Pairwise Accelerated Failure Time Regression Models for Infectious Disease Transmission in Close-Contact Groups With External Sources of Infection.

Many important questions in infectious disease epidemiology involve associations between covariates (e.g., age or vaccination status) and infectiousness or susceptibility. Because disease transmission produces dependent outcomes, these questions are difficult or impossible to address using standard regression models from biostatistics. Pairwise survival analysis handles dependent outcomes by calculating likelihoods in terms of contact interval distributions in ordered pairs of individuals. The contact interval in the ordered pair i j $$ ij $$ is the time from the onset of infectiousness in i $$ i $$ to infectious contact from i $$ i $$ to j $$ j $$ , where an infectious contact is sufficient to infect j $$ j $$ if they are susceptible. Here, we introduce a pairwise accelerated failure time regression model for infectious disease transmission that allows the rate parameter of the contact interval distribution to depend on individual-level infectiousness covariates for i $$ i $$ , individual-level susceptibility covariates for j $$ j $$ , and pair-level covariates (e.g., type of relationship). This model can simultaneously handle internal infections (caused by transmission between individuals under observation) and external infections (caused by environmental or community sources of infection). We show that this model produces consistent and asymptotically normal parameter estimates. In a simulation study, we evaluate bias and confidence interval coverage probabilities, explore the role of epidemiologic study design, and investigate the effects of model misspecification. We use this regression model to analyze household data from Los Angeles County during the 2009 influenza A (H1N1) pandemic, where we find that the ability to account for external sources of infection increases the statistical power to estimate the effect of antiviral prophylaxis.

Superspreading of SARS-CoV-2: a systematic review and meta-analysis of event attack rates and individual transmission patterns.

SARS-CoV-2 superspreading occurs when transmission is highly efficient and/or an individual infects many others, contributing to rapid spread. To better quantify heterogeneity in SARS-CoV-2 transmission, particularly superspreading, we performed a systematic review of transmission events with data on secondary attack rates or contact tracing of individual index cases published before September 2021 prior to the emergence of variants of concern and widespread vaccination. We reviewed 592 distinct events and 9,883 index cases from 491 papers. A meta-analysis of secondary attack rates identified substantial heterogeneity across 12 chosen event types/settings, with the highest transmission (25-35%) in co-living situations including households, nursing homes, and other congregate housing. Among index cases, 67% reported zero secondary cases and only 3% (287) infected >5 secondary cases ("superspreaders"). Index case demographic data were limited, with only 55% of individuals reporting age, sex, symptoms, real-time polymerase chain reaction (PCR) cycle threshold values, or total contacts. With the data available, we identified a higher percentage of superspreaders among symptomatic individuals, individuals aged 49-64 years, and individuals with over 100 total contacts. Addressing gaps in the literature regarding transmission events and contact tracing is needed to properly explain the heterogeneity in transmission and facilitate control efforts for SARS-CoV-2 and other infections.

Prevalence of nontuberculous mycobacteria and the emergence of rare species in Henan Province, China.

Nontuberculous mycobacteria (NTM) is a large group of mycobacteria other than the Mycobacterium tuberculosis complex and Mycobacterium leprae. Epidemiological investigations have found that the incidence of NTM infections is increasing in China, and it is naturally resistant to many antibiotics. Therefore, studies of NTM species in clinical isolates are useful for understanding the epidemiology of NTM infections. The present study aimed to investigate the incidence of NTM infections and types of NTM species. Of the 420 samples collected, 285 were positive for M. tuberculosis, 62 samples were negative, and the remaining 73 samples contained NTM, including 35 (8.3%) only NTM and 38 (9%) mixed (M. tuberculosis and NTM). The most prevalent NTM species were Mycobacterium intracellulare (30.1%), followed by Mycobacterium abscessus (15%) and M. triviale (12%). M. gordonae infection was detected in 9.5% of total NTM-positive cases. Moreover, this study reports the presence of Mycobacterium nonchromogenicum infection and a high prevalence of M. triviale for the first time in Henan. M. intracellulare is the most prevalent, accompanied by some emerging NTM species, including M. nonchromogenicum and a high prevalence of M. triviale in Henan Province. Monitoring NTM transmission and epidemiology could enhance mycobacteriosis management in future.

Case-control study of behavioural and societal risk factors for sporadic SARS-CoV-2 infections, Germany, 2020-2021 (CoViRiS study).

During the COVID-19 pandemic in Germany, a variety of societal activities were restricted to minimize direct personal interactions and, consequently, reduce SARS-CoV-2 transmission. The aim of the CoViRiS study was to investigate whether certain behaviours and societal factors were associated with the risk of sporadic symptomatic SARS-CoV-2 infections. Adult COVID-19 cases and frequency-matched population controls were interviewed by telephone regarding activities that involved contact with other people during the 10 days before illness onset (cases) or before the interview (controls). Associations between activities and symptomatic SARS-CoV-2 infection were analysed using logistic regression models adjusted for potential confounding variables. Data of 859 cases and 1 971 controls were available for analysis. The risk of symptomatic SARS-CoV-2 infection was lower for individuals who worked from home (adjusted odds ratio (aOR) 0.5; 95% confidence interval (CI) 0.3-0.6). Working in a health care setting was associated with a higher risk (aOR: 1.5; 95% CI: 1.1-2.1) as were private indoor contacts, personal contacts that involved shaking hands or hugging, and overnight travelling within Germany. Our results are in line with some of the public health recommendations aimed at reducing interpersonal contacts during the COVID-19 pandemic.

Looking under the lamp-post: quantifying the performance of contact tracing in the United States during the SARS-CoV-2 pandemic.

Contact tracing forms a crucial part of the public-health toolbox in mitigating and understanding emergent pathogens and nascent disease outbreaks. Contact tracing in the United States was conducted during the pre-Omicron phase of the ongoing COVID-19 pandemic. This tracing relied on voluntary reporting and responses, often using rapid antigen tests due to lack of accessibility to PCR tests. These limitations, combined with SARS-CoV-2's propensity for asymptomatic transmission, raise the question "how reliable was contact tracing for COVID-19 in the United States"? We answered this question using a Markov model to examine the efficiency with which transmission could be detected based on the design and response rates of contact tracing studies in the United States. Our results suggest that contact tracing protocols in the U.S. are unlikely to have identified more than 1.65% (95% uncertainty interval: 1.62-1.68%) of transmission events with PCR testing and 1.00% (95% uncertainty interval 0.98-1.02%) with rapid antigen testing. When considering a more robust contact tracing scenario, based on compliance rates in East Asia with PCR testing, this increases to 62.7% (95% uncertainty interval: 62.6-62.8%). We did not assume presence of asymptomatic transmission or superspreading, making our estimates upper bounds on the actual percentages traced. These findings highlight the limitations in interpretability for studies of SARS-CoV-2 disease spread based on U.S. contact tracing and underscore the vulnerability of the population to future disease outbreaks, for SARS-CoV-2 and other pathogens.

A practical guide to mathematical methods for estimating infectious disease outbreak risks.

Mathematical models are increasingly used throughout infectious disease outbreaks to guide control measures. In this review article, we focus on the initial stages of an outbreak, when a pathogen has just been observed in a new location (e.g., a town, region or country). We provide a beginner's guide to two methods for estimating the risk that introduced cases lead to sustained local transmission (i.e., the probability of a major outbreak), as opposed to the outbreak fading out with only a small number of cases. We discuss how these simple methods can be extended for epidemiological models with any level of complexity, facilitating their wider use, and describe how estimates of the probability of a major outbreak can be used to guide pathogen surveillance and control strategies. We also give an overview of previous applications of these approaches. This guide is intended to help quantitative researchers develop their own epidemiological models and use them to estimate the risks associated with pathogens arriving in new host populations. The development of these models is crucial for future outbreak preparedness. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Usefulness of linked data for infectious disease events: a systematic review.

Surveillance is a key public health function to enable early detection of infectious disease events and inform public health action. Data linkage may improve the depth of data for response to infectious disease events. This study aimed to describe the uses of linked data for infectious disease events. A systematic review was conducted using Pubmed, CINAHL and Web of Science. Studies were included if they used data linkage for an acute infectious disease event (e.g. outbreak of disease). We summarised the event, study aims and designs; data sets; linkage methods; outcomes reported; and benefits and limitations. Fifty-four studies were included. Uses of linkage for infectious disease events included assessment of severity of disease and risk factors; improved case finding and contact tracing; and vaccine uptake, safety and effectiveness. The ability to conduct larger scale population level studies was identified as a benefit, in particular for rarer exposures, risk factors or outcomes. Limitations included timeliness, data quality and inability to collect additional variables. This review demonstrated multiple uses of data linkage for infectious disease events. As infectious disease events occur without warning, there is a need to establish pre-approved protocols and the infrastructure for data-linkage to enhance information available during an event.

Demographic and clinical characteristics of children and adolescents hospitalised with laboratory-confirmed COVID-19 in the Tel-Aviv District, Israel, 2020-2022.

Our study population consisted of all children and adolescents, with laboratory-confirmed SARS-Co-V-2 infection, hospitalised from February 2020 through February 2022, among residents of the Tel Aviv (TA) District, Israel. There were 491 children and adolescents hospitalised with Sars-CoV-2 infection. Among them, 281 (57%) admitted with coronavirus disease 2019 (COVID-19) as the primary cause of admission (rate of 39 per 100 000). Among all children and adolescents in the TA District, the highest hospitalisation rates were observed among infants and children below the age of 4 years (rate of 311 per 100 000 population). Severe disease was observed mostly among children with multiple underlying medical conditions. Admission rates were also elevated among residents of the ultra-orthodox community (rate ratio (RR) compared to the rest of the district; 95% confidence interval (CI) 2.38-3.82). Admission rates with COVID-19 as primary cause of admission were higher during Omicron compared to Delta predominance period (RR 1.7; 95% CI 1.22-2.32). Targeted social and public health policies should be put in place when rates of disease start to increase, such as encouraging vaccine uptake for eligible children and social distancing when necessary, taking into account already existing social and learning gaps, in order to reduce the burden of disease.

High prevalence of asymptomatic malaria in Forest Guinea: Results from a rapid community survey.

Malaria is endemic in Guinea; however, the extent and role in transmission of asymptomatic malaria are not well understood. In May 2023, we conducted a rapid community survey to determine Plasmodium falciparum (P. falciparum) prevalence among asymptomatic individuals in Middle Guinea (Prefecture Dalaba) and Forest Guinea (Prefecture Guéckédou). In Dalaba, 6 of 239 (2.1%, confidence interval (CI) 0.9-4.8%) individuals tested positive for P. falciparum by a rapid diagnostic test (RDT), while in Guéckédou, 147 of 235 (60.9%, CI 54.5-66.9%) participants tested positive. Asymptomatic malaria needs to be considered more strongly as a driver of transmission when designing control strategies, especially in Forest Guinea and potentially other hyper-endemic settings.

Genetic and epidemiological analyses of infection load and its relationship with psychiatric disorders.

Severe infections and psychiatric disorders have a large impact on both society and the individual. Studies investigating these conditions and the links between them are therefore important. Most past studies have focused on binary phenotypes of particular infections or overall infection, thereby losing some information regarding susceptibility to infection as reflected in the number of specific infection types, or sites, which we term infection load. In this study we found that infection load was associated with increased risk for attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, depression, schizophrenia and overall psychiatric diagnosis. We obtained a modest but significant heritability for infection load (h2 = 0.0221), and a high degree of genetic correlation between it and overall psychiatric diagnosis (rg = 0.4298). We also found evidence supporting a genetic causality for overall infection on overall psychiatric diagnosis. Our genome-wide association study for infection load identified 138 suggestive associations. Our study provides further evidence for genetic links between susceptibility to infection and psychiatric disorders, and suggests that a higher infection load may have a cumulative association with psychiatric disorders, beyond what has been described for individual infections.

Comparing Mycobacterium tuberculosis transmission reconstruction models from whole genome sequence data.

Genomic epidemiology is routinely used worldwide to interrogate infectious disease dynamics. Multiple computational tools exist that reconstruct transmission networks by coupling genomic data with epidemiological models. Resulting inferences can improve our understanding of pathogen transmission dynamics, and yet the performance of these tools has not been evaluated for tuberculosis (TB), a disease process with complex epidemiology including variable latency and within-host heterogeneity. Here, we performed a systematic comparison of six publicly available transmission reconstruction models, evaluating their accuracy when predicting transmission events in simulated and real-world Mycobacterium tuberculosis outbreaks. We observed variability in the number of transmission links that were predicted with high probability (P ≥ 0.5) and low accuracy of these predictions against known transmission in simulated outbreaks. We also found a low proportion of epidemiologically supported case-contact pairs were identified in our real-world TB clusters. The specificity of all models was high, and a relatively high proportion of the total transmission events predicted by some models were true links, notably with TransPhylo, Outbreaker2, and Phybreak. Our findings may inform the choice of tools in TB transmission analyses and underscore the need for caution when interpreting transmission networks produced using probabilistic approaches.

Mapping the stability of febrile illness hotspots in Punjab from 2012 to 2019- a spatial clustering and regression analysis.

INTRODUCTION: Febrile illnesses (FI) represent a typical spectrum of diseases in low-resource settings, either in isolation or with other common symptoms. They contribute substantially to morbidity and mortality in India. The primary objective was to study the burden of FI based on Integrated Disease Surveillance Programme (IDSP) data in Punjab, analyze geospatial and temporal trends and patterns, and identify the potential hotspots for effective intervention. METHODS: A retrospective ecological study used the district-level IDSP reports between 2012 and 2019. Diseases responsible for FI on a large scale, like Dengue, Chikungunya, Malaria (Plasmodium Falciparum, P. Vivax), Enteric fever, and Pyrexia of Unknown Origin (PUO), were included in the analysis. The digital map of Punjab was obtained from GitHub. Spatial autocorrelation and cluster analysis were done using Moran's I and Getis-Ord G* to determine hotspots of FI using the incidence and crude disease numbers reported under IDSP. Further, negative binomial regression was used to determine the association between Spatio-temporal and population variables per the census 2011. Stable hotspots were depicted using heat maps generated from district-wise yearly data. RESULTS: PUO was the highest reported FI. We observed a rising trend in the incidence of Dengue, Chikungunya, and Enteric fever, which depicted occasional spikes during the study period. FI expressed significant inter-district variations and clustering during the start of the study period, with more dispersion in the latter part of the study period. P.Vivax malaria depicted stable hotspots in southern districts of Punjab. In contrast, P. Falciparum malaria, Chikungunya, and PUO expressed no spatial patterns. Enteric Fever incidence was high in central and northeastern districts but depicted no stable spatial patterns. Certain districts were common incidence hotspots for multiple diseases. The number of cases in each district has shown over-dispersion for each disease and has little dependence on population, gender, or residence as per regression analysis. CONCLUSIONS: The study demonstrates that information obtained through IDSP can describe the spatial epidemiology of FI at crude spatial scales and drive concerted efforts against FI by identifying actionable points.

A direct comparison of methods for assessing the threat from emerging infectious diseases in seasonally varying environments.

Seasonal variations in environmental conditions lead to changing infectious disease epidemic risks at different times of year. The probability that early cases initiate a major epidemic depends on the season in which the pathogen enters the population. The instantaneous epidemic risk (IER) can be tracked. This quantity is straightforward to calculate, and corresponds to the probability of a major epidemic starting from a single case introduced at time t=t0, assuming that environmental conditions remain identical from that time onwards (i.e. for all t≥t0). However, the threat when a pathogen enters the population in fact depends on changes in environmental conditions occurring within the timescale of the initial phase of the outbreak. For that reason, we compare the IER with a different metric: the case epidemic risk (CER). The CER corresponds to the probability of a major epidemic starting from a single case entering the population at time t=t0, accounting for changes in environmental conditions after that time. We show how the IER and CER can be calculated using different epidemiological models (the stochastic Susceptible-Infectious-Removed model and a stochastic host-vector model that is parameterised using temperature data for Miami) in which transmission parameter values vary temporally. While the IER is always easy to calculate numerically, the adaptable method we provide for calculating the CER for the host-vector model can also be applied easily and solved using widely available software tools. In line with previous research, we demonstrate that, if a pathogen is likely to either invade the population or fade out on a fast timescale compared to changes in environmental conditions, the IER closely matches the CER. However, if this is not the case, the IER and the CER can be significantly different, and so the CER should be used. This demonstrates the need to consider future changes in environmental conditions carefully when assessing the risk posed by emerging pathogens.

Estimating local outbreak risks and the effects of non-pharmaceutical interventions in age-structured populations: SARS-CoV-2 as a case study.

During the COVID-19 pandemic, non-pharmaceutical interventions (NPIs) including school closures, workplace closures and social distancing policies have been employed worldwide to reduce transmission and prevent local outbreaks. However, transmission and the effectiveness of NPIs depend strongly on age-related factors including heterogeneities in contact patterns and pathophysiology. Here, using SARS-CoV-2 as a case study, we develop a branching process model for assessing the risk that an infectious case arriving in a new location will initiate a local outbreak, accounting for the age distribution of the host population. We show that the risk of a local outbreak depends on the age of the index case, and we explore the effects of NPIs targeting individuals of different ages. Social distancing policies that reduce contacts outside of schools and workplaces and target individuals of all ages are predicted to reduce local outbreak risks substantially, whereas school closures have a more limited impact. In the scenarios considered here, when different NPIs are used in combination the risk of local outbreaks can be eliminated. We also show that heightened surveillance of infectious individuals reduces the level of NPIs required to prevent local outbreaks, particularly if enhanced surveillance of symptomatic cases is combined with efforts to find and isolate nonsymptomatic infected individuals. Our results reflect real-world experience of the COVID-19 pandemic, during which combinations of intense NPIs have reduced transmission and the risk of local outbreaks. The general modelling framework that we present can be used to estimate local outbreak risks during future epidemics of a range of pathogens, accounting fully for age-related factors.

Protective effect of a first SARS-CoV-2 infection from reinfection: a matched retrospective cohort study using PCR testing data in England.

The duration of immunity after first severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and the extent to which prior immunity prevents reinfection is uncertain and remains an important question within the context of new variants. This is a retrospective population-based matched observational study where we identified the first polymerase chain reaction (PCR) positive of primary SARS-CoV-2 infection case tests between 1 March 2020 and 30 September 2020. Each case was matched by age, sex, upper tier local authority of residence and testing route to one individual testing negative in the same week (controls) by PCR. After a 90-day pre-follow-up period for cases and controls, any subsequent positive tests up to 31 December 2020 and deaths within 28 days of testing positive were identified, this encompassed an essentially vaccine-free period. We used a conditional logistic regression to analyse the results. There were 517 870 individuals in the matched cohort with 2815 reinfection cases and 12 098 first infections. The protective effect of a prior SARS-CoV-2 PCR-positive episode was 78% (odds ratio (OR) 0.22, 0.21-0.23). Protection rose to 82% (OR 0.18, 0.17-0.19) after a sensitivity analysis excluded 933 individuals with a first test between March and May and a subsequent positive test between June and September 2020. Amongst individuals testing positive by PCR during follow-up, reinfection cases had 77% lower odds of symptoms at the second episode (adjusted OR 0.23, 0.20-0.26) and 45% lower odds of dying in the 28 days after reinfection (adjusted OR 0.55, 0.42-0.71). Prior SARS-CoV-2 infection offered protection against reinfection in this population. There was some evidence that reinfections increased with the alpha variant compared to the wild-type SARS-CoV-2 variant highlighting the importance of continued monitoring as new variants emerge.

Estimated incidence and transmission intensity of rubella infection in Zambia pre-vaccine era 2005-2016.

The rubella disease burden in Zambia may be under-estimated. Using models, we describe the transmission dynamics, determine the incidence estimates and assess the level of underestimation of the real burden of rubella infection in Zambia during the pre-vaccination period 2005-2016. This study used both the deterministic compartmental model and likelihood-based method using a Bayesian framework to describe the epidemiology of rubella. A total of 1313 cases of rubella were confirmed with the highest annual number of 255 new cases recorded in 2008. However, 2014 recorded the highest monthly median positivity rate of 9.0%. The observed median rubella cases were 5.5. There was a seasonal pattern in the occurrence of laboratory-confirmed rubella, with higher test positivity rates of rubella infection usually recorded in the months of September, October and November. The modelled monthly median incidence of rubella infection among the general population was 76 and 20 among pregnant women. The incidence of rubella among the non-pregnant women was 44. The average effective reproductive number (Rt) between 2005 and 2016 was estimated as 1.2 with the peak of infection occurring in 2016. The measles surveillance system underestimates the observed burden of rubella. A mass vaccination campaign conducted between January and July is recommended.

Replacement dynamics and the pathogenesis of the Alpha, Delta and Omicron variants of SARS-CoV-2.

New SARS-CoV-2 variants causing COVID-19 are a major risk to public health worldwide due to the potential for phenotypic change and increases in pathogenicity, transmissibility and/or vaccine escape. Recognising signatures of new variants in terms of replacing growth and severity are key to informing the public health response. To assess this, we aimed to investigate key time periods in the course of infection, hospitalisation and death, by variant. We linked datasets on contact tracing (Contact Tracing Advisory Service), testing (the Second-Generation Surveillance System) and hospitalisation (the Admitted Patient Care dataset) for the entire length of contact tracing in the England - from March 2020 to March 2022. We modelled, for England, time delay distributions using a Bayesian doubly interval censored modelling approach for the SARS-CoV-2 variants Alpha, Delta, Delta Plus (AY.4.2), Omicron BA.1 and Omicron BA.2. This was conducted for the incubation period, the time from infection to hospitalisation and hospitalisation to death. We further modelled the growth of novel variant replacement using a generalised additive model with a negative binomial error structure and the relationship between incubation period length and the risk of a fatality using a Bernoulli generalised linear model with a logit link. The mean incubation periods for each variant were: Alpha 4.19 (95% credible interval (CrI) 4.13-4.26) days; Delta 3.87 (95% CrI 3.82-3.93) days; Delta Plus 3.92 (95% CrI 3.87-3.98) days; Omicron BA.1 3.67 (95% CrI 3.61-3.72) days and Omicron BA.2 3.48 (95% CrI 3.43-3.53) days. The mean time from infection to hospitalisation was for Alpha 11.31 (95% CrI 11.20-11.41) days, Delta 10.36 (95% CrI 10.26-10.45) days and Omicron BA.1 11.54 (95% CrI 11.38-11.70) days. The mean time from hospitalisation to death was, for Alpha 14.31 (95% CrI 14.00-14.62) days; Delta 12.81 (95% CrI 12.62-13.00) days and Omicron BA.2 16.02 (95% CrI 15.46-16.60) days. The 95th percentile of the incubation periods were: Alpha 11.19 (95% CrI 10.92-11.48) days; Delta 9.97 (95% CrI 9.73-10.21) days; Delta Plus 9.99 (95% CrI 9.78-10.24) days; Omicron BA.1 9.45 (95% CrI 9.23-9.67) days and Omicron BA.2 8.83 (95% CrI 8.62-9.05) days. Shorter incubation periods were associated with greater fatality risk when adjusted for age, sex, variant, vaccination status, vaccination manufacturer and time since last dose with an odds ratio of 0.83 (95% confidence interval 0.82-0.83) (P value < 0.05). Variants of SARS-CoV-2 that have replaced previously dominant variants have had shorter incubation periods. Conversely co-existing variants have had very similar and non-distinct incubation period distributions. Shorter incubation periods reflect generation time advantage, with a reduction in the time to the peak infectious period, and may be a significant factor in novel variant replacing growth. Shorter times for admission to hospital and death were associated with variant severity - the most severe variant, Delta, led to significantly earlier hospitalisation, and death. These measures are likely important for future risk assessment of new variants, and their potential impact on population health.

A large outbreak of COVID-19 in a UK prison, October 2020 to April 2021.

Prisons are susceptible to outbreaks. Control measures focusing on isolation and cohorting negatively affect wellbeing. We present an outbreak of coronavirus disease 2019 (COVID-19) in a large male prison in Wales, UK, October 2020 to April 2021, and discuss control measures.We gathered case-information, including demographics, staff-residence postcode, resident cell number, work areas/dates, test results, staff interview dates/notes and resident prison-transfer dates. Epidemiological curves were mapped by prison location. Control measures included isolation (exclusion from work or cell-isolation), cohorting (new admissions and work-area groups), asymptomatic testing (case-finding), removal of communal dining and movement restrictions. Facemask use and enhanced hygiene were already in place. Whole-genome sequencing (WGS) and interviews determined the genetic relationship between cases plausibility of transmission.Of 453 cases, 53% (n = 242) were staff, most aged 25-34 years (11.5% females, 27.15% males) and symptomatic (64%). Crude attack-rate was higher in staff (29%, 95% CI 26-64%) than in residents (12%, 95% CI 9-15%).Whole-genome sequencing can help differentiate multiple introductions from person-to-person transmission in prisons. It should be introduced alongside asymptomatic testing as soon as possible to control prison outbreaks. Timely epidemiological investigation, including data visualisation, allowed dynamic risk assessment and proportionate control measures, minimising the reduction in resident welfare.

Immunity acquired by a minority active fraction of the population could explain COVID-19 spread in Greater Buenos Aires (June-November 2020).

The coronavirus disease 2019 (COVID-19) pandemic had an uneven development in different countries. In Argentina, the pandemic began in March 2020 and, during the first 3 months, the vast majority of cases were concentrated in a densely populated region that includes the city of Buenos Aires (country capital) and the Greater Buenos Aires (GBA) area that surrounds it. This work focuses on the spread of COVID-19 between June and November 2020 in GBA. Within this period of time there was no vaccine, basically only the early wild strain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was present, and the official restriction and distancing measures in this region remained more or less constant. Under these particular conditions, the incidences show a sharp rise from June 2020 and begin to decrease towards the end of August until the end of November 2020. In this work we study, through mathematical modelling and available epidemiological information, the spread of COVID-19 in this region and period of time. We show that a coherent explanation of the evolution of incidences can be obtained assuming that only a minority fraction of the population got involved in the spread process, so that the incidences decreased as this group of people was becoming immune. The observed evolution of the incidences could then be a consequence at the population level of lasting immunity conferred by SARS-CoV-2.

Social risk factors for SARS-CoV-2 acquisition in university students: cross sectional survey.

The objectives of this study were to define risk factors for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in University of Cambridge (UoC) students during a period of increased incidence in October and November 2020. The study design was a survey.Routine public health surveillance identified an increase in the numbers of UoC students with confirmed SARS-CoV-2 positivity in the 10 days after a national lockdown was announced in the UK on 5th November 2020. Cases were identified both through symptom-triggered testing and a universal asymptomatic testing programme. An online questionnaire was sent to all UoC students on 25 November to investigate risk factors for testing positive in the period after 30th October 2020. This asked about symptoms, SARS-CoV-2 test results, aspects of university life, and attendance at social events in the week prior to lockdown. Univariate and multivariable analyses were undertaken evaluating potential risk factors for SARS-CoV-2 positivity.Among 3980 students responding to the questionnaire, 99 (2.5%) reported testing SARS-CoV-2 positive in the period studied; 28 (28%) were asymptomatic. We found strong independent associations with SARS-CoV-2 positivity and attendance at two social settings in the City of Cambridge (adjusted odds ratio favouring disease 13.0 (95% CI 6.2-26.9) and 14.2 (95% CI 2.9-70)), with weaker evidence of association with three further social settings. By contrast, we did not observe strong independent associations between disease risk and accommodation type or attendance at a range of activities associated with the university curriculum.To conclude attendance at social settings can facilitate widespread SARS-CoV-2 transmission in university students. Constraint of transmission in higher education settings needs to emphasise risks outside university premises, as well as a COVID-safe environment within university premises.