Impact of molecular sequence data completeness on HIV cluster detection and a network science approach to enhance detection.

Detection of viral transmission clusters using molecular epidemiology is critical to the response pillar of the Ending the HIV Epidemic initiative. Here, we studied whether inference with an incomplete dataset would influence the accuracy of the reconstructed molecular transmission network. We analyzed viral sequence data available from ~ 13,000 individuals with diagnosed HIV (2012-2019) from Houston Health Department surveillance data with 53% completeness (n = 6852 individuals with sequences). We extracted random subsamples and compared the resulting reconstructed networks versus the full-size network. Increasing simulated completeness was associated with an increase in the number of detected clusters. We also subsampled based on the network node influence in the transmission of the virus where we measured Expected Force (ExF) for each node in the network. We simulated the removal of nodes with the highest and then lowest ExF from the full dataset and discovered that 4.7% and 60% of priority clusters were detected respectively. These results highlight the non-uniform impact of capturing high influence nodes in identifying transmission clusters. Although increasing sequence reporting completeness is the way to fully detect HIV transmission patterns, reaching high completeness has remained challenging in the real world. Hence, we suggest taking a network science approach to enhance performance of molecular cluster detection, augmented by node influence information.

A comparison assessment of landfill waste incineration and methane capture in the central region of Mexico.

This article aims to conduct a techno-economic feasibility assessment of producing energy by waste incineration and methane capture in the central region of Mexico. Three scenarios at different efficiency rates were considered: 50, 80 and 100%. For the methane project, yields and power capacity were determined using the potential generation rate and the degradable organic carbon content through the LandGEM model. For incineration, the waste calorific potential and the average moisture content were used to estimate the achievable electrical performance. The estimated annual energy was 35,018 GWh for methane, compared to 537.71 GWh for incineration. Both projects reported financial economic feasibilities when evaluated at a discount rate of 12%. Incineration reported an net present value of US$49,942,534 and an internal rate of return of 26% in contrast to US$4,054,109 and 17% for the methane project. Although the payback period for incineration was lower than for methane, its levelized cost of energy was significantly higher. These results are intended to assist the decision-making process when planning and developing waste management strategies under principles of circular economy in Mexico and similar regions worldwide.

A Review of Mammarenaviruses and Rodent Reservoirs in the Americas.

In the Americas, infectious viral diseases caused by viruses of the genus Mammarenavirus have been reported since the 1960s. Such diseases have commonly been associated with land use changes, which favor abundance of generalist rodent species. In the Americas-where the rates of land use change are among the highest worldwide-at least 1326 of all 2277 known rodent species have been reported. We conducted a literature review of studies between 1960 and 2020, to establish the current and historical knowledge about genotypes of mammarenaviruses and their rodent reservoirs in the Americas. Our overall goal was to show the importance of focusing research efforts on the American continent, since the conditions exist for future viral hemorrhagic fever (VHF) outbreaks caused by rodent-borne viruses, in turn, carried by widely distributed rodents. We found 47 species identified down to the species level, and one species identified only down to the genus level (Oryzomys sp.), reported in the Americas as reservoirs of mammarenaviruses, most these are ecological generalists. These species associate with 29 genotypes of Mammarenavirus, seven of which have been linked to VHFs in humans. We also highlight the need to monitor these species, in order to prevent viral disease outbreaks in the region.

Identifying counties at risk of high overdose mortality burden during the emerging fentanyl epidemic in the USA: a predictive statistical modelling study.

BACKGROUND: The emergence of fentanyl around 2013 represented a new, deadly stage of the opioid epidemic in the USA. We aimed to develop a statistical regression approach to identify counties at the highest risk of high overdose mortality in the subsequent years by predicting annual county-level overdose death rates across the contiguous USA and to validate our approach against observed overdose mortality data collected between 2013 and 2018. METHODS: We fit mixed-effects negative binomial regression models to predict overdose death rates in the subsequent year for 2013-18 for all contiguous state counties in the USA (ie, excluding Alaska and Hawaii). We used publicly available county-level data related to health-care access, drug markets, socio-demographics, and the geographical spread of opioid overdose as model predictors. The crude number of county-level overdose deaths was extracted from restricted US Centers for Disease Control and Prevention mortality records. To predict county-level overdose rates for the year 201X: (1) a model was trained on county-level predictor data for the years 2010-201(X-2) paired with county-level overdose deaths for the year 2011-201(X-1); (2) county-level predictor data for the year 201(X-1) was fed into the model to predict the 201X county-level crude number of overdose deaths; and (3) the latter were converted to a population-adjusted rate. For comparison, we generated a benchmark set of predictions by applying the observed slope of change in overdose death rates in the previous year to 201(X-1) rates. To assess the predictive performance of the model, we compared predicted values (of both the model and benchmark) to observed values by (1) calculating the mean average error, root mean squared error, and Spearman's correlation coefficient and (2) assessing the proportion of counties in the top decile (10%) of overdose death rates that were correctly predicted as such. Finally, in a post-hoc analysis, we sought to identify variables with greatest predictive utility. FINDINGS: Between 2013 and 2018, among the 3106 US counties included, our modelling approach outperformed the benchmark strategy across all metrics. The observed average county-level overdose death rate rose from 11·8 per 100 000 people in 2013 to 15·4 in 2017 before falling to 14·6 in 2018. Our negative binomal modelling approach similarly identified an increasing trend, predicting an average 11·8 deaths per 100 000 in 2013, up to 15·1 in 2017, and increasing further to 16·4 in 2018. The benchmark model over-predicted average death rates each year, ranging from 13·0 per 100 000 in 2013 to 18·3 in 2018. Our modelling approach successfully ranked counties by overdose death rate identifying between 42% and 57% of counties in the top decile of overdose mortality (compared with 29% and 43% using the benchmark) each year and identified 194 of the 808 counties with emergent overdose outbreaks (ie, newly entered the top decile) across the study period, versus 31 using the benchmark. In the post-hoc analysis, we identified geospatial proximity of overdose in nearby counties, opioid prescription rate, presence of an urgent care facility, and several economic indicators as the variables with the greatest predictive utility. INTERPRETATION: Our model shows that a regression approach can effectively predict county-level overdose death rates and serve as a risk assessment tool to identify future high mortality counties throughout an emerging drug use epidemic. FUNDING: National Institute on Drug Abuse.

Methodological approaches for the prediction of opioid use-related epidemics in the United States: a narrative review and cross-disciplinary call to action.

The opioid crisis in the United States has been defined by waves of drug- and locality-specific Opioid use-Related Epidemics (OREs) of overdose and bloodborne infections, among a range of health harms. The ability to identify localities at risk of such OREs, and better yet, to predict which ones will experience them, holds the potential to mitigate further morbidity and mortality. This narrative review was conducted to identify and describe quantitative approaches aimed at the "risk assessment," "detection" or "prediction" of OREs in the United States. We implemented a PubMed search composed of the: (1) objective (eg, prediction), (2) epidemiologic outcome (eg, outbreak), (3) underlying cause (ie, opioid use), (4) health outcome (eg, overdose, HIV), (5) location (ie, US). In total, 46 studies were included, and the following information extracted: discipline, objective, health outcome, drug/substance type, geographic region/unit of analysis, and data sources. Studies identified relied on clinical, epidemiological, behavioral and drug markets surveillance and applied a range of methods including statistical regression, geospatial analyses, dynamic modeling, phylogenetic analyses and machine learning. Studies for the prediction of overdose mortality at national/state/county and zip code level are rapidly emerging. Geospatial methods are increasingly used to identify hotspots of opioid use and overdose. In the context of infectious disease OREs, routine genetic sequencing of patient samples to identify growing transmission clusters via phylogenetic methods could increase early detection capacity. A coordinated implementation of multiple, complementary approaches would increase our ability to successfully anticipate outbreak risk and respond preemptively. We present a multi-disciplinary framework for the prediction of OREs in the US and reflect on challenges research teams will face in implementing such strategies along with good practices.