The COVID19 pandemic, a unique and devastating respiratory disease outbreak,
has affected global populations as the disease spreads rapidly. Recent Deep
Learning breakthroughs may improve COVID19 prediction and forecasting as a tool
of precise and fast detection, however, current methods are still being
examined to achieve higher accuracy and precision. This study analyzed the
collection contained 8055 CT image samples, 5427 of which were COVID cases and
2628 non COVID. The 9544 Xray samples included 4044 COVID patients and 5500 non
COVID cases. The most accurate models are MobileNet V3 (97.872 percent),
DenseNet201 (97.567 percent), and GoogleNet Inception V1 (97.643 percent). High
accuracy indicates that these models can make many accurate predictions, as
well as others, are also high for MobileNetV3 and DenseNet201. An extensive
evaluation using accuracy, precision, and recall allows a comprehensive
comparison to improve predictive models by combining loss optimization with
scalable batch normalization in this study. Our analysis shows that these
tactics improve model performance and resilience for advancing COVID19
prediction and detection and shows how Deep Learning can improve disease
handling. The methods we suggest would strengthen healthcare systems,
policymakers, and researchers to make educated decisions to reduce COVID19 and
other contagious diseases.
CCS CONCEPTS Covid,Deep Learning, Image Processing
KEYWORDS Covid, Deep Learning, DenseNet201, MobileNet, ResNet, DenseNet,
GoogleNet, Image Processing, Disease Detection.

COVID19 预测和检测的深度学习方法在此研究中得到了改进，并且高精度的预测模型可为医疗系统、决策者和研究者做出准确决策以减少 COVID-19 及其它传染病。

高效 COVID19 检测中释放改进的深度学习模型

Unleashing Modified Deep Learning Models in Efficient COVID19 Detection

Vaccine supply chain optimization can benefit from hierarchical time series
forecasting, when grouping the vaccines by type or location. However, forecasts
of different hierarchy levels become incoherent when higher levels do not match
the sum of the lower levels forecasts, which can be addressed by reconciliation
methods.
In this paper, we tackle the vaccine sale forecasting problem by modeling
sales data from GSK between 2010 and 2021 as a hierarchical time series. After
forecasting future values with several ARIMA models, we systematically compare
the performance of various reconciliation methods, using statistical tests. We
also compare the performance of the forecast before and after COVID. The
results highlight Minimum Trace and Weighted Least Squares with Structural
scaling as the best performing methods, which provided a coherent forecast
while reducing the forecast error of the baseline ARIMA.

本文通过对 2010 年至 2021 年葛兰素史克疫苗销售数据进行建模，使用多种 ARIMA 模型预测未来数值，并运用统计检验系统地比较各种调和方法的表现，比较 COVID 前后预测的表现。结果表明，最小迹线和加权最小二乘法与结构缩放是最佳表现的方法，提供了连贯的预测，同时减少了基准 ARIMA 的预测误差。