Estimation of the test to test distribution as a proxy for generation interval distribution for the Omicron variant in England

Background Early estimates from South Africa indicated that the Omicron COVID-19 variant may be both more transmissible and have greater immune escape than the previously dominant Delta variant. The rapid turnover of the latest epidemic wave in South Africa as well as initial evidence from contact tracing and household infection studies has prompted speculation that the generation time of the Omicron variant may be shorter in comparable settings than the generation time of the Delta variant. Methods We estimated daily growth rates for the Omicron and Delta variants in each UKHSA region from the 23rd of November to the 23rd of December 2021 using surveillance case counts by date of specimen and S-gene target failure status with an autoregressive model that allowed for time-varying differences in the transmission advantage of the Delta variant where the evidence supported this. By assuming a gamma distributed generation distribution we then estimated the generation time distribution and transmission advantage of the Omicron variant that would be required to explain this time varying advantage. We repeated this estimation process using two different prior estimates for the generation time of the Delta variant first based on household transmission and then based on its intrinsic generation time. Results Visualising our growth rate estimates provided initial evidence for a difference in generation time distributions. Assuming a generation time distribution for Delta with a mean of 2.5-4 days (90% credible interval) and a standard deviation of 1.9-3 days we estimated a shorter generation time distribution for Omicron with a mean of 1.5-3.2 days and a standard deviation of 1.3-4.6 days. This implied a transmission advantage for Omicron in this setting of 160%-210% compared to Delta. We found similar relative results using an estimate of the intrinsic generation time for Delta though all estimates increased in magnitude due to the longer assumed generation time. Conclusions We found that a reduction in the generation time of Omicron compared to Delta was able to explain the observed variation over time in the transmission advantage of the Omicron variant. However, this analysis cannot rule out the role of other factors such as differences in the populations the variants were mixing in, differences in immune escape between variants or bias due to using the test to test distribution as a proxy for the generation time distribution.

Reconstructing transmission chains of SARS-CoV-2 amid multiple outbreaks in a geriatric acute-care hospital

Background There is ongoing uncertainty regarding transmission chains and the respective roles of healthcare workers (HCWs) and elderly patients in nosocomial outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS–CoV–2) in geriatric settings. Methods We performed a retrospective cohort study including patients with nosocomial coronavirus disease 2019 (COVID–19) in four outbreak–affected wards, and all SARS–CoV–2 RT–PCR positive HCWs from a Swiss university–affiliated geriatric acute–care hospital that admitted both Covid–19 and non–Covid–19 patients during the first pandemic wave in Spring 2020. We combined epidemiological and genetic sequencing data using a Bayesian modelling framework, and reconstructed transmission dynamics of SARS–CoV–2 involving patients and HCWs, in order to determine who infected whom. We evaluated general transmission patterns according to type of case (HCWs working in dedicated Covid–19 cohorting wards: HCWcovid; HCWs working in non–Covid–19 wards where outbreaks occurred: HCWoutbreak; patients with nosocomial Covid–19: patientnoso) by deriving the proportion of infections attributed to each type of case across all posterior trees and comparing them to random expectations. Results During the study period (March 1 to May 7, 2020) we included 180 SARS–CoV–2 positive cases: 127 HCWs (91 HCWcovid, 36 HCWoutbreak) and 53 patients. The attack rates ranged from 10–19% for patients, and 21% for HCWs. We estimated that there were 16 importation events (3 patients, 13 HCWs) that jointly led to 16 secondary cases. Most patient–to–patient transmission events involved patients having shared a ward (97.6%, 95% credible interval [CrI] 90.4–100%), in contrast to those having shared a room (44.4%, 95%CrI 27.8–62.5%). Transmission events tended to cluster by type of case: patientnoso were almost twice as likely to be infected by other patientnoso than expected (observed:expected ratio 1.91, 95%CrI 1.08 – 4.00, p = 0.02); similarly, HCWoutbreak were more than twice as likely to be infected by other HCWoutbreak than expected (2.25, 95%CrI 1.00–8.00, p = 0.04). The proportion of infectors of HCWcovid were as expected as random. The proportions of high transmitters (≥2 secondary cases) were significantly higher among HCWoutbreak than patientnoso in the late phases (26.2% vs. 13.4%, p<2.2e–16) of the outbreak. Conclusions Most importation events were linked to HCW. Unexpectedly, transmission between HCWcovid was more limited than transmission between patients and HCWoutbreak. This highlights gaps in infection control and suggests possible areas of improvements to limit the extent of nosocomial transmission.

Quantifying within-city inequalities in child mortality across neighbourhoods in Accra, Ghana: a Bayesian spatial analysis

ObjectiveCountries in sub-Saharan Africa suffer the highest rates of child mortality worldwide. Urban areas tend to have lower mortality than rural areas, but these comparisons likely mask large within-city inequalities. We aimed to estimate rates of under-five mortality (U5M) at the neighbourhood level for Ghana’s Greater Accra Metropolitan Area (GAMA) and measure the extent of intraurban inequalities.MethodsWe accessed data on >700 000 women aged 25–49 years living in GAMA using the most recent Ghana census (2010). We summarised counts of child births and deaths by five-year age group of women and neighbourhood (n=406) and applied indirect demographic methods to convert the summaries to yearly probabilities of death before age five years. We fitted a Bayesian spatiotemporal model to the neighbourhood U5M probabilities to obtain estimates for the year 2010 and examined their correlations with indicators of neighbourhood living and socioeconomic conditions.ResultsU5M varied almost five-fold across neighbourhoods in GAMA in 2010, ranging from 28 (95% credible interval (CrI) 8 to 63) to 138 (95% CrI 111 to 167) deaths per 1000 live births. U5M was highest in neighbourhoods of the central urban core and industrial areas, with an average of 95 deaths per 1000 live births across these neighbourhoods. Peri-urban neighbourhoods performed better, on average, but rates varied more across neighbourhoods compared with neighbourhoods in the central urban areas. U5M was negatively correlated with multiple indicators of improved living and socioeconomic conditions among peri-urban neighbourhoods. Among urban neighbourhoods, correlations with these factors were weaker or, in some cases, reversed, including with median household consumption and women’s schooling.ConclusionReducing child mortality in high-burden urban neighbourhoods in GAMA, where a substantial portion of the urban population resides, should be prioritised as part of continued efforts to meet the Sustainable Development Goal national target of less than 25 deaths per 1000 live births.

Quantifying transmissibility of SARS-CoV-2 and impact of intervention within long-term healthcare facilities

Estimates of the basic reproduction number ( R 0 ) for COVID-19 are particularly variable in the context of transmission within locations such as long-term healthcare (LTHC) facilities. We sought to characterize the heterogeneity of R 0 across known outbreaks within these facilities. We used a unique comprehensive dataset of all outbreaks that occurred within LTHC facilities in British Columbia, Canada as of 21 September 2020. We estimated R 0 in 18 LTHC outbreaks with a novel Bayesian hierarchical dynamic model of susceptible, exposed, infected and recovered individuals, incorporating heterogeneity of R 0 between facilities. We further compared these estimates to those obtained with standard methods that use the exponential growth rate and maximum likelihood. The total size of outbreaks varied dramatically, with range of attack rates 2%–86%. The Bayesian analysis provided an overall estimate of R 0 = 2.51 (90% credible interval 0.47–9.0), with individual facility estimates ranging between 0.56 and 9.17. Uncertainty in these estimates was more constrained than standard methods, particularly for smaller outbreaks informed by the population-level model. We further estimated that intervention led to 61% (52%–69%) of all potential cases being averted within the LTHC facilities, or 75% (68%–79%) when using a model with multi-level intervention effect. Understanding of transmission risks and impact of intervention are essential in planning during the ongoing global pandemic, particularly in high-risk environments such as LTHC facilities.

Visit-to-Visit Blood Pressure Variability and Longitudinal Tau Accumulation in Older Adults

Background: Elevated blood pressure variability (BPV) is predictive of dementia, independent of average blood pressure levels, but neuropathological mechanisms remain unclear. We examined whether BPV in older adults is related to tau accumulation in brain regions vulnerable to Alzheimer disease and whether relationships are modified by apoϵ4 carrier status. Methods: Two hundred eighty-six Alzheimer’s Disease Neuroimaging Initiative participants without history of dementia underwent 3 to 4 blood pressure measurements over 12 months and ≥1 tau positron emission tomography thereafter. BPV was calculated as variability independent of mean. Each scan determined tau burden (standardized uptake value ratio) for a temporal meta–region of interest, including burden from entorhinal cortex, amygdala, parahippocampus, fusiform, inferior temporal, and middle temporal. Bayesian linear growth modeling examined the role of BPV, apolipoprotein ϵ4 carrier status, and time on regional tau accumulation after controlling for several variables, including baseline hypertension. Results: Elevated BPV was related to tau accumulation at follow-up in a temporal meta-region, independent of average blood pressure levels (ß, 0.89 [95% credible interval, 0.86–0.92]) and especially in entorhinal cortex (ß, 2.57 [95% credible interval, 2.15–2.99]). Apoϵ4 carriers with elevated BPV had the fastest tau accumulation at follow-up (ß, 1.73 [95% credible interval, 0.47–3.03]). Conclusions: BPV is related to tau accumulation in brain regions vulnerable to Alzheimer disease, independent of average blood pressure. APOEϵ4 modified this relationship. Bidirectionality of findings is possible. BPV may represent a marker of vascular dysfunction related to early-stage tau pathology contributing to Alzheimer disease.

Spatial Patterns and Climate Drivers of Malaria in Three Border Areas of Brazil, Venezuela and Guyana, 2016-2018

IntroductionIn 2020, 77% of malaria cases in the Americas were concentrated in Venezuela, Brazil, and Colombia. These countries are characterized by a heterogeneous malaria landscape and malaria hotspots. Furthermore, the political unrest in Venezuela has led to significant cross-border population movement. Hence, the aim of this study was to describe spatial patterns and identify significant climatic drivers of malaria transmission along the Venezuela-Brazil-Guyana border, focusing on Bolivar state, Venezuela and Roraima state, Brazil.MethodsMalaria case data, stratified by species from 2016-2018, were obtained from the Brazilian Malaria Epidemiology Surveillance Information System, the Guyana Vector Borne Diseases Program, the Venezuelan Ministry of Health, and civil society organizations. Spatial autocorrelation in malaria incidence was explored using Getis-Ord (Gi*) statistics. A Poisson regression model was developed with a conditional autoregressive prior structure and posterior parameters were estimated using the Bayesian Markov chain Monte Carlo simulation with Gibbs sampling. Climatic covariates were precipitation and minimum and maximum temperature. ResultsThere were 685,498 malaria cases during the study period. Plasmodium vivax was the predominant species (71.7%, 490,861). Malaria hotspots were located in eight municipalities along the Venezuela and Guyana international borders with Brazil. Plasmodium falciparum decreased by 1.6% (95% credible interval [CrI] 1.5%, 2.3%) and 9.6% (95% CrI 1.5%, 25.2%) per 1 cm increase in six-month lagged precipitation and each 1°C increase of minimum temperature without lag. Each 1°C increase of one-month lagged maximum temperature increased P. falciparum by 6.6% (95% CrI 4.8%, 21.7%). P. vivax cases decreased by 1.0% (95% CrI 1.0%, 1.1%) and 7.0% (95% CrI 6.5%, 7.5%) for each 1 cm increase of precipitation lagged at six-months and 1°C increase in minimum temperature lagged at six-months. There was no significant residual spatial clustering after accounting for climatic covariates.ConclusionMalaria hotspots were located along the Venezuela and Guyana international border with Roraima state, Brazil. In addition to population movement, climatic variables are important drivers of malaria transmission in these areas.

Classical and Bayesian Estimation of the Inverse Weibull Distribution: Using Progressive Type-I Censoring Scheme

The challenge of estimating the parameters for the inverse Weibull (IW) distribution employing progressive censoring Type-I (PCTI) will be addressed in this study using Bayesian and non-Bayesian procedures. To address the issue of censoring time selection, qauntiles from the IW lifetime distribution will be implemented as censoring time points for PCTI. Focusing on the censoring schemes, maximum likelihood estimators (MLEs) and asymptotic confidence intervals (ACI) for unknown parameters are constructed. Under the squared error (SEr) loss function, Bayes estimates (BEs) and concomitant maximum posterior density credible interval estimations are also produced. The BEs are assessed using two methods: Lindley’s approximation (LiA) technique and the Metropolis-Hasting (MH) algorithm utilizing Markov Chain Monte Carlo (MCMC). The theoretical implications of MLEs and BEs for specified schemes of PCTI samples are shown via a simulation study to compare the performance of the different suggested estimators. Finally, application of two real data sets will be employed.

Effectiveness of casirivimab and imdevimab, and sotrovimab during Delta variant surge: a prospective cohort study and comparative effectiveness randomized trial

IMPORTANCE The effectiveness of monoclonal antibodies (mAbs), casirivimab and imdevimab, and sotrovimab, for patients with mild to moderate Covid-19 from the Delta variant is unknown. OBJECTIVE To evaluate the effectiveness of mAbs for the Delta variant compared to no treatment, and the comparative effectiveness between mAbs. DESIGN, SETTING, AND PARTICIPANTS Two parallel studies among patients who met Emergency Use Authorization criteria for mAbs from July 14, 2021 to September 29, 2021: i.) prospective observational cohort study comparing mAb treatment to no mAb treatment and, ii.) Bayesian adaptive randomized trial comparing the effectiveness of casirivimab-imdevimab versus sotrovimab. In the observational study, we compared eligible patients who received mAb at an outpatient infusion center at UPMC, to nontreated patients with a positive SARS-CoV-2 test. In the comparative effectiveness trial, we randomly allocated casirivimab-imdevimab or sotrovimab to patients presenting to infusion centers and emergency departments, per system therapeutic interchange policy. EXPOSURE Intravenous mAb per their EUA criteria. MAIN OUTCOMES AND MEASURES For the observational study, risk ratio estimates for hospitalization or death by 28 days were compared between mAb treatment to no mAb treatment using propensity matched models. For the comparative effectiveness trial, the primary outcome was hospital-free days (days alive and free of hospital) within 28 days, where patients who died were assigned -1 day) in a Bayesian cumulative logistic model, adjusted for treatment location, age, sex, and time. Inferiority was defined as a 99% posterior probability of an odds ratio <1. Equivalence was defined as a 95% posterior probability that the odds ratio is within a given bound. RESULTS Among 3,558 patients receiving mAb, the mean age was 54 (SD 18 years), 1,511 (43%) were treated in an infusion center, and 450 (13%) were hospitalized or died by day 28. In propensity matched models, mAb treatment was associated with reduced risk of hospitalization or death compared to no treatment (risk ratio (RR)=0.40, 95% CI: 0.28-0.57). Both casirivimab and imdevimab (RR=0.31, 95% CI: 0.20-0.50), and sotrovimab (RR=0.60, 95% CI: 0.37-1.00) reduced hospitalization or death compared to no mAb treatment. Among patients allocated randomly to casirivimab and imdevimab (n=2,454) or sotrovimab (n=1,104), the median hospital-free days were 28 (IQR 28-28) for both groups, 28-day mortality was 0.5% (n=12) and 0.6% (n=7), and hospitalization by day 28 was 12% (n=291) and 12% (n=140), respectively. Compared to casirivimab and imdevimab, the median adjusted odds ratio for hospital-free days was 0.88 (95% credible interval, 0.70-1.11) for sotrovimab. This odds ratio yielded 86% probability of inferiority of sotrovimab versus casirivimab and imdevimab, and 79% probability of equivalence. CONCLUSIONS AND RELEVANCE In non-hospitalized patients with mild to moderate Covid-19 due to the Delta variant, casirivimab and imdevimab and sotrovimab were both associated with a reduced risk of hospitalization or death. The comparative effectiveness of mAbs appeared similar, though prespecified criteria for statistical inferiority or equivalence were not met. TRIAL REGISTRATION ClinicalTrials.gov: NCT04790786

Serial interval and basic reproduction number of SARS-CoV-2 Omicron variant in South Korea

South Korea is experiencing the community transmission of the SARS-CoV-2 Omicron variant (B.1.1.529). We estimated that the mean of the serial interval was 2.22 days, and the basic reproduction number was 1.90 (95% Credible Interval, 1.50-2.43) for the Omicron variant outbreak in South Korea.

Statistical Inferences of Burr XII Lifetime Models under Joint Type-1 Competing Risks Samples

The problem of statistical inference under joint censoring samples has received considerable attention in the past few years. In this paper, we adopted this problem when units under the test fail with different causes of failure which is known by the competing risks model. The model is formulated under consideration that only two independent causes of failure and the unit are collected from two lines of production and its life distributed with Burr XII lifetime distribution. So, under Type-I joint competing risks samples, we obtained the maximum likelihood (ML) and Bayes estimators. Interval estimation is discussed through asymptotic confidence interval, bootstrap confidence intervals, and Bayes credible interval. The numerical computations which described the quality of theoretical results are discussed in the forms of real data analyzed and Monte Carlo simulation study. Finally, numerical results are discussed and listed through some points as a brief comment.