS

One hundred and eight patients who were treated with IVT for acute ischaemic stroke (AIS) during January 2021 to July 2023 were analysed retrospectively. The infarct location was classified as cortical or subcortical in accordance with the Alberta Stroke Program Early CT Score (ASPECTS) system. Logistic regression and receiver operating characteristic curve analyses were performed to determine the relationship between ischaemic variables and HT.

RESULTS

Of the total, 18 (16.7%) patients had HT and seven (6.5%) had symptomatic intracerebral haemorrhage (sICH). Multivariate analysis revealed that cortical ASPECTS was independently associated with HT (odds ratio [OR], 0.197; 95% confidence interval [CI], 0.076-0.511; p=0.001) and cortical ASPECTS was independently associated with sICH (OR, 0.066; 95% CI, 0.009-0.510; p=0.009). To predict HT and sICH, cortical ASPECTS (HT area under the curve [AUC] = 0.881, sICH AUC = 0.971) provided a higher AUC compared with ASPECTS (HT AUC = 0.850, sICH AUC = 0.918).

CONCLUSION

Cortical ASPECTS seen on CT at the time of admission is associated with HT and sICH after IVT.

[Current status and prospects for the application of robot-assisted spine surgery]

Traditional spine surgery frequently encounters difficulties with inadequate surgical visualization and high risk.Robot-assisted spine surgery is quickly evolving,particularly in screw placement,providing three-dimensional imaging and precise positioning to optimize the surgical process. Robot-assisted systems can increase surgical precision,reduce operating time and radiation exposure,and improve patient prognosis. They also have strong image recognition and analysis capabilities,reducing intraoperative instability and fatigue and allowing remote manipulation.While robot-assisted spine surgery has demonstrated noteworthy advantages in regards to screw placement accuracy and reduced radiation exposure,its effects on operative time remain subject to debate,with cost being a significant hindrance to widespread implementation.Long-term clinical validation and studies of outcomes are necessary for the extensive use of robotic-assisted spine surgery.Future priorities include the enhancement of surgical navigation and imaging,integration of artificial intelligence,improvement of telesurgical capabilities,expansion of robotic functionality,and the development of policy guidance and clinical guidelines to accompany the growth of technology.Robot-assisted spine surgery enhances accuracy and safety,and is anticipated to assume an increasingly crucial role in spine surgery as technology advances and becomes more widely available.

Association of blood heavy metal concentrations with hearing loss: a systematic review and meta-analysis

OBJECTIVES

This study aimed to assess the associations between blood heavy metal concentrations and hearing loss.

STUDY DESIGN

This was a systematic review and meta-analysis.

METHODS

A comprehensive literature search was performed using Embase, PubMed, Web of Science, Cochrane Library, China National Knowledge Infrastructure, Chinese Biomedical Literature, Wanfang and Weipu databases. Ten studies were included, and a random or fixed-effects model was used for the meta-analysis. Review Manager 5.4 software was used for data synthesis, and Stata 15.1 software was used for the publication bias and sensitivity analyses.

RESULTS

Blood lead concentrations were significantly and substantially associated with hearing loss (mean difference (MD) = 1.14; 95% confidence interval [CI] = 0.03, 2.26; P = 0.04; I2 = 81%), and iron deficiency was significantly related to hearing loss (MD = -0.42; 95% CI = -0.66, -0.18; P = 0.12; I2 = 60%).

CONCLUSIONS

These results suggest an association between blood heavy metal concentrations and hearing loss. However, there were limitations: confounding factors, lack of description for the specific methods of blinding and independent verification of case definition, limited sample size, Chinese publications comprising half of the primary data and the lack of assessment of the relationship between different blood heavy metal concentrations and the severity of hearing loss. Therefore, larger and well-designed prospective cohort studies are required for further exploration.

[Serum glycocalyx markers in patients after cardiac arrest: association with outcomes]

Objective: To assess the levels of serum glycocalyx markers in the first 24 hours after cardiac arrest (CA) and investigate their relationship with 30-day outcomes. Methods: A retrospective cohort study was conducted on prospectively collected data from CA patients, who were admitted to the intensive care units of the Affiliated Hospital of Xuzhou Medical University and obtained return of spontaneous circulation for more than 24 hours between September 2021 and October 2022. Serum samples obtained at the 24-hour after CA were utilized to measure the levels of glycocalyx markers, including heparan sulfate (HS), hyaluronic acid (HA), and syndecan-1 (Sdc-1). Patients were allocated into good function (CPC1-2) and poor function (CPC3-5) groups on the basis of cerebral performance category (CPC) at 30 days post-CA. Logistic regression analysis was used to determine the association between serum glycocalyx markers and neurological outcomes. Patients were regrouped in light of 30-d mortality and Cox regression analysis was used to determine the association between serum glycocalyx markers and 30-d mortality. Results: A total of 71 patients were included in the study, including 31 (43.7%) females and 40 (56.3%) males, with an average age of (59.0±17.0) years. The poor function group (n=49) demonstrated significantly elevated levels of HS and HA when compared to the good function group (n=22) [HS: 2 461.0(1 623.0, 5 492.0) μg/L vs 1 492.0 (914.0, 2 550.0) μg/L, P=0.008; HA: 124.0(97.0, 365.0)μg/L vs 337.0(135.0, 1 421.0) μg/L, P=0.033]. Adjusted logistic regression analysis revealed that HS was independently associated with poor neurological outcome [odds ratio (OR)=0.389, 95% confidence interval (CI): 0.182-0.828, P=0.014]. In the 30-day mortality analysis, the death group (n=32) exhibited significantly higher levels of HS and HA when compared to the survival group (n=39) [HS: 1 880.0(1 011.0, 3 554.0) μg/L vs 2 500.0(1 726.0, 6 276.0) μg/L, P=0.027; HA: 162.0(99.0, 537.0) μg/L vs 813.0(148.0, 1 531.0) μg/L, P=0.025]. Adjusted Cox regression analysis indicated that elevated levels of HS and HA were independent risk factors (HS: HR=1.697, 95%CI: 1.126-2.557, P=0.011; HA: HR=1.336, 95%CI: 1.047-1.705, P=0.020) for 30-day mortality. Conclusions: High level of serum HS in 24 hours after CA may serve as a potential predictive marker for both neurological function and 30-day mortality. However, high level of serum HA appears to primarily predict 30-day mortality. Sdc-1 does not seem to contribute to outcome prediction.

Experimental study of modified Tavis-Cummings model with directly-coupled superconducting artificial atoms

The Tavis-Cummings model is intensively investigated in quantum optics and has important applications in generation of multi-atom entanglement. Here, we employ a superconducting circuit quantum electrodynamic system to study a modified Tavis-Cummings model with directly-coupled atoms. In our device, three superconducting artificial atoms are arranged in a chain with direct coupling through fixed capacitors and strongly coupled to a transmission line resonator. By performing transmission spectrum measurements, we observe different anticrossing structures when one or two qubits are resonantly coupled to the resonator. In the case of the two-qubit Tavis-Cummings model without qubit-qubit interaction, we observe two dips at the resonance point of the anticrossing. The splitting of these dips is determined by Δ λ=2g12+g32, where g1 and g3 are the coupling strengths between Qubit 1 and the resonator, and Qubit 3 and the resonator, respectively. The direct coupling J12 between the two qubits results in three dressed states in the two-qubit Tavis-Cummings model at the frequency resonance point, leading to three dips in the transmission spectrum. In this case, the distance between the two farthest and asymmetrical dips, arising from the energy level splitting, is larger than in the previous case. The frequency interval between these two dips is determined by the difference in eigenvalues (Δ λ=ε 1+-ε 1-), obtained through numerical calculations. What we believe as novel and intriguing experimental results may potentially advance quantum optics experiments, providing valuable insights for future research.

Transmission risk assessment of HIV/AIDS epidemic resulting from sexual transmission in China, 2013-2017

To estimate the risk of human immunodeficiency virus (HIV) infection through sexual transmission in China from 2013 to 2017 accurately, we divide the total population into three groups, namely, men who have sex with men (MSM) group; non-marital and commercial sex group: female sex workers (FSW) and their clients (FSWC); non-marital and non-commercial sex group: general women (GW) and general men (GM). First, the risks of HIV infection among men who have contacts with infected men or infected women decrease annually. Second, the number of contacts between susceptible FSWC and infected FSW per unit time is greater than that between susceptible MSM and infected MSM, and also greater than that between susceptible FSW and infected FSWC, which suggests that the intervention for commercial sex of heterosexual men should be strengthened. Third, the effective reproduction numbers of the MSM group and non-commercial sex group decrease annually, while the effective reproduction number of the commercial sex group decreases first, then increases, because the risk of women being infected by men decreases first, then increases. Additionally, the effective reproduction number of the commercial sex group exceeds that of the MSM group after 2015, which indicates that commercial sex contributes more and more to the HIV/AIDS epidemic.

Effectiveness of the booster dose of inactivated COVID-19 vaccine against Omicron BA.5 infection: a matched cohort study of adult close contacts

BACKGROUND

Although COVID-19 vaccines and their booster regimens protect against symptomatic infections and severe outcomes, there is limited evidence about their protection against asymptomatic and symptomatic infections in real-world settings, particularly when considering that the majority of SARS-CoV-2 Omicron infections were asymptomatic. We aimed to assess the effectiveness of the booster dose of inactivated vaccines in mainland China, i.e., Sinopharm (BBIBP-CorV) and Sinovac (CoronaVac), against Omicron infection in an Omicron BA.5 seeded epidemic.

METHODS

Based on an infection-naive but highly vaccinated population in Urumqi, China, the study cohort comprised all 37,628 adults who had a contact history with individuals having SARS-CoV-2 infections, i.e., close contacts, between August 1 and September 7, 2022. To actively detect SARS-CoV-2 infections, RT-PCR tests were performed by local authorities on a daily basis for all close contacts, and a testing-positive status was considered a laboratory-confirmed outcome. The cohort of close contacts was matched at a ratio of 1:5 with the fully vaccinated (i.e., 2 doses) and booster vaccinated groups (i.e., 3 doses) according to sex, age strata, calendar date, and contact settings. Multivariate conditional logistic regression models were adopted to estimate the marginal effectiveness of the booster dose against Omicron BA.5 infection after adjusting for confounding variables. Subgroup analyses were performed to assess vaccine effectiveness (VE) in different strata of sex, age, the time lag from the last vaccine dose to exposure, and the vaccination status of the source case. Kaplan-Meier curves were employed to visualize the follow-up process and testing outcomes among different subgroups of the matched cohort.

FINDINGS

Before matching, 37,099 adult close contacts were eligible for cohort enrolment. After matching, the 2-dose and 3-dose groups included 3317 and 16,051 contacts, and the proportions with Omicron infections were 1.03% and 0.62% among contacts in the 2-dose and 3-dose groups, respectively. We estimated that the adjusted effectiveness of the inactivated booster vaccine versus 2 doses against Omicron infection was 35.5% (95% CI 2.0, 57.5). The booster dose provided a higher level of protection, with an effectiveness of 60.2% (95% CI 22.8, 79.5) for 15-180 days after vaccination, but this VE decreased to 35.0% (95% CI 2.8, 56.5) after 180 days. Evidence for the protection of the booster dose was detected among young adults aged 18-39 years, but was not detected for those aged 40 years or older.

INTERPRETATION

The receipt of the inactivated vaccine booster dose was associated with a significantly lower Omicron infection risk, and our findings confirmed the vaccine effectiveness (VE) of booster doses against Omicron BA.5 variants. Given the rapid evolution of SARS-CoV-2, we highlight the importance of continuously monitoring the protective performance of vaccines against the genetic variants of SARS-CoV-2, regardless of existing vaccine coverage.

Age-period-cohort analysis and projection of cancer mortality in Hong Kong, 1998-2030

OBJECTIVES

To explore the relationship between immigration groups and cancer mortality, this study aimed to explore age, period, birth cohort effects and effects across genders and immigration groups on mortality rates of lung, pancreatic, colon, liver, prostate and stomach cancers and their projections.

DESIGN, SETTING, AND PARTICIPANTS

Death registry data in Hong Kong between 1998 and 2021, which were stratified by age, sex and immigration status. Immigration status was classified into three groups: locals born in Hong Kong, long-stay immigrants and short-stay immigrants.

METHODS

Age-period-cohort (APC) analysis was used to examine age, period, and birth cohort effects for genders and immigration groups from 1998 to 2021. Bayesian APC models were applied to predict the mortality rates from 2022 to 2030.

RESULTS

Short-stay immigrants revealed pronounced fluctuations of mortality rates by age and of relative risks by cohort and period effects for six types of cancers than those of long-stay immigrants and locals. Immigrants for each type of cancer and gender will be at a higher mortality risk than locals. After 2021, decreasing trends (p<0.05) or plateau (p>0.05) of forecasting mortality rates of cancers occur for all immigration groups, except for increasing trends for short-stay male immigrants with colon cancer (p<0.05, Avg+0.30 deaths/100 000 per annum from 15.47 to 18.50 deaths/100 000) and long-stay male immigrants with pancreatic cancer (p<0.05, Avg+0.72 deaths/100 000 per annum from 16.30 to 23.49 deaths/100 000).

CONCLUSIONS

Findings underscore the effect of gender and immigration status in Hong Kong on mortality risks of cancers that immigrants for each type of cancer and gender will be at a higher mortality risk than locals.

Distinguishing the Vaccine Effectiveness of Inactivated BBIBP-CorV Vaccine Booster Against the Susceptibility, Infectiousness, and Transmission of Omicron Stains: A Retrospective Cohort Study in Urumqi, China

INTRODUCTION

With COVID-19 vaccination rolled out globally, increasing numbers of studies have shown that booster vaccines can enhance an individual's protection against the infection, hospitalization, and death caused by SARS-CoV-2. This study evaluated the effectiveness of COVID-19 vaccine BBIBP-CorV booster against being infected (susceptibility), infecting others (infectiousness), and spreading the disease from one to another (transmission).

METHODS

This retrospective cohort study investigated the close contacts of all officially ascertained COVID-19 confirmed cases in Urumqi, China between August 1 and September 7, 2022. Eligible records were divided into four subcohorts based on the vaccination status of both the close contact and their source case: group 2-2, 2-dose contacts seeded by 2-dose source case (as the reference level); group 2-3, 3-dose contacts seeded by 2-dose source case; group 3-2, 2-dose contacts seeded by 3-dose source case; and group 3-3, 3-dose contacts seeded by 3-dose source case. In the four subcohorts, multivariate logistic regression models were used to examine the vaccine effectiveness (VE) for the BBIBP-CorV booster dose. We adjusted for potential confounding variables, including the sex and age of source cases and close contacts, the calendar week of contact history and contact settings. We evaluated the statistical uncertainty using a 95% confidence interval (CI). In addition, we conducted subgroup analyses to evaluate VE by sex.

RESULTS

The sample sizes of groups 2-2, 2-3, 3-2, and 3-3 were 1184, 3773, 4723, and 27,136 individuals, respectively. Overall VE against susceptibility (group 2-3 vs 2-2) was 42.1% (95% CI 10.6, 62.5), VE against infectiousness (group 3-2 vs 2-2) was 62.0% (95% CI 37.2, 77.0), and VE against transmission (group 3-3 vs 2-2) was 83.7% (95% CI 75.1, 89.4). In the sex-stratified subgroups, male close contacts showed similar VE compared to the overall. However, among female close contacts, while the booster dose improved VE against infectiousness and VE against susceptibility, the VEs were not significantly different from zero.

CONCLUSION

BBIBP-CorV vaccine booster was associated with mild to moderate levels of protection against Omicron susceptibility, infectiousness, and transmission. Real-world assessment of protective performance of COVID-19 vaccines against the risk of Omicron strains is continuously needed, and may provide information that helps vaccination strategy.

A multiline fitting method for measuring ethylene concentration based on WMS-2f/1f

Coal spontaneous combustion risk assessment is a global technical challenge for the sustainable development of deep mining technology, and C2H4 is a key indicator for early warning of coal spontaneous combustion. Tunable diode laser absorption spectroscopy (TDLAS) has the advantages of high selectivity, high sensitivity, high accuracy and real-time on-line measurement, and it can detect multiple gases simultaneously, so it has significant advantages in the accurate detection of coal spontaneous combustion indicator gases. To address the problem of cross-interference between the near-infrared absorption lines of CH4 and C2H4, which are the indicator gases of spontaneous combustion in coal, a multi-line fitting method was proposed in this study to calibrate the concentration of C2H4. The high-precision Environics2000 automatic standard gas dispenser from the United States, which has a built-in CPU computer control and data control and processing system, was used. Its gas concentration accuracy: ± 1.0%, gas flow accuracy: ± 1.0%, gas repeatability accuracy: ± 1.0%, flow linearity accuracy: ± 0.5%, and inlet operating pressure: minimum 10 psig (0.67 bar) ~ 75 psig (5.04 bar). The measured and simulated WMS-2f/1f signals were multilinearly fitted using a multilinear fitting algorithm and wavelength modulation spectroscopy (WMS), and the measurement of C2H4 concentration was achieved based on the extracted spectral line information. The results show that the maximum relative error of C2H4 concentration measurement is 2.40%, which is 54% lower than that of the conventional 2f peak measurement method, thus demonstrating the effectiveness of the multilinear fitting algorithm in the inversion of C2H4 concentration under the interference of absorption lines. In addition, this study has far-reaching implications for the application of TDLAS technology in the accurate detection of coal spontaneous combustion indicator gases.

Getting Jab or Regular Test: Observations from an Impulsive Epidemic COVID-19 Model

Several safe and effective vaccines are available to prevent individuals from experiencing severe illness or death as a result of COVID-19. Widespread vaccination is widely regarded as a critical tool in the fight against the disease. However, some individuals may choose not to vaccinate due to vaccine hesitancy or other medical conditions. In some sectors, regular compulsory testing is required for such unvaccinated individuals. Interestingly, different sectors require testing at various frequencies, such as weekly or biweekly. As a result, it is essential to determine the optimal testing frequency and identify underlying factors. This study proposes a population-based model that can accommodate different personal decision choices, such as getting vaccinated or undergoing regular tests, as well as vaccine efficacies and uncertainties in epidemic transmission. The model, formulated as impulsive differential equations, uses time instants to represent the reporting date for the test result of an unvaccinated individual. By employing well-accepted indices to measure transmission risk, including the basic reproduction number, the peak time, the final size, and the number of severe infections, the study shows that an optimal testing frequency is highly sensitive to parameters involved in the transmission process, such as vaccine efficacy, disease transmission rate, test accuracy, and existing vaccination coverage. The testing frequency should be appropriately designed with the consideration of all these factors, as well as the control objectives measured by epidemiological quantities of great concern.

Differences in case-fatality-rate of emerging SARS-CoV-2 variants

Objects

Variants of Severe-Acute-Respiratory-Syndrome Coronavirus-2 (SARS-CoV-2) has caused tremendous impact globally. It has been widely reported that the Omicron (B.1.1.529) variant is less deadly than the Delta (B.1.617.2) variant, presumably due to immunity from vaccination and previous infection. When measuring the severity of a variant, Case-Fatality-Rate (CFR) is often estimated. The purpose of this work is to calculate the change in CFR of different variants over time from a large number of countries/regions since the start of the pandemic in 2020.

Study design

A Cross-sectional study.

Methods

We extend the comparison to all previous VOCs in 58 counties/regions. We use reported death divided by reported cases in 30-day sliding window with a two-week shift between reported death and reported cases.

Results

The drop from Delta variant to Omicron variant is substantial and the difference between subvariants of Omicron is not evident.

Conclusion

We showed that the CFR dropped over time, presumably due to vaccine-induced immune and infection induced immune. Population age structure and prevalence of comorbidity influence CFR.

Rapid global spread of variants of concern of SARS-CoV-2

Objectives

Variants of concern (VOCs) of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), such as the Delta variant and the Omicron variant, have reached all countries/regions of the world and have had a tremendous impact. This study analyses the global spread of VOCs of SARS-CoV-2.

Methods

Biweekly aggregated numbers of several VOCs were retrieved for 58 locations. The time interval for the proportion of VOC samples to exceed 60% (indicating dominance) among all samples sequenced in each location was calculated. The times taken for a VOC to become dominant in 12 (or 36) locations was defined in order to quantify the speed of spread.

Results

It took 63, 56 and 28 days for the Alpha, Delta and Omicron variants to become dominant in 12 locations, respectively, and 133, 70 and 28 days for the Alpha, Delta and Omicron variants to become dominant in 36 locations.

Conclusions

The Omicron variant has much higher transmission potential compared with the Delta variant, and the Delta variant has higher transmission potential compared with the pre-Delta VOCs.

The immune evasion ability of Delta variant is comparable to that of Beta variant in South Africa

BACKGROUND

The high immune evasion ability of SARS-COV-2 Omicron variant surprised the world and appears to be far stronger than any previous variant. Previous to Omicron it has been difficult to assess and compare immune evasion ability of different variants, including the Beta and Delta variants, because of the relatively small numbers of reinfections and because of the problems in correctly identifying reinfections in the population. This has led to different claims appearing in the literature. Thus we find claims of both high and low immune evasion for the Beta variant. Some findings have suggested that the Beta variant has a higher immune evasion ability than the Delta variant in South Africa, and others that it has a lower ability.

METHOD

In this brief report, we re-analyse a unique dataset of variant-specific reinfection data and a simple model to correct for the infection attack rates of different variants.

RESULT

We find that a model with the Delta variant having  an equal or higher immune evasion ability than Beta variant is compatible with the data.

CONCLUSION

We conclude that the immune evasion ability of Beta variant is not stronger than Delta variant, and indeed, the immune evasion abilities of both variants are weak in South Africa.

Resolving the enigma of Iquitos and Manaus: A modeling analysis of multiple COVID-19 epidemic waves in two Amazonian cities

The two nearby Amazonian cities of Iquitos and Manaus endured explosive COVID-19 epidemics and may well have suffered the world's highest infection and death rates over 2020, the first year of the pandemic. State-of-the-art epidemiological and modeling studies estimated that the populations of both cities came close to attaining herd immunity (>70% infected) at the termination of the first wave and were thus protected. This makes it difficult to explain the more deadly second wave of COVID-19 that struck again in Manaus just months later, simultaneous with the appearance of a new P.1 variant of concern, creating a catastrophe for the unprepared population. It was suggested that the second wave was driven by reinfections, but the episode has become controversial and an enigma in the history of the pandemic. We present a data-driven model of epidemic dynamics in Iquitos, which we also use to explain and model events in Manaus. By reverse engineering the multiple epidemic waves over 2 y in these two cities, the partially observed Markov process model inferred that the first wave left Manaus with a highly susceptible and vulnerable population (≈40% infected) open to invasion by P.1, in contrast to Iquitos (≈72% infected). The model reconstructed the full epidemic outbreak dynamics from mortality data by fitting a flexible time-varying reproductive number [Formula: see text] while estimating reinfection and impulsive immune evasion. The approach is currently highly relevant given the lack of tools available to assess these factors as new SARS-CoV-2 virus variants appear with different degrees of immune evasion.

The impact of COVID-19 vaccination campaign in Hong Kong SAR China and Singapore

Background

Vaccination has been the most important measure to mitigate the COVID-19 pandemic. The vaccination coverage was relatively low in Hong Kong Special Administrative Region China, compared to Singapore, in early 2022. Hypothetically, if the two regions, Hong Kong (HK) and Singapore (SG), swap their vaccination coverage rate, what outcome would occur?

Method

We adopt the Susceptible - Vaccinated - Exposed - Infectious - Hospitalized - Death - Recovered model with a time-varying transmission rate and fit the model to weekly reported COVID-19 deaths (the data up to 2022 Nov 4) in HK and SG using R package POMP. After we obtain a reasonable fitting, we rerun our model with the estimated parameter values and swap the vaccination rates between HK and SG to explore what would happen.

Results

Our model fits the data well. The reconstructed transmission rate was higher in HK than in SG in 2022. With a higher vaccination rate as in SG, the death total reported in HK would decrease by 37.5% and the timing of the peak would delay by 3 weeks. With a lower vaccination rate as in HK, the death total reported in SG would increase to 5.5-fold high with a peak 6 weeks earlier than the actual during the Delta variant period.

Conclusions

Vaccination rate changes in HK and SG may lead to very different outcomes. This is likely due that the estimated transmission rates were very different in HK and SG which reflect the different control policies and dominant variants. Because of strong control measures, HK avoided large-scale community transmission of the Delta variant. Given the high breakthrough infection rate and transmission rate of the Omicron variant, increasing the vaccination rate in HK will likely yield a mild (but significant) contribution in terms of lives saved. While in SG, lower vaccination coverage to the level of HK will be disastrous.

Exome-wide screening identifies novel rare risk variants for bone mineral density

Bone mineral density (BMD) is an independent risk factor of osteoporosis-related fractures. We performed gene-based burden tests to assess the association between rare variants and BMD, and identified several BMD candidate genes.

PURPOSE

BMD is highly heritable and a major predictor of osteoporotic fractures, but its genetic basis remains unclear. We aimed to identify rare risk variants contributing to BMD.

METHODS

Utilizing the newly released UK Biobank 200,643 exome dataset, we conducted a gene-based exome-wide association study in males and females, respectively. First, 100,639 males and 117,338 females with BMD values were included in the polygenic risk scores (PRS) analysis. Among individuals with lower 30% PRS, cases were individuals with top 10% BMD, and individuals with bottom 10% BMD were the controls. Considering the effects of vitamin D (VD), individuals with the highest 30% VD concentration were selected for VD-BMD analysis. After quality control, 741 males and 697 females were included in the BMD analysis, and 717 males and 708 females were included in the VD-BMD analysis. The variants were annotated by ANNOVAR software, then BMD and VD-BMD qualified variants were imported into the SKAT R-package to perform gene-based burden tests, respectively.

RESULTS

The gene-based burden test of the exonic variants identified genome-wide candidate associations in ANKRD18A (P = 1.60 × 10^-5, P_{Bonferroni adjust} = 2.11 × 10^-3), C22orf31 (P = 3.49 × 10^-4, P_{Bonferroni adjust} = 3.17 × 10^-2), and SPATC1L (P = 1.09 × 10^-5, P_{Bonferroni adjust} = 8.80 × 10^-3). For VD-BMD analysis, three genes were associated with BMD, such as NIPAL1 (P = 1.06 × 10^-3, P_{Bonferroni adjust} = 3.91 × 10^-2).

CONCLUSIONS

Our study suggested that rare variants contribute to BMD, providing new sights for broadening the genetic structure of BMD.

Propensity score analysis with missing data using a multi-task neural network

BACKGROUND

Propensity score analysis is increasingly used to control for confounding factors in observational studies. Unfortunately, unavoidable missing values make estimating propensity scores extremely challenging. We propose a new method for estimating propensity scores in data with missing values.

MATERIALS AND METHODS

Both simulated and real-world datasets are used in our experiments. The simulated datasets were constructed under 2 scenarios, the presence (T = 1) and the absence (T = 0) of the true effect. The real-world dataset comes from LaLonde's employment training program. We construct missing data with varying degrees of missing rates under three missing mechanisms: MAR, MCAR, and MNAR. Then we compare MTNN with 2 other traditional methods in different scenarios. The experiments in each scenario were repeated 20,000 times. Our code is publicly available at https://github.com/ljwa2323/MTNN .

RESULTS

Under the three missing mechanisms of MAR, MCAR and MNAR, the RMSE between the effect and the true effect estimated by our proposed method is the smallest in simulations and in real-world data. Furthermore, the standard deviation of the effect estimated by our method is the smallest. In situations where the missing rate is low, the estimation of our method is more accurate.

CONCLUSIONS

MTNN can perform propensity score estimation and missing value filling at the same time through shared hidden layers and joint learning, which solves the dilemma of traditional methods and is very suitable for estimating true effects in samples with missing values. The method is expected to be broadly generalized and applied to real-world observational studies.

Resurgence of different influenza types in China and the US in 2021

Various nonpharmaceutical interventions (NPIs) were implemented to alleviate the COVID-19 pandemic since its outbreak. The transmission dynamics of other respiratory infectious diseases, such as seasonal influenza, were also affected by these interventions. The drastic decline of seasonal influenza caused by such interventions would result in waning of population immunity and may trigger the seasonal influenza epidemic with the lift of restrictions during the post-pandemic era. We obtained weekly influenza laboratory confirmations from FluNet to analyse the resurgence patterns of seasonal influenza in China and the US. Our analysis showed that due to the impact of NPIs including travel restrictions between countries, the influenza resurgence was caused by influenza virus A in the US while by influenza virus B in China.

Twice evasions of Omicron variants explain the temporal patterns in six Asian and Oceanic countries

BACKGROUND

The ongoing coronavirus 2019 (COVID-19) pandemic has emerged and caused multiple pandemic waves in the following six countries: India, Indonesia, Nepal, Malaysia, Bangladesh and Myanmar. Some of the countries have been much less studied in this devastating pandemic. This study aims to assess the impact of the Omicron variant in these six countries and estimate the infection fatality rate (IFR) and the reproduction number [Formula: see text] in these six South Asia, Southeast Asia and Oceania countries.

METHODS

We propose a Susceptible-Vaccinated-Exposed-Infectious-Hospitalized-Death-Recovered model with a time-varying transmission rate [Formula: see text] to fit the multiple waves of the COVID-19 pandemic and to estimate the IFR and [Formula: see text] in the aforementioned six countries. The level of immune evasion and the intrinsic transmissibility advantage of the Omicron variant are also considered in this model.

RESULTS

We fit our model to the reported deaths well. We estimate the IFR (in the range of 0.016 to 0.136%) and the reproduction number [Formula: see text] (in the range of 0 to 9) in the six countries. Multiple pandemic waves in each country were observed in our simulation results.

CONCLUSIONS

The invasion of the Omicron variant caused the new pandemic waves in the six countries. The higher [Formula: see text] suggests the intrinsic transmissibility advantage of the Omicron variant. Our model simulation forecast implies that the Omicron pandemic wave may be mitigated due to the increasing immunized population and vaccine coverage.

Effects of vaccination on mitigating COVID-19 outbreaks: a conceptual modeling approach

This paper is devoted to investigating the impact of vaccination on mitigating COVID-19 outbreaks. In this work, we propose a compartmental epidemic ordinary differential equation model, which extends the previous so-called SEIRD model ^[1,2,3,4] by incorporating the birth and death of the population, disease-induced mortality and waning immunity, and adding a vaccinated compartment to account for vaccination. Firstly, we perform a mathematical analysis for this model in a special case where the disease transmission is homogeneous and vaccination program is periodic in time. In particular, we define the basic reproduction number $ \mathcal{R}_0 $ for this system and establish a threshold type of result on the global dynamics in terms of $ \mathcal{R}_0 $. Secondly, we fit our model into multiple COVID-19 waves in four locations including Hong Kong, Singapore, Japan, and South Korea and then forecast the trend of COVID-19 by the end of 2022. Finally, we study the effects of vaccination again the ongoing pandemic by numerically computing the basic reproduction number $ \mathcal{R}_0 $ under different vaccination programs. Our findings indicate that the fourth dose among the high-risk group is likely needed by the end of the year.

Estimation of the serial interval of monkeypox during the early outbreak in 2022

With increased transmissibility and novel transmission mode, monkeypox poses new threats to public health globally in the background of the ongoing COVID-19 pandemic. Estimates of the serial interval, a key epidemiological parameter of infectious disease transmission, could provide insights into the virus transmission risks. As of October 2022, little was known about the serial interval of monkeypox due to the lack of contact tracing data. In this study, public-available contact tracing data of global monkeypox cases were collected and 21 infector-infectee transmission pairs were identified. We proposed a statistical method applied to real-world observations to estimate the serial interval of the monkeypox. We estimated a mean serial interval of 5.6 days with the right truncation and sampling bias adjusted and calculated the reproduction number of 1.33 for the early monkeypox outbreaks at a global scale. Our findings provided a preliminary understanding of the transmission potentials of the current situation of monkeypox outbreaks. We highlighted the need for continuous surveillance of monkeypox for transmission risk assessment.

The mechanism shaping the logistic growth of mutation proportion in epidemics at population scale

Virus evolution is a common process of pathogen adaption to host population and environment. Frequently, a small but important fraction of virus mutations are reported to contribute to higher risks of host infection, which is one of the major determinants of infectious diseases outbreaks at population scale. The key mutations contributing to transmission advantage of a genetic variant often grow and reach fixation rapidly. Based on classic epidemiology theories of disease transmission, we proposed a mechanistic explanation of the process that between-host transmission advantage may shape the observed logistic curve of the mutation proportion in population. The logistic growth of mutation is further generalized by incorporating time-varying selective pressure to account for impacts of external factors on pathogen adaptiveness. The proposed model is implemented in real-world data of COVID-19 to capture the emerging trends and changing dynamics of the B.1.1.7 strains of SARS-CoV-2 in England. The model characterizes and establishes the underlying theoretical mechanism that shapes the logistic growth of mutation in population.

Differences in the superspreading potentials of COVID-19 across contact settings

BACKGROUND

Superspreading events (SSEs) played a critical role in fueling the COVID-19 outbreaks. Although it is well-known that COVID-19 epidemics exhibited substantial superspreading potential, little is known about the risk of observing SSEs in different contact settings. In this study, we aimed to assess the potential of superspreading in different contact settings in Japan.

METHOD

Transmission cluster data from Japan was collected between January and July 2020. Infector-infectee transmission pairs were constructed based on the contact tracing history. We fitted the data to negative binomial models to estimate the effective reproduction number (R) and dispersion parameter (k). Other epidemiological issues relating to the superspreading potential were also calculated.

RESULTS

The overall estimated R and k are 0.561 (95% CrI: 0.496, 0.640) and 0.221 (95% CrI: 0.186, 0.262), respectively. The transmission in community, healthcare facilities and school manifest relatively higher superspreading potentials, compared to other contact settings. We inferred that 13.14% (95% CrI: 11.55%, 14.87%) of the most infectious cases generated 80% of the total transmission events. The probabilities of observing superspreading events for entire population and community, household, health care facilities, school, workplace contact settings are 1.75% (95% CrI: 1.57%, 1.99%), 0.49% (95% CrI: 0.22%, 1.18%), 0.07% (95% CrI: 0.06%, 0.08%), 0.67% (95% CrI: 0.31%, 1.21%), 0.33% (95% CrI: 0.13%, 0.94%), 0.32% (95% CrI: 0.21%, 0.60%), respectively.

CONCLUSION

The different potentials of superspreading in contact settings highlighted the need to continuously monitoring the transmissibility accompanied with the dispersion parameter, to timely identify high risk settings favoring the occurrence of SSEs.

Global stability and optimal control for a COVID-19 model with vaccination and isolation delays

COVID-19 pandemic remains serious around the world and causes huge deaths and economic losses. To investigate the effect of vaccination and isolation delays on the transmission of COVID-19, we propose a mathematical model of COVID-19 transmission with vaccination and isolation delays. The basic reproduction number is computed, and the global dynamics of the model are proved. When R 0 < 1 , the disease-free equilibrium is globally asymptotically stable. The unique endemic equilibrium is globally asymptotically stable if R 0 > 1 . Based on the public information, parameter values are estimated, and sensitivity analysis is carried out by the partial rank correlation coefficients (PRCCs) and the extended version of the Fourier amplitude sensitivity test (eFAST). Our results suggest that the isolation rates of asymptomatic and symptomatic infectious individuals have a significant impact on the transmission of COVID-19. When the COVID-19 is epidemic, the optimal control strategies of our model with vaccination and isolation delays are analyzed. Under the limited resource with constant and time-varying isolation rates, we find that the optimal isolation rates may minimize the cumulative number of infected individuals and the cost of disease control, and effectively contain the transmission of COVID-19. Our study may help public health to prevent and control the COVID-19 spread.

Unravelling the dynamics of Lassa fever transmission with differential infectivity: Modeling analysis and control strategies

Epidemic models have been broadly used to comprehend the dynamic behaviour of emerging and re-emerging infectious diseases, predict future trends, and assess intervention strategies. The symptomatic and asymptomatic features and environmental factors for Lassa fever (LF) transmission illustrate the need for sophisticated epidemic models to capture more vital dynamics and forecast trends of LF outbreaks within countries or sub-regions on various geographic scales. This study proposes a dynamic model to examine the transmission of LF infection, a deadly disease transmitted mainly by rodents through environment. We extend prior LF models by including an infectious stage to mild and severe as well as incorporating environmental contributions from infected humans and rodents. For model calibration and prediction, we show that the model fits well with the LF scenario in Nigeria and yields remarkable prediction results. Rigorous mathematical computation divulges that the model comprises two equilibria. That is disease-free equilibrium, which is locally-asymptotically stable (LAS) when the basic reproduction number, $ {\mathcal{R}}_{0} $, is $ < 1 $; and endemic equilibrium, which is globally-asymptotically stable (GAS) when $ {\mathcal{R}}_{0} $ is $ > 1 $. We use time-dependent control strategy by employing Pontryagin's Maximum Principle to derive conditions for optimal LF control. Furthermore, a partial rank correlation coefficient is adopted for the sensitivity analysis to obtain the model's top rank parameters requiring precise attention for efficacious LF prevention and control.

Evaluation of Effectiveness of Global COVID-19 Vaccination Campaign

To model estimated deaths averted by COVID-19 vaccines, we used state-of-the-art mathematical modeling, likelihood-based inference, and reported COVID-19 death and vaccination data. We estimated that >1.5 million deaths were averted in 12 countries. Our model can help assess effectiveness of the vaccination program, which is crucial for curbing the COVID-19 pandemic.

Estimating the incubation period of SARS-CoV-2 Omicron BA.1 variant in comparison with that during the Delta variant dominance in South Korea

Based on exposure history and symptom onset of 22 Omicron BA.1 cases in South Korea from November to December 2021, we estimated mean incubation period of 3.5 days (95% CI: 2.5, 3.8), and then compared to that of 6.5 days (95% CI: 5.3, 7.7) for 64 cases during Delta variants' dominance in June 2021. For Omicron BA.1 variants, we found that 95% of symptomatic cases developed clinical conditions within 6.0 days (95% CI: 4.3, 6.6) after exposure. Thus, a shorter quarantine period may be considered based on symptoms, or similarly laboratory testing, when Omicron BA.1 variants are circulating.

A simple model to estimate the transmissibility of the Beta, Delta, and Omicron variants of SARS-COV-2 in South Africa

The COVID-19 pandemic caused multiple waves of mortality in South Africa, where three genetic variants of SARS-COV-2 and their ancestral strain dominated consecutively. State-of-the-art mathematical modeling approach was used to estimate the time-varying transmissibility of SARS-COV-2 and the relative transmissibility of Beta, Delta, and Omicron variants. The transmissibility of the three variants were about 73%, 87%, and 276% higher than their preceding variants. To the best of our knowledge, our model is the first simple model that can simulate multiple mortality waves and three variants' replacements in South Africa. The transmissibility of the Omicron variant is substantially higher than that of previous variants.

Reduction in the infection fatality rate of Omicron variant compared with previous variants in South Africa

OBJECTIVE

The SARS-CoV-2 Omicron (B.1.1.529) variant has caused global concern. Previous studies have shown that the variant has enhanced immune evasion ability and transmissibility and reduced severity.

METHODS

In this study, we developed a mathematical model with time-varying transmission rate, vaccination, and immune evasion. We fit the model to reported case and death data up to February 6, 2022 to estimate the transmissibility and infection fatality ratio of the Omicron variant in South Africa.

RESULTS

We found that the high relative transmissibility of the Omicron variant was mainly due to its immune evasion ability, whereas its infection fatality rate substantially decreased by approximately 78.7% (95% confidence interval: 66.9%, 85.0%) with respect to previous variants.

CONCLUSION

On the basis of data from South Africa and mathematical modeling, we found that the Omicron variant is highly transmissible but with significantly lower infection fatality rates than those of previous variants of SARS-CoV-2.

Transmission dynamics of COVID-19 pandemic with combined effects of relapse, reinfection and environmental contribution: A modeling analysis

Reinfection and reactivation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have recently raised public health pressing concerns in the fight against the current pandemic globally. In this study, we propose a new dynamic model to study the transmission of the coronavirus disease 2019 (COVID-19) pandemic. The model incorporates possible relapse, reinfection and environmental contribution to assess the combined effects on the overall transmission dynamics of SARS-CoV-2. The model's local asymptotic stability is analyzed qualitatively. We derive the formula for the basic reproduction number ( R 0 ) and final size epidemic relation, which are vital epidemiological quantities that are used to reveal disease transmission status and guide control strategies. Furthermore, the model is validated using the COVID-19 reported situations in Saudi Arabia. Moreover, sensitivity analysis is examined by implementing a partial rank correlation coefficient technique to obtain the ultimate rank model parameters to control or mitigate the pandemic effectively. Finally, we employ a standard Euler technique for numerical simulations of the model to elucidate the influence of some crucial parameters on the overall transmission dynamics. Our results highlight that contact rate, hospitalization rate, and reactivation rate are the fundamental parameters that need particular emphasis for the prevention, mitigation and control.

Regional heterogeneity of in-hospital mortality of COVID-19 in Brazil

Background

The ongoing Coronavirus disease of 2019 (COVID-19) pandemic has hit Brazil hard in period of different dominant variants. Different COIVD-19 variants have swept through the region, resulting that the total number of cases in Brazil is the third highest in the world. This study is aimed at investigating the regional heterogeneity of in-hospital mortality of COVID-19 in Brazil and the effects of vaccination and social inequality.

Methods

We fitted a multivariate mixed-effects Cox model to a national database of inpatient data in Brazil who were admitted for COVID-19 from February 27, 2020 to March 15, 2022. The in-hospital mortality risks of vaccinated and unvaccinated patients were compared, with adjustment for age, state, ethnicity, education and comorbidities. And the effects of variables to in-hospital mortality were also compared. Stratified analysis was conducted across different age groups and vaccine types.

Results

By fitting the multivariate mixed-effects Cox model, we concluded that age was the most important risk factor for death. With regards to educational level, illiterate patients (hazard ratio: 1.63, 95% CI: 1.56–1.70) had a higher risk than those with a university or college degree. Some common comorbidities were more dangerous for hospitalized patients, such as liver disease (HR: 1.46, 95% CI: 1.34–1.59) and immunosuppression (HR:1.32, 95% CI: 1.26–1.40). In addition, the states involving Sergipe (HR: 1.75, 95% CI: 1.46–2.11), Roraima (HR: 1.65, 95% CI: 1.43–1.92), Maranhão (HR: 1.57, 95% CI: 1.38–1.79), Acre (HR: 1.44, 95% CI: 1.12–1.86), and Rondônia (HR: 1.26, 95% CI: 1.10–1.44) in the north and the northeast region tended to have higher hazard ratios than other area. In terms of vaccine protection, vaccination did not significantly reduce mortality among hospitalized patients. Sinovac and AstraZeneca offered different protection in different regions, and no vaccine provided high protection in all regions.

Conclusion

The study revealed the regional heterogeneity of in-hospital mortality of Covid-19 in Brazil and the effects of vaccination and social inequality. We found that ethnic concentrations were consistent with higher proportion of death cases relative to population size. White Brazilians had more frequent international travel opportunities. As race revealed the intersection of social connections, we speculated that uneven interactions with residential communities partially contribute to the spread of the epidemic. Additionally, the vaccine showed different protection in different regions. In the northern and northeastern regions, AstraZeneca was much more protective than Sinovac, while Sinovac was more protective for hospitalized patients with varying numbers of comorbidities in the Central-west, Southeast and South regions.

Characterizing superspreading potential of infectious disease: Decomposition of individual transmissibility

In the context of infectious disease transmission, high heterogeneity in individual infectiousness indicates that a few index cases can generate large numbers of secondary cases, a phenomenon commonly known as superspreading. The potential of disease superspreading can be characterized by describing the distribution of secondary cases (of each seed case) as a negative binomial (NB) distribution with the dispersion parameter, k. Based on the feature of NB distribution, there must be a proportion of individuals with individual reproduction number of almost 0, which appears restricted and unrealistic. To overcome this limitation, we generalized the compound structure of a Poisson rate and included an additional parameter, and divided the reproduction number into independent and additive fixed and variable components. Then, the secondary cases followed a Delaporte distribution. We demonstrated that the Delaporte distribution was important for understanding the characteristics of disease transmission, which generated new insights distinct from the NB model. By using real-world dataset, the Delaporte distribution provides improvements in describing the distributions of COVID-19 and SARS cases compared to the NB distribution. The model selection yielded increasing statistical power with larger sample sizes as well as conservative type I error in detecting the improvement in fitting with the likelihood ratio (LR) test. Numerical simulation revealed that the control strategy-making process may benefit from monitoring the transmission characteristics under the Delaporte framework. Our findings highlighted that for the COVID-19 pandemic, population-wide interventions may control disease transmission on a general scale before recommending the high-risk-specific control strategies.

Superspreading is one of the key transmission features of many infectious diseases and is considered a consequence of the heterogeneity in infectiousness of individual cases. To characterize the superspreading potential, we divided individual infectiousness into two independent and additive components, including a fixed baseline and a variable part. Such decomposition produced an improvement in the fit of the model explaining the distribution of real-world datasets of COVID-19 and SARS that can be captured by the classic statistical tests. Disease control strategies may be developed by monitoring the characteristics of superspreading. For the COVID-19 pandemic, population-wide interventions are suggested first to limit the transmission at a scale of general population, and then high-risk-specific control strategies are recommended subsequently to lower the risk of superspreading.

Modelling COVID-19 outbreak on the Diamond Princess ship using the public surveillance data

The novel coronavirus disease 2019 (COVID-19) outbreak on the Diamond Princess (DP) ship has caused over 634 cases as of February 20, 2020. We model the transmission process on DP ship as a stochastic branching process, and estimate the reproduction number at the innitial phase of 2.9 (95%CrI: 1.7–7.7). The epidemic doubling time is 3.4 days, and thus timely actions on COVID-19 control were crucial. We estimate the COVID-19 transmissibility reduced 34% after the quarantine program on the DP ship which was implemented on February 5. According to the model simulation, relocating the population at risk may sustainably decrease the epidemic size, postpone the timing of epidemic peak, and thus relieve the tensive demands in the healthcare. The lesson learnt on the ship should be considered in other similar settings.

Heterogeneous epidemic modelling within an enclosed space and corresponding Bayesian estimation

Since March 11th, 2020, COVID-19 has been a global pandemic for more than one years due to a long and infectious incubation period. This paper establishes a heterogeneous epidemic model that divides the incubation period into infectious and non-infectious and employs the Bayesian framework to model the ‘Diamond Princess’ enclosed space incident. The heterogeneity includes two different identities, two transmission methods, two different-size rooms, and six transmission stages. This model is also applicable to similar mixed structures, including closed schools, hospitals, and communities. As the COVID-19 pandemic continues, our mathematical modeling can provide management insights to the governments and policymakers on how the COVID-19 disease has spread and what prevention strategies still need to be taken.

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A completely data-driven linear heterogeneous epidemic model for a relatively enclosed space composed of subspaces up to three people is developed.

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The heterogeneity of the model includes the following five aspects: two different identities, two infection sources, two transmission methods, two different-size rooms, and six transmission stages.

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A probabilistic algorithm to calculate the change in the subspace distribution of people is constructed.

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A simple method is introduced to calculate the basic reproduction number R0 for different infection sources.

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An improved approximate Bayesian updating computation method based on entirely non-informative priors that simultaneously infers any number of unknown parameters is proposed.

Superspreading potential of SARS-CoV-2 Delta variants under intensive disease control measures in China

Given the heterogeneity in individual transmissibility, we estimated the superspreading potential of SARS-CoV-2 Delta variants. Using case series of Delta variants in Guangdong, China, we found 15% (95%CrI: 12, 19) of cases seeded 80% of offspring cases.

Impact of low vaccine coverage on the resurgence of COVID-19 in Central and Eastern Europe

The coronavirus disease 2019 (COVID-19) pandemic has caused a tremendous global impact both socially and economically. The mechanisms behind the disparity in the severity, vaccine coverage, and variant replacement patterns across European countries are unclear. In this work, we aim to reveal the possible reasons via data visualization and model fitting. We developed a model with a vaccination component to simulate the mortality waves in these countries. Deaths averted by the vaccination campaign were estimated. Finally, we discuss the potential reasons behind the differences in vaccine coverage across European countries. Contemporary transportation and global trade bring significant convenience to our daily life but also facilitate the spread of the novel virus COVID-19 to anywhere globally within a short time. The observations and results in this work highlight the importance of the global campaign to mitigate the COVID-19 pandemic and future pandemics under the One Health approach.

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We reveal disparity in COVID-19 vaccine coverage across European counties.

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We reveal different patterns of COVID-19 variants across European countries.

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Using a mathematical model, we calculate deaths averted by the vaccine in Europe.

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We discuss the reasons behind the disparity in vaccine coverage in Europe.