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We propose a new framework to model the COVID-19 epidemic of the United Kingdom at the local authority level. The model fits within a general framework for semi-mechanistic Bayesian models of the epidemic based on renewal equations, with some important innovations, including a random walk modelling the reproduction number, incorporating information from different sources, including surveys to estimate the time-varying proportion of infections that lead to reported cases or deaths, and modelling the underlying infections as latent random variables. The model is designed to be updated daily using publicly available data. We envisage the model to be useful for now-casting and short-term projections of the epidemic as well as estimating historical trends. The model fits are available on a public website: https://imperialcollegelondon.github.io/covid19local. The model is currently being used by the Scottish government to inform their interventions.

Although recent research has shown a clear link between alopecia areata (AA) and anxiety and depression, the association of AA with other psychiatric comorbidities has been poorly studied. In this National Institutes of Health database study, we show AA to be significantly associated with alcohol use disorder, attention–deficit hyperactivity disorder and insomnia. Dermatologists may be uniquely situated to screen for these previously under-recognized comorbidities and refer people to mental health services when appropriate.

Introduction Human herpesvirus-6 (HHV-6) is a ubiquitous lymphotropic betaherpesvirus that can reactivate in drug rash with eosinophilia and systemic symptoms (DRESS). Despite recent publications advancing our understanding of HHV-6 in DRESS, the exact role of HHV-6 in disease pathogenesis remains unclear. Methods A scoping review with the PubMed query “(HHV 6 AND (drug OR DRESS OR DIHS)) OR (HHV6 AND (drug OR DRESS OR DIHS))” was conducted in accordance with PRISMA guidelines. Articles containing original data on at least one DRESS patient with HHV-6 testing were included. Results Our search returned a total of 373 publications, of which 89 met eligibility criteria. HHV-6 reactivation occurred in 63% of DRESS patients (n = 748), which was significantly more often than other herpesviruses. HHV-6 reactivation was associated with worse outcomes and greater severity in controlled studies. Case reports have demonstrated sometimes fatal HHV-6-related multi-organ involvement. Temporally, HHV-6 reactivation typically occurs 2 to 4 weeks after DRESS onset and has been linked to markers of immunologic signaling, such as OX40 (CD134), an HHV-6 entry receptor. Efficacy of antiviral or immunoglobulin treatment has only been demonstrated anecdotally, and steroid use may affect HHV-6 reactivation. Conclusion HHV-6 is implicated in DRESS more than in any other dermatologic condition. It is still unclear whether HHV-6 reactivation is cause or consequence of DRESS dysregulation. Similar pathogenic mechanisms precipitated by HHV-6 in other contexts may be relevant in DRESS. Future randomized controlled studies to assess effects of viral suppression on clinical outcomes is needed.

UNSTRUCTURED Altmetric Score (AS) is a novel measure of publication impact that is calculated through an automated algorithm by the number of mentions on numerous social media websites including Twitter and Facebook. It may be advantageous to traditional bibliometrics in the context of analyzing medical student research, as AS peaks relatively quickly and measures qualitative data. The authors hypothesize that dermatology matriculants who graduated from higher-ranked medical schools published more articles with greater impact (i.e. higher AS) than those from lower-ranked institutions.

The UK and Sweden have among the worst per-capita COVID-19 mortality in Europe. Sweden stands out for its greater reliance on voluntary, rather than mandatory, control measures. We explore how the timing and effectiveness of control measures in the UK, Sweden and Denmark shaped COVID-19 mortality in each country, using a counterfactual assessment: what would the impact have been, had each country adopted the others’ policies? Using a Bayesian semi-mechanistic model without prior assumptions on the mechanism or effectiveness of interventions, we estimate the time-varying reproduction number for the UK, Sweden and Denmark from daily mortality data. We use two approaches to evaluate counterfactuals which transpose the transmission profile from one country onto another, in each country’s first wave from 13th March (when stringent interventions began) until 1st July 2020. UK mortality would have approximately doubled had Swedish policy been adopted, while Swedish mortality would have more than halved had Sweden adopted UK or Danish strategies. Danish policies were most effective, although differences between the UK and Denmark were significant for one counterfactual approach only. Our analysis shows that small changes in the timing or effectiveness of interventions have disproportionately large effects on total mortality within a rapidly growing epidemic.

The standard treatment of basal cell carcinoma (BCC) consists of conventional excision or Mohs micrographic surgery. However, surgical excision is not feasible in specific cases, particularly in patients with several BCCs such as those with Gorlin syndrome or individuals receiving immunosuppression after solid-organ transplantation. Additionally, the geriatric population may not be appropriate candidates for surgery. Thus, alternative therapies are needed for these populations.Hedgehog (Hh) inhibitors are approved and effective but are currently available only in oral formulations. These agents such as vismodegib and sonidegib are associated with short-lived responses as well as significant adverse effects including myalgias, dysgeusia, and alopecia. Patidegib and itraconazole are two topical Hh inhibitors agents emerging as alternatives to oral Hh inhibiton for difficult-to-treat BCCs. These agents exhibit limited systemic absorption, leading to improved tolerability; however, an optimal formulation is needed to maximize efficacy and is currently being investigated.Ongoing and recent clinical studies on topical Hedgehog inhibitors show great promise for the development of an agent with a high therapeutic index and limited adverse effects. If patidegib continues to show clinical efficacy in randomized controlled trials, it may become a universal therapy for all subtypes of difficult-to-treat BCC.

INTRODUCTION: After initial declines, in mid-2020, a sustained resurgence in the transmission of novel coronavirus disease (COVID-19) occurred in the United States. Throughout the US epidemic, considerable heterogeneity existed among states, both in terms of overall mortality and infection, but also in the types and stringency of nonpharmaceutical interventions. Despite these stark differences among states, little is known about the relationship between interventions, contact patterns, and infections, or how this varies by age and demographics. A useful tool for studying these dynamics is individual, age-specific mobility data. In this study, we use detailed mobile-phone data from more than 10 million individuals and establish a mechanistic relationship between individual contact patterns and COVID-19 mortality data. RATIONALE: As the pandemic progresses, disease control responses are becoming increasingly nuanced and targeted. Understanding fine-scale patterns of how individuals interact with each other is essential to mounting an efficient public health control program. For example, the choice of closing workplaces, closing schools, limiting hospitality sectors, or prioritizing vaccination to certain population groups should be informed by the demographics currently driving and sustaining transmission. To develop the tools to answer such questions, we introduce a new framework that links mobility to mortality through age-specific contact patterns and then use this rich relationship to reconstruct accurate transmission dynamics (see figure panel A). RESULTS: We find that as of 29 October 2020, adults aged 20 to 34 and 35 to 49 are the only age groups that have sustained SARS-CoV-2 transmission with reproduction numbers (transmission rates) consistently above one. The high reproduction numbers from adults are linked both to rebounding mobility over the summer and elevated transmission risks per venue visit among adults aged 20 to 49. Before school reopening, we estimate that 75 of 100 COVID-19 infections originated from adults aged 20 to 49, and the share of young adults aged 20 to 34 among COVID-19 infections was highly variable geographically. After school reopening, we reconstruct relatively modest shifts in the age-specific sources of resurgent COVID-19 toward younger individuals, with less than 5% of SARS-CoV-2 transmissions attributable to children aged 0 to 9 and less than 10% attributable to early adolescents and teenagers aged 10 to 19. Thus, adults aged 20 to 49 continue to be the only age groups that contribute disproportionately to COVID-19 spread relative to their size in the population (see figure panel B). However, because children and teenagers seed infections among adults who are more transmission efficient, we estimate that overall, school opening is indirectly associated with a 26% increase in SARS-CoV-2 transmission. CONCLUSION: We show that considering transmission through the lens of contact patterns is fundamental to understanding which population groups are driving disease transmission. Over time, the share of age groups among reported deaths has been markedly constant, and the data provide no evidence that transmission shifted to younger age groups before school reopening, and no evidence that young adults aged 20 to 34 were the primary source of resurgent epidemics since the summer of 2020. Our key conclusion is that in locations where novel, highly transmissible SARS-CoV-2 lineages have not yet become established, additional interventions among adults aged 20 to 49, such as mass vaccination with transmission-blocking vaccines, could bring resurgent COVID-19 epidemics under control and avert deaths.

As of 1st June 2020, the US Centres for Disease Control and Prevention reported 104,232 confirmed or probable COVID-19-related deaths in the US. This was more than twice the number of deaths reported in the next most severely impacted country. We jointly model the US epidemic at the state-level, using publicly available death data within a Bayesian hierarchical semi-mechanistic framework. For each state, we estimate the number of individuals that have been infected, the number of individuals that are currently infectious and the time-varying reproduction number (the average number of secondary infections caused by an infected person). We use changes in mobility to capture the impact that non-pharmaceutical interventions and other behaviour changes have on the rate of transmission of SARS-CoV-2. We estimate that Rt was only below one in 23 states on 1st June. We also estimate that 3.7% [3.4%–4.0%] of the total population of the US had been infected, with wide variation between states, and approximately 0.01% of the population was infectious. We demonstrate good 3 week model forecasts of deaths with low error and good coverage of our credible intervals., High numbers of COVID-19-related deaths have been reported in the United States, but estimation of the true numbers of infections is challenging. Here, the authors estimate that on 1 June 2020, 3.7% of the US population was infected with SARS-CoV-2, and 0.01% was infectious, with wide variation by state.

We propose a new framework to model the COVID-19 epidemic of the United Kingdom at the level of local authorities. The model fits within a general framework for semi-mechanistic Bayesian models of the epidemic, with some important innovations: we model the proportion of infections that result in reported deaths and cases as random variables. This is in contrast to standard frameworks that model the latent infection as a deterministic function of time varying reproduction number, Rt. The model is tailored and designed to be updated daily based on publicly available data. We envisage the model to be useful for now-casting and short-term projections of the epidemic as well as estimating historical trends. The model fits are available on a public website, https://imperialcollegelondon.github.io/covid19local. The model is currently being used by the Scottish government in their decisions on interventions within Scotland [1, issue 24 to now].

The impact of estrogen exposure in preventing or treating cardiovascular disease is controversial. But it is clear that estrogen has important effects on vascular physiology and pathophysiology, with potential therapeutic implications. Therefore, the goal of this review is to summarize, using an integrated approach, current knowledge of the vascular effects of estrogen, both in humans and in experimental animals. Aspects of estrogen synthesis and receptors, as well as general mechanisms of estrogenic action are reviewed with an emphasis on issues particularly relevant to the vascular system. Recent understanding of the impact of estrogen on mitochondrial function suggests that the longer lifespan of women compared with men may depend in part on the ability of estrogen to decrease production of reactive oxygen species in mitochondria. Mechanisms by which estrogen increases endothelial vasodilator function, promotes angiogenesis, and modulates autonomic function are summarized. Key aspects of the relevant pathophysiology of inflammation, atherosclerosis, stroke, migraine, and thrombosis are reviewed concerning current knowledge of estrogenic effects. A number of emerging concepts are addressed throughout. These include the importance of estrogenic formulation and route of administration and the impact of genetic polymorphisms, either in estrogen receptors or in enzymes responsible for estrogen metabolism, on responsiveness to hormone treatment. The importance of local metabolism of estrogenic precursors and the impact of timing for initiation of treatment and its duration are also considered. Although consensus opinions are emphasized, controversial views are presented to stimulate future research.

As of 1st June 2020, the US Centers for Disease Control and Prevention reported 104,232 confirmed or probable COVID-19-related deaths in the US. This was more than twice the number of deaths reported in the next most severely impacted country. We jointly modelled the US epidemic at the state-level, using publicly available death data within a Bayesian hierarchical semi-mechanistic framework. For each state, we estimate the number of individuals that have been infected, the number of individuals that are currently infectious and the time-varying reproduction number (the average number of secondary infections caused by an infected person). We used changes in mobility to capture the impact that non-pharmaceutical interventions and other behaviour changes have on the rate of transmission of SARS-CoV-2. Nationally, we estimated 3.7% [3.4%-4.0%] of the population had been infected by 1st June 2020, with wide variation between states, and approximately 0.01% of the population was infectious. We also demonstrated that good model forecasts of deaths for the next 3 weeks with low error and good coverage of our credible intervals.

1AbstractBrazil is currently reporting the second highest number of COVID-19 deaths in the world. Here we characterise the initial dynamics of COVID-19 across the country and assess the impact of non-pharmaceutical interventions (NPIs) that were implemented using a semi-mechanistic Bayesian hierarchical modelling approach. Our results highlight the significant impact these NPIs had across states, reducing an average Rt > 3 to an average of 1.5 by 9-May-2020, but that these interventions failed to reduce Rt < 1, congruent with the worsening epidemic Brazil has experienced since. We identify extensive heterogeneity in the epidemic trajectory across Brazil, with the estimated number of days to reach 0.1% of the state population infected since the first nationally recorded case ranging from 20 days in São Paulo compared to 60 days in Goiás, underscoring the importance of sub-national analyses in understanding asynchronous state-level epidemics underlying the national spread and burden of COVID-19.

Summary Italy was the first European country to experience sustained local transmission of COVID-19. As of 1st May 2020, the Italian health authorities reported 28,238 deaths nationally. To control the epidemic, the Italian government implemented a suite of non-pharmaceutical interventions (NPIs), including school and university closures, social distancing and full lockdown involving banning of public gatherings and non essential movement. In this report, we model the effect of NPIs on transmission using data on average mobility. We estimate that the average reproduction number (a measure of transmission intensity) is currently below one for all Italian regions, and significantly so for the majority of the regions. Despite the large number of deaths, the proportion of population that has been infected by SARS-CoV-2 (the attack rate) is far from the herd immunity threshold in all Italian regions, with the highest attack rate observed in Lombardy (13.18% [10.66%-16.70%]). Italy is set to relax the currently implemented NPIs from 4th May 2020. Given the control achieved by NPIs, we consider three scenarios for the next 8 weeks: a scenario in which mobility remains the same as during the lockdown, a scenario in which mobility returns to pre-lockdown levels by 20%, and a scenario in which mobility returns to pre-lockdown levels by 40%. The scenarios explored assume that mobility is scaled evenly across all dimensions, that behaviour stays the same as before NPIs were implemented, that no pharmaceutical interventions are introduced, and it does not include transmission reduction from contact tracing, testing and the isolation of confirmed or suspected cases. New interventions, such as enhanced testing and contact tracing are going to be introduced and will likely contribute to reductions in transmission; therefore our estimates should be viewed as pessimistic projections. We find that, in the absence of additional interventions, even a 20% return to pre-lockdown mobility could lead to a resurgence in the number of deaths far greater than experienced in the current wave in several regions. Future increases in the number of deaths will lag behind the increase in transmission intensity and so a second wave will not be immediately apparent from just monitoring of the daily number of deaths. Our results suggest that SARS-CoV-2 transmission as well as mobility should be closely monitored in the next weeks and months. To compensate for the increase in mobility that will occur due to the relaxation of the currently implemented NPIs, adherence to the recommended social distancing measures alongside enhanced community surveillance including swab testing, contact tracing and the early isolation of infections are of paramount importance to reduce the risk of resurgence in transmission. SUGGESTED CITATION Michaela A. C. Vollmer, Swapnil Mishra, H Juliette T Unwin, Axel Gandy et al. Using mobility to estimate the transmission intensity of COVID-19 in Italy: a subnational analysis with future scenarios. Imperial College London (2020) doi:https://doi.org/10.25561/78677 This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Following the detection of the new coronavirus