scholarly journals COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms

2021 ◽  
pp. 154-169
Author(s):  
Rohan Sukumaran ◽  
Parth Patwa ◽  
Sethuraman T V ◽  
Sheshank Shankar ◽  
Rishank Kanaparti ◽  
...  
Keyword(s):  
New York ◽  
Confidence Interval ◽  
Relative Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Disease Prevalence ◽  
Antibody Testing ◽  
Mean Relative Error ◽  
Alternative Method ◽  
Testing Data

It is crucial for policymakers to understand the community prevalence of COVID-19 so combative resources can be effectively allocated and prioritized during the COVID-19 pandemic. Traditionally, community prevalence has been assessed through diagnostic and antibody testing data. However, despite the increasing availability of COVID-19 testing, the required level has not been met in parts of the globe, introducing a need for an alternative method for communities to determine disease prevalence. This is further complicated by the observation that COVID-19 prevalence and spread vary across different spatial, temporal, and demographic verticals. In this study, we study trends in the spread of COVID-19 by utilizing the results of self-reported COVID-19 symptoms surveys as a complement to COVID-19 testing reports. This allows us to assess community disease prevalence, even in areas with low COVID-19 testing ability. Using individually reported symptom data from various populations, our method predicts the likely percentage of the population that tested positive for COVID-19. We achieved a mean absolute error (MAE) of 1.14 and mean relative error (MRE) of 60.40% with 95% confidence interval as [60.12, 60.67]. This implies that our model predicts +/- 1140 cases than the original in a population of 1 million. In addition, we forecast the location-wise percentage of the population testing positive for the next 30 days using self-reported symptoms data from previous days. The MAE for this method is as low as 0.15 (MRE of 11.28% with 95% confidence interval [10.9, 11.6]) for New York. We present an analysis of these results, exposing various clinical attributes of interest across different demographics. Lastly, we qualitatively analyze how various policy enactments (testing, curfew) affect the prevalence of COVID-19 in a community.

scholarly journals Relationship between photosynthetically active radiation and global radiation in Petrolina and Brasília, Brazil

2021 ◽  
Vol 25 (9) ◽  
pp. 612-619
Author(s):  
Lady L. M. Custódio ◽  
Bernardo B. da Silva ◽  
Carlos A. C. dos Santos
Keyword(s):  
Photosynthetically Active Radiation ◽  
Mean Absolute Error ◽  
Global Radiation ◽  
Absolute Error ◽  
Global Solar Radiation ◽  
Clearness Index ◽  
Active Radiation ◽  
Mean Relative Error ◽  
Instantaneous Values ◽  
The Relationship

ABSTRACT Photosynthetically active radiation (PAR) comprises the spectral range of global solar radiation (Rs) that is highly related to vegetation productivity. The study aimed to evaluate the relationship between PAR and Rs in Petrolina, PE, and Brasília, DF, Brazil, with data measured in 2011 and 2013 at two stations of the Sistema Nacional de Organização de Dados Ambientais located in Petrolina, PE and Brasília, DF, Brazil, and the obtained models were evaluated using the measurements of 2014. It was verified that the PAR, in instantaneous values (μmol m-2 s-1), can be estimated at 2.31 times the Rs (W m-2) measured in Petrolina, while for daily values of PAR (MJ m-2) is equal to 50% of Rs (MJ m-2). In Brasília, PAR (μmol m-2 s-1) is 2.05 times the Rs (W m-2) and, in daily values, equal to 44% of Rs (MJ m-2). The variability of the PAR/Rs ratio followed the local variations of clearness index (Kt) and Rs. The models presented an adequate performance based on statistical indices mean absolute error, mean relative error, and root mean square error and can be used to estimate PAR.

scholarly journals MO877PREVALENCE AND PERSISTENCE OF COVID-19 ANTIBODIES IN NEW YORK CITY HEMODIALYSIS CLINIC

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Ohnmar Thwin ◽  
Nadja Grobe ◽  
Xiaoling (Janice) Ye ◽  
Priscila Preciado Rojas ◽  
Leticia M Tapia Silva ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
Confidence Interval ◽  
Clinical Diagnosis ◽  
Phase 1 ◽  
Phase 2 ◽  
Igg Antibodies ◽  
Rt Pcr ◽  
Antibody Testing ◽  
Luminescence Signal

Abstract Background and Aims Dialysis patients are at higher risk for severe acute respiratory syndrome coronavirus (SARS-CoV-2) infection. Longevity of antibody response to SARS-CoV-2 infection remains unclear. It is reported that maintenance hemodialysis (MHD) patients can mount an antibody response that is similar in intensity and timing to the non-dialysis population. We aim to investigate the prevalence and persistence of antibodies in hemodialysis patients. Method We measured IgG and IgM antibodies in MHD patients as part of a quality improvement project. Four New York City dialysis clinics participated in this study. Strict policy of RT-PCR testing was implemented in clinics for patients with signs and symptoms of Coronavirus Disease 2019 (COVID-19). Initial antibody testing was done on June 10 and July 13, 2020 (phase 1) and retesting was done for previously positive patients between December 9 and 17, 2020 (phase 2). Upon obtaining verbal consent, 3.5 ml of pre-dialysis blood samples were taken via vascular access. SARS-CoV-2 antibodies were determined using the emergency use authorized Diazyme DZ-Lite SARS-CoV-2 IgM / IgG CLIA assays with 100% sensitivity and 98% specificity. Detection of formed immune-complexes is achieved with N-(4-amino-butyl)-N-ethyl-isoluminol; the luminescence signal is reported as units per ml (AU/ml), values ≥ 1.00 AU/ml are considered as “reactive” and < 1.00 AU/ml as “non-reactive.” Results A total of 429 MHD patients were studied in phase 1. Antibodies were present in 130 (30.3%) and only 55 patients with Covid-19 diagnosis confirmed by RT-PCR test were reactive for IgG antibodies. The time to antibody testing was 73 days (median 77; range 30-111) days. In the phase 2 antibody testing, IgG antibodies were only detected in 47 patients (85.5%) 242 days (median 245, range 204 to 268) after clinical diagnosis of Covid-19. Between the two phases of antibody testing, the luminescence signal declined by 40.9 AU/mL (95% confidence interval 31.5 to 50.3) from 54.1±45.3 to 13.2±20.9 AU/mL (P<0.0001 by paired t-test; Figure 1). In univariate logistic regression, a higher number of days between clinical diagnosis of COVID-19 and the second antibody measurement was associated with a lower seropositivity rate (odds ratio 0.929, 95% confidence interval 0.864 to 0.998, P=0.044). Antibody persistence was not associated with age, gender, race, and ethnicity. Conclusion We observed that about 6 out of 7 MHD patients maintain SARS-CoV-2 antibodies over 6-9 months but there is a significant decline of IgG level. The time between clinical diagnosis and IgG testing was associated with IgG decline. Follow up study to understand antibody dynamics in MHD population is a crucial step once vaccines become available.

scholarly journals Approach to the Highest HXR Yield in Plasma Focus Device Using Adaptive Neurofuzzy Inference System to Optimize Anode Configuration

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
M. Mahtab ◽  
M. Taghipour ◽  
G. H. Roshani ◽  
M. Habibi
Keyword(s):  
Relative Error ◽  
Plasma Focus ◽  
Low Cost ◽  
Discharge Voltage ◽  
Inference System ◽  
X Ray ◽  
Mean Relative Error ◽  
Testing Data ◽  
Adaptive Neurofuzzy Inference System ◽  
Good Agreement

Adaptive neurofuzzy inference system (ANFIS) is investigated to optimize the configuration of anode shape in plasma focus devices to achieve the highest X-ray yield. Variables of discharge voltage, filling gas pressure, and angles of anode slopes (Φ1 and Φ2) are chosen as input parameters, while the output is designated to be the radiated hard X-ray intensity. The trained ANFIS has achieved good agreement with the experimental results and has mean relative error percentages (MRE%) 1.12% and 2.18% for training and testing data, respectively. The study demonstrates that adaptive neurofuzzy inference system is useful, reliable, and low-cost way to interpret the highest X-ray yield and corresponding anode configuration in plasma focus devices.

scholarly journals Evaluation of Nowcasting for Real-Time COVID-19 Tracking — New York City, March–May 2020

2020 ◽  
Author(s):  
Sharon K. Greene ◽  
Sarah F. McGough ◽  
Gretchen M. Culp ◽  
Laura E. Graf ◽  
Marc Lipsitch ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
Real Time ◽  
York City ◽  
Negative Binomial ◽  
Situational Awareness ◽  
Prediction Interval ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Moving Window

AbstractTo account for delays between specimen collection and report, the New York City Department of Health and Mental Hygiene used a time-correlated Bayesian nowcasting approach to support real-time COVID-19 situational awareness. We retrospectively evaluated nowcasting performance for case counts among residents diagnosed during March–May 2020, a period when the median reporting delay was 2 days. Nowcasts with a 2-week moving window and a negative binomial distribution had lower mean absolute error, lower relative root mean square error, and higher 95% prediction interval coverage than nowcasts conducted with a 3-week moving window or with a Poisson distribution. Nowcasts conducted toward the end of the week outperformed nowcasts performed earlier in the week, given fewer patients diagnosed on weekends and lack of day-of-week adjustments. When estimating case counts for weekdays only, metrics were similar across days the nowcasts were conducted, with Mondays having the lowest mean absolute error, of 183 cases in the context of an average daily weekday case count of 2,914. Nowcasting ensured that recent decreases in observed case counts were not overinterpreted as true declines and supported health department leadership in anticipating the magnitude and timing of hospitalizations and deaths and allocating resources geographically.

scholarly journals Nowcasting for Real-Time COVID-19 Tracking in New York City: An Evaluation Using Reportable Disease Data From Early in the Pandemic (Preprint)

2020 ◽  
Author(s):  
Sharon K Greene ◽  
Sarah F McGough ◽  
Gretchen M Culp ◽  
Laura E Graf ◽  
Marc Lipsitch ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
Real Time ◽  
York City ◽  
Negative Binomial ◽  
Prediction Interval ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Moving Window ◽  
Trend Monitoring

BACKGROUND Nowcasting approaches enhance the utility of reportable disease data for trend monitoring by correcting for delays, but implementation details affect accuracy. OBJECTIVE To support real-time COVID-19 situational awareness, the New York City Department of Health and Mental Hygiene used nowcasting to account for testing and reporting delays. We conducted an evaluation to determine which implementation details would yield the most accurate estimated case counts. METHODS A time-correlated Bayesian approach called Nowcasting by Bayesian Smoothing (NobBS) was applied in real time to line lists of reportable disease surveillance data, accounting for the delay from diagnosis to reporting and the shape of the epidemic curve. We retrospectively evaluated nowcasting performance for confirmed case counts among residents diagnosed during the period from March to May 2020, a period when the median reporting delay was 2 days. RESULTS Nowcasts with a 2-week moving window and a negative binomial distribution had lower mean absolute error, lower relative root mean square error, and higher 95% prediction interval coverage than nowcasts conducted with a 3-week moving window or with a Poisson distribution. Nowcasts conducted toward the end of the week outperformed nowcasts performed earlier in the week, given fewer patients diagnosed on weekends and lack of day-of-week adjustments. When estimating case counts for weekdays only, metrics were similar across days when the nowcasts were conducted, with Mondays having the lowest mean absolute error of 183 cases in the context of an average daily weekday case count of 2914. CONCLUSIONS Nowcasting using NobBS can effectively support COVID-19 trend monitoring. Accounting for overdispersion, shortening the moving window, and suppressing diagnoses on weekends—when fewer patients submitted specimens for testing—improved the accuracy of estimated case counts. Nowcasting ensured that recent decreases in observed case counts were not overinterpreted as true declines and supported officials in anticipating the magnitude and timing of hospitalizations and deaths and allocating resources geographically.

Contactless Core-Temperature Monitoring by Infrared Thermal Sensor using Mean Absolute Error Analysis

2020 ◽  
Vol 15 ◽  
Author(s):  
Fahad Layth Malallah ◽  
Baraa T. Shareef ◽  
Mustafah Ghanem Saeed ◽  
Khaled N. Yasen
Keyword(s):  
Body Temperature ◽  
Embedded System ◽  
Core Temperature ◽  
Integrated Circuit ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Thermal Sensor ◽  
Human Face ◽  
Body Contact ◽  
Arduino Microcontroller

Aims: Normally, the temperature increase of individuals leads to the possibility of getting a type of disease, which might be risky to other people such as coronavirus. Traditional techniques for tracking core-temperature require body contact either by oral, rectum, axillary, or tympanic, which are unfortunately considered intrusive in nature as well as causes of contagion. Therefore, sensing human core-temperature non-intrusively and remotely is the objective of this research. Background: Nowadays, increasing level of medical sectors is a necessary targets for the research operations, especially with the development of the integrated circuit, sensors and cameras that made the normal life easier. Methods: The solution is by proposing an embedded system consisting of the Arduino microcontroller, which is trained with a model of Mean Absolute Error (MAE) analysis for predicting Contactless Core-Temperature (CCT), which is the real body temperature. Results: The Arduino is connected to an Infrared-Thermal sensor named MLX90614 as input signal, and connected to the LCD to display the CCT. To evaluate the proposed system, experiments are conducted by participating 31-subject sensing contactless temperature from the three face sub-regions: forehead, nose, and cheek. Conclusion: Experimental results approved that CCT can be measured remotely depending on the human face, in which the forehead region is better to be dependent, rather than nose and cheek regions for CCT measurement due to the smallest

scholarly journals Modeling Spatiotemporal Population Changes by Integrating DMSP-OLS and NPP-VIIRS Nighttime Light Data in Chongqing, China

2021 ◽  
Vol 13 (2) ◽  
pp. 284
Author(s):  
Dan Lu ◽  
Yahui Wang ◽  
Qingyuan Yang ◽  
Kangchuan Su ◽  
Haozhe Zhang ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Relative Error ◽  
Urban Areas ◽  
Large Scale ◽  
Population Distribution ◽  
Spatial Optimization ◽  
Distribution Data ◽  
Mountainous Areas ◽  
Mean Relative Error ◽  
Nighttime Light

The sustained growth of non-farm wages has led to large-scale migration of rural population to cities in China, especially in mountainous areas. It is of great significance to study the spatial and temporal pattern of population migration mentioned above for guiding population spatial optimization and the effective supply of public services in the mountainous areas. Here, we determined the spatiotemporal evolution of population in the Chongqing municipality of China from 2000–2018 by employing multi-period spatial distribution data, including nighttime light (NTL) data from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). There was a power function relationship between the two datasets at the pixel scale, with a mean relative error of NTL integration of 8.19%, 4.78% less than achieved by a previous study at the provincial scale. The spatial simulations of population distribution achieved a mean relative error of 26.98%, improved the simulation accuracy for mountainous population by nearly 20% and confirmed the feasibility of this method in Chongqing. During the study period, the spatial distribution of Chongqing’s population has increased in the west and decreased in the east, while also increased in low-altitude areas and decreased in medium-high altitude areas. Population agglomeration was common in all of districts and counties and the population density of central urban areas and its surrounding areas significantly increased, while that of non-urban areas such as northeast Chongqing significantly decreased.

scholarly journals Towards Tracking of Deep Brain Stimulation Electrodes Using an Integrated Magnetometer

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2670
Author(s):  
Thomas Quirin ◽  
Corentin Féry ◽  
Dorian Vogel ◽  
Céline Vergne ◽  
Mathieu Sarracanie ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Mean Absolute Error ◽  
Tracking System ◽  
Three Dimensional ◽  
Absolute Error ◽  
Magnetic Tracking ◽  
Magnetic Camera ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Brain

This paper presents a tracking system using magnetometers, possibly integrable in a deep brain stimulation (DBS) electrode. DBS is a treatment for movement disorders where the position of the implant is of prime importance. Positioning challenges during the surgery could be addressed thanks to a magnetic tracking. The system proposed in this paper, complementary to existing procedures, has been designed to bridge preoperative clinical imaging with DBS surgery, allowing the surgeon to increase his/her control on the implantation trajectory. Here the magnetic source required for tracking consists of three coils, and is experimentally mapped. This mapping has been performed with an in-house three-dimensional magnetic camera. The system demonstrates how magnetometers integrated directly at the tip of a DBS electrode, might improve treatment by monitoring the position during and after the surgery. The three-dimensional operation without line of sight has been demonstrated using a reference obtained with magnetic resonance imaging (MRI) of a simplified brain model. We observed experimentally a mean absolute error of 1.35 mm and an Euclidean error of 3.07 mm. Several areas of improvement to target errors below 1 mm are also discussed.

scholarly journals A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3719
Author(s):  
Aoxin Ni ◽  
Arian Azarang ◽  
Nasser Kehtarnavaz
Keyword(s):  
Heart Rate ◽  
Deep Learning ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Video Camera ◽  
Measurement Methods ◽  
Rate Measurement ◽  
Learning Methods ◽  
The Public ◽  
Heart Rate Measurement

The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning methods have been used to improve the performance of conventional contactless methods for heart rate measurement. After providing a review of the related literature, a comparison of the deep learning methods whose codes are publicly available is conducted in this paper. The public domain UBFC dataset is used to compare the performance of these deep learning methods for heart rate measurement. The results obtained show that the deep learning method PhysNet generates the best heart rate measurement outcome among these methods, with a mean absolute error value of 2.57 beats per minute and a mean square error value of 7.56 beats per minute.

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