SARS-COV-2: SIR Model Limitations and Predictive Constraints

Background: The main purpose of this research is to describe the mathematical asymmetric patterns of susceptible, infectious, or recovered (SIR) model equation application in the light of coronavirus disease 2019 (COVID-19) skewness patterns worldwide. Methods: The research modeled severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) spreading and dissemination patterns sensitivity by redesigning time series data extraction of daily new cases in terms of deviation consistency concerning variables that sustain COVID-19 transmission. The approach opened a new scenario where seasonality forcing behavior was introduced to understand SARS-COV-2 non-linear dynamics due to heterogeneity and confounding epidemics scenarios. Results: The main research results are the elucidation of three birth- and death-forced seasonality persistence phases that can explain COVID-19 skew patterns worldwide. They are presented in the following order: (1) the environmental variables (Earth seasons and atmospheric conditions); (2) health policies and adult learning education (HPALE) interventions; (3) urban spaces (local indoor and outdoor spaces for transit and social-cultural interactions, public or private, with natural physical features (river, lake, terrain). Conclusions: Three forced seasonality phases (positive to negative skew) phases were pointed out as a theoretical framework to explain uncertainty found in the predictive SIR model equations that might diverge in outcomes expected to express the disease’s behaviour.

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SARS-COV-2 THREE FORCING SEASONALITIES: POLICIES, ENVIRONMENT AND URBAN SPACES

10.1101/2020.07.15.20154823 ◽

2020 ◽

Author(s):

Charles Roberto Telles

Keyword(s):

Time Series Data ◽

Data Extraction ◽

Sir Model ◽

Urban Spaces ◽

Series Data ◽

Predictive Analysis ◽

The Earth ◽

Model Equations ◽

And Control ◽

North And South

This research investigated if pandemic of SARS-COV-2 follows the Earth seasonality ε comparing countries cumulative daily new infections incidence over Earth periodic time of interest for north and south hemisphere. It was found that no seasonality in this form ε occurs as far as a seasonality forcing behavior ε' assumes most of the influence in SARS-COV-2 spreading patterns. Putting in order ε' of influence, there were identified three main forms of SARS-COV-2 of transmission behavior: during epidemics growth, policies are the main stronger seasonality forcing behavior of the epidemics followed by secondary and weaker environmental and urban spaces driving patterns of transmission. At outbreaks and control phase, environmental and urban spaces are the main seasonality forcing behavior due to policies/ALE limitations to address heterogeneity and confounding scenario of infection. Finally regarding S and R compartments of SIR model equations, control phases are the most reliable phase to predictive analysis. These seasonality forcing behaviors cause environmental driven seasonality researches to face hidden or false observations due to policy/ALE interventions for each country and urban spaces characteristics. And also, it causes policies/ALE limitations to address urban spaces and environmental seasonality instabilities, thus generating posterior waves or uncontrolled patterns of transmission (fluctuations). All this components affect the SARS-COV-2 spreading patterns simultaneously being not possible to observe environmental seasonality not associated intrinsically with policies/ALE and urban spaces, therefore conferring to these three forms of transmission spreading patterns, specific regions of analysis for time series data extraction.

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Inference for Ecological Dynamical Systems: A Case Study of Two Endemic Diseases

Computational and Mathematical Methods in Medicine ◽

10.1155/2012/390694 ◽

2012 ◽

Vol 2012 ◽

pp. 1-13

Author(s):

Daniel A. Vasco

Keyword(s):

Time Series ◽

Statistical Theory ◽

Time Series Data ◽

Sample Path ◽

Sir Model ◽

Series Data ◽

Parameter Inference ◽

Transmission Process ◽

Endemic Diseases

A Bayesian Markov chain Monte Carlo method is used to infer parameters for an open stochastic epidemiological model: the Markovian susceptible-infected-recovered (SIR) model, which is suitable for modeling and simulating recurrent epidemics. This allows exploring two major problems of inference appearing in many mechanistic population models. First, trajectories of these processes are often only partly observed. For example, during an epidemic the transmission process is only partly observable: one cannot record infection times. Therefore, one only records cases (infections) as the observations. As a result some means of imputing or reconstructing individuals in the susceptible cases class must be accomplished. Second, the official reporting of observations (cases in epidemiology) is typically done not as they are actually recorded but at some temporal interval over which they have been aggregated. To address these issues, this paper investigates the following problems. Parameter inference for a perfectly sampled open Markovian SIR is first considered. Next inference for an imperfectly observed sample path of the system is studied. Although this second problem has been solved for the case of closed epidemics, it has proven quite difficult for the case of open recurrent epidemics. Lastly, application of the statistical theory is made to measles and pertussis epidemic time series data from 60 UK cities.

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Locality-Based Visual Outlier Detection Algorithm for Time Series

Security and Communication Networks ◽

10.1155/2017/1869787 ◽

2017 ◽

Vol 2017 ◽

pp. 1-10

Author(s):

Zhihua Li ◽

Ziyuan Li ◽

Ning Yu ◽

Steven Wen

Keyword(s):

Time Series ◽

Outlier Detection ◽

Extraction Method ◽

Time Series Data ◽

Data Extraction ◽

Detection Algorithm ◽

Series Data ◽

Practical Application ◽

Detection Model ◽

Efficient Data

Physiological theories indicate that the deepest impression for time series data with respect to the human visual system is its extreme value. Based on this principle, by researching the strategies of extreme-point-based hierarchy segmentation, the hierarchy-segmentation-based data extraction method for time series, and the ideas of locality outlier, a novel outlier detection model and method for time series are proposed. The presented algorithm intuitively labels an outlier factor to each subsequence in time series such that the visual outlier detection gets relatively direct. The experimental results demonstrate the average advantage of the developed method over the compared methods and the efficient data reduction capability for time series, which indicates the promising performance of the proposed method and its practical application value.

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A Parameter-Free Model Comparison Test Using Differential Algebra

Complexity ◽

10.1155/2019/6041981 ◽

2019 ◽

Vol 2019 ◽

pp. 1-15

Author(s):

Heather A. Harrington ◽

Kenneth L. Ho ◽

Nicolette Meshkat

Keyword(s):

Time Course ◽

Model Comparison ◽

Time Series Data ◽

Dynamic Properties ◽

Differential Algebra ◽

Series Data ◽

Comparison Test ◽

Competing Models ◽

Model Equations ◽

Parameter Values

We present a method for rejecting competing models from noisy time-course data that does not rely on parameter inference. First we characterize ordinary differential equation models in only measurable variables using differential-algebra elimination. This procedure gives input-output equations, which serve as invariants for time series data. We develop a model comparison test using linear algebra and statistics to reject incorrect models from their invariants. This algorithm exploits the dynamic properties that are encoded in the structure of the model equations without recourse to parameter values, and, in this sense, the approach is parameter-free. We demonstrate this method by discriminating between different models from mathematical biology.

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Mathematical Modelling of Transmission Dynamics of COVID-19: A Case Study of Nepal

Prithvi Academic Journal ◽

10.3126/paj.v3i1.31283 ◽

2020 ◽

pp. 19-38

Author(s):

Laxman Bahadur Kunwar

Keyword(s):

Public Health Policy ◽

Time Series Data ◽

Sir Model ◽

Transmission Dynamics ◽

Series Data ◽

Basic Reproduction Ratio ◽

Reproduction Ratio ◽

Prime Objective ◽

India And China

In this study, the SIR compartmental mathematical model has been proposed to predict the transmission dynamics of COVID-19 in Nepal. The model is analysed by deriving some important expressions such as the basic reproduction ratio and possible maximum number of infectives in the future. This study examines the applicability of the SIR model for the study of the COVID-19 pandemic and other similar infectious diseases. The prime objective of the study is to analyse and forecast the COVID-19 pandemic in Nepal for the upcoming time. The estimation of the parameters of the model is based upon data from January 20, 2020 to July 14, 2020. The model presented in the paper fitted to the time-series data well for the whole Nepal and its neighbouring countries such as India and China. The findings suggest that there is a potential for this model to contribute to better public health policy in combating COVID-19.

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A Time Series Weather Radar Simulator Based on High-Resolution Atmospheric Models

Journal of Atmospheric and Oceanic Technology ◽

10.1175/2007jtecha923.1 ◽

2008 ◽

Vol 25 (2) ◽

pp. 230-243 ◽

Cited By ~ 35

Author(s):

B. L. Cheong ◽

R. D. Palmer ◽

M. Xue

Keyword(s):

Time Series ◽

High Resolution ◽

Time Series Data ◽

Three Dimensional ◽

Weather Radar ◽

Series Data ◽

Atmospheric Conditions ◽

Moment Maps ◽

Weather Model ◽

Radar Simulator

Abstract A three-dimensional radar simulator capable of generating simulated raw time series data for a weather radar has been designed and implemented. The characteristics of the radar signals (amplitude, phase) are derived from the atmospheric fields from a high-resolution numerical weather model, although actual measured fields could be used. A field of thousands of scatterers is populated within the field of view of the virtual radar. Reflectivity characteristics of the targets are determined from well-known parameterization schemes. Doppler characteristics are derived by forcing the discrete scatterers to move with the three-dimensional wind field. Conventional moment-generating radar simulators use atmospheric conditions and a set of weighting functions to produce theoretical moment maps, which allow for the study of radar characteristics and limitations given particular configurations. In contrast to these radar simulators, the algorithm presented here is capable of producing sample-to-sample time series data that are collected by a radar system of virtually any design. Thus, this new radar simulator allows for the test and analysis of advanced topics, such as phased array antennas, clutter mitigation schemes, waveform design studies, and spectral-based methods. Limited examples exemplifying the usefulness and flexibility of the simulator will be provided.

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Correction: The Impact of COVID-19 Management Policies Tailored to Airborne SARS-CoV-2 Transmission: Policy Analysis (Preprint)

10.2196/preprints.30007 ◽

2021 ◽

Author(s):

Charles Roberto Telles ◽

Archisman Roy ◽

Mohammad Rehan Ajmal ◽

Syed Khalid Mustafa ◽

Mohammad Ayaz Ahmad ◽

...

Keyword(s):

South Korea ◽

Time Series Data ◽

Virus Transmission ◽

Series Data ◽

Social Distancing ◽

Main Research ◽

Johns Hopkins University ◽

Geographical Regions ◽

And Control ◽

The Impact

UNSTRUCTURED Daily new COVID-19 cases from January to April 2020 demonstrate varying patterns of SARS-CoV-2 transmission across different geographical regions. Constant infection rates were observed in some countries, whereas China and South Korea had a very low number of daily new cases. In fact, China and South Korea successfully and quickly flattened their COVID-19 curve. To understand why this was the case, this paper investigated possible aerosol-forming patterns in the atmosphere and their relationship to the policy measures adopted by select countries. The main research objective was to compare the outcomes of policies adopted by countries between January and April 2020. Policies included physical distancing measures that in some cases were associated with mask use and city disinfection. We investigated whether the type of social distancing framework adopted by some countries (ie, without mask use and city disinfection) led to the continual dissemination of SARS-CoV-2 (daily new cases) in the community during the study period. We examined the policies used as a preventive framework for virus community transmission in some countries and compared them to the policies adopted by China and South Korea. Countries that used a policy of social distancing by 1-2 m were divided into two groups. The first group consisted of countries that implemented social distancing (1-2 m) only, and the second comprised China and South Korea, which implemented distancing with additional transmission/isolation measures using masks and city disinfection. Global daily case maps from Johns Hopkins University were used to provide time-series data for the analysis. The results showed that virus transmission was reduced due to policies affecting SARS-CoV-2 propagation over time. Remarkably, China and South Korea obtained substantially better results than other countries at the beginning of the epidemic due to their adoption of social distancing (1-2 m) with the additional use of masks and sanitization (city disinfection). These measures proved to be effective due to the atmosphere carrier potential of SARS-CoV-2 transmission. Our findings confirm that social distancing by 1-2 m with mask use and city disinfection yields positive outcomes. These strategies should be incorporated into prevention and control policies and be adopted both globally and by individuals as a method to fight the COVID-19 pandemic.

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Elevator Fault Detection Using Profile Extraction and Deep Autoencoder Feature Extraction for Acceleration and Magnetic Signals

Applied Sciences ◽

10.3390/app9152990 ◽

2019 ◽

Vol 9 (15) ◽

pp. 2990 ◽

Cited By ~ 3

Author(s):

Krishna Mohan Mishra ◽

Kalevi Huhtala

Keyword(s):

Feature Extraction ◽

Random Forest ◽

Fault Detection ◽

Time Series Data ◽

Data Extraction ◽

False Positives ◽

Sensor Data ◽

Series Data ◽

False Alarms ◽

Maintenance Systems

In this paper, we propose a new algorithm for data extraction from time-series data, and furthermore automatic calculation of highly informative deep features to be used in fault detection. In data extraction, elevator start and stop events are extracted from sensor data including both acceleration and magnetic signals. In addition, a generic deep autoencoder model is also developed for automated feature extraction from the extracted profiles. After this, extracted deep features are classified with random forest algorithm for fault detection. Sensor data are labelled as healthy and faulty based on the maintenance actions recorded. The remaining healthy data are used for validation of the model to prove its efficacy in terms of avoiding false positives. We have achieved above 90% accuracy in fault detection along with avoiding false positives based on new extracted deep features, which outperforms results using existing features. Existing features are also classified with random forest to compare results. Our developed algorithm provides better results due to the new deep features extracted from the dataset when compared to existing features. This research will help various predictive maintenance systems to detect false alarms, which will in turn reduce unnecessary visits of service technicians to installation sites.

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Atmospheric Correction Thresholds for Ground-Based Radar Interferometry Deformation Monitoring Estimated Using Time Series Analyses

Remote Sensing ◽

10.3390/rs12142236 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2236 ◽

Cited By ~ 1

Author(s):

Tomasz Owerko ◽

Przemysław Kuras ◽

Łukasz Ortyl

Keyword(s):

Time Series ◽

Satellite Data ◽

Time Series Data ◽

Learning Algorithm ◽

Atmospheric Correction ◽

Deformation Monitoring ◽

Radar Interferometry ◽

Series Data ◽

Atmospheric Conditions ◽

The Stability

Ground-based radar interferometry (GBSAR) is a useful method to control the stability of engineering objects and elements of geographical spaces at risk of deformation or displacement. To secure accurate and credible measurement results, it is crucial to consider atmospheric conditions as they influence the corrections to distance measurements. These conditions are especially important considering the radar bandwidth used. Measurements for the stability of engineering objects are not always performed in locations where meteorological monitoring is prevalent; however, information about the range of variability in atmospheric corrections is always welcome. The authors present a hybrid method to estimate the probable need of atmospheric corrections, which allows partly eliminating false positive alarms of deformations as caused by atmospheric fluctuations. Unlike the numerous publications on atmospheric reductions focused on the current state of the atmosphere, the proposed solution is based on applying a classic machine learning algorithm designed for the SARIMAX (Seasonal Autoregressive Integrated Moving Average with covariate at time) time series data model for satellite data shared by NASA (National Aeronautics and Space Administration) during the Landsat MODIS (Moderate Resolution Imaging Spectroradiometer) mission before performing residual estimation during the monitoring phase. Example calculations (proof of concept) were made for ten-year satellite data covering a region for experimental flood bank stability observations as performed using the IBIS-L (Image by Interferometric Survey—Landslide) radar and for target monitoring data (ground measurements).

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