Bayesian analysis of joint quantile regression for multi-response longitudinal data with application to primary biliary cirrhosis sequential cohort study.

This article proposes a Bayesian approach for jointly estimating marginal conditional quantiles of multi-response longitudinal data with multivariate mixed effects model. The multivariate asymmetric Laplace distribution is employed to construct the working likelihood of the considered model. Penalization priors on regression parameters are incorporated into the working likelihood to conduct Bayesian high-dimensional inference. Markov chain Monte Carlo algorithm is used to obtain the fully conditional posterior distributions of all parameters and latent variables. Monte Carlo simulations are conducted to evaluate the sample performance of the proposed joint quantile regression approach. Finally, we analyze a longitudinal medical dataset of the primary biliary cirrhosis sequential cohort study to illustrate the real application of the proposed modeling method.

Imputed mean tensor regression for near-sited spatial temporal data.

Modern spatial temporal data are often collected from sensor networks. Missing data problems are common to this kind of data. Making accurate imputation is important for many applications. In the unsupervised setting, one technique is to minimize the rank of a tensor or matrix. If we add related covariates, can we get more accurate imputation results? To address this, we transform the original sensor×time measurements to high order tensors by adding additional temporal dimensions and then integrate tensor regression with tensor completion using nuclear norm penalty. One advantage is we can simultaneously estimate parameters and impute missing values due to clear spatial consistency for near-sited spatial-temporal data. The proposed method doesn't assume missing mechanism of the response. Theoretical properties of the proposed estimator are investigated. Simulation studies and real data analysis are conducted to verify the efficiency of the estimation procedure.

Hepatitis B time series in Xinjiang, China (2006-2021): change point detection based on the Mann-Kendall-Sneyers test.

Hepatitis B is a major global challenge, but there is a lack of epidemiological research on hepatitis B incidence from a change point perspective. This study aimed to fill this gap by identifying significant change points and trends in hepatitis time series in Xinjiang, China. The datasets were obtained from the Xinjiang Information System for Disease Control and Prevention. The Mann-Kendall-Sneyers (MKS) test was used to detect change points and trend changes on the hepatitis B time series of 14 regions in Xinjiang, and the effectiveness of this method was validated by comparing it with the binary segmentation (BS) and segment regression (SR) methods. Based on the results of change point analysis, the prevention and control policies and measures of hepatitis in Xinjiang were discussed. The results showed that 8 regions (57.1%) with at least one change fell within the 95% confidence interval (CI) in all 14 regions by the MKS test, where five regions (Turpan (TP), Hami (HM), Bayingolin (BG), Kyzylsu Kirgiz (KK), Altai (AT)) were identified at one change point, two change points existed for two regions (Aksu (AK), Hotan (HT)) and three change points was detected in 1 region (Bortala (BT)). Most of the change points occurred at both ends of the sequence. More change points indicated an upward trend in the front half of the sequence, while in the latter half, many change points indicated a downward trend prominently. Finally, in comparing the results of the three change point tests, the MKS test showed a 61.5% agreement (8/13) with the BS and SR.

Interaction between air pollutants and meteorological factors on pulmonary tuberculosis in northwest China: A case study of eight districts in Urumqi.

Meteorological factors and air pollutants are associated with the spread of pulmonary tuberculosis (PTB), but few studies have examined the effects of their interactions on PTB. Therefore, this study investigated the impact of meteorological factors and air pollutants and their interactions on the risk of PTB in Urumqi, a city with a high prevalence of PTB and a high level of air pollution. The number of new PTB cases in eight districts of Urumqi from 2014 to 2019 was collected, along with data on meteorological factors and air pollutants for the same period. A generalized additive model was applied to explore the effects of meteorological factors and air pollutants and their interactions on the risk of PTB incidence. Segmented linear regression was used to estimate the nonlinear characteristics of the impact of meteorological factors on PTB. During 2014-2019, a total of 14,402 new cases of PTB were reported in eight districts, with March to May being the months of high PTB incidence. The exposure-response curves for temperature (Temp), relative humidity (RH), wind speed (WS), air pressure (AP), and diurnal temperature difference (DTR) were generally inverted "U" shaped, with the corresponding threshold values of - 5.411 °C, 52.118%, 3.513 m/s, 1021.625 hPa, and 8.161 °C, respectively. The effects of air pollutants on PTB were linear and lagged. All air pollutants were positively associated with PTB, except for O3, which was not associated with PTB, and the ER values for the effects on PTB were as follows: 0.931 (0.255, 1.612) for PM2.5, 1.028 (0.301, 1.760) for PM10, 5.061 (0.387, 9.952) for SO2, 2.830 (0.512, 5.200) for NO2, and 5.789 (1.508, 10.251) for CO. Meteorological factors and air pollutants have an interactive effect on PTB. The risk of PTB incidence was higher when in high Temp-high air pollutant, high RH-high air pollutant, high WS-high air pollutant, lowAP-high air pollutant, and high DTR-high air pollutant. In conclusion, both meteorological and pollutant factors had an influence on PTB, and the influence on PTB may have an interaction.

Multiple change point analysis of hepatitis B reports in Xinjiang, China from 2006 to 2021.

Objective: Hepatitis B (HB) is a major global challenge, but there has been a lack of epidemiological studies on HB incidence in Xinjiang from a change-point perspective. This study aims to bridge this gap by identifying significant change points and trends. Method: The datasets were obtained from the Xinjiang Information System for Disease Control and Prevention. Change points were identified using binary segmentation for full datasets and a segmented regression model for five age groups. Results: The results showed four change points for the quarterly HB time series, with the period between the first change point (March 2007) and the second change point (March 2010) having the highest mean number of HB reports. In the subsequent segments, there was a clear downward trend in reported cases. The segmented regression model showed different numbers of change points for each age group, with the 30-50, 51-80, and 15-29 age groups having higher growth rates. Conclusion: Change point analysis has valuable applications in epidemiology. These findings provide important information for future epidemiological studies and early warning systems for HB.

Robust and sparse learning of varying coefficient models with high-dimensional features.

Varying coefficient model (VCM) is extensively used in various scientific fields due to its capability of capturing the changing structure of predictors. Classical mean regression analysis is often complicated in the existence of skewed, heterogeneous and heavy-tailed data. For this purpose, this work employs the idea of model averaging and introduces a novel comprehensive approach by incorporating quantile-adaptive weights across different quantile levels to further improve both least square (LS) and quantile regression (QR) methods. The proposed procedure that adaptively takes advantage of the heterogeneous and sparse nature of input data can gain more efficiency and be well adapted to extreme event case and high-dimensional setting. Motivated by its nice properties, we develop several robust methods to reveal the dynamic close-to-truth structure for VCM and consistently uncover the zero and nonzero patterns in high-dimensional scientific discoveries. We provide a new iterative algorithm that is proven to be asymptotic consistent and can attain the optimal nonparametric convergence rate given regular conditions. These introduced procedures are highlighted with extensive simulation examples and several real data analyses to further show their stronger predictive power compared with LS, composite quantile regression (CQR) and QR methods.

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.

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.

Quantile regression for static panel data models with time-invariant regressors.

This paper proposes two new weighted quantile regression estimators for static panel data model with time-invariant regressors. The two new estimators can improve the estimation of the coefficients with time-invariant regressors, which are computationally convenient and simple to implement. Also, the paper shows consistency and asymptotic normality of the two proposed estimator for sequential and simultaneous N, T asymptotics. Monte Carlo simulation in various parameters sets proves the validity of the proposed approach. It has an empirical application to study the effects of the influence factors of China's exports using the trade gravity model.

A Bayesian multistage spatio-temporally dependent model for spatial clustering and variable selection.

In spatio-temporal epidemiological analysis, it is of critical importance to identify the significant covariates and estimate the associated time-varying effects on the health outcome. Due to the heterogeneity of spatio-temporal data, the subsets of important covariates may vary across space and the temporal trends of covariate effects could be locally different. However, many spatial models neglected the potential local variation patterns, leading to inappropriate inference. Thus, this article proposes a flexible Bayesian hierarchical model to simultaneously identify spatial clusters of regression coefficients with common temporal trends, select significant covariates for each spatial group by introducing binary entry parameters and estimate spatio-temporally varying disease risks. A multistage strategy is employed to reduce the confounding bias caused by spatially structured random components. A simulation study demonstrates the outperformance of the proposed method, compared with several alternatives based on different assessment criteria. The methodology is motivated by two important case studies. The first concerns the low birth weight incidence data in 159 counties of Georgia, USA, for the years 2007 to 2018 and investigates the time-varying effects of potential contributing covariates in different cluster regions. The second concerns the circulatory disease risks across 323 local authorities in England over 10 years and explores the underlying spatial clusters and associated important risk factors.

Smoothed quantile regression for partially functional linear models in high dimensions.

Practitioners of current data analysis are regularly confronted with the situation where the heavy-tailed skewed response is related to both multiple functional predictors and high-dimensional scalar covariates. We propose a new class of partially functional penalized convolution-type smoothed quantile regression to characterize the conditional quantile level between a scalar response and predictors of both functional and scalar types. The new approach overcomes the lack of smoothness and severe convexity of the standard quantile empirical loss, considerably improving the computing efficiency of partially functional quantile regression. We investigate a folded concave penalized estimator for simultaneous variable selection and estimation by the modified local adaptive majorize-minimization (LAMM) algorithm. The functional predictors can be dense or sparse and are approximated by the principal component basis. Under mild conditions, the consistency and oracle properties of the resulting estimators are established. Simulation studies demonstrate a competitive performance against the partially functional standard penalized quantile regression. A real application using Alzheimer's Disease Neuroimaging Initiative data is utilized to illustrate the practicality of the proposed model.

Case clustering, contact stratification, and transmission heterogeneity of SARS-CoV-2 Omicron BA.5 variants in Urumqi, China: An observational study.

Background: From August to September 2022, Urumqi, the capital of the Xinjiang Uygur Autonomous Region in China, faced its largest COVID-19 outbreak caused by the emergence of the SARS-CoV-2 Omicron BA.5.2 variants. Although the superspreading of COVID-19 played an important role in triggering large-scale outbreaks, little was known about the superspreading potential and heterogeneity in the transmission of Omicron BA.5 variants. Methods: In this retrospective observational, contact tracing study, we identified 1139 laboratory-confirmed COVID-19 cases of Omicron BA.5.2 variants, and 51 323 test-negative close contacts in Urumqi from 7 August to 7 September 2022. By using detailed contact tracing information and exposure history of linked case-contact pairs, we described stratification in contact and heterogeneity in transmission across different demographic strata, vaccine statuses, and contact settings. We adopted beta-binomial models to characterise the secondary attack rate (SAR) distribution among close contacts and modelled COVID-19 transmission as a branching process with heterogeneity in transmission governed by negative binomial models. Results: After the city lockdown, the mean case cluster size decreased from 2.0 (before lockdown) to 1.6, with decreased proportions of contacts in workplace and community settings compared with household settings. We estimated that 14% of the most infectious index cases generated 80% transmission, whereas transmission in the community setting presented the highest heterogeneity, with 5% index cases seeding 80% transmission. Compared with zero, one, and two doses of inactivated vaccine (Sinopharm), index cases with three doses of vaccine had a lower risk of generating secondary cases in terms of the reproduction number. Contacts of female cases, cases with ages 0-17 years, and household settings had relatively higher SAR. Conclusions: In the context of intensive control measures, active case detection, and relatively high vaccine coverage, but with an infection-naive population, our findings suggested high heterogeneity in the contact and transmission risks of Omicron BA.5 variants across different demographic strata, vaccine statuses, and contact settings. Given the rapid evolution of SARS-CoV-2, investigating the distribution of transmission not only helped promote public awareness and preparedness among high-risk groups, but also highlighted the importance of continuously monitoring the transmission characteristics of genetic variants of SARS-CoV-2.

Sparse regression for low-dimensional time-dynamic varying coefficient models with application to air quality data.

Time dynamic varying coefficient models play an important role in applications of biology, medicine, environment, finance, etc. Traditional methods, such as kernel smoothing and spline smoothing, are popular. But explicit expressions are unavailable using these methods, and the convergence rate of coefficient function estimators is slow. To address these problems, we expand the varying component with appropriate basis functions. And then we solve a sparse regression problem via a sequential thresholded least-squares estimator. The "parameterization" leads to explicit expressions and fast computation speed. Convergence of the sequential thresholded least squares algorithm is guaranteed. The asymptotic distribution of the coefficient function estimator is derived under certain assumptions. Our simulation shows the proposed method has higher precision and computing speed. Finally, our proposed method is applied to the study of PM 2.5 concentration in Beijing. We analyze the relationship between PM 2.5 and other impact factors.

An efficient model-free approach to interaction screening for high dimensional data.

An innovated model-free interaction screening procedure called the MCVIS is proposed for high dimensional data analysis. Specifically, we adopt the introduced MCV index for quantifying the importance of an interaction effect among predictors. Our proposed method is fully nonparametric and is capable of successfully selecting interactions even if the signal of parental main effects is weak. The MCVIS procedure has many distinctive features: (i) it can work with discrete, categorical and continuous covariates; (ii) it can deal with both categorical and continuous response, even handle the missing response; (iii) it is robust for heavy-tailed distributions, thus well accommodates heterogeneity typically caused by high dimensionality; (iv) it enjoys the sure screening and ranking consistency properties, therefore achieves dimension reduction without information loss. In another respect, computational feasibility is a top concern in high dimensional data analysis, by transforming our MCV into several variants, the MCVIS procedure is simple and fast to implement. Extensive numerical experiments and comparisons confirm the effectiveness and wide applicability of our MCVIS procedure. We further illustrate the proposed methodology by empirical study of two real datasets. Supplementary materials are available online.

Superspreading potentials of SARS-CoV-2 Delta variants across different contact settings in Eastern China: A retrospective observational study.

OBJECTIVES: As the genetic variants of SARS-CoV-2 continuously pose threats to global health, evaluating superspreading potentials of emerging genetic variants is of importance for region-wide control of COVID-19 outbreaks. METHODS: By using detailed epidemiological contact tracing data of test-positive COVID-19 cases collected between July and August 2021 in Nanjing and Yangzhou, China, we assessed the superspreading potential of outbreaks seeded by SARS-CoV-2 Delta variants. The transmission chains and case-clusters were constructed according to the individual-based surveillance data. We modelled the disease transmission as a classic branching process with transmission heterogeneity governed by negative binomial models. Subgroup analysis was conducted by different contact settings and age groups. RESULTS: We reported a considerable heterogeneity in the contact patterns and transmissibility of Delta variants in eastern China. We estimated an expected 14% (95% CI: 11-16%) of the most infectious cases generated 80% of the total transmission. CONCLUSIONS: Delta variants demonstrated a significant potential of superspreading under strict control measures and active COVID-19 detecting efforts. Enhancing the surveillance on disease transmissibility especially in high-risk settings, along with rapid contact tracing and case isolations would be one of the key factors to mitigate the epidemic caused by the emerging genetic variants of SARS-CoV-2.

Analysis of the effect of temperature on tuberculosis incidence by distributed lag non-linear model in Kashgar city, China.

The aim of this study was to explore the effect of temperature on tuberculosis (TB) incidence using the distributed lag non-linear model (DLNM) from 2017 to 2021 in Kashgar city, the region with higher TB incidence than national levels, and assist public health prevention and control measures. From January 2017 to December 2021, a total of 8730 cases of TB were reported, with the higher incidence of male than that of female. When temperature was below 1 °C, it was significantly correlated with TB incidence compared to the median observed temperature (15 °C) at lag 7, 14, and 21, and lower temperatures showed larger RR (relative risk) values. High temperature produced a protective effect on TB transmission, and higher temperature from 16 to 31 °C has lower RR. In discussion stratified by gender, the maximum RRs were achieved for both male group and female group at - 15 °C with lag 21, reporting 4.28 and 2.02, respectively. At high temperature (higher than 20 °C), the RR value of developing TB for female group was significantly larger than 1. In discussion stratified by age, the maximum RRs were achieved for all age groups (≤ 35, 36-64, ≥ 65) at - 15 °C with lag 21, reporting 3.20, 2.07, and 3.45, respectively. When the temperature was higher than 20 °C, the RR of the 36-64-year-old group and the ≥ 65-year-old group was significantly larger than 1 at lag 21, while significantly smaller than 1 for cumulative RR at lag 21, reporting 0.11, 95% confidence interval (CI) (0.01, 0.83) and 0.06, 95% CI (0.01, 0.44), respectively. In conclusion, low temperature, especially in extreme level, acts as a high-risk factor inducing TB transmission in Kashgar city. Males exhibit a significantly higher RR of developing TB at low temperature than female, as well as the elderly group in contrast to the young or middle-aged groups. High temperature has a protective effect on TB transmission in the total population, but female and middle-aged and elderly groups are also required to be alert to the delayed RR induced by it.

Prediction study of electric energy production in important power production base, China.

Xinjiang is an important power production base in China, and its electric energy production needs not only meet the demand of Xinjiang's electricity consumption, but also make up for the shortage of electricity in at least 19 provinces or cities in China. Therefore, it is of great significance to know ahead of time the electric energy production of Xinjiang in the future. In such terms, accurate electric energy production forecasts are imperative for decision makers to develop an optimal strategy that includes not only risk reduction, but also the betterment of the economy and society as a whole. According to the characteristics of the historical data of monthly electricity generation in Xinjiang from January 2001 to August 2020 , the suitable and widely used SARIMA (Seasonal autoregressive integrated moving mean model) method and Holt-winter method were used to construct the monthly electric energy production in Xinjiang for the first time. The results of our analysis showed that the established SARIMA((1,2,3,4,6,7,11),2,1)(1,0,1)12 model had higher prediction accuracy than that of the established Holt-Winters' multiplicative model. We predicted the monthly electric energy production from August 2021 to August 2022 by the SARIMA((1,2,3,4,6,7,11),2,1)(1,0,1)12 model, and errors are very small compared to the actual values, indicating that our model has a very good prediction performance. Therefore, based on our study, we provided a simple and easy scientific tool for the future power output prediction in Xinjiang. Our research methods and research ideas can also provide scientific reference for the prediction of electric energy production elsewhere.

Exploration of the impact of political ideology disparity on COVID-19 transmission in the United States.

BACKGROUND: Based on individual-level studies, previous literature suggested that conservatives and liberals in the United States had different perceptions and behaviors when facing the COVID-19 threat. From a state-level perspective, this study further explored the impact of personal political ideology disparity on COVID-19 transmission before and after the emergence of Omicron. METHODS: A new index was established, which depended on the daily cumulative number of confirmed cases in each state and the corresponding population size. Then, by using the 2020 United States presidential election results, the values of the built index were further divided into two groups concerning the political party affiliation of the winner in each state. In addition, each group was further separated into two parts, corresponding to the time before and after Omicron predominated. Three methods, i.e., functional principal component analysis, functional analysis of variance, and function-on-scalar linear regression, were implemented to statistically analyze and quantify the impact. RESULTS: Findings reveal that the disparity of personal political ideology has caused a significant discrepancy in the COVID-19 crisis in the United States. Specifically, the findings show that at the very early stage before the emergence of Omicron, Democratic-leaning states suffered from a much greater severity of the COVID-19 threat but, after July 2020, the severity of COVID-19 transmission in Republican-leaning states was much higher than that in Democratic-leaning states. Situations were reversed when the Omicron predominated. Most of the time, states with Democrat preferences were more vulnerable to the threat of COVID-19 than those with Republican preferences, even though the differences decreased over time. CONCLUSIONS: The individual-level disparity of political ideology has impacted the nationwide COVID-19 transmission and such findings are meaningful for the government and policymakers when taking action against the COVID-19 crisis in the United States.

The Association Between Clinical Severity and Incubation Period of SARS-CoV-2 Delta Variants: Retrospective Observational Study.

BACKGROUND: As of August 25, 2021, Jiangsu province experienced the largest COVID-19 outbreak in eastern China that was seeded by SARS-CoV-2 Delta variants. As one of the key epidemiological parameters characterizing the transmission dynamics of COVID-19, the incubation period plays an essential role in informing public health measures for epidemic control. The incubation period of COVID-19 could vary by different age, sex, disease severity, and study settings. However, the impacts of these factors on the incubation period of Delta variants remains uninvestigated. OBJECTIVE: The objective of this study is to characterize the incubation period of the Delta variant using detailed contact tracing data. The effects of age, sex, and disease severity on the incubation period were investigated by multivariate regression analysis and subgroup analysis. METHODS: We extracted contact tracing data of 353 laboratory-confirmed cases of SARS-CoV-2 Delta variants' infection in Jiangsu province, China, from July to August 2021. The distribution of incubation period of Delta variants was estimated by using likelihood-based approach with adjustment for interval-censored observations. The effects of age, sex, and disease severity on the incubation period were expiated by using multivariate logistic regression model with interval censoring. RESULTS: The mean incubation period of the Delta variant was estimated at 6.64 days (95% credible interval: 6.27-7.00). We found that female cases and cases with severe symptoms had relatively longer mean incubation periods than male cases and those with nonsevere symptoms, respectively. One-day increase in the incubation period of Delta variants was associated with a weak decrease in the probability of having severe illness with an adjusted odds ratio of 0.88 (95% credible interval: 0.71-1.07). CONCLUSIONS: In this study, the incubation period was found to vary across different levels of sex, age, and disease severity of COVID-19. These findings provide additional information on the incubation period of Delta variants and highlight the importance of continuing surveillance and monitoring of the epidemiological characteristics of emerging SARS-CoV-2 variants as they evolve.

Effects and Interaction of Meteorological Factors on Pulmonary Tuberculosis in Urumqi, China, 2013-2019.

Background: Most existing studies have only investigated the delayed effect of meteorological factors on pulmonary tuberculosis (PTB). However, the effect of extreme climate and the interaction between meteorological factors on PTB has been rarely investigated. Methods: Newly diagonsed PTB cases and meteorological factors in Urumqi in each week between 2013 and 2019 were collected. The lag-exposure-response relationship between meteorological factors and PTB was analyzed using the distributed lag non-linear model (DLNM). The generalized additive model (GAM) was used to visualize the interaction between meteorological factors. Stratified analysis was used to explore the impact of meteorological factors on PTB in different stratification and RERI, AP and SI were used to quantitatively evaluate the interaction between meteorological factors. Results: A total of 16,793 newly diagnosed PTB cases were documented in Urumqi, China from 2013 to 2019. The median (interquartile range) temperature, relative humidity, wind speed, and PTB cases were measured as 11.3°C (-5.0-20.5), 57.7% (50.7-64.2), 4.1m/s (3.4-4.7), and 47 (37-56), respectively. The effects of temperature, relative humidity and wind speed on PTB were non-linear, which were found with the "N"-shaped, "L"-shaped, "N"-shaped distribution, respectively. With the median meteorological factor as a reference, extreme low temperature was found to have a protective effect on PTB. However, extreme high temperature, extreme high relative humidity, and extreme high wind speed were found to increase the risk of PTB and peaked at 31.8°C, 83.2%, and 7.6 m/s respectively. According to the existing monitoring data, no obvious interaction between meteorological factors was found, but low temperature and low humidity (RR = 1.149, 95%CI: 1.003-1.315), low temperature and low wind speed (RR = 1.273, 95%CI: 1.146-1.415) were more likely to cause the high incidence of PTB. Conclusion: Temperature, relative humidity and wind speed were found to play vital roles in PTB incidence with delayed and non-linear effects. Extreme high temperature, extreme high relative humidity, and extreme high wind speed could increase the risk of PTB. Moreover, low temperature and low humidity, low temperature and low wind speed may increase the incidence of PTB.