Digital SARS-CoV-2 detection among hospital employees: A Participatory Surveillance study. (Preprint)

2021 ◽  
Author(s):  
Onicio Leal-Neto ◽  
Thomas Egger ◽  
Matthias Schlegel ◽  
Domenica Flury ◽  
Johannes Summer ◽  
...  
Keyword(s):  
Random Forest ◽  
Disease Surveillance ◽  
Classification Tree ◽  
Population Level ◽  
Surveillance Systems ◽  
Surveillance Strategy ◽  
Random Forest Classification ◽  
Runny Nose ◽  
Participatory Surveillance ◽  
Red Eyes

BACKGROUND The implementation of novel techniques represents an additional opportunity for the rapid analysis acting as a complement to the traditional disease surveillance systems. OBJECTIVE The objective of this work is to describe a web-based participatory surveillance strategy among healthcare workers (HCW) in two Swiss hospitals during the first wave of COVID-19. METHODS A prospective cohort of HCW was initiated in March 2020 at the Cantonal Hospital of St. Gallen and the Eastern Switzerland Children’s Hospital. For data analysis, we used a combination of the following techniques: loess regression, spearman correlation, anomaly detection and random forest. RESULTS From March 23rd to August 23rd 2020, 127,684 SMS were sent generating 90,414 valid reports among 1,004 participants, achieving a weekly average of 4.5 reports per user (SD 1.9). The symptom showing the strongest correlation with a positive PCR result was loss of taste. Symptoms like red eyes or runny nose were negatively associated with a positive test. The area under the ROC curve showed favorable performance of the classification tree, with an accuracy of 88% for the training and 89% for the test data. Nevertheless, while the prediction matrix showed good specificity (80.0%), sensitivity was low at 10.6%. Loss of taste was the symptom which paralleled best with COVID-19 activity on the population level. On the resident level, using machine-learning based random forest classification, reporting of loss of taste and limb/muscle pain, as well as absence of runny nose and red eyes were the best predictors of COVID-19. CONCLUSIONS Nevertheless, we deem the presented surveillance tool highly useful in monitoring and predicting COVID-19 activity among our HCW.

scholarly journals AN OBJECT-BASED METHOD FOR CHINESE LANDFORM TYPES CLASSIFICATION

2016 ◽  
Vol XLI-B7 ◽  
pp. 213-217 ◽  
Author(s):  
Hu Ding ◽  
Fei Tao ◽  
Wufan Zhao ◽  
Jiaming Na ◽  
Guo’an Tang
Keyword(s):  
Random Forest ◽  
Classification Tree ◽  
Unsupervised Classification ◽  
Entropy Method ◽  
Random Forest Classification ◽  
Landform Classification ◽  
Forest Classification ◽  
Object Based ◽  
Importance Analysis ◽  
Occurrence Matrix

Landform classification is a necessary task for various fields of landscape and regional planning, for example for landscape evaluation, erosion studies, hazard prediction, et al. This study proposes an improved object-based classification for Chinese landform types using the factor importance analysis of random forest and the gray-level co-occurrence matrix (GLCM). In this research, based on 1km DEM of China, the combination of the terrain factors extracted from DEM are selected by correlation analysis and Sheffield's entropy method. Random forest classification tree is applied to evaluate the importance of the terrain factors, which are used as multi-scale segmentation thresholds. Then the GLCM is conducted for the knowledge base of classification. The classification result was checked by using the 1:4,000,000 Chinese Geomorphological Map as reference. And the overall classification accuracy of the proposed method is 5.7% higher than ISODATA unsupervised classification, and 15.7% higher than the traditional object-based classification method.

scholarly journals Are Nomothetic or Ideographic Approaches Superior in Predicting Daily Exercise Behaviors?

2017 ◽  
Vol 56 (06) ◽  
pp. 452-460 ◽  
Author(s):  
Ying Cheung ◽  
Pei-Yun Hsueh ◽  
Min Qian ◽  
Sunmoo Yoon ◽  
Laura Meli ◽  
...  
Keyword(s):  
Random Forest ◽  
Healthy Lifestyle ◽  
Classification Tree ◽  
Full Range ◽  
Population Level ◽  
Mobile App ◽  
Exercise Behavior ◽  
Classification Error ◽  
Temperature And Precipitation ◽  
Nomothetic Approach

SummaryObjectives: The understanding of how stress influences health behavior can provide insights into developing healthy lifestyle interventions. This understanding is traditionally attained through observational studies that examine associations at a population level. This nomothetic approach, however, is fundamentally limited by the fact that the environment- person milieu that constitutes stress exposure and experience can vary substantially between individuals, and the modifiable elements of these exposures and experiences are individual-specific. With recent advances in smartphone and sensing technologies, it is now possible to conduct idiographic assessment in users’ own environment, leveraging the full-range observations of actions and experiences that result in differential response to naturally occurring events. The aim of this paper is to explore the hypothesis that an ideographic N-of-1 model can better capture an individual’s stress- behavior pathway (or the lack thereof) and provide useful person-specific predictors of exercise behavior.Methods: This paper used the data collected in an observational study in 79 participants who were followed for up to a 1-year period, wherein their physical activity was continuously and objectively monitored by actigraphy and their stress experience was recorded via ecological momentary assessment on a mobile app. In addition, our analyses considered exogenous and environmental variables retrieved from public archive such as day in a week, daylight time, temperature and precipitation. Leveraging the multiple data sources, we developed prediction algorithms for exercise behavior using random forest and classification tree techniques using a nomothetic approach and an N-of-1 approach. The two approaches were compared based on classification errors in predicting personalized exercise behavior.Results: Eight factors were selected by random forest for the nomothetic decision model, which was used to predict whether a participant would exercise on a particular day. The predictors included previous exercise behavior, emotional factors (e.g., midday stress), external factors such as weather (e.g., temperature), and self-determination factors (e.g., expectation of exercise). The nomothetic model yielded an average classification error of 36%. The ideographic N-of-1 models used on average about two predictors for each individual, and had an average classification error of 25%, which represented an improvement of 11 percentage points.Conclusions: Compared to the traditional one-size-fits-all, nomothetic model that generalizes population-evidence for individuals, the proposed N-of-1 model can better capture the individual difference in their stressbehavior pathways. In this paper, we demonstrate it is feasible to perform personalized exercise behavior prediction, mainly made possible by mobile health technology and machine learning analytics.

scholarly journals AN OBJECT-BASED METHOD FOR CHINESE LANDFORM TYPES CLASSIFICATION

2016 ◽  
Vol XLI-B7 ◽  
pp. 213-217
Author(s):  
Hu Ding ◽  
Fei Tao ◽  
Wufan Zhao ◽  
Jiaming Na ◽  
Guo’an Tang
Keyword(s):  
Random Forest ◽  
Classification Tree ◽  
Unsupervised Classification ◽  
Entropy Method ◽  
Random Forest Classification ◽  
Landform Classification ◽  
Forest Classification ◽  
Object Based ◽  
Importance Analysis ◽  
Occurrence Matrix

Landform classification is a necessary task for various fields of landscape and regional planning, for example for landscape evaluation, erosion studies, hazard prediction, et al. This study proposes an improved object-based classification for Chinese landform types using the factor importance analysis of random forest and the gray-level co-occurrence matrix (GLCM). In this research, based on 1km DEM of China, the combination of the terrain factors extracted from DEM are selected by correlation analysis and Sheffield's entropy method. Random forest classification tree is applied to evaluate the importance of the terrain factors, which are used as multi-scale segmentation thresholds. Then the GLCM is conducted for the knowledge base of classification. The classification result was checked by using the 1:4,000,000 Chinese Geomorphological Map as reference. And the overall classification accuracy of the proposed method is 5.7% higher than ISODATA unsupervised classification, and 15.7% higher than the traditional object-based classification method.

AVADAR Application: a learning model for designing a mobile-based disease surveillance system using participatory community reporting in Africa (Preprint)

2019 ◽  
Author(s):  
Godwin Akpan ◽  
Johnson Muluh Ticha ◽  
Lara M.F. Paige ◽  
Daniel Rasheed Oyaole ◽  
Patrick Briand ◽  
...  
Keyword(s):  
Community Participation ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Health Workers ◽  
Digital Health ◽  
Black Box ◽  
Project Planning ◽  
Acceptance Testing ◽  
Surveillance Strategy ◽  
Design And Implementation

BACKGROUND Acute Flaccid Paralysis (AFP) surveillance is the bedrock of polio case detection. The Auto Visual AFP Detection and Reporting (AVADAR) is a digital health intervention designed as a supplemental community surveillance system. OBJECTIVE This paper describes the design and implementation process that made AVADAR a successful disease surveillance strategy at the community level. METHODS This paper outlines the methods for the design and implementation of the AVADAR application. It explains the co-design of the application, the implementation of a helpdesk support structure, the process involved in trouble shooting the application, the benefits of utilizing a closed user group for telecommunication requirements, and the use of a consented video. We also describe how these features combined led to user acceptance testing using black box methodology. RESULTS A total of 198 community informants across two provinces, four districts and 32 settlements were interviewed about application performance, usability, security, load, stress and functionality testing black box components. The responses showed most community participants giving positive reviews. Data from the Blackbox testing yielded optimum acceptance ratings from over 90% of the users involved in the testing. A total of 22380 AFP Alerts were sent out by community informants and 21589 (95%) were investigated by health workers or WHO AVADAR coordinators. Overall there was 93% assimilation at regional level. About 83% of investigations were done in the vicinity of the alerts in 2018 compared to 77% in 2017. CONCLUSIONS AVADAR implementation model offers a simplistic step by step model that includes community participation as an integral tool for the successful deployment of a mobile based surveillance reporting tool. AVADAR can be a veritable source of project planning data and a mobile application for other interventions that target using community participation to influence health outcomes.

scholarly journals At the tip of an iceberg: citizen science and active surveillance collaborating to broaden the known distribution of Aedes japonicus in Spain

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Roger Eritja ◽  
Sarah Delacour-Estrella ◽  
Ignacio Ruiz-Arrondo ◽  
Mikel A. González ◽  
Carlos Barceló ◽  
...  
Keyword(s):  
Citizen Science ◽  
Active Surveillance ◽  
Mosquito Control ◽  
Mosquito Species ◽  
Surveillance Systems ◽  
Northern Spain ◽  
Surveillance Strategy ◽  
Aedes Japonicus ◽  
Invasive Mosquito Species ◽  
Invasive Mosquito

Abstract Background Active surveillance aimed at the early detection of invasive mosquito species is usually focused on seaports and airports as points of entry, and along road networks as dispersion paths. In a number of cases, however, the first detections of colonizing populations are made by citizens, either because the species has already moved beyond the implemented active surveillance sites or because there is no surveillance in place. This was the case of the first detection in 2018 of the Asian bush mosquito, Aedes japonicus, in Asturias (northern Spain) by the citizen science platform Mosquito Alert. Methods The collaboration between Mosquito Alert, the Ministry of Health, local authorities and academic researchers resulted in a multi-source surveillance combining active field sampling with broader temporal and spatial citizen-sourced data, resulting in a more flexible and efficient surveillance strategy. Results Between 2018 and 2020, the joint efforts of administrative bodies, academic teams and citizen-sourced data led to the discovery of this species in northern regions of Spain such as Cantabria and the Basque Country. This raised the estimated area of occurrence of Ae. japonicus from < 900 km2 in 2018 to > 7000 km2 in 2020. Conclusions This population cluster is geographically isolated from any other population in Europe, which raises questions about its origin, path of introduction and dispersal means, while also highlighting the need to enhance surveillance systems by closely combining crowd-sourced surveillance with public health and mosquito control agencies’ efforts, from local to continental scales. This multi-actor approach for surveillance (either passive and active) shows high potential efficiency in the surveillance of other invasive mosquito species, and specifically the major vector Aedes aegypti which is already present in some parts of Europe. Graphical abstract

scholarly journals Quantitative measurement of retinal ganglion cell populations via histology-based random forest classification

2016 ◽  
Vol 146 ◽  
pp. 370-385 ◽  
Author(s):  
Adam Hedberg-Buenz ◽  
Mark A. Christopher ◽  
Carly J. Lewis ◽  
Kimberly A. Fernandes ◽  
Laura M. Dutca ◽  
...  
Keyword(s):  
Random Forest ◽  
Ganglion Cell ◽  
Retinal Ganglion Cell ◽  
Quantitative Measurement ◽  
Cell Populations ◽  
Retinal Ganglion ◽  
Random Forest Classification ◽  
Forest Classification
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