Early Projections Research Articles

Slipping Away: Slippage in Hazard Ratios Over Datacuts and Its Impact on Immuno-oncology Combination Economic Evaluations

ObjectivesThis study examines the impact of slippage in hazard ratios (tending toward the null over subsequent datacuts) for overall survival for combination treatment with a PD-(L)-1 inhibitor and a tyrosine kinase inhibitor in advanced renal cell carcinoma. MethodsFour trials’ Kaplan-Meier curves were digitized over several datacuts and fitted with standard parametric curves. Accuracy and consistency of early data projections were calculated versus observed restricted mean survival time and fitted lifetime survival from the longest follow-up datacut. The change in economically justifiable price (eJP) was calculated fitting the same curve to both arms, using an assumed average utility of 0.7 and willingness-to-pay threshold of £30 000 per quality-adjusted life-year. The eJP represents the lifetime justifiable price increment for the new treatment, including differences in drug-, administration-, and disease-related costs. ResultsSlippage in hazard ratios was observed in trials with longer follow-up, potentially influenced by subsequent PD-(L)-1 use after tyrosine kinase inhibitor monotherapy, early stoppage of PD-(L)-1, and development of resistance. Lognormal and log-logistic curves were more likely to overpredict the observed result; Gompertz and gamma underpredicted. Statistical measures for goodness of fit did not reliably predict the restricted mean survival time. Large differences in incremental mean life-years were observed between even the penultimate and final datacuts for most of the fitted curves, meaningfully affecting the eJP. ConclusionsThis work demonstrates the challenge in predicting treatment benefits with novel therapies using immature data. Incorporating information on the impact of subsequent treatment is likely to play a key role in improving predictions.

Prediction of Fetal Health Status Using Machine Learning

The goal of this promising area of study is to enhance prenatal care and lower fetal morbidity and mortality by utilizingmachine learning to anticipate fetal disease. In this study, we present a machine learning-based strategy for predicting fetaldiseases from clinical data. First, we gathered a sizable collection of clinical information from expectant mothers with various fetal disorders. Using clinical guidelines, we pre-processed the data and retrieved pertinent features. We integrated a range of machine learning algorithms, including logistic regression, support vector machines, decision trees, and random forests, to train and test our model. We evaluated the performance of our model using severalfactors, such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC).The results of this study demonstrate how machine learning algorithms can accurately forecast fetal health status. The developed models achieve good accuracy and AUC-ROC ratings todistinguish between healthy and at-risk fetuses. The interpretability study identifies key clinical characteristics that have a significant impact on the prediction, providing medical practitioners with useful information when making decisions about prenatal care. Through the provision of more unbiasedand precise assessments of fetal health status, machine learning techniques incorporated into prenatal care have the potential to transform the industry. By providing accurate and early projections, this technology can assist healthcare professionals in identifying high-risk pregnancies and carrying out the necessary procedures, improving mother and fetal outcomes. Future research should concentrate on verifying and improving predictive models on larger and more varied datasets to ensure real-world applicability and reliability

Endogenous mutant Huntingtin alters the corticogenesis via lowering Golgi recruiting ARF1 in cortical organoid

Pathogenic mutant huntingtin (mHTT) infiltrates the adult Huntington’s disease (HD) brain and impairs fetal corticogenesis. However, most HD animal models rarely recapitulate neuroanatomical alterations in adult HD and developing brains. Thus, the human cortical organoid (hCO) is an alternative approach to decode mHTT pathogenesis precisely during human corticogenesis. Here, we replicated the altered corticogenesis in the HD fetal brain using HD patient-derived hCOs. Our HD-hCOs had pathological phenotypes, including deficient junctional complexes in the neural tubes, delayed postmitotic neuronal maturation, dysregulated fate specification of cortical neuron subtypes, and abnormalities in early HD subcortical projections during corticogenesis, revealing a causal link between impaired progenitor cells and chaotic cortical neuronal layering in the HD brain. We identified novel long, oriented, and enriched polyQ assemblies of HTTs that hold large flat Golgi stacks and scaffold clathrin+ vesicles in the neural tubes of hCOs. Flat Golgi stacks conjugated polyQ assemblies by ADP-ribosylation factor 1 (ARF1). Inhibiting ARF1 activation with Brefeldin A (BFA) disassociated polyQ assemblies from Golgi. PolyQ assembles with mHTT scaffolded fewer ARF1 and formed shorter polyQ assembles with fewer and shorter Golgi and clathrin vesicles in neural tubes of HD-hCOs compared with those in hCOs. Inhibiting the activation of ARF1 by BFA in healthy hCOs replicated impaired junctional complexes in the neural tubes. Together, endogenous polyQ assemblies with mHTT reduced the Golgi recruiting ARF1 in the neuroepithelium, impaired the Golgi structure and activities, and altered the corticogenesis in HD-hCO.

Model‐informed health system reorganization during emergencies

The COVID‐19 pandemic presented the world to a novel class of problems highlighting distinctive features that rendered standard academic research and participatory processes less effective in properly informing public health interventions in a timely way. The urgency and rapidity of the emergency required tight integration of novel and high‐quality simulation modeling with public health policy implementation. By introducing flexibility and agility into standard participatory processes, we aligned the modeling effort with the imposed reality of the emergency to rapidly develop a regional system dynamics (SD) model integrating diverse streams of data that could reliably inform both health system restructuring and public health policy. Using Lombardy data, our SD model was able to generate early projections for the diffusion of the pandemic in neighbor Ticino. Later, it projected the timing and size of peak patient demand. Our work also supported the need for reorganization of the healthcare system and volume flexibility strategies increasing hospital capacity (e.g., intensive care unit [ICU] and ward beds, medical and nursing staff, and oxygen supply) in Ticino. Counterfactual analyses quantify the impact of the decisions supported by our interventions. Our research contributes to our understanding of volume flexibility strategies used by healthcare organizations during emergencies, highlighting the critical role played by available response time in the deployment of strategies that either prioritize critical services or leverage available resources. It also contributes to the literature on participatory systems modeling by describing a flexible and agile participatory process that was successfully deployed in a rapidly evolving high‐stakes emergency.

Revisiting Poverty Trends and the Role of Social Protection Systems in Africa during the COVID-19 Pandemic

AbstractQuantifying the impact of the COVID-19 pandemic on poverty in Africa has been as difficult as predicting the path of the pandemic, mainly due to data limitations. The advent of new data sources, including national accounts and phone survey data, provides an opportunity for a thorough reassessment of the impact of the pandemic and the subsequent expansion of social protection systems on the evolution of poverty in Africa. In this paper, we combine per capita GDP growth from national accounts with data from High-Frequency Phone Surveys for several countries to estimate the net impact of the pandemic on poverty. We find that the pandemic has increased poverty in Africa by 1.5 to 1.7 percentage points in 2020, relatively smaller than early estimates and projections. We also find that countries affected by Fragility, Conflict and Violence experienced the greatest increases in poverty, about 2.1 percentage points in 2020. Furthermore, we assess and synthesize empirical evidence on the role that social protection systems played in mitigating the adverse impact of the COVID-19 crisis in Africa. We review social protection responses in various African countries, mainly focusing on the impact of these programs and effectiveness of targeting systems. Although the evidence base on the protective role of social protection programs during the pandemic remains scarce, we highlight important findings on the impacts of these programs while also uncovering some vulnerabilities in social protection programming in Africa. We finally draw important lessons related to the delivery, targeting and impact of various social protection programs launched in Africa in response to the pandemic.

Poor insight and future thinking in early dementia limit patient projections of potential utility of technological innovations and advanced care planning.

Cognitive psychology posits that thinking about the future relies on memory such that those with memory impairment may have trouble imaging their future technology and other needs. We conducted a content analysis of qualitative data from interviews with six patients with MCI or early dementia regarding potential adaptations to a mobile telepresence robot. Using a matrix analysis approach, we explored perceptions of (1) what technology could help with day-to-day functioning in the present and future and (2) what technology may help people with memory problems or dementia stay home alone safely. Very few participants could identify any technology to assist themselves or other people with memory problems and could not provide suggestions on what technology may help them stay home alone safely. Most perceived that they would never need robotic assistance. These findings suggest individuals with MCI or early dementia have limited perspectives on their own functional abilities now and in the future. Consideration of the individuals' diminished understanding of their own future illness trajectory is crucial when engaging in research or considering novel technological management solutions and may have implications for other aspects of advanced care planning.

COVID-19 and suicide: Evidence from Japan

Early projections painted a bleak picture of a suicide epidemic following the emergence of the COVID-19. This stemmed from widespread concerns that an unintended consequence of the health mandates designed to limit COVID-19 infections was deteriorating mental health and that this could lead to increases in suicides. This was amplified by poor media reporting1 and by early studies forecasting high suicide rates as a consequence of changing health and economic conditions. Yet the evidence to date suggests rising suicide rates have not occurred in most countries.

Recurrent Neural Networks for Feature Extraction from Dengue Fever.

Dengue fever modelling in endemic locations is critical to reducing outbreaks and improving vector-borne illness control. Early projections of dengue are a crucial tool for disease control because of the unavailability of treatments and universal vaccination. Neural networks have made significant contributions to public health in a variety of ways. In this paper, we develop a deep learning modelling using random forest (RF) that helps extract the features of the dengue fever from the text datasets. The proposed modelling involves the data collection, preprocessing of the input texts, and feature extraction. The extracted features are studied to test how well the feature extraction using RF is effective on dengue datasets. The simulation result shows that the proposed method achieves higher degree of accuracy that offers an improvement of more than 12% than the existing methods in extracting the features from the input datasets than the other feature extraction methods. Further, the study reduces the errors associated with feature extraction that is 10% lesser than the other existing methods, and this shows the efficacy of the model.

Modeling the Waves of Covid-19

The challenges with modeling the spread of Covid-19 are its power-type growth during the middle stages of the waves with the exponents depending on time, and that the saturation of the waves is mainly due to the protective measures and other restriction mechanisms working in the same direction. The two-phase solution we propose for modeling the total number of detected cases of Covid-19 describes the actual curves for many its waves and in many countries almost with the accuracy of physics laws. Bessel functions play the key role in our approach. The differential equations we obtain are of universal type and can be used in behavioral psychology, invasion ecology (transient processes), etc. The initial transmission rate and the intensity of the restriction mechanisms are the key parameters. This theory provides a convincing explanation of the surprising uniformity of the Covid-19 waves in many places, and can be used for forecasting the epidemic spread. For instance, the early projections for the 3rd wave in the USA appeared sufficiently exact. The Delta-waves (2021) in India, South Africa, UK, and the Netherlands are discussed at the end.

Real-Time Projections of SARS-CoV-2 B.1.1.7 Variant in a University Setting, Texas, USA.

We used the incidence of spike gene target failures identified during PCR testing to provide an early projection of the prevalence of severe acute respiratory syndrome coronavirus 2 variant B.1.1.7 in a university setting in Texas, USA, before sequencing results were available. Findings from a more recent evaluation validated those early projections.

An Agro-Pastoral Phenological Water Balance Framework for Monitoring and Predicting Growing Season Water Deficits and Drought Stress

Sharing simple ideas across a broad community of practitioners helps them to work together more effectively. For this reason, drought early warning systems spend a considerable effort on describing how hazards are detected and defined. Well-articulated definitions of drought provide a shared basis for collaboration, response planning, and impact mitigation. One very useful measure of agricultural drought stress has been the “Water Requirement Satisfaction Index” (WRSI). In this study, we develop a new, simpler metric of water requirement satisfaction, the Phenological Water Balance (PWB). We describe this metric, compare it to WRSI and yield statistics in a food-insecure region (east Africa), and show how it can be easily combined with analog-based rainfall forecasts to produce end-of-season estimates of growing season water deficits. In dry areas, the simpler PWB metric is very similar to the WRSI. In these regions, we show that the coupling between rainfall deficits and increased reference evapotranspiration amplifies the impacts of droughts. In wet areas, on the other hand, our new metric provides useful information about water excess—seasons that are so wet that they may not be conducive to good agricultural outcomes. Finally, we present a PWB-based forecast example, demonstrating how this framework can be easily used to translate assumptions about seasonal rainfall outcomes into predictions of growing season water deficits. Effective humanitarian relief efforts rely on early projections of these deficits to design and deploy appropriate targeted responses. At present, it is difficult to combine gridded satellite-gauge precipitation forecasts with climate forecasts. Our new metric helps overcome this obstacle. Future extensions could use the water requirement framework to contextualize other water supply indicators, like actual evapotranspiration values derived from satellite observations or hydrologic models.

The intersection of genetics and COVID-19 in 2021: preview of the 2021 Rodney Howell Symposium

The intersection of genetics and COVID-19 in 2021: preview of the 2021 Rodney Howell Symposium

Syntactic parallelism and the co-production of syntactic units in Mandarin Chinese

AbstractCross-linguistic studies on co-production of syntactic units and compound sentence formats have found that the location of predicates affects the projectability of the language, in that languages like English allow early projections while languages like Japanese later projections. In Mandarin Chinese, we found that syntactic parallelism often occurs before co-constructions, impacting the projectability of syntactic structures in one way or another. Based on the theories of dialogic syntax (Du Bois 2007,2014) and the principles of interactional linguistics, this study explores the relationship between syntactic parallelism and co-production of syntactic structures across turns. The co-production of four syntactic and sentential structures were closely examined, namely,Copula V+Complement, (be) Adjectival Predicate, the conditionalIF X THEN Yconstruction(如果ruguo……就/会jiu/hui……), and compound sentences withto-clause of purpose. Also observed is the emergent new sequence as interactionally relevant syntax. Upon inspection, we found that turn units with parallel syntactic structures may help narrow down the category of the projected final component, thus inspiring the second speaker to come in early and jointly complete the syntax-in-progress. Apart from co-producing syntax-in-progress, co-produced structures can also develop into interactionally relevant sequences with independent internal structures, thereby executing new social actions.

Revisiting the initial COVID-19 pandemic projections

Early projections of the COVID-19 pandemic prompted federal governments to action. One critical report, published on March 16, 2020, received international attention when it predicted 2 200 000 deaths in the USA and 510 000 deaths in the UK without some kind of coordinated pandemic response.1 This information became foundational in decisions to implement physical distancing and adherence to other public health measures because it established the upper boundary for any worst-case scenarios.

Temporal Strategies and Firms’ Speedy Responses to COVID‐19

Temporal Strategies and Firms’ Speedy Responses to COVID‐19

Development and plasticity of the corpus callosum.

The corpus callosum (CC) connects the cerebral hemispheres and is the major mammalian commissural tract. It facilitates bilateral sensory integration and higher cognitive functions, and is often affected in neurodevelopmental diseases. Here, we review the mechanisms that contribute to the development of CC circuits in animal models and humans. These species comparisons reveal several commonalities. First, there is an early period of massive axonal projection. Second, there is a postnatal temporal window, varying between species, in which early callosal projections are selectively refined. Third, sensory-derived activity influences axonal refinement. We also discuss how defects in CC formation can lead to mild or severe CC congenital malformations.

Evolution and early government responses to COVID-19 in South America.

This article analyses the evolution of COVID-19 and early government responses to the pandemic in eight South American countries. To this aim, this study explores indicators which trace the progression of the pandemic and analyses factors related of state capacity which impacted on the early response of governments of implementing restrictive policies of social distancing associated with a suppression strategy. The pressure on the health systems is evaluated with early projections of the growth-phase of the epidemic, which is incorporated as an indicator in the analysis of early interventions based on Cox proportional hazards models. The results indicate that fiscal expenditure on health, regional and local government capacity, and pressure on the health system accelerate government response with stringent interventions. A counter-intuitive finding is that the economic strength of a country delays these types of reactions. The effect of these interventions is something that should be studied in greater depth, considering, for example, sociocultural factors. Lastly, only cases such as Uruguay and Paraguay show some signs of having the pandemic relatively under control by mid-May, while Brazil and Peru face very adverse scenarios. In this context, considering the characteristics of the states in the region and the level of informal employment, it will be a public policy challenge to keep the equilibrium between restrictive measures and the economic and social problems which these responses imply in the medium term.

Projecting Population Change by Age and Birth Parity: the Third Generation of Population Projections

Early population projections described future changes in total population and could foresee unsustainable population growth. Age-specific population projections could identify trends in population ageing and demographic dividends, and they have been widely used in recent decades owing to the efforts of collecting and estimating demographic data by age. In recent years, data are becoming available to allow for population projections by age and birth parity, which could help understanding future changes in family structures. To take advantage of the opportunity created by these increasingly accessible data, this paper extends the cohort component method to project populations by age and birth parity and provides an application for Canada.

Post-Hype Uses of Drones in News Reporting: Revealing the Site and Presenting Scope

Camera-equipped drones have emerged as an increasingly commonplace tool for media to acquire aerial imagery. Previous research has mainly focused on the innovative aspects and creative potential of the technology. This article argues that early optimistic projections reflected a novelty effect, typical of a culturally embedded idea that new and better technologies continuously replace older ones. Using a historical theory which distinguishes techno-optimistic innovation discourse from actual observations of technology in use, photojournalists were interviewed on the role of drones in news reporting. The results show that the practitioners historicise drones, relating them to previous aerial technologies, and they reflect on current and future uses of drones in journalism based on a notion of phases, where early hype gives way to subsequent drone fatigue. Drones are seen by many as a more convenient tool to do things that journalism has done before, but the convenience increases the use of aerial imagery. The results also show that, although photojournalists see a wide range of potential uses, there are also limitations, including the ideals of the invisible observer, safety concerns, and the perils of over-aesthetic imagery. The post-hype uses of drone photography were summarized in two categories: (a) revealing the site, establishing ‘this happened here’ and (b) presenting scope, or showing how vast or large something is.

Pandemics, tourism and global change: a rapid assessment of COVID-19

The novel coronavirus (COVID-19) is challenging the world. With no vaccine and limited medical capacity to treat the disease, nonpharmaceutical interventions (NPI) are the main strategy to contain the pandemic. Unprecedented global travel restrictions and stay-at-home orders are causing the most severe disruption of the global economy since World War II. With international travel bans affecting over 90% of the world population and wide-spread restrictions on public gatherings and community mobility, tourism largely ceased in March 2020. Early evidence on impacts on air travel, cruises, and accommodations have been devastating. While highly uncertain, early projections from UNWTO for 2020 suggest international arrivals could decline by 20 to 30% relative to 2019. Tourism is especially susceptible to measures to counteract pandemics because of restricted mobility and social distancing. The paper compares the impacts of COVID-19 to previous epidemic/pandemics and other types of global crises and explores how the pandemic may change society, the economy, and tourism. It discusses why COVID-19 is an analogue to the ongoing climate crisis, and why there is a need to question the volume growth tourism model advocated by UNWTO, ICAO, CLIA, WTTC and other tourism organizations.