Accuracy of US CDC COVID-19 forecasting models.

Accurate predictive modeling of pandemics is essential for optimally distributing biomedical resources and setting policy. Dozens of case prediction models have been proposed but their accuracy over time and by model type remains unclear. In this study, we systematically analyze all US CDC COVID-19 forecasting models, by first categorizing them and then calculating their mean absolute percent error, both wave-wise and on the complete timeline. We compare their estimates to government-reported case numbers, one another, as well as two baseline models wherein case counts remain static or follow a simple linear trend. The comparison reveals that around two-thirds of models fail to outperform a simple static case baseline and one-third fail to outperform a simple linear trend forecast. A wave-by-wave comparison of models revealed that no overall modeling approach was superior to others, including ensemble models and errors in modeling have increased over time during the pandemic. This study raises concerns about hosting these models on official public platforms of health organizations including the US CDC which risks giving them an official imprimatur and when utilized to formulate policy. By offering a universal evaluation method for pandemic forecasting models, we expect this study to serve as the starting point for the development of more accurate models.

COVID-19 activity risk calculator as a gamified public health intervention tool.

The Coronavirus disease 2019 (COVID-19) pandemic, caused by the virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has impacted over 200 countries leading to hospitalizations and deaths of millions of people. Public health interventions, such as risk estimators, can reduce the spread of pandemics and epidemics through influencing behavior, which impacts risk of exposure and infection. Current publicly available COVID-19 risk estimation tools have had variable effectiveness during the pandemic due to their dependency on rapidly evolving factors such as community transmission levels and variants. There has also been confusion surrounding certain personal protective strategies such as risk reduction by mask-wearing and vaccination. In order to create a simple easy-to-use tool for estimating different individual risks associated with carrying out daily-life activity, we developed COVID-19 Activity Risk Calculator (CovARC). CovARC is a gamified public health intervention as users can "play with" how different risks associated with COVID-19 can change depending on several different factors when carrying out routine daily activities. Empowering the public to make informed, data-driven decisions about safely engaging in activities may help to reduce COVID-19 levels in the community. In this study, we demonstrate a streamlined, scalable and accurate COVID-19 risk calculation system. Our study also demonstrates the quantitative impact of vaccination and mask-wearing during periods of high case counts. Validation of this impact could inform and support policy decisions regarding case thresholds for mask mandates, and other public health interventions.

Using survey data to estimate the impact of the omicron variant on vaccine efficacy against COVID-19 infection.

Symptoms-based detection of SARS-CoV-2 infection is not a substitute for precise diagnostic tests but can provide insight into the likely level of infection in a given population. This study uses symptoms data collected in the Global COVID-19 Trends and Impact Surveys (UMD Global CTIS), and data on variants sequencing from GISAID. This work, conducted in January of 2022 during the emergence of the Omicron variant (subvariant BA.1), aims to improve the quality of infection detection from the available symptoms and to use the resulting estimates of infection levels to assess the changes in vaccine efficacy during a change of dominant variant; from the Delta dominant to the Omicron dominant period. Our approach produced a new symptoms-based classifier, Random Forest, that was compared to a ground-truth subset of cases with known diagnostic test status. This classifier was compared with other competing classifiers and shown to exhibit an increased performance with respect to the ground-truth data. Using the Random Forest classifier, and knowing the vaccination status of the subjects, we then proceeded to analyse the evolution of vaccine efficacy towards infection during different periods, geographies and dominant variants. In South Africa, where the first significant wave of Omicron occurred, a significant reduction of vaccine efficacy is observed from August-September 2021 to December 2021. For instance, the efficacy drops from 0.81 to 0.30 for those vaccinated with 2 doses (of Pfizer/BioNTech), and from 0.51 to 0.09 for those vaccinated with one dose (of Pfizer/BioNTech or Johnson & Johnson). We also extended the study to other countries in which Omicron has been detected, comparing the situation in October 2021 (before Omicron) with that of December 2021. While the reduction measured is smaller than in South Africa, we still found, for instance, an average drop in vaccine efficacy from 0.53 to 0.45 among those vaccinated with two doses. Moreover, we found a significant negative (Pearson) correlation of around - 0.6 between the measured prevalence of Omicron in several countries and the vaccine efficacy in those same countries. This prediction, in January of 2022, of the decreased vaccine efficacy towards Omicron is in line with the subsequent increase of Omicron infections in the first half of 2022.

Brain molecular aging, promotion of neurological disease and modulation by sirtuin 5 longevity gene polymorphism.

Mechanisms determining characteristic age-of-onset for neurological diseases are largely unknown. Normal brain aging associates with robust and progressive transcriptome changes ("molecular aging"), but the intersection with disease pathways is mostly uncharacterized. Here, using cross-cohort microarray analysis of four human brain areas, we show that neurological disease pathways largely overlap with molecular aging and that subjects carrying a newly-characterized low-expressing polymorphism in a putative longevity gene (Sirtuin5; SIRT5(prom2)) have older brain molecular ages. Specifically, molecular aging was remarkably conserved across cohorts and brain areas, and included numerous developmental and transcription-regulator genes. Neurological disease-associated genes were highly overrepresented within age-related genes and changed almost unanimously in pro-disease directions, together suggesting an underlying genetic "program" of aging that progressively promotes disease. To begin testing this putative pathway, we developed and used an age-biosignature to assess five candidate longevity gene polymorphisms' association with molecular aging rates. Most robustly, aging was accelerated in cingulate, but not amygdala, of subjects carrying a SIRT5 promoter polymorphism (+9 years, p=0.004), in concordance with cingulate-specific decreased SIRT5 expression. This effect was driven by a set of core transcripts (+24 years, p=0.0004), many of which were mitochondrial, including Parkinson's disease genes, PINK-1 and DJ-1/PARK7, hence suggesting that SIRT5(prom2) may represent a risk factor for mitochondrial dysfunction-related diseases, including Parkinson's, through accelerated molecular aging of disease-related genes. Based on these results we speculate that a "common mechanism" may underlie age-of-onset across several neurological diseases. Confirming this pathway and its regulation by common genetic variants would provide new strategies for predicting, delaying, and treating neurological diseases.

Rate of brain aging and APOE ε4 are synergistic risk factors for Alzheimer's disease.

Advanced age and the APOE ε4 allele are the two biggest risk factors for Alzheimer's disease (AD) and declining cognitive function. We describe a universal gauge to measure molecular brain age using transcriptome analysis of four human postmortem cohorts (n = 673, ages 25-97) free of neurological disease. In a fifth cohort of older subjects with or without neurological disease (n = 438, ages 67-108), we show that subjects with brains deviating in the older direction from what would be expected based on chronological age show an increase in AD, Parkinson's disease, and cognitive decline. Strikingly, a younger molecular age (-5 yr than chronological age) protects against AD even in the presence of APOE ε4 An established DNA methylation gauge for age correlates well with the transcriptome gauge for determination of molecular age and assigning deviations from the expected. Our results suggest that rapid brain aging and APOE ε4 are synergistic risk factors, and interventions that slow aging may substantially reduce risk of neurological disease and decline even in the presence of APOE ε4.

Nicotine promotes neuron survival and partially protects from Parkinson's disease by suppressing SIRT6.

Parkinson's disease is characterized by progressive death of dopaminergic neurons, leading to motor and cognitive dysfunction. Epidemiological studies consistently show that the use of tobacco reduces the risk of Parkinson's. We report that nicotine reduces the abundance of SIRT6 in neuronal culture and brain tissue. We find that reduction of SIRT6 is partly responsible for neuroprotection afforded by nicotine. Additionally, SIRT6 abundance is greater in Parkinson's patient brains, and decreased in the brains of tobacco users. We also identify SNPs that promote SIRT6 expression and simultaneously associate with an increased risk of Parkinson's. Furthermore, brain-specific SIRT6 knockout mice are protected from MPTP-induced Parkinson's, while SIRT6 overexpressing mice develop more severe pathology. Our data suggest that SIRT6 plays a pathogenic and pro-inflammatory role in Parkinson's and that nicotine can provide neuroprotection by accelerating its degradation. Inhibition of SIRT6 may be a promising strategy to ameliorate Parkinson's and neurodegeneration.

Avoiding a lost generation of scientists.

By sharing their experiences, early-career scientists can help to make the case for increased government funding for researchers.

Between destiny and disease: genetics and molecular pathways of human central nervous system aging.

Aging of the human brain is associated with "normal" functional, structural, and molecular changes that underlie alterations in cognition, memory, mood and motor function, amongst other processes. Normal aging also imposes a robust constraint on the onset of many neurological diseases, ranging from late onset neurodegenerative diseases, such as Alzheimer's (AD) and Parkinson's diseases (PD), to early onset psychiatric disorders, such as bipolar disorder (BPD) and schizophrenia (SCZ). The molecular mechanisms and genetic underpinnings of age-related changes in the brain are understudied, and, while they share some overlap with peripheral mechanisms of aging, many are unique to the largely non-mitotic brain. Hence, understanding mechanisms of brain aging and identifying associated modulators may have profound consequences for the prevention and treatment of age-related impairments and diseases. Here we review current knowledge on age-related functional and structural changes, their molecular and genetic underpinnings, and discuss how these pathways may contribute to the vulnerability to develop age-related neurological diseases. We highlight recent findings from human post-mortem brain microarray studies, which we hypothesize, point to a potential genetically controlled transcriptional program underlying molecular changes and age-gating of neurological diseases. Finally, we discuss the implications of this model for understanding basic mechanisms of brain aging and for the future investigation of therapeutic approaches.