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This letter describes an immunocompromised patient who had persistent infection with SARS-CoV-2 over a period of months, despite several courses of remdesivir. Phylogenetic analysis showed accelerated viral evolution.

Gay, bisexual, and other men who have sex with men (MSM) continue to have disproportionately high burdens of HIV infection in countries of low, middle, and high income in 2016. 4 years after publication of a Lancet Series on MSM and HIV, progress on reducing HIV incidence, expanding sustained access to treatment, and realising human rights gains for MSM remains markedly uneven and fraught with challenges. Incidence densities in MSM are unacceptably high in countries as diverse as China, Kenya, Thailand, the UK, and the USA, with substantial disparities observed in specific communities of MSM including young and minority populations. Although some settings have achieved sufficient coverage of treatment, pre-exposure prophylaxis (PrEP), and human rights protections for sexual and gender minorities to change the trajectory of the HIV epidemic in MSM, these are exceptions. The roll-out of PrEP has been notably slow and coverage nowhere near what will be required for full use of this new preventive approach. Despite progress on issues such as marriage equality and decriminalisation of same-sex behaviour in some countries, there has been a marked increase in anti-gay legislation in many countries, including Nigeria, Russia, and The Gambia. The global epidemic of HIV in MSM is ongoing, and global efforts to address it remain insufficient. This must change if we are ever to truly achieve an AIDS-free generation.

[...]if one accepts that the boundaries between disease outbreaks and their political economic determinants/sequelae are blurred,3 the same question should also be asked of other ‘expert’ modelers, economists in particular. When it comes to forecasting epidemic trends, however, their contributions—from specious metrics4 like the 2019 Global Health Security Index5 to kaleidoscopic computational models of communicable disease transmission—have limited predictive power (as experience in global health has repeatedly shown). During the 2013–2016, Ebola virus outbreak in West Africa, modelers devised a dizzying array of forecasts,6 ranging from the WHO’s supposition early on that the outbreak would be contained at a few hundred cases to the US Centers for Disease Control and Prevention’s estimate of up to 1.4 million cases by January 2015.7 Interestingly, this latter model was least consistent with the observed epidemic; at the same time, however, it was claimed to be the most useful (as an advocacy tool to muster a robust international response).8 9 This is not quite what the statistician George E. P. Box had in mind when he wrote his famous dictum, ‘All models are wrong but some are useful.’10 More recently, suppositious models of the SARS-CoV-2 outbreak in the UK posited that half the country (some 34 million people) might already be infected (as of 19 March 2020)11 and that the ‘herd immunity’ approach initially adopted by the UK government was defensible.12 In the USA, health economists Bendavid and Bhattacharya upped the ante questioning whether universal quarantine measures were worth their costs to the economy.13 The duo’s neoliberal proclivities,14 coupled with this current offering in the Wall Street Journal, underscore the ideological presumptions intrinsic to any modeling exercise. For the most part, mathematical models of infectious disease transmission serve not as forecasts,24 25 but rather as a means for setting epistemic confines to the understanding of why some groups live sicker lives than others—confines that sustain predatory accumulation rather than challenge it.26 27 Similar to the role philanthropy plays in occulting ecnonomic exploitation,28 29 the modest improvements in well-being offered by the right hand of public health ‘science’ often disguise what global elites and their looting machines30 have expropriated with the left.31 That being the case, the field is in clear need of decolonising; however, it is producing some potentially useful, although structurally naïve,32 work to support the containment of SARS-CoV-2 within countries.

High-income countries have used neocolonial negotiating power, global policy leverage and capital to procure enough doses to cover 245% of their citizens while leaving few doses for poorer countries.2 As a result, lower-income countries may not be able to vaccinate their populations until 2023.3 Such inequity is yet another example of how the interests of racial capitalism run roughshod over the golden rule of global solidarity—attend to the highest risk first.4 Currently, older and medically vulnerable individuals are dying from COVID-19 disproportionately in poor countries, while young, healthy individuals are getting vaccinated in wealthy ones.5 Vaccine apartheid is a not novel phenomenon. The notion that only certain corners of the world get to benefit from life-saving treatments is an everyday reality of a global health system driven by a capitalist, philanthropic model.6 7 But in times of crises—and as new variants threaten the vaccination plans of wealthy countries—these inequities and their solutions come to the forefront of global debate.8 Policy-makers in rich nations are aware of these issues. Over 50 years ago, Kwame Nkrumah observed how aid is a ‘revolving credit’ which returns to countries of the global North in the form of increased profits.19 To the extent that COVAX is being leveraged to protect corporate patents and profits, Nkrumah’s words continue to be germane. The focus on a donor-based model of aid in achieving vaccine equity has distracted leaders from the ideologies, economic systems and trade regulations that leave access to medicine to the forces of the marketplace rather than global health priorities.32 Achieving global vaccine justice requires a rapid shift in trade regulations and contract transparency that streamlines IP sharing and technology transfers.

This review examines key events, challenges, and responses to the mpox public health emergency following the Africa CDC's declaration of a Public Health Emergency of Continental Concern on August 13, 2024. In response to the crisis, over 3.6 million vaccine doses and more than $150 million in funding have been mobilized globally, with contributions from the United States, European Union, and Japan. However, challenges persist, particularly in the Democratic Republic of Congo, where a humanitarian crisis in Kinshasa has complicated mpox diagnostics and treatment. In response, the Africa CDC has deployed its One Continental Incident Management Support team, with a focus on decentralizing diagnostics and enhancing sample movement through additional PCR equipment, funded by the Pandemic Fund and USAID. To reinforce laboratory diagnostics, surveillance, and case management, the Africa CDC has adopted a comprehensive \"One Team, One Plan, One Budget, One M&E\" approach and has deployed 72 epidemiologists to improve data integration. Collaborative efforts with WHO, GAVI, and UNICEF aim to expedite vaccine distribution, with a target of 10 million doses by 2025, alongside enhanced vaccine safety monitoring.

A detailed analysis of how these historical forces become embodied as viral disease is beyond the scope of this study (and we have conducted such analyses elsewhere).4–7 Rather, in this article, we argue that epidemiological studies that claim to capture the social dynamics of disease transmission in health-seeking behaviours all too readily serve as a smokescreen that enables and perpetuates ongoing structural inequities—notably, by omitting consideration of global power relations, colonial history and contemporary extractive political economies. Interpretive injustice Epidemiological studies of health-seeking behaviours conducted during the Ebola outbreaks in West Africa and Great Lakes region have rarely included an analysis of the historical sources of these countries’ poverty or poor health systems. Grounded in our experience with patients and ethnographic interlocutors in both the DRC and West Africa, we have come to appreciate alternative viewpoints, one of which is that mistrust may serve as a practical way of engaging with the ‘history and ongoing effects of atrocities inflicted on African people, particularly when committed by non-Africans’.14 Mistrust, as such, represents one mediator among many in a determinative web of human rights abuses that stretches back in time and across the region, linking the DRC and West Africa to distant continents.8 From this perspective, studies like the one described earlier lend themselves to what we consider ‘interpretive injustice’15 by (1) denying mistrust as a valid critique of the colonial legacies that persist to this day and (2) instantiating logics of causality that obscure how centuries of abusive power dynamics factor into contemporary public health emergencies.4 8 16 17 While some researchers may counter that the tendency to conduct proximal, synchronic analyses is a consequence of available data and epidemiological research methods, we will show further that some quantitative social-science researchers have already begun to parameterise historical and geopolitical factors in an attempt to fashion an epidemiological inquiry capable of grappling with historical social forces. Take for example a 2016 study published in the New England Journal of Medicine, which presented data from the 2013–2016 Ebola outbreak in West Africa and concluded that approximately 20% of individuals (ie, ‘superspreaders’) generated 80% of cases of EVD.18 We do not doubt the descriptive accuracy of the study; we trust the math and the observations that the virus moved through communities in such a pattern.

Evidence for minimally symptomatic Ebola virus (EBOV) infection is limited. During the 2013-16 outbreak in West Africa, it was not considered epidemiologically relevant to published models or projections of intervention effects. In order to improve our understanding of the transmission dynamics of EBOV in humans, we investigated the occurrence of minimally symptomatic EBOV infection in quarantined contacts of reported Ebola virus disease cases in a recognized 'hotspot.' We conducted a cross-sectional serosurvey in Sukudu, Kono District, Sierra Leone, from October 2015 to January 2016. A blood sample was collected from 187 study participants, 132 negative controls (individuals with a low likelihood of previous exposure to Ebola virus), and 30 positive controls (Ebola virus disease survivors). IgG responses to Ebola glycoprotein and nucleoprotein were measured using Alpha Diagnostic International ELISA kits with plasma diluted at 1:200. Optical density was read at 450 nm (subtracting OD at 630nm to normalize well background) on a ChroMate 4300 microplate reader. A cutoff of 4.7 U/mL for the anti-GP ELISA yielded 96.7% sensitivity and 97.7% specificity in distinguishing positive and negative controls. We identified 14 seropositive individuals not known to have had Ebola virus disease. Two of the 14 seropositive individuals reported only fever during quarantine while the remaining 12 denied any signs or symptoms during quarantine. By using ELISA to measure Zaire Ebola virus antibody concentrations, we identified a significant number of individuals with previously undetected EBOV infection in a 'hotspot' village in Sierra Leone, approximately one year after the village outbreak. The findings provide further evidence that Ebola, like many other viral infections, presents with a spectrum of clinical manifestations, including minimally symptomatic infection. These data also suggest that a significant portion of Ebola transmission events may have gone undetected during the outbreak. Further studies are needed to understand the potential risk of transmission and clinical sequelae in individuals with previously undetected EBOV infection.

Despite an improvement in the overall TB cure rate from 40-74% between 1995 and 2011, TB incidence in South Africa continues to increase. The epidemic is notably disquieting in schools because the vulnerable population is compelled to be present. Older learners (age 15-19) are at particular risk given a smear-positive rate of 427 per 100,000 per year and the significant amount of time they spend indoors. High schools are therefore important locations for potential TB infection and thus prevention efforts. Using portable carbon dioxide monitors, we measured CO2 in classrooms under non-steady state conditions. The threshold for tuberculosis transmission was estimated using a carbon dioxide-based risk equation. We determined a critical rebreathed fraction of carbon dioxide (ƒ(c)) of 1 · 6%, which correlates with an indoor CO2 concentration of 1000 ppm. These values correspond with a ventilation rate of 8 · 6 l/s per person or 12 air exchanges per hour (ACH) for standard classrooms of 180 m(3). Given the high smear positive rate of high-school adolescents in South Africa, the proposal to achieve CO2 levels of 1000 ppm through natural ventilation (in the amount 12 ACH) will not only help achieve WHO guidelines for providing children with healthy indoor environments, it will also provide a low-cost intervention for helping control the TB epidemic in areas of high prevalence.

As of May 27, 2018, 6 suspected, 13 probable and 35 confirmed cases of Ebola virus disease (EVD) had been reported in Équateur Province, Democratic Republic of Congo. We used reported case counts and time series from prior outbreaks to estimate the total outbreak size and duration with and without vaccine use. We modeled Ebola virus transmission using a stochastic branching process model that included reproduction numbers from past Ebola outbreaks and a particle filtering method to generate a probabilistic projection of the outbreak size and duration conditioned on its reported trajectory to date; modeled using high (62%), low (44%), and zero (0%) estimates of vaccination coverage (after deployment). Additionally, we used the time series for 18 prior Ebola outbreaks from 1976 to 2016 to parameterize the Thiel-Sen regression model predicting the outbreak size from the number of observed cases from April 4 to May 27. We used these techniques on probable and confirmed case counts with and without inclusion of suspected cases. Probabilistic projections were scored against the actual outbreak size of 54 EVD cases, using a log-likelihood score. With the stochastic model, using high, low, and zero estimates of vaccination coverage, the median outbreak sizes for probable and confirmed cases were 82 cases (95% prediction interval [PI]: 55, 156), 104 cases (95% PI: 58, 271), and 213 cases (95% PI: 64, 1450), respectively. With the Thiel-Sen regression model, the median outbreak size was estimated to be 65.0 probable and confirmed cases (95% PI: 48.8, 119.7). Among our three mathematical models, the stochastic model with suspected cases and high vaccine coverage predicted total outbreak sizes closest to the true outcome. Relatively simple mathematical models updated in real time may inform outbreak response teams with projections of total outbreak size and duration.

Climate policy needs to take a reparative stance