scholarly journals The Effectiveness of Governmental Nonpharmaceutical Interventions Against COVID-19 On The Control of Seasonal Influenza Transmission: An Ecological Study

2022 ◽  
Author(s):  
Zekai Qiu ◽  
Zicheng Cao ◽  
Min Zou ◽  
Kang Tang ◽  
Chi Zhang ◽  
...  
Keyword(s):  
Seasonal Influenza ◽  
International Travel ◽  
School Closure ◽  
Gradient Boosting ◽  
Positive Interaction ◽  
Precise Control ◽  
Extreme Gradient Boosting ◽  
Influenza Transmission ◽  
Nonpharmaceutical Interventions ◽  
Suppression Index

Abstract Background: A range of strict nonpharmaceutical interventions (NPIs) had been implemented in many countries to combat the COVID-19 pandemic. These NPIs might also be effective in controlling the seasonal influenza virus, which share the same transmission path with SARS-CoV-2. The aim of this study is to evaluate the effect of different NPIs for control of seasonal influenza.Methods: Data on 14 NPIs implemented in 33 countries and corresponding data on influenza virologic surveillance were collected. The influenza suppression index was calculated as the difference between the influenza-positive rate during its decline period from 2019 to 2020 and that during influenza epidemic seasons in the previous 9 years. A machine learning model was developed by using extreme gradient boosting tree (XGBoost) regressor to fit NPI data and influenza suppression index. SHapley Additive exPlanations (SHAP) was used to characterize NPIs in suppressing influenza.Results: Gathering limitation contributed the most (37.60%) among all NPIs in suppressing influenza transmission in the 2019-2020 influenza season. The top three effective NPIs were gathering limitation, international travel restriction, and school closure. Regarding the three NPIs, their intensity threshold to generate effect were restrictions on the size of gatherings less than 1000 people, travel bans on all regions or total border closure, and closing only some categories of schools, respectively. There was a strong positive interaction effect between mask wearing requirement and gathering limitation, whereas merely implementing mask wearing requirement but ignoring other NPIs would dilute mask wearing requirement’s effectiveness in suppressing influenza.Conclusions: Gathering limitation, travel bans on all regions or total border closure, and closing some levels of schools are the most effective NPIs to suppress influenza transmission. Mask wearing requirement is advised to be combined with gathering limitation and other NPIs. Our findings could facilitate the precise control of future influenza epidemics and potential pandemics.

scholarly journals Effects of School Holidays on Seasonal Influenza in South Korea, 2014–2016

2020 ◽  
Vol 222 (5) ◽  
pp. 832-835 ◽  
Author(s):  
Sukhyun Ryu ◽  
Sheikh Taslim Ali ◽  
Benjamin J Cowling ◽  
Eric H Y Lau
Keyword(s):  
South Korea ◽  
Seasonal Influenza ◽  
School Closure ◽  
Surveillance Data ◽  
Influenza Surveillance ◽  
School Closures ◽  
School Aged Children ◽  
Severe Influenza ◽  
Influenza Transmission

Abstract School closures are considered as a potential nonpharmaceutical intervention to mitigate severe influenza epidemics and pandemics. In this study, we assessed the effects of scheduled school closure on influenza transmission using influenza surveillance data before, during, and after spring breaks in South Korea, 2014–2016. During the spring breaks, influenza transmission was reduced by 27%–39%, while the overall reduction in transmissibility was estimated to be 6%–23%, with greater effects observed among school-aged children.

scholarly journals Increased urbanization reduced the effectiveness of school closures on seasonal influenza epidemics in China

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Hao Lei ◽  
Hangjin Jiang ◽  
Nan Zhang ◽  
Xiaoli Duan ◽  
Tao Chen ◽  
...  
Keyword(s):  
Seasonal Influenza ◽  
Southern China ◽  
Social Contact ◽  
School Closure ◽  
Surveillance Data ◽  
Reproductive Number ◽  
Influenza Surveillance ◽  
Sirs Model ◽  
Influenza Transmission ◽  
The Impact

Abstract Background School closure is a common mitigation strategy during severe influenza epidemics and pandemics. However, the effectiveness of this strategy remains controversial. In this study, we aimed to explore the effectiveness of school closure on seasonal influenza epidemics in provincial-level administrative divisions (PLADs) with varying urbanization rates in China. Methods This study analyzed influenza surveillance data between 2010 and 2019 provided by the Chinese National Influenza Center. Taking into consideration the climate, this study included a region with 3 adjacent PLADs in Northern China and another region with 4 adjacent PLADs in Southern China. The effect of school closure on influenza transmission was evaluated by the reduction of the effective reproductive number of seasonal influenza during school winter breaks compared with that before school winter breaks. An age-structured Susceptible-Infected-Recovered-Susceptible (SIRS) model was built to model influenza transmission in different levels of urbanization. Parameters were determined using the surveillance data via robust Bayesian method. Results Between 2010 and 2019, in the less urbanized provinces: Hebei, Zhejiang, Jiangsu and Anhui, during school winter breaks, the effective reproductive number of seasonal influenza epidemics reduced 14.6% [95% confidential interval (CI): 6.2–22.9%], 9.6% (95% CI: 2.5–16.6%), 7.3% (95% CI: 0.1–14.4%) and 8.2% (95% CI: 1.1–15.3%) respectively. However, in the highly urbanized cities: Beijing, Tianjin and Shanghai, it reduced only 5.2% (95% CI: -0.7–11.2%), 4.1% (95% CI: -0.9–9.1%) and 3.9% (95% CI: -1.6–9.4%) respectively. In China, urbanization is associated with decreased proportion of children and increased social contact. According to the SIRS model, both factors could reduce the impact of school closure on seasonal influenza epidemics, and the proportion of children in the population is thought to be the dominant influencing factor. Conclusions Effectiveness of school closure on the epidemics varies with the age structure in the population and social contact patterns. School closure should be recommended in the low urbanized regions in China in the influenza seasons. Graphical abstract

scholarly journals Nonpharmaceutical Interventions Used to Control COVID-19 Reduced Seasonal Influenza Transmission in China

2020 ◽  
Vol 222 (11) ◽  
pp. 1780-1783 ◽  
Author(s):  
Hao Lei ◽  
Modi Xu ◽  
Xiao Wang ◽  
Yu Xie ◽  
Xiangjun Du ◽  
...  
Keyword(s):  
Incidence Rate ◽  
Seasonal Influenza ◽  
Chinese Government ◽  
Influenza Activity ◽  
Influenza Transmission ◽  
Means Of Transmission ◽  
Nonpharmaceutical Interventions

Abstract To suppress the ongoing COVID-19 pandemic, the Chinese government has implemented nonpharmaceutical interventions (NPIs). Because COVID-19 and influenza have similar means of transmission, NPIs targeting COVID-19 may also affect influenza transmission. In this study, the extent to which NPIs targeting COVID-19 have affected seasonal influenza transmission was explored. Indicators of seasonal influenza activity in the epidemiological year 2019–2020 were compared with those in 2017–2018 and 2018–2019. The incidence rate of seasonal influenza reduced by 64% in 2019–2020 (P < .001). These findings suggest that NPIs aimed at controlling COVID-19 significantly reduced seasonal influenza transmission in China.

Predicting Undesired Treatment Outcome in Mental Healthcare: Machine Learning Study (Preprint)

2019 ◽  
Author(s):  
Kasper Van Mens ◽  
Joran Lokkerbol ◽  
Richard Janssen ◽  
Robert de Lange ◽  
Bea Tiemens
Keyword(s):  
Machine Learning ◽  
Treatment Outcome ◽  
Mental Health Treatment ◽  
Mental Healthcare ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Trade Off ◽  
Trade Offs ◽  
Outcome Monitoring ◽  
Extreme Gradient Boosting

BACKGROUND It remains a challenge to predict which treatment will work for which patient in mental healthcare. OBJECTIVE In this study we compare machine algorithms to predict during treatment which patients will not benefit from brief mental health treatment and present trade-offs that must be considered before an algorithm can be used in clinical practice. METHODS Using an anonymized dataset containing routine outcome monitoring data from a mental healthcare organization in the Netherlands (n = 2,655), we applied three machine learning algorithms to predict treatment outcome. The algorithms were internally validated with cross-validation on a training sample (n = 1,860) and externally validated on an unseen test sample (n = 795). RESULTS The performance of the three algorithms did not significantly differ on the test set. With a default classification cut-off at 0.5 predicted probability, the extreme gradient boosting algorithm showed the highest positive predictive value (ppv) of 0.71(0.61 – 0.77) with a sensitivity of 0.35 (0.29 – 0.41) and area under the curve of 0.78. A trade-off can be made between ppv and sensitivity by choosing different cut-off probabilities. With a cut-off at 0.63, the ppv increased to 0.87 and the sensitivity dropped to 0.17. With a cut-off of at 0.38, the ppv decreased to 0.61 and the sensitivity increased to 0.57. CONCLUSIONS Machine learning can be used to predict treatment outcomes based on routine monitoring data.This allows practitioners to choose their own trade-off between being selective and more certain versus inclusive and less certain.

XGBoost and Network Analysis for Prediction of Proteins Affecting Insulin based on Protein Protein Interactions

2020 ◽  
pp. 253-262
Author(s):  
Mohammad Hamim Zajuli Al Faroby ◽  
Mohammad Isa Irawan ◽  
Ni Nyoman Tri Puspaningsih
Keyword(s):  
Protein Interactions ◽  
Interaction Analysis ◽  
Synthesis Process ◽  
Gradient Boosting ◽  
Protein Protein Interactions ◽  
Central Function ◽  
Extreme Gradient Boosting ◽  
Main Protein ◽  
The Right ◽  
Roc Score

Protein Interaction Analysis (PPI) can be used to identify proteins that have a supporting function on the main protein, especially in the synthesis process. Insulin is synthesized by proteins that have the same molecular function covering different but mutually supportive roles. To identify this function, the translation of Gene Ontology (GO) gives certain characteristics to each protein. This study purpose to predict proteins that interact with insulin using the centrality method as a feature extractor and extreme gradient boosting as a classification algorithm. Characteristics using the centralized method produces  features as a central function of protein. Classification results are measured using measurements, precision, recall and ROC scores. Optimizing the model by finding the right parameters produces an accuracy of  and a ROC score of . The prediction model produced by XGBoost has capabilities above the average of other machine learning methods.

scholarly journals Evaluation of Three Different Machine Learning Methods for Object-Based Artificial Terrace Mapping—A Case Study of the Loess Plateau, China

2021 ◽  
Vol 13 (5) ◽  
pp. 1021
Author(s):  
Hu Ding ◽  
Jiaming Na ◽  
Shangjing Jiang ◽  
Jie Zhu ◽  
Kai Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Loess Plateau ◽  
Water Conservation ◽  
Nearest Neighbor ◽  
Gradient Boosting ◽  
K Nearest Neighbor ◽  
The Loess Plateau ◽  
Object Based ◽  
Extreme Gradient Boosting

Artificial terraces are of great importance for agricultural production and soil and water conservation. Automatic high-accuracy mapping of artificial terraces is the basis of monitoring and related studies. Previous research achieved artificial terrace mapping based on high-resolution digital elevation models (DEMs) or imagery. As a result of the importance of the contextual information for terrace mapping, object-based image analysis (OBIA) combined with machine learning (ML) technologies are widely used. However, the selection of an appropriate classifier is of great importance for the terrace mapping task. In this study, the performance of an integrated framework using OBIA and ML for terrace mapping was tested. A catchment, Zhifanggou, in the Loess Plateau, China, was used as the study area. First, optimized image segmentation was conducted. Then, features from the DEMs and imagery were extracted, and the correlations between the features were analyzed and ranked for classification. Finally, three different commonly-used ML classifiers, namely, extreme gradient boosting (XGBoost), random forest (RF), and k-nearest neighbor (KNN), were used for terrace mapping. The comparison with the ground truth, as delineated by field survey, indicated that random forest performed best, with a 95.60% overall accuracy (followed by 94.16% and 92.33% for XGBoost and KNN, respectively). The influence of class imbalance and feature selection is discussed. This work provides a credible framework for mapping artificial terraces.

scholarly journals Computational Intelligence-Based Model for Mortality Rate Prediction in COVID-19 Patients

2021 ◽  
Vol 18 (12) ◽  
pp. 6429
Author(s):  
Irfan Ullah Khan ◽  
Nida Aslam ◽  
Malak Aljabri ◽  
Sumayh S. Aljameel ◽  
Mariam Moataz Aly Kamaleldin ◽  
...  
Keyword(s):  
Mortality Rate ◽  
Computational Intelligence ◽  
Nearest Neighbor ◽  
Gradient Boosting ◽  
K Nearest Neighbor ◽  
Detection And Identification ◽  
Proposed Model ◽  
Extreme Gradient Boosting ◽  
The World ◽  
Detection And Diagnosis

The COVID-19 outbreak is currently one of the biggest challenges facing countries around the world. Millions of people have lost their lives due to COVID-19. Therefore, the accurate early detection and identification of severe COVID-19 cases can reduce the mortality rate and the likelihood of further complications. Machine Learning (ML) and Deep Learning (DL) models have been shown to be effective in the detection and diagnosis of several diseases, including COVID-19. This study used ML algorithms, such as Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) and DL model (containing six layers with ReLU and output layer with sigmoid activation), to predict the mortality rate in COVID-19 cases. Models were trained using confirmed COVID-19 patients from 146 countries. Comparative analysis was performed among ML and DL models using a reduced feature set. The best results were achieved using the proposed DL model, with an accuracy of 0.97. Experimental results reveal the significance of the proposed model over the baseline study in the literature with the reduced feature set.

scholarly journals A Machine Learning Method for Predicting Vegetation Indices in China

2021 ◽  
Vol 13 (6) ◽  
pp. 1147
Author(s):  
Xiangqian Li ◽  
Wenping Yuan ◽  
Wenjie Dong
Keyword(s):  
Machine Learning ◽  
Growing Season ◽  
Crop Growth ◽  
Spatiotemporal Distribution ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Severe Drought ◽  
Vegetation Growth ◽  
Extreme Gradient Boosting ◽  
Boosting Method

To forecast the terrestrial carbon cycle and monitor food security, vegetation growth must be accurately predicted; however, current process-based ecosystem and crop-growth models are limited in their effectiveness. This study developed a machine learning model using the extreme gradient boosting method to predict vegetation growth throughout the growing season in China from 2001 to 2018. The model used satellite-derived vegetation data for the first month of each growing season, CO2 concentration, and several meteorological factors as data sources for the explanatory variables. Results showed that the model could reproduce the spatiotemporal distribution of vegetation growth as represented by the satellite-derived normalized difference vegetation index (NDVI). The predictive error for the growing season NDVI was less than 5% for more than 98% of vegetated areas in China; the model represented seasonal variations in NDVI well. The coefficient of determination (R2) between the monthly observed and predicted NDVI was 0.83, and more than 69% of vegetated areas had an R2 > 0.8. The effectiveness of the model was examined for a severe drought year (2009), and results showed that the model could reproduce the spatiotemporal distribution of NDVI even under extreme conditions. This model provides an alternative method for predicting vegetation growth and has great potential for monitoring vegetation dynamics and crop growth.

scholarly journals A partisan pandemic: state government public health policies to combat COVID-19 in Brazil

2021 ◽  
Vol 6 (6) ◽  
pp. e005223
Author(s):  
Michael Touchton ◽  
Felicia Marie Knaul ◽  
Héctor Arreola-Ornelas ◽  
Thalia Porteny ◽  
Mariano Sánchez ◽  
...  
Keyword(s):  
Public Space ◽  
State Government ◽  
Public Transportation ◽  
National Level ◽  
International Travel ◽  
School Closure ◽  
Population Mobility ◽  
Mato Grosso ◽  
First Case ◽  
National Response

IntroductionTo present an analysis of the Brazilian health system and subnational (state) variation in response to the COVID-19 pandemic, based on 10 non-pharmaceutical interventions (NPIs).Materials and methodsWe collected daily information on implementation of 10 NPI designed to inform the public of health risks and promote distancing and mask use at the national level for eight countries across the Americas. We then analyse the adoption of the 10 policies across Brazil’s 27 states over time, individually and using a composite index. We draw on this index to assess the timeliness and rigour of NPI implementation across the country, from the date of the first case, 26 February 2020. We also compile Google data on population mobility by state to describe changes in mobility throughout the COVID-19 pandemic.ResultsBrazil’s national NPI response was the least stringent among countries analysed. In the absence of a unified federal response to the pandemic, Brazilian state policy implementation was neither homogenous nor synchronised. The median NPI was no stay-at-home order, a recommendation to wear masks in public space but not a requirement, a full school closure and partial restrictions on businesses, public transportation, intrastate travel, interstate travel and international travel. These restrictions were implemented 45 days after the first case in each state, on average. Rondônia implemented the earliest and most rigorous policies, with school closures, business closures, information campaigns and restrictions on movement 24 days after the first case; Mato Grosso do Sul had the fewest, least stringent restrictions on movement, business operations and no mask recommendation.ConclusionsThe study identifies wide variation in national-level NPI responses to the COVID-19 pandemic. Our focus on Brazil identifies subsequent variability in how and when states implemented NPI to contain COVID-19. States’ NPIs and their scores on the composite policy index both align with the governors’ political affiliations: opposition governors implemented earlier, more stringent sanitary measures than those supporting the Bolsonaro administration. A strong, unified national response to a pandemic is essential for keeping the population safe and disease-free, both at the outset of an outbreak and as communities begin to reopen. This national response should be aligned with state and municipal implementation of NPI, which we show is not the case in Brazil.

scholarly journals Machine Learning-Based Energy System Model for Tissue Paper Machines

Processes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 655
Author(s):  
Huanhuan Zhang ◽  
Jigeng Li ◽  
Mengna Hong
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Gradient Boosting ◽  
Paper Machine ◽  
Steam Consumption ◽  
Tissue Paper ◽  
Paper Machines ◽  
Energy Consumption Model ◽  
Extreme Gradient Boosting ◽  
Consumption Model

With the global energy crisis and environmental pollution intensifying, tissue papermaking enterprises urgently need to save energy. The energy consumption model is essential for the energy saving of tissue paper machines. The energy consumption of tissue paper machine is very complicated, and the workload and difficulty of using the mechanism model to establish the energy consumption model of tissue paper machine are very large. Therefore, this article aims to build an empirical energy consumption model for tissue paper machines. The energy consumption of this model includes electricity consumption and steam consumption. Since the process parameters have a great influence on the energy consumption of the tissue paper machines, this study uses three methods: linear regression, artificial neural network and extreme gradient boosting tree to establish the relationship between process parameters and power consumption, and process parameters and steam consumption. Then, the best power consumption model and the best steam consumption model are selected from the models established by linear regression, artificial neural network and the extreme gradient boosting tree. Further, they are combined into the energy consumption model of the tissue paper machine. Finally, the models established by the three methods are evaluated. The experimental results show that using the empirical model for tissue paper machine energy consumption modeling is feasible. The result also indicates that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The experimental results show that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The mean absolute percentage error of the electricity consumption model and the steam consumption model built by the extreme gradient boosting tree is approximately 2.72 and 1.87, respectively. The root mean square errors of these two models are about 4.74 and 0.03, respectively. The result also indicates that using the empirical model for tissue paper machine energy consumption modeling is feasible, and the extreme gradient boosting tree is an efficient method for modeling energy consumption of tissue paper machines.

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