scholarly journals Development and Validation of Diagnostic Models for Hand-Foot-and-Mouth Disease in Children

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Feng Zhuo ◽  
Mengjie Yu ◽  
Qiang Chen ◽  
Nuoya Li ◽  
Li Luo ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Predictive Model ◽  
Predictive Models ◽  
Prediction Models ◽  
Multivariate Logistic Regression Analysis ◽  
Foot And Mouth Disease ◽  
Mouth Disease ◽  
Risk Markers ◽  
Failure Group ◽  
Foot And Mouth

Objective. To find risk markers and develop new clinical predictive models for the differential diagnosis of hand-foot-and-mouth disease (HFMD) with varying degrees of disease. Methods. 19766 children with HFMD and 64 clinical indexes were included in this study. The patients included in this study were divided into the mild patients’ group (mild) with 12292 cases, severe patients’ group (severe) with 6508 cases, and severe patients with respiratory failure group (severe-RF) with 966 cases. Single-factor analysis was carried out on 64 indexes collected from patients when they were admitted to the hospital, and the indexes with statistical differences were selected as the prediction factors. Binary multivariate logistic regression analysis was used to construct the prediction models and calculate the adjusted odds ratio (OR). Results. SP, DP, NEUT#, NEUT%, RDW-SD, RDW-CV, GGT, CK/CK-MB, and Glu were risk markers in mild/severe, mild/severe-RF, and severe/severe-RF. Glu was a diagnostic marker for mild/severe-RF ( AUROC = 0.80 , 95% CI: 0.78-0.82); the predictive model constructed by temperature, SP, MOMO%, EO%, RDW-SD, GLB, CRP, Glu, BUN, and Cl could be used for the differential diagnosis of mild/severe ( AUROC > 0.84 ); the predictive model constructed by SP, age, NEUT#, PCT, TBIL, GGT, Mb, β2MG, Glu, and Ca could be used for the differential diagnosis of severe/severe-RF ( AUROC > 0.76 ). Conclusion. By analyzing clinical indicators, we have found the risk markers of HFMD and established suitable predictive models.

Mitigation Strategies for Foot and Mouth Disease

2013 ◽  
pp. 176-203
Author(s):  
Sohini Roy Chowdhury ◽  
Caterina Scoglio ◽  
William H. Hsu
Keyword(s):  
Predictive Models ◽  
Spatial Information ◽  
Ethical Issues ◽  
Foot And Mouth Disease ◽  
Simulation Models ◽  
Cost Effective ◽  
Mouth Disease ◽  
Mitigation Strategies ◽  
Foot And Mouth ◽  
The Cost

Prediction of epidemics such as Foot and Mouth Disease (FMD) is a global necessity in addressing economic, political and ethical issues faced by the affected countries. In the absence of precise and accurate spatial information regarding disease dynamics, learning- based predictive models can be used to mimic latent spatial parameters so as to predict the spread of epidemics in time. This paper analyzes temporal predictions from four such learning-based models, namely: neural network, autoregressive, Bayesian network, and Monte-Carlo simulation models. The prediction qualities of these models have been validated using FMD incidence reports in Turkey. Additionally, the authors perform simulations of mitigation strategies based on the predictive models to curb the impact of the epidemic. This paper also analyzes the cost-effectiveness of these mitigation strategies to conclude that vaccinations and movement ban strategies are more cost-effective than premise culls before the onset of an epidemic outbreak; however, in the event of existing epidemic outbreaks, premise culling is more effective at controlling FMD.

scholarly journals Differential diagnosis of vesicular stomatitis and foot-and-mouth disease. Examination of virus samples from Mexico with special reference to complement fixation

1949 ◽  
Vol 47 (4) ◽  
pp. 384-389 ◽  
Author(s):  
J. B. Brooksby
Keyword(s):  
Differential Diagnosis ◽  
Special Reference ◽  
Complement Fixation Test ◽  
Complement Fixation ◽  
Foot And Mouth Disease ◽  
Mouth Disease ◽  
The Other ◽  
Practical Applications ◽  
Foot And Mouth ◽  
Vesicular Stomatitis

The usefulness of the complement-fixation test in differential diagnosis between vesicular stomatitis and foot-and-mouth disease has been demonstrated on two ‘field’ specimens of virus from Mexico. Examples are given of the practical applications of the other methods for this differential diagnosis.

scholarly journals Tracking and predicting hand, foot, and mouth disease (HFMD) epidemics in China by Baidu queries

2017 ◽  
Vol 145 (8) ◽  
pp. 1699-1707 ◽  
Author(s):  
Q. Y. XIAO ◽  
H. J. LIU ◽  
M. W. FELDMAN
Keyword(s):  
Predictive Model ◽  
Pearson Correlation ◽  
Foot And Mouth Disease ◽  
Mouth Disease ◽  
Surveillance Systems ◽  
Pearson Correlation Coefficient ◽  
Search Queries ◽  
Foot And Mouth ◽  
The Mean ◽  
Traditional Surveillance

SUMMARYHand, foot, and mouth disease (HFMD) is highly prevalent in China, and more efficient methods of epidemic detection and early warning need to be developed to augment traditional surveillance systems. In this paper, a method that uses Baidu search queries to track and predict HFMD epidemics is presented, and the outbreaks of HFMD in China during the 60-month period from January 2011 to December 2015 are predicted. The Pearson correlation coefficient (R) of the predictive model and the mean absolute percentage errors between observed HFMD case counts and the predicted number show that our predictive model gives excellent fit to the data. This implies that Baidu search queries can be used in China to track and reliably predict HFMD epidemics, and can serve as a supplement to official systems for HFMD epidemic surveillance.

Mitigation Strategies for Foot and Mouth Disease

2011 ◽  
Vol 2 (2) ◽  
pp. 42-76 ◽  
Author(s):  
Sohini Roy Chowdhury ◽  
Caterina Scoglio ◽  
William H. Hsu
Keyword(s):  
Predictive Models ◽  
Spatial Information ◽  
Ethical Issues ◽  
Foot And Mouth Disease ◽  
Simulation Models ◽  
Cost Effective ◽  
Mouth Disease ◽  
Mitigation Strategies ◽  
Foot And Mouth ◽  
The Impact

Prediction of epidemics such as Foot and Mouth Disease (FMD) is a global necessity in addressing economic, political and ethical issues faced by the affected countries. In the absence of precise and accurate spatial information regarding disease dynamics, learning- based predictive models can be used to mimic latent spatial parameters so as to predict the spread of epidemics in time. This paper analyzes temporal predictions from four such learning-based models, namely: neural network, autoregressive, Bayesian network, and Monte-Carlo simulation models. The prediction qualities of these models have been validated using FMD incidence reports in Turkey. Additionally, the authors perform simulations of mitigation strategies based on the predictive models to curb the impact of the epidemic. This paper also analyzes the cost-effectiveness of these mitigation strategies to conclude that vaccinations and movement ban strategies are more cost-effective than premise culls before the onset of an epidemic outbreak; however, in the event of existing epidemic outbreaks, premise culling is more effective at controlling FMD.

Rapid and differential diagnosis of foot-and-mouth disease, swine vesicular disease, and vesicular stomatitis by a new multiplex RT-PCR assay

2008 ◽  
Vol 147 (2) ◽  
pp. 301-311 ◽  
Author(s):  
Jovita Fernández ◽  
Montserrat Agüero ◽  
Luis Romero ◽  
Carmen Sánchez ◽  
Sándor Belák ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Foot And Mouth Disease ◽  
Mouth Disease ◽  
Rt Pcr ◽  
Pcr Assay ◽  
Foot And Mouth ◽  
Vesicular Stomatitis ◽  
Vesicular Disease

scholarly journals Analysis and prediction of hand, foot and mouth disease incidence in China using Random Forest and XGBoost

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261629
Author(s):  
Delin Meng ◽  
Jun Xu ◽  
Jijun Zhao
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Social Factors ◽  
Prediction Models ◽  
Mainland China ◽  
Foot And Mouth Disease ◽  
Influential Factors ◽  
Mouth Disease ◽  
Extreme Gradient Boosting ◽  
Foot And Mouth

Hand, foot and mouth disease (HFMD) is an increasingly serious public health problem, and it has caused an outbreak in China every year since 2008. Predicting the incidence of HFMD and analyzing its influential factors are of great significance to its prevention. Now, machine learning has shown advantages in infectious disease models, but there are few studies on HFMD incidence based on machine learning that cover all the provinces in mainland China. In this study, we proposed two different machine learning algorithms, Random Forest and eXtreme Gradient Boosting (XGBoost), to perform our analysis and prediction. We first used Random Forest to examine the association between HFMD incidence and potential influential factors for 31 provinces in mainland China. Next, we established Random Forest and XGBoost prediction models using meteorological and social factors as the predictors. Finally, we applied our prediction models in four different regions of mainland China and evaluated the performance of them. Our results show that: 1) Meteorological factors and social factors jointly affect the incidence of HFMD in mainland China. Average temperature and population density are the two most significant influential factors; 2) Population flux has different delayed effect in affecting HFMD incidence in different regions. From a national perspective, the model using population flux data delayed for one month has better prediction performance; 3) The prediction capability of XGBoost model was better than that of Random Forest model from the overall perspective. XGBoost model is more suitable for predicting the incidence of HFMD in mainland China.

Biophysical Measurement of Molecular Weight of Foot-and-Mouth Disease RNA Fragments

1974 ◽  
Vol 32 ◽  
pp. 262-263
Author(s):  
Sydney S. Breese ◽  
Howard L. Bachrach
Keyword(s):  
Chemical Properties ◽  
Foot And Mouth Disease ◽  
Mouth Disease ◽  
Distilled Water ◽  
Bovine Plasma ◽  
Mouth Disease Virus ◽  
Foot And Mouth ◽  
Carbon Coated ◽  
Rna Fragments ◽  
Physical And Chemical

Continuing studies on the physical and chemical properties of foot-and-mouth disease virus (FMDV) have included electron microscopy of RNA strands released when highly purified virus (1) was dialyzed against demlneralized distilled water. The RNA strands were dried on formvar-carbon coated electron microscope screens pretreated with 0.1% bovine plasma albumin in distilled water. At this low salt concentration the RNA strands were extended and were stained with 1% phosphotungstic acid. Random dispersions of strands were recorded on electron micrographs, enlarged to 30,000 or 40,000 X and the lengths measured with a map-measuring wheel. Figure 1 is a typical micrograph and Fig. 2 shows the distributions of strand lengths for the three major types of FMDV (A119 of 6/9/72; C3-Rezende of 1/5/73; and O1-Brugge of 8/24/73.

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