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We conduct this research with a two-fold aim: providing a quantitative analysis of the opioid epidemic in the United States (U.S.), and exploring the impact of the COVID-19 pandemic on opioid-related mortality. The duration and persistence of the opioid epidemic lends itself to the need for an overarching analysis with extensive scope. Additionally, studying the ramifications of these concurrent severe public health crises is vital for informing policies to avoid preventable mortality. Using data from CDC WONDER, we consider opioid-related deaths grouped by Census Region spanning January 1999 to October 2022 inclusive, and later add on a demographic component with gender-stratification. Through the lens of key events in the opioid epidemic, we build an interrupted time series model to reveal statistically significant drivers of opioid-related mortality. We then employ a counterfactual to approximate trends in the absence of COVID-19, and estimate excess opioid-related deaths (defined as observed opioid-related deaths minus projected opioid-related deaths) associated with the pandemic. According to our model, the proliferation of fentanyl contributed to sustained increases in opioid-related death rates across three of the four U.S. census regions, corroborating existing knowledge in the field. Critically, each region has an immediate increase to its opioid-related monthly death rate of at least 0.31 deaths per 100,000 persons at the start of the pandemic, highlighting the nationwide knock-on effects of COVID-19. There are consistent positive deviations from the expected monthly opioid-related death rate and a sizable burden from cumulative excess opioid-related deaths, surpassing 60,000 additional deaths nationally from March 2020 to October 2022, ∼70% of which were male. These results suggest that robust, multi-faceted measures are even more important in light of the COVID-19 pandemic to prevent overdoses and educate users on the risks associated with potent synthetic opioids such as fentanyl.

Land use change—for example, the conversion of natural habitats to agricultural or urban ecosystems—is widely recognized to influence the risk and emergence of zoonotic disease in humans 1 , 2 . However, whether such changes in risk are underpinned by predictable ecological changes remains unclear. It has been suggested that habitat disturbance might cause predictable changes in the local diversity and taxonomic composition of potential reservoir hosts, owing to systematic, trait-mediated differences in species resilience to human pressures 3 , 4 . Here we analyse 6,801 ecological assemblages and 376 host species worldwide, controlling for research effort, and show that land use has global and systematic effects on local zoonotic host communities. Known wildlife hosts of human-shared pathogens and parasites overall comprise a greater proportion of local species richness (18–72% higher) and total abundance (21–144% higher) in sites under substantial human use (secondary, agricultural and urban ecosystems) compared with nearby undisturbed habitats. The magnitude of this effect varies taxonomically and is strongest for rodent, bat and passerine bird zoonotic host species, which may be one factor that underpins the global importance of these taxa as zoonotic reservoirs. We further show that mammal species that harbour more pathogens overall (either human-shared or non-human-shared) are more likely to occur in human-managed ecosystems, suggesting that these trends may be mediated by ecological or life-history traits that influence both host status and tolerance to human disturbance 5 , 6 . Our results suggest that global changes in the mode and the intensity of land use are creating expanding hazardous interfaces between people, livestock and wildlife reservoirs of zoonotic disease. Wildlife communities in human-managed ecosystems contain proportionally more species that share human pathogens, and at a higher abundance, than undisturbed habitats, suggesting that landscape transformation creates increasing opportunities for contact between humans and potential hosts of human disease.

The increasing burden of dengue, and the relative failure of traditional vector control programs highlight the need to develop new control methods. SIT using self-limiting genetic technology is one such promising method. A self-limiting strain of Aedes aegypti, OX513A, has already reached the stage of field evaluation. Sustained releases of OX513A Ae. aegypti males led to 80% suppression of a target wild Ae. aegypti population in the Cayman Islands in 2010. Here we describe sustained series of field releases of OX513A Ae. aegypti males in a suburb of Juazeiro, Bahia, Brazil. This study spanned over a year and reduced the local Ae. aegypti population by 95% (95% CI: 92.2%-97.5%) based on adult trap data and 81% (95% CI: 74.9-85.2%) based on ovitrap indices compared to the adjacent no-release control area. The mating competitiveness of the released males (0.031; 95% CI: 0.025-0.036) was similar to that estimated in the Cayman trials (0.059; 95% CI: 0.011-0.210), indicating that environmental and target-strain differences had little impact on the mating success of the OX513A males. We conclude that sustained release of OX513A males may be an effective and widely useful method for suppression of the key dengue vector Ae. aegypti. The observed level of suppression would likely be sufficient to prevent dengue epidemics in the locality tested and other areas with similar or lower transmission.

In the face of rapidly changing data, a range of case fatality ratio estimates for coronavirus disease 2019 (COVID-19) have been produced that differ substantially in magnitude. We aimed to provide robust estimates, accounting for censoring and ascertainment biases. We collected individual-case data for patients who died from COVID-19 in Hubei, mainland China (reported by national and provincial health commissions to Feb 8, 2020), and for cases outside of mainland China (from government or ministry of health websites and media reports for 37 countries, as well as Hong Kong and Macau, until Feb 25, 2020). These individual-case data were used to estimate the time between onset of symptoms and outcome (death or discharge from hospital). We next obtained age-stratified estimates of the case fatality ratio by relating the aggregate distribution of cases to the observed cumulative deaths in China, assuming a constant attack rate by age and adjusting for demography and age-based and location-based under-ascertainment. We also estimated the case fatality ratio from individual line-list data on 1334 cases identified outside of mainland China. Using data on the prevalence of PCR-confirmed cases in international residents repatriated from China, we obtained age-stratified estimates of the infection fatality ratio. Furthermore, data on age-stratified severity in a subset of 3665 cases from China were used to estimate the proportion of infected individuals who are likely to require hospitalisation. Using data on 24 deaths that occurred in mainland China and 165 recoveries outside of China, we estimated the mean duration from onset of symptoms to death to be 17·8 days (95% credible interval [CrI] 16·9–19·2) and to hospital discharge to be 24·7 days (22·9–28·1). In all laboratory confirmed and clinically diagnosed cases from mainland China (n=70 117), we estimated a crude case fatality ratio (adjusted for censoring) of 3·67% (95% CrI 3·56–3·80). However, after further adjusting for demography and under-ascertainment, we obtained a best estimate of the case fatality ratio in China of 1·38% (1·23–1·53), with substantially higher ratios in older age groups (0·32% [0·27–0·38] in those aged <60 years vs 6·4% [5·7–7·2] in those aged ≥60 years), up to 13·4% (11·2–15·9) in those aged 80 years or older. Estimates of case fatality ratio from international cases stratified by age were consistent with those from China (parametric estimate 1·4% [0·4–3·5] in those aged <60 years [n=360] and 4·5% [1·8–11·1] in those aged ≥60 years [n=151]). Our estimated overall infection fatality ratio for China was 0·66% (0·39–1·33), with an increasing profile with age. Similarly, estimates of the proportion of infected individuals likely to be hospitalised increased with age up to a maximum of 18·4% (11·0–37·6) in those aged 80 years or older. These early estimates give an indication of the fatality ratio across the spectrum of COVID-19 disease and show a strong age gradient in risk of death. UK Medical Research Council.

Amid profound climate change, incidence of dengue continues to rise and expand in distribution across the world. Here, we analysed dengue in three coastal departments of Peru which have recently experienced public health emergencies during the worst dengue crises in Latin American history. We developed a climate-based spatiotemporal modelling framework to model monthly incidence of new dengue cases in Piura, Tumbes, and Lambayeque over 140 months from 2010 to 2021. The framework enabled accurate description of in-sample and out-of-sample dengue incidence trends across the departments, as well as the characterisation of the timing, structure, and intensity of climatic relationships with human dengue incidence. In terms of dengue incidence rate (DIR) risk factors, we inferred non-linear and delayed effects of greater monthly mean maximum temperatures, extreme precipitation, sustained drought conditions, and extremes of a Peruvian-specific indicator of the El Niño Southern Oscillation. Building on our model-based understanding of climatic influences, we performed climate-model-based forecasting of dengue incidence across 2018 to 2021 with a forecast horizon of one month. Our framework enabled representative, reliable forecasts of future dengue outbreaks, including correct classification of 100% of all future outbreaks with DIR ≥ 50 (or 150) per 100,000, whilst retaining relatively low probability of 0.12 (0.05) for false alarms. Therefore, our model framework and analysis may be used by public health authorities to i) understand climatic drivers of dengue incidence, and ii) alongside our forecasts, to mitigate impacts of dengue outbreaks and potential public health emergencies by informing early warning systems and deployment of vector control resources.

We find that epidemic resurgence, defined as an upswing in the effective reproduction number ( R ) of the contagion from subcritical to supercritical values, is fundamentally difficult to detect in real time. Inherent latencies in pathogen transmission, coupled with smaller and intrinsically noisier case incidence across periods of subcritical spread, mean that resurgence cannot be reliably detected without significant delays of the order of the generation time of the disease, even when case reporting is perfect. In contrast, epidemic suppression (where R falls from supercritical to subcritical values) may be ascertained 5–10 times faster due to the naturally larger incidence at which control actions are generally applied. We prove that these innate limits on detecting resurgence only worsen when spatial or demographic heterogeneities are incorporated. Consequently, we argue that resurgence is more effectively handled proactively, potentially at the expense of false alarms. Timely responses to recrudescent infections or emerging variants of concern are more likely to be possible when policy is informed by a greater quality and diversity of surveillance data than by further optimisation of the statistical models used to process routine outbreak data.

Infection with SARS-CoV-2 virus is associated with a wide range of symptoms. The REal-time Assessment of Community Transmission − 1 (REACT-1) study monitored the spread and clinical manifestation of SARS-CoV-2 among random samples of the population in England from 1 May 2020 to 31 March 2022. We show changing symptom profiles associated with the different variants over that period, with lower reporting of loss of sense of smell or taste for Omicron compared to previous variants, and higher reporting of cold-like and influenza-like symptoms, controlling for vaccination status. Contrary to the perception that recent variants have become successively milder, Omicron BA.2 was associated with reporting more symptoms, with greater disruption to daily activities, than BA.1. With restrictions lifted and routine testing limited in many countries, monitoring the changing symptom profiles associated with SARS-CoV-2 infection and effects on daily activities will become increasingly important. In this study, the authors assess changing symptom profiles associated with different SARS-CoV-2 variants from May 2020 to March 2022 in England. Using data from the REACT-1 study, they find that Omicron infection is more often associated with cold and influenza-like symptoms, and less with loss of taste and smell.

The COVID-19 pandemic is having a lasting impact on health and well-being. We compare current self-reported health, quality of life and symptom profiles for people with ongoing symptoms following COVID-19 to those who have never tested positive for SARS-CoV-2 infection and those who have recovered from COVID-19. Overall, 276,840/800,000 (34·6%) of invited participants took part. Mental health and health-related quality of life were worse among participants with ongoing persistent symptoms post-COVID compared with those who had never had COVID-19 or had recovered. In this study, median duration of COVID-related symptoms (N = 130,251) was 1·3 weeks (inter-quartile range 6 days to 2 weeks), with 7·5% and 5·2% reporting ongoing symptoms ≥12 weeks and ≥52 weeks respectively. Female sex, ≥1 comorbidity and being infected when Wild-type variant was dominant were associated with higher probability of symptoms lasting ≥12 weeks and longer recovery time in those with persistent symptoms. Although COVID-19 is usually of short duration, some adults experience persistent and burdensome illness. The duration of symptomatic COVID-19 and its impacts on health and quality of life are not well understood. In this study, the authors report results from a survey of ~275,000 adults in England and find that persistent COVID-19 is relatively rare but is associated with poorer mental health and health-related quality of life.

England has experienced a large outbreak of SARS-CoV-2, disproportionately affecting people from disadvantaged and ethnic minority communities. It is unclear how much of this excess is due to differences in exposure associated with structural inequalities. Here, we report from the REal-time Assessment of Community Transmission-2 (REACT-2) national study of over 100,000 people. After adjusting for test characteristics and re-weighting to the population, overall antibody prevalence is 6.0% (95% CI: 5.8-6.1). An estimated 3.4 million people had developed antibodies to SARS-CoV-2 by mid-July 2020. Prevalence is two- to three-fold higher among health and care workers compared with non-essential workers, and in people of Black or South Asian than white ethnicity, while age- and sex-specific infection fatality ratios are similar across ethnicities. Our results indicate that higher hospitalisation and mortality from COVID-19 in minority ethnic groups may reflect higher rates of infection rather than differential experience of disease or care. REACT-2 is a large-scale community study of SARS-CoV-2 seroprevalence in England. Here, the authors estimate that 6% of adults in England had been infected by mid-July 2020, with health and long-term care workers and those of Black or South Asian ethnicity disproportionately affected.

The novel Middle East respiratory syndrome coronavirus (MERS-CoV) had, as of Aug 8, 2013, caused 111 virologically confirmed or probable human cases of infection worldwide. We analysed epidemiological and genetic data to assess the extent of human infection, the performance of case detection, and the transmission potential of MERS-CoV with and without control measures. We assembled a comprehensive database of all confirmed and probable cases from public sources and estimated the incubation period and generation time from case cluster data. Using data of numbers of visitors to the Middle East and their duration of stay, we estimated the number of symptomatic cases in the Middle East. We did independent analyses, looking at the growth in incident clusters, the growth in viral population, the reproduction number of cluster index cases, and cluster sizes to characterise the dynamical properties of the epidemic and the transmission scenario. The estimated number of symptomatic cases up to Aug 8, 2013, is 940 (95% CI 290–2200), indicating that at least 62% of human symptomatic cases have not been detected. We find that the case-fatality ratio of primary cases detected via routine surveillance (74%; 95% CI 49–91) is biased upwards because of detection bias; the case-fatality ratio of secondary cases was 20% (7–42). Detection of milder cases (or clinical management) seemed to have improved in recent months. Analysis of human clusters indicated that chains of transmission were not self-sustaining when infection control was implemented, but that R in the absence of controls was in the range 0·8–1·3. Three independent data sources provide evidence that R cannot be much above 1, with an upper bound of 1·2–1·5. By showing that a slowly growing epidemic is underway either in human beings or in an animal reservoir, quantification of uncertainty in transmissibility estimates, and provision of the first estimates of the scale of the epidemic and extent of case detection biases, we provide valuable information for more informed risk assessment. Medical Research Council, Bill & Melinda Gates Foundation, EU FP7, and National Institute of General Medical Sciences.