Geographic risk assessment of COVID-19 transmission using recent data

Jen, Tung-Hui; Chien, Tsair‐Wei; Yeh, Yu-Tsen; Lin, Jui-Chung John; Kuo, Shu‐Chun; Chou, Willy

doi:10.1097/md.0000000000020774

Cited by 11 publications

(16 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Geographic attributes have been shown to be linked to COV-ID-19 epidemics in several countries. [33][34][35][36] The geographic differences of COVID-19 incidence and deaths in states have been reported in a previous study, 37 which, unfortunately, did not conduct quantitative trend analyses. The states' neighboring relationship with the epicenter (New York) in the USA was linked to the COV-ID-19 epidemics in our multivariable models, while the number of states under SAHO, proportion of positive COVID-19 testing and the number and proportion of the populations under SAHO were not linked to them.…”

Section: Resultsmentioning

confidence: 85%

Associations of Stay-at-Home Order and Face-Masking Recommendation with Trends in Daily New Cases and Deaths of Laboratory-Confirmed COVID-19 in the United States

Xu¹,

Hussain²,

Lu³

et al. 2020

Exploratory Research and Hypothesis in Medicine

View full text Add to dashboard Cite

Background and objectives: Public health interventions have reduced coronavirus disease 2019 (COVID-19) transmission in several countries, but their impacts on COVID-19 epidemics in the USA are unclear. We examined associations of stay-at-home order (SAHO) and face-masking recommendation with COVID-19 epidemics in the USA. Methods: In this quasi-experimental interrupted time-series study, we modeled temporal trends in daily new cases and deaths of laboratory-confirmed COVID-19 cases, and COVID-19 time-varying reproduction numbers in the USA between March 1 and April 20, 2020. In addition, we conducted simulation analyses. Results: The number of residents under SAHO increased since March 19 and plateaued at 290,829,980 (88.6% of the U.S. population) on April 7. Trends in COVID-19 time-varying reproduction numbers peaked on March 23, further reduced on April 3, and fell below/around 1.0 on April 13. Early-implementation and early-lift of SAHO would reduce and increase COVID-19 epidemics, respectively. Multivariable piecewise log-linear regression revealed the states' neighboring relationship with New York was linked to COVID-19 daily new cases and deaths. There were two turning points in daily new-case trend, being March 28 (slope-changes = −0.09) and April 3 (slope-changes = −0.09), which appeared to be associated with implementation of SAHO on March 28 (affecting 48.5% of the US population in 22 states and District of Columbia), and face-masking recommendation on April 3, respectively. There were also two turning points in daily new-death trend, being April 9 (slope-changes = −0.06) and April 19 (slope-changes = −0.90). Conclusions: We identified two turning points of COVID-19 daily new cases or deaths in the USA, which seem to

show abstract

Section: Resultsmentioning

confidence: 85%

Associations of Stay-at-Home Order and Face-Masking Recommendation with Trends in Daily New Cases and Deaths of Laboratory-Confirmed COVID-19 in the United States

Xu¹,

Hussain²,

Lu³

et al. 2020

Exploratory Research and Hypothesis in Medicine

View full text Add to dashboard Cite

show abstract

“…When a new disease (e.g., COVID-19) starts to spread, many questions emerge. [ 5 , 6 ] One of the most frequently asked questions is how high is the case fatality rate (CFR) [ 5 ] and what is the model to predict patients at risk of high mortality. [ 1 , 7 ] If we ignore the classification of patients at high risk with proper treatments, the condition of patients might rapidly deteriorate.…”

Section: Introductionmentioning

confidence: 99%

An artificial neural network model to predict the mortality of COVID-19 patients using routine blood samples at the time of hospital admission

et al. 2021

Self Cite

View full text Add to dashboard Cite

Background: In a pandemic situation (e.g., COVID-19), the most important issue is to select patients at risk of high mortality at an early stage and to provide appropriate treatments. However, a few studies applied the model to predict in-hospital mortality using routine blood samples at the time of hospital admission. This study aimed to develop an app, name predict the mortality of COVID-19 patients (PMCP) app, to predict the mortality of COVID-19 patients at hospital-admission time. Methods: We downloaded patient records from 2 studies, including 361 COVID-19 patients in Wuhan, China, and 106 COVID-19 patients in 3 Korean medical institutions. A total of 30 feature variables were retrieved, consisting of 28 blood biomarkers and 2 demographic variables (i.e., age and gender) of patients. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared with each other across 2 scenarios using raw laboratory versus normalized data and training vs testing datasets (n = 361 and n = 106/361≅30%) to verify the model performance (e.g., sensitivity [SENS], specificity [SPEC], and area under the receiver operating characteristic curve [AUC]). An app for predicting the mortality of COVID-19 patients was developed using the model's estimated parameters for the prediction and classification of PMCP at an earlier stage. Feature variables and prediction results were visualized using the forest plot and category probability curves shown on Google Maps. Results: We observed that the normalized dataset gains a relatively higher AUC(>0.9) when compared to that(<0.9) in the raw-laboratory dataset based on training data, the normalized dataset in ANN yielded a high AUC of 0.96 that that(=0.91) in CNN based on testing data, and a ready and available app, where anyone can access the model to predict mortality, for PMCP was developed in this study. Conclusions: Our new PMCP app with ANN model accurately predicts the mortality probability for COVID-19 patients. It is publicly available and aims to help health care providers fight COVID-19 and improve patients’ classifications against treatment risk.

show abstract

“…Geographic visualization shows the geographical location and related information of all epidemic areas in an intuitive way; timeline visualization provides visualize temporal data in a specific period; spatiotemporal visualization combines geographical area and time interval to explore the deeper meaning of data. Those approaches are widely used in the COVID-19 pandemic, such as assessing the geographic risk of COVID-19 transmission [ 3 ], monitoring the control measures [ 4 ], presenting the timeline of COVID-19 outbreak [ 5 ], etc. Despite the variety of data visualization methods, for the pandemic disease like COVID-19, there is limited method to comprehensively demonstrate the global epidemic in one graph through constructing multi-dimensional core indicators.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal visualization for the global COVID-19 surveillance by balloon chart

Pang

Liang

Cheng³

et al. 2021

Infect Dis Poverty

View full text Add to dashboard Cite

Background Considering the widespread of coronavirus disease 2019 (COVID-19) pandemic in the world, it is important to understand the spatiotemporal development of the pandemic. In this study, we aimed to visualize time-associated alterations of COVID-19 in the context of continents and countries. Methods Using COVID-19 case and death data from February to December 2020 offered by Johns Hopkins University, we generated time-associated balloon charts with multiple epidemiological indicators including crude case fatality rate (CFR), morbidity, mortality and the total number of cases, to compare the progression of the pandemic within a specific period across regions and countries, integrating seven related dimensions together. The area chart is used to supplement the display of the balloon chart in daily new COVID-19 case changes in UN geographic regions over time. Javascript and Vega-Lite were chosen for programming and mapping COVID-19 data in browsers for visualization. Results From February 1st to December 20th 2020, the COVID-19 pandemic spread across UN subregions in the chronological order. It was first reported in East Asia, and then became noticeable in Europe (South, West and North), North America, East Europe and West Asia, Central and South America, Southern Africa, Caribbean, South Asia, North Africa, Southeast Asia and Oceania, causing several waves of epidemics in different regions. Since October, the balloons of Europe, North America and West Asia have been rising rapidly, reaching a dramatically high morbidity level ranging from 200 to 500/10 000 by December, suggesting an emerging winter wave of COVID-19 which was much bigger than the previous ones. By late December 2020, some European and American countries displayed a leading mortality as high as or over 100/100 000, represented by Belgium, Czechia, Spain, France, Italy, UK, Hungary, Bulgaria, Peru, USA, Argentina, Brazil, Chile and Mexico. The mortality of Iran was the highest in Asia (over 60/100 000), and that of South Africa topped in Africa (40/100 000). In the last 15 days, the CFRs of most countries were at low levels of less than 5%, while Mexico had exceptional high CFR close to 10%. Conclusions We creatively used visualization integrating 7-dimensional epidemiologic and spatiotemporal indicators to assess the progression of COVID-19 pandemic in terms of transmissibility and severity. Such methodology allows public health workers and policy makers to understand the epidemics comparatively and flexibly.

show abstract

Geographic risk assessment of COVID-19 transmission using recent data

Cited by 11 publications

References 20 publications

Associations of Stay-at-Home Order and Face-Masking Recommendation with Trends in Daily New Cases and Deaths of Laboratory-Confirmed COVID-19 in the United States

Associations of Stay-at-Home Order and Face-Masking Recommendation with Trends in Daily New Cases and Deaths of Laboratory-Confirmed COVID-19 in the United States

An artificial neural network model to predict the mortality of COVID-19 patients using routine blood samples at the time of hospital admission

Spatiotemporal visualization for the global COVID-19 surveillance by balloon chart

Contact Info

Product

Resources

About