Modeling The Impact of Vaccination On COVID-19 And Its Delta Variant

Vaccination is an important means to fight against the spread of the SARS-CoV-2 virus and its variants. In this work, we propose a general susceptible-vaccinated-exposed-infected-hospitalized-removed (SVEIHR) model and derive its basic and effective reproduction numbers. We set Hong Kong as an example to prove the validity of our model. The model shows how the number of confirmed COVID-19 cases in Hong Kong during the second and third waves of the COVID-19 pandemic would have been reduced had vaccination been available then. We then investigate the relationships between various model parameters and the cumulative number of hospitalized COVID-19 cases in Hong Kong for the ancestral and Delta strains of the virus. Next, we compare the evolution of the SVEIHR model to the traditional “herd immunity” threshold where the proportion of vaccinated individuals is static and no further vaccination takes place after model initialization. Numerical results for Hong Kong demonstrate that the static herd immunity threshold corresponds to a cumulative hospitalization ratio of about one percent (assuming the current hospitalization rate of infected individuals is maintained). We also demonstrate that when the vaccination rate is high, the initial proportion of vaccinated individuals can be lowered for while still maintaining the same proportion of cumulative hospitalized individuals.

Numerical procedure to effectively assess sequestration capacity of geological structures

The paper presents a numerical procedure of estimating the sequestration capacity of an underground geological structure as a potential sequestration site. The procedure adopts a reservoir simulation model of the structure and performs multiple simulation runs of the sequestration process on the model according to a pre-defined optimization scheme. It aims at finding the optimum injection schedule for existing and/or planned injection wells. Constraints to be met for identifying the sequestration capacity of the structure include a no-leakage operation for an elongated period of the sequestration performance that contains a relaxation phase in addition to the injection one. The leakage risk analysis includes three basic leakage pathways: leakage to the overburden of a storage formation, leakage beyond the structural trap boundary, leakage via induced fractures. The procedure is implemented as a dedicated script of the broadly used Petrel package and tested on an example of a synthetic geologic structure. The script performs all the tasks of the procedure flowchart including: input data definitions, simulation model initialization, iteration loops control, simulation launching, simulation results processing and analysis. Results of the procedure are discussed in detail with focus put on various leakage mechanisms and their handling in the adopted scheme. The overall results of the proposed procedure seem to confirm its usefulness and effectiveness as a numerical tool to facilitate estimation of the sequestration capacity of an underground geological structure. In addition, by studying details of the procedure runs and its intermediate results, it may be also very useful for the estimation of various leakage risks.

Hydrologic-land surface modelling of the Canadian sporadic-discontinuous permafrost: initialization and uncertainty propagation

Permafrost thaw has been observed in recent decades in the Northern Hemisphere and is expected to accelerate with continued global warming. Predicting the future of permafrost requires proper representation of the interrelated surface/subsurface thermal and hydrologic regimes. Land surface models (LSMs) are well suited for such predictions, as they couple heat and water interactions across soil-vegetation-atmosphere interfaces and can be applied over large scales. LSMs, however, are challenged by the long-term thermal and hydraulic memories of permafrost and the paucity of historical records to represent permafrost dynamics under transient climate conditions. In this study, we address the challenge of model initialization by characterizing the impact of initial climate conditions and initial soil frozen and liquid water contents on the simulation length required to reach equilibrium. Further, we quantify how the uncertainty in model initialization propagates to simulated permafrost dynamics. Modelling experiments are conducted with the Modélisation Environmentale Communautaire – Surface and Hydrology (MESH) framework and its embedded Canadian Land Surface Scheme (CLASS). The study area is in the Liard River basin in the Northwest Territories of Canada with sporadic and discontinuous regions. Results show that uncertainty in model initialization controls various attributes of simulated permafrost, especially the active layer thickness, which could change by 0.5-1.5m depending on the initial condition chosen. The least number of spin-up cycles is achieved with near field capacity condition, but the number of cycles varies depending on the spin-up year climate. We advise an extended spin-up of 200-1000 cycles to ensure proper model initialization under different climatic conditions and initial soil moisture contents.

Impact of the observed SST frequency in the model initialization on the BSISO prediction

The impact of the observed sea surface temperature (SST) frequency in the model initialization on the prediction of the boreal summer intraseasonal oscillation (BSISO) over the Western North Pacific (WNP) is investigated using the Beijing Climate Center Climate System Model. Three sets of hindcast experiments initialized by the observed monthly, weekly and daily SST data (referred to as the Exp_MSST, Exp_WSST and Exp_DSST, respectively) are conducted with 3-month integration starting from the 1st, 11th, and 21st day of each month in June–August during 2000–2014, respectively. The results show that the useful prediction skill of BSISO index reaches out to about 10 days in the Exp_MSST, and further increases by 1–2 days in the Exp_WSST and Exp_DSST. The skill differences among various hindcast experiments are especially apparent during the forecast time of 6–20 days. Focusing on the strong BSISO cases in this period, the BSISO activity and its related moist static energy (MSE) characteristics over the WNP are further diagnosed. It is found that from the Exp_MSST to the Exp_WSST and Exp_DSST, the enhanced BSISO prediction skill is associated with the more realistic variations of intraseasonal MSE and its tendency. Among the various budget terms that dominate the MSE tendency, the surface latent heat flux and MSE advection are evidently improved, with reduction of mean biases by more than 21% and 10%, respectively. Therefore, the better reproduced MSE variation may contribute to the more skillful BSISO forecast through improving the surface evaporation as well as atmospheric convergence and divergence that related to the BSISO activity. Our findings suggest the necessity of increasing the observed SST frequency (i.e., from monthly to weekly or daily) in the initialization process of coupled models to enhance the actual BSISO predictability, since some current subseasonal forecast operations and researches still use relatively low-frequency SST observations for the model initialization.

Understanding the Processes Causing the Early Intensification of Hurricane Dorian through an Ensemble of the Hurricane Analysis and Forecast System (HAFS)

The early stages of a tropical cyclone can be a challenge to forecast, as a storm consolidates and begins to grow based on the local and environmental conditions. A high-resolution ensemble of the Hurricane Analysis and Forecast System (HAFS) is used to study the early intensification of Hurricane Dorian, a catastrophic 2019 storm in which the early period proved challenging for forecasters. There was a clear connection in the ensemble between early storm track and intensity: stronger members moved more northeast initially, although this result did not have much impact on the long-term track. The ensemble results show several key factors determining the early evolution of Dorian. Large-scale divergence northeast of the tropical cyclone (TC) appeared to favor intensification, and this structure was present at model initialization. There was also greater moisture northeast of the TC for stronger members at initialization, favoring more intensification and downshear development of the circulation as these members evolved. This study highlights the complex interplay between synoptic and storm scale processes in the development and intensification of early-stage tropical cyclones.

Toward the Improvement of High-Impact Probabilistic Forecasts with a Sensitivity-Based Convective-Scale Ensemble Subsetting Technique

Ensemble sensitivity analysis (ESA) is a useful and computationally inexpensive tool for analyzing how features in the flow at early forecast times affect different relevant forecast features later in the forecast. Given the frequency of observations measured between model initialization times that remain unused, ensemble sensitivity may be used to increase predictability and forecast accuracy through an objective ensemble subsetting technique. This technique identifies ensemble members with the smallest errors in regions of high sensitivity to produce a smaller, more accurate ensemble subset. Ensemble subsets can significantly reduce synoptic-scale forecast errors, but applying this strategy to convective-scale forecasts presents additional challenges. Objective verification of the sensitivity-based ensemble subsetting technique is conducted for ensemble forecasts of 2–5-km updraft helicity (UH) and simulated reflectivity. Many degrees of freedom are varied to identify the lead times, subset sizes, forecast thresholds, and atmospheric predictors that provide most forecast benefit. Subsets vastly reduce error of UH forecasts in an idealized framework but tend to degrade fractions skill scores and reliability in a real-world framework. Results reveal this discrepancy is a result of verifying probabilistic UH forecasts with storm-report-based observations, which effectively dampens technique performance. The potential of ensemble subsetting and likely other postprocessing techniques is limited by tuning UH forecasts to predict severe reports. Additional diagnostic ideas to improve postprocessing tool optimization for convection-allowing models are discussed.

Assessing ecological uncertainty and simulation model sensitivity to evaluate an invasive plant species’ potential impacts to the landscape

Ecological forecasts of the extent and impacts of invasive species can inform conservation management decisions. Such forecasts are hampered by ecological uncertainties associated with non-analog conditions resulting from the introduction of an invader to an ecosystem. We developed a state-and-transition simulation model tied to a fire behavior model to simulate the spread of buffelgrass (Cenchrus ciliaris) in Saguaro National Park, AZ, USA over a 30-year period. The simulation models forecast the potential extent and impact of a buffelgrass invasion including size and frequency of fire events and displacement of saguaro cacti and other native species. Using simulation models allowed us to evaluate how model uncertainties affected forecasted landscape outcomes. We compared scenarios covering a range of parameter uncertainties including model initialization (landscape susceptibility to invasion) and expert-identified ecological uncertainties (buffelgrass patch infill rates and precipitation). Our simulations showed substantial differences in the amount of buffelgrass on the landscape and the size and frequency of fires for dry years with slow patch infill scenarios compared to wet years with fast patch infill scenarios. We identified uncertainty in buffelgrass patch infill rates as a key area for research to improve forecasts. Our approach could be used to investigate novel processes in other invaded systems.

When Low Resource NLP Meets Unsupervised Language Model: Meta-Pretraining then Meta-Learning for Few-Shot Text Classification (Student Abstract)

Text classification tends to be difficult when data are deficient or when it is required to adapt to unseen classes. In such challenging scenarios, recent studies have often used meta-learning to simulate the few-shot task, thus negating implicit common linguistic features across tasks. This paper addresses such problems using meta-learning and unsupervised language models. Our approach is based on the insight that having a good generalization from a few examples relies on both a generic model initialization and an effective strategy for adapting this model to newly arising tasks. We show that our approach is not only simple but also produces a state-of-the-art performance on a well-studied sentiment classification dataset. It can thus be further suggested that pretraining could be a promising solution for few-shot learning of many other NLP tasks. The code and the dataset to replicate the experiments are made available at https://github.com/zxlzr/FewShotNLP.