Mapping the pandemic: a review of Geographical Information Systems-based spatial modeling of Covid-19

According to the World Health Organization (WHO), COVID-19 has caused more than 6.5 million deaths, while over 600 million people are infected. With regard to the tools and techniques of disease analysis, spatial analysis is increasingly being used to analyze the impact of COVID-19. The present review offers an assessment of research that used regional data systems to study the COVID-19 epidemic published between 2020 and 2022. The research focuses on: categories of the area, authors, methods, and procedures used by the authors and the results of their findings. This input will enable the contrast of different spatial models used for regional data systems with COVID-19. Our outcomes showed increased use of geographically weighted regression and Moran I spatial statistical tools applied to better spatial and time-based gauges. We have also found an increase in the use of local models compared to other spatial statistics models/methods.

Systematic Evaluation of Local and Global Machine Learning Models for the Prediction of ADME Properties

Machine learning (ML) has become an indispensable tool to predict absorption, distribution, metabolism, and excretion (ADME) properties in pharmaceutical research. ML algorithms are trained on molecular structures and corresponding ADME assay data to develop quantitative structure-property relationship (QSPR) models. Traditional QSPR models were trained on compound sets of limited size. With the advent of more complex ML algorithms and data availability, training sets have become larger and more diverse. Most common training approaches consist in either training a model with a small set of similar compounds, namely, compounds designed for the same drug discovery project or chemical series (local model approach) or with a larger set of diverse compounds (global model approach). Global models are built with all experimental data available for an assay, combining compound data from different projects and disease areas. Despite the ML progress made so far, the choice of the appropriate data composition for building ML models is still unclear. Herein, a systematic evaluation of local and global ML models was performed for 10 different experimental assays and 112 drug discovery projects. Results show a consistent superior performance of global models for ADME property predictions. Diagnostic analyses were also carried out to investigate the influence of training set size, structural diversity, and data shift in the relative performance of local and global ML models. Training set and structural diversity did not have an impact in the relative performance on the methods. Instead, data shift helped to identify the projects with larger performance differences between local and global models. Results presented in this work can be leveraged to improve ML-based ADME properties predictions and thus decision-making in drug discovery projects.

Spatial Associations of Long-term Exposure to Diesel Particulate Matter with Seasonal and Annual Mortality Due to COVID-19 in the Contiguous United States

Background People with certain underlying respiratory and cardiovascular conditions might be at an increased risk for severe illness from COVID-19. Diesel Particulate Matter (DPM) exposure may affect the pulmonary and cardiovascular systems. The study aims to assess if DPM was spatially associated with COVID-19 mortality across three waves of the disease and throughout 2020. Methods We tested an ordinary least square (OLS) model, then two global models, spatial lag model (SLM) and spatial error model (SEM), designed to explore spatial dependence, and a geographically weighted regression (GWR) model designed to explore local associations. Results The GWR model found that associations between COVID-19 deaths and DPM concentrations may increase up to 57, 36, 43, and 58 deaths per 100,000 people in some US counties for every 1 µg/m ³ increase in DPM concentration. Relative significant positive association are observed in New York, New Jersey, eastern Pennsylvania, and western Connecticut for the wave from January to May, and in southern Florida and southern Texas for June to September. The period from October to December exhibit a negative association in most parts of the US, which seems to have influenced the year-long relationship due to the large number of deaths during that wave of the disease. Conclusions Our models provided a picture in which long-term DPM exposure may have influenced COVID-19 mortality during the early stages of the disease, but that influence appears to have waned over time as transmission patterns evolved.

Importance of disturbance history on net primary productivity in the world's most productive forests and implications for the global carbon cycle

Analysis of growth and biomass turnover in natural forests of Eucalyptus regnans, the world's tallest angiosperm, reveals it is also the world's most productive forest type, with fire disturbance an important mediator of net primary productivity (NPP). A comprehensive empirical database was used to calculate the averaged temporal pattern of NPP from regeneration to 250 years age. NPP peaks at 23.1 ± 3.8 (95% interquantile range) Mg C ha^-1  year^-1 at age 14 years, and declines gradually to about 9.2 ± 0.8 Mg C ha^-1  year^-1 at 130 years, with an average NPP over 250 years of 11.4 ± 1.1 Mg C ha^-1  year^-1 , a value similar to the most productive temperate and tropical forests around the world. We then applied the age-class distribution of E. regnans resulting from relatively recent historical fires to estimate current NPP for the forest estate. Values of NPP were 40% higher (13 Mg C ha^-1  year^-1 ) than if forests were assumed to be at maturity (9.2 Mg C ha^-1  year^-1 ). The empirically derived NPP time series for the E. regnans estate was then compared against predictions from 21 global circulation models, showing that none of them had the capacity to simulate a post-disturbance peak in NPP, as found in E. regnans. The potential importance of disturbance impacts on NPP was further tested by applying a similar approach to the temperate forests of conterminous United States and of China. Allowing for the effects of disturbance, NPP summed across both regions was on average 11% (or 194 Tg C/year) greater than if all forests were assumed to be in a mature state. The results illustrate the importance of accounting for past disturbance history and growth stage when estimating forest primary productivity, with implications for carbon balance modelling at local to global scales.

Near-Infrared Spectroscopy Calibrations Performed on Oven-Dried Green Forages for the Prediction of Chemical Composition and Nutritive Value of Preserved Forage for Ruminants

Predicting forage feed value is a vital part of estimating ruminant performances. Most near-infrared (NIR) reflectance calibration models have been developed on oven-dried green forages, but preserved forages such as hays or silages are a significant part of real-world farm practice. Fresh and preserved forages give largely similar fodder, but drying or ensiling processes could modify preserved forage spectra which would make the oven-dried green forage model unsuitable to use on preserved forage samples. The aim of this study was to monitor the performance of oven-dried green forage calibration models on a set of hay and silage to predict their nutritive value. Local and global approaches were tested and 1025 green permanent grassland forages, 46 types of hay, and 27 types of silage were used. The samples were scanned by NIR spectroscopy and analyzed for nitrogen, neutral detergent fiber, acid detergent fiber, and pepsin-cellulase dry matter digestibility (PCDMD). Local and global calibrations were developed on 975 oven-dried green forage spectra and tested on 50 samples of oven-dried green forages, 46 samples of hay, and 27 samples of silage. For oven-dried green forage and hay validation sets, Mahalanobis distance (H) between these samples and the calibration population center was lower than 3. No significant standard error of prediction differences was obtained when calibration models were applied to oven-dried green forage and hay validation sets. For silage, the H-distance was higher than 3, meaning that calibration models built from oven-dried green forages cannot be applied to silage samples. We conclude that local calibration outperforms global strategy on predicting the PCDMD of oven-dried green forages and hay.

Role of updraft velocity in temporal variability of global cloud hydrometeor number

Understanding how dynamical and aerosol inputs affect the temporal variability of hydrometeor formation in climate models will help to explain sources of model diversity in cloud forcing, to provide robust comparisons with data, and, ultimately, to reduce the uncertainty in estimates of the aerosol indirect effect. This variability attribution can be done at various spatial and temporal resolutions with metrics derived from online adjoint sensitivities of droplet and crystal number to relevant inputs. Such metrics are defined and calculated from simulations using the NASA Goddard Earth Observing System Model, Version 5 (GEOS-5) and the National Center for Atmospheric Research Community Atmosphere Model Version 5.1 (CAM5.1). Input updraft velocity fluctuations can explain as much as 48% of temporal variability in output ice crystal number and 61% in droplet number in GEOS-5 and up to 89% of temporal variability in output ice crystal number in CAM5.1. In both models, this vertical velocity attribution depends strongly on altitude. Despite its importance for hydrometeor formation, simulated vertical velocity distributions are rarely evaluated against observations due to the sparsity of relevant data. Coordinated effort by the atmospheric community to develop more consistent, observationally based updraft treatments will help to close this knowledge gap.