scholarly journals Artificial Neural Network Modeling of Novel Coronavirus (COVID-19) Incidence Rates across the Continental United States

2020 ◽  
Vol 17 (12) ◽  
pp. 4204 ◽  
Author(s):  
Abolfazl Mollalo ◽  
Kiara M. Rivera ◽  
Behzad Vahedi
Keyword(s):  
Neural Network ◽  
United States ◽  
Disease Incidence ◽  
Ground Truth ◽  
The United States ◽  
Incidence Rates ◽  
Machine Learning Algorithms ◽  
Neural Network Modeling ◽  
Public Health Decision ◽  
Explanatory Variables

Prediction of the COVID-19 incidence rate is a matter of global importance, particularly in the United States. As of 4 June 2020, more than 1.8 million confirmed cases and over 108 thousand deaths have been reported in this country. Few studies have examined nationwide modeling of COVID-19 incidence in the United States particularly using machine-learning algorithms. Thus, we collected and prepared a database of 57 candidate explanatory variables to examine the performance of multilayer perceptron (MLP) neural network in predicting the cumulative COVID-19 incidence rates across the continental United States. Our results indicated that a single-hidden-layer MLP could explain almost 65% of the correlation with ground truth for the holdout samples. Sensitivity analysis conducted on this model showed that the age-adjusted mortality rates of ischemic heart disease, pancreatic cancer, and leukemia, together with two socioeconomic and environmental factors (median household income and total precipitation), are among the most substantial factors for predicting COVID-19 incidence rates. Moreover, results of the logistic regression model indicated that these variables could explain the presence/absence of the hotspots of disease incidence that were identified by Getis-Ord Gi* (p < 0.05) in a geographic information system environment. The findings may provide useful insights for public health decision makers regarding the influence of potential risk factors associated with the COVID-19 incidence at the county level.

scholarly journals ESTIMATING CORN YIELD IN THE UNITED STATES WITH MODIS EVI AND MACHINE LEARNING METHODS

2016 ◽  
Vol III-8 ◽  
pp. 131-136 ◽  
Author(s):  
K. Kuwata ◽  
R. Shibasaki
Keyword(s):  
Neural Network ◽  
United States ◽  
Machine Learning ◽  
Crop Yield ◽  
The United States ◽  
Machine Learning Algorithms ◽  
Corn Yield ◽  
County Level ◽  
Entire Area ◽  
Modis Evi

Satellite remote sensing is commonly used to monitor crop yield in wide areas. Because many parameters are necessary for crop yield estimation, modelling the relationships between parameters and crop yield is generally complicated. Several methodologies using machine learning have been proposed to solve this issue, but the accuracy of county-level estimation remains to be improved. In addition, estimating county-level crop yield across an entire country has not yet been achieved. In this study, we applied a deep neural network (DNN) to estimate corn yield. We evaluated the estimation accuracy of the DNN model by comparing it with other models trained by different machine learning algorithms. We also prepared two time-series datasets differing in duration and confirmed the feature extraction performance of models by inputting each dataset. As a result, the DNN estimated county-level corn yield for the entire area of the United States with a determination coefficient (&lt;i&gt;R&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt;) of 0.780 and a root mean square error (&lt;i&gt;RMSE&lt;/i&gt;) of 18.2 bushels/acre. In addition, our results showed that estimation models that were trained by a neural network extracted features from the input data better than an existing machine learning algorithm.

scholarly journals Trends in peripheral arterial disease incidence and mortality in EU15+ countries 1990–2017

2020 ◽  
pp. 204748731989962 ◽  
Author(s):  
Richard Goodall ◽  
Justin D Salciccioli ◽  
Alun Huw Davies ◽  
Dominic Marshall ◽  
Joseph Shalhoub
Keyword(s):  
United States ◽  
Peripheral Arterial Disease ◽  
Medical Therapy ◽  
Disease Incidence ◽  
Arterial Disease ◽  
The United States ◽  
Mortality Rates ◽  
Incidence Rates ◽  
Incidence And Mortality ◽  
Peripheral Arterial

Aims The aim was to assess trends in peripheral arterial disease (PAD) incidence and mortality rates in European Union(15+) countries between 1990 and 2017. Methods and Results This observational study used data obtained from the 2017 Global Burden of Disease study. Age-standardised mortality and incidence rates from PAD were extracted from the Global Health Data Exchange for EU15+ countries for the years 1990–2017. Trends were analysed using Joinpoint regression analysis. Between 1990 and 2017, the incidence of PAD decreased in all 19 EU15+ countries for females, and in 18 of 19 countries for males. Increasing PAD incidence was observed only for males in the United States (+1.4%). In 2017, the highest incidence rates were observed in Denmark and the United States for males (213.6 and 202.3 per 100,000, respectively) and in the United States and Canada for females (194.8 and 171.1 per 100,000, respectively). There was a concomitant overall trend for increasing age-standardised mortality rates in all EU15+ countries for females, and in 16 of 19 EU15+ countries for males between 1990 and 2017. Italy (–25.1%), Portugal (–1.9%) and Sweden (–0.6%) were the only countries with reducing PAD mortality rates in males. The largest increases in mortality rates were observed in the United Kingdom (males +140.4%, females +158.0%) and the United States (males +125.7%, females +131.2%). Conclusions We identify shifting burden of PAD in EU15+ countries, with increasing mortality rates despite reducing incidence. Strong evidence supports goal-directed medical therapy in reducing PAD mortality – population-wide strategies to improve compliance to optimal goal-directed medical therapy are warranted.

scholarly journals Epidemiology of mucopolysaccharidoses (MPS) in United States: challenges and opportunities

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Yana Puckett ◽  
Alejandra Mallorga-Hernández ◽  
Adriana M. Montaño
Keyword(s):  
United States ◽  
Disease Incidence ◽  
State Level ◽  
The United States ◽  
Incidence Rates ◽  
Mandated Reporting ◽  
Prevalence Rates ◽  
Lysosomal Storage ◽  
Live Births ◽  
The Us

Abstract Background Mucopolysaccharidoses (MPS) are rare, inherited lysosomal storage disorders characterized by progressive multiorgan involvement. Previous studies on incidence and prevalence of MPS mainly focused on countries other than the United States (US), showing considerable variation by country. This study aimed to identify MPS incidence and prevalence in the US at a national and state level to guide clinicians and policy makers. Methods This retrospective study examined all diagnosed cases of MPS from 1995 to 2015 in the US using the National MPS Society database records. Data included year of birth, patient geographic location, and MPS variant type. US population information was obtained from the National Center for Health Statistics. The incidence and prevalence rates were calculated for each disease. Incidence rates were calculated for each state. Results We obtained information from 789 MPS patients during a 20-year period. Incidence of MPS in the US was found to be 0.98 per 100,000 live births. Prevalence was found to be 2.67 per 1 million. MPS I, II, and III had the highest incidence rate at birth (0.26/100,000) and prevalence rates of 0.70–0.71 per million. Birth incidences of MPS IV, VI, and VII were 0.14, 0.04 and 0.027 per 100,000 live births. Conclusions This is the most comprehensive review of MPS incidence and prevalence rates in the US. Due to the large US population and state fragmentation, US incidence and prevalence were found to be lower than other countries. Nonetheless, state-level studies in the US supported these figures. Efforts should be focused in the establishment of a national rare disease registry with mandated reporting from every state as well as newborn screening of MPS.

scholarly journals Delay in the Effect of Restricting Community Mobility on the Spread of COVID-19 During the First Wave in the United States

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Shan He ◽  
Jooyoung Lee ◽  
Benjamin Langworthy ◽  
Junyi Xin ◽  
Peter James ◽  
...  
Keyword(s):  
United States ◽  
Incidence Rate ◽  
Human Mobility ◽  
Disease Incidence ◽  
Human Movement ◽  
Credible Interval ◽  
The United States ◽  
Incidence Rates ◽  
Grocery Stores ◽  
Minimal Impact

Abstract Background It remains unclear how changes in human mobility shaped the transmission dynamic of coronavirus disease 2019 (COVID-19) during its first wave in the United States. Methods By coupling a Bayesian hierarchical spatiotemporal model with reported case data and Google mobility data at the county level, we found that changes in movement were associated with notable changes in reported COVID-19 incidence rates about 5 to 7 weeks later. Results Among all movement types, residential stay was the most influential driver of COVID-19 incidence rate, with a 10% increase 7 weeks ago reducing the disease incidence rate by 13% (95% credible interval, 6%–20%). A 10% increase in movement from home to workplaces, retail and recreation stores, public transit, grocery stores, and pharmacies 7 weeks ago was associated with an increase of 5%–8% in the COVID-10 incidence rate. In contrast, parks-related movement showed minimal impact. Conclusions Policy-makers should anticipate such a delay when planning intervention strategies restricting human movement.

scholarly journals ESTIMATING CORN YIELD IN THE UNITED STATES WITH MODIS EVI AND MACHINE LEARNING METHODS

2016 ◽  
Vol III-8 ◽  
pp. 131-136 ◽  
Author(s):  
K. Kuwata ◽  
R. Shibasaki
Keyword(s):  
Neural Network ◽  
United States ◽  
Machine Learning ◽  
Crop Yield ◽  
The United States ◽  
Machine Learning Algorithms ◽  
Corn Yield ◽  
County Level ◽  
Entire Area ◽  
Modis Evi

Satellite remote sensing is commonly used to monitor crop yield in wide areas. Because many parameters are necessary for crop yield estimation, modelling the relationships between parameters and crop yield is generally complicated. Several methodologies using machine learning have been proposed to solve this issue, but the accuracy of county-level estimation remains to be improved. In addition, estimating county-level crop yield across an entire country has not yet been achieved. In this study, we applied a deep neural network (DNN) to estimate corn yield. We evaluated the estimation accuracy of the DNN model by comparing it with other models trained by different machine learning algorithms. We also prepared two time-series datasets differing in duration and confirmed the feature extraction performance of models by inputting each dataset. As a result, the DNN estimated county-level corn yield for the entire area of the United States with a determination coefficient (<i>R</i><sup>2</sup>) of 0.780 and a root mean square error (<i>RMSE</i>) of 18.2 bushels/acre. In addition, our results showed that estimation models that were trained by a neural network extracted features from the input data better than an existing machine learning algorithm.

scholarly journals 55 Malignant primary brain and other central nervous system tumours diagnosed in the Canadian population from 2009 to 2013

2018 ◽  
Vol 45 (S3) ◽  
pp. S16-S17
Author(s):  
EV Walker ◽  
F Davis ◽  
Keyword(s):  
United States ◽  
Central Nervous System ◽  
Nervous System ◽  
Brain Tumour ◽  
Age Distribution ◽  
Paediatric Population ◽  
The United States ◽  
Incidence Rates ◽  
Canadian Population ◽  
Cns Tumours

The Canadian Brain Tumour Registry (CBTR) project was established in 2016 with the aim of enhancing infrastructure for surveillance and clinical research to improve health outcomes for brain tumour patients in Canada. We present a national surveillance report on malignant primary brain and central nervous system (CNS) tumours diagnosed in the Canadian population from 2009-2013. Patients were identified through the Canadian Cancer Registry (CCR); an administrative dataset that includes cancer incidence data from all provinces/territories in Canada. Cancer diagnoses are coded using the ICD-O3 system. Tumour types were classified by site and histology using The Central Brain Tumour Registry of the United States definitions. Incidence rates (IR) and 95% confidence intervals (CI) were calculated per 100,000 person-years and standardized to the 2011 census population age-distribution. Overall, 12,115 malignant brain and CNS tumours were diagnosed in the Canadian population from 2009-2013 (IR:8.43;95%CI:8.28,8.58). Of these, 6,845 were diagnosed in males (IR:9.72;95%CI:9.49,9.95) and 5,270 in females (IR:7.20;95%CI:7.00,7.39). The most common histology overall was glioblastoma (IR:4.06;95%CI:3.95,4.16). Among those aged 0-19 years, 1,130 malignant brain and CNS tumours were diagnosed from 2009-2013 (IR:3.36;95%CI:3.16,3.56). Of these, 625 were diagnosed in males (IR:3.32;95%CI:3.34,3.92) and 505 in females (IR:3.08;95%CI:2.81,3.36). The most common histology among the paediatric population was pilocytic astrocytoma (IR:0.73;95%CI:0.64,0.83). The presentation will include: IRs for other histologies, the geographic distribution of cases and a comparison between Canada and the United States.

Incidence rates of liver cirrhosis and related diseases in Baltimore and selected areas of the United States

1979 ◽  
Vol 32 (8) ◽  
pp. 543-554 ◽  
Author(s):  
Cedric F. Garagliano ◽  
Abraham M. Lilienfeld ◽  
Albert I. Mendeloff
Keyword(s):  
United States ◽  
Liver Cirrhosis ◽  
The United States ◽  
Incidence Rates

scholarly journals Annual Incidence Rates of Herpes Zoster among an Immunocompetent Population In The United States

2015 ◽  
Vol 18 (3) ◽  
pp. A232
Author(s):  
B.H. Johnson ◽  
J. Gatwood ◽  
L.A. Palmer ◽  
G. Lenhart ◽  
K. Kawai ◽  
...  
Keyword(s):  
United States ◽  
Herpes Zoster ◽  
The United States ◽  
Incidence Rates ◽  
Annual Incidence

scholarly journals A Survey of Genetic Variation in Streptomyces Isolates Causing Potato Common Scab in the United States

2006 ◽  
Vol 96 (12) ◽  
pp. 1363-1371 ◽  
Author(s):  
Leslie A. Wanner
Keyword(s):  
United States ◽  
Genetic Variation ◽  
Common Scab ◽  
Disease Incidence ◽  
The United States ◽  
Rrna Gene ◽  
Population Genetic Study ◽  
Variable Regions ◽  
Genus Streptomyces ◽  
Characteristic Features

Common scab is a serious disease of potatoes and other root and tuber crops, affecting crop quality and market value. The disease is caused by gram positive soil bacteria in the genus Streptomyces. Disease incidence and severity vary in different locations and years; this is due in part to variation in the environment (weather) and genetic variation in potato cultivars. Little information is available on the contribution of genetic variation by the pathogen. To examine genetic diversity in different locations within the United States, streptomycetes were isolated from lesions on field-grown potatoes from six states. Isolates were classified into species based on sequence of variable regions in the 16s rRNA gene. The presence of genes associated with the recently described S. turgidiscabies pathogenicity island (PAI) was also determined. About half of the isolates belonged to S. scabies or S. europaeiscabiei based on 16s rDNA sequence, and had characteristic features of the PAI. They were found in all six states, and were pathogenic on potato and radish. The remaining isolates included pathogens and nonpathogens. They were varied in appearance, and represent several species, including one pathogenic species not previously reported. Some pathogenic isolates lacked one or more genes characteristic of the PAI, although all had genes for biosynthesis of the pathogenicity determinant thaxtomin. In this relatively small survey, regional differences in scab-causing streptomycetes were seen. This report furnishes tools and baseline data for population genetic study of scab-causing streptomycetes in the United States.

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