scholarly journals Predicting Long-term Evolution of COVID-19 by On-going Data using Bayesian Susceptible-Infected-Removed Model

2020 ◽  
Author(s):  
Shohei Hidaka ◽  
Takuma Torii
Keyword(s):  
Noisy Data ◽  
Sir Model ◽  
Current Data ◽  
Accurate Estimation ◽  
Cumulative Number ◽  
Term Evolution ◽  
Large Numbers ◽  
Simulated Test ◽  
Day By Day

AbstractIn this study, we propose a novel statistical method to predict a long-term epidemic evolution based on a on-going data. We developed a Bayesian framework for the Susceptible-Infected-Removed model (Bayesian SIR), and estimated its underlying parameters based on day-by-day timeseries of the cumulative number of infectious individuals. The new Baysian framework extends the deterministic SIR model to a probabilistic form, which provides an accurate estimation of the underlying system by a short and noisy data. We applied it to the data reported on the Coronavirus Disease 2019 (COVID-19), and made a month long prediction on its evolution. Our simulated test using past timeseries to predict the current data gives a reasonable reliablity of the proposed method. Our analysis of the current data detected and warned a rising trend in the countries in Central Asia, Middle East, and South America, while United States or European countries, which have already experienced large numbers of infected cases, are predicted to slow down in the increase.

scholarly journals Predicting the cumulative medical load of COVID-19 outbreaks after the peak in daily fatalities

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0247272
Author(s):  
Claudius Gros ◽  
Roser Valenti ◽  
Lukas Schneider ◽  
Benedikt Gutsche ◽  
Dimitrije Marković
Keyword(s):  
Phase Space ◽  
Sir Model ◽  
Reaction Model ◽  
Space Representation ◽  
Cumulative Number ◽  
Short Term ◽  
Before And After ◽  
The Status ◽  
Short And Long Term

The distinct ways the COVID-19 pandemic has been unfolding in different countries and regions suggest that local societal and governmental structures play an important role not only for the baseline infection rate, but also for short and long-term reactions to the outbreak. We propose to investigate the question of how societies as a whole, and governments in particular, modulate the dynamics of a novel epidemic using a generalization of the SIR model, the reactive SIR (short-term and long-term reaction) model. We posit that containment measures are equivalent to a feedback between the status of the outbreak and the reproduction factor. Short-term reaction to an outbreak corresponds in this framework to the reaction of governments and individuals to daily cases and fatalities. The reaction to the cumulative number of cases or deaths, and not to daily numbers, is captured in contrast by long-term reaction. We present the exact phase space solution of the controlled SIR model and use it to quantify containment policies for a large number of countries in terms of short and long-term control parameters. We find increased contributions of long-term control for countries and regions in which the outbreak was suppressed substantially together with a strong correlation between the strength of societal and governmental policies and the time needed to contain COVID-19 outbreaks. Furthermore, for numerous countries and regions we identified a predictive relation between the number of fatalities within a fixed period before and after the peak of daily fatality counts, which allows to gauge the cumulative medical load of COVID-19 outbreaks that should be expected after the peak. These results suggest that the proposed model is applicable not only for understanding the outbreak dynamics, but also for predicting future cases and fatalities once the effectiveness of outbreak suppression policies is established with sufficient certainty. Finally, we provide a web app (https://itp.uni-frankfurt.de/covid-19/) with tools for visualising the phase space representation of real-world COVID-19 data and for exporting the preprocessed data for further analysis.

scholarly journals Investigating the long-term evolution of galaxies: Noise, cuspy halos and bars

2003 ◽  
Vol 208 ◽  
pp. 215-226
Author(s):  
Martin D. Weinberg
Keyword(s):  
Galaxy Evolution ◽  
First Generation ◽  
Qualitative Difference ◽  
Halo Structure ◽  
Stable Pattern ◽  
Term Evolution ◽  
Large Numbers ◽  
Pattern Speed ◽  
Low Torque

I review the arguments for the importance of halo structure in driving galaxy evolution and coupling a galaxy to its environment. We begin with a general discussion of the key dynamics and examples of structure dominated by modes. We find that simulations with large numbers of particles (N ≳ 106) are required to resolve the dynamics. Finally, I will describe some new results which demonstrates that a disk bar can produce cores in a cuspy CDM dark-matter profile within a gigayear. An inner Lindblad-like resonance couples the rotating bar to halo orbits at all radii through the cusp, rapidly flattening it. This resonance disappears for profiles with cores and is responsible for a qualitative difference in bar-driven halo evolution with and without a cusp. Although the bar gives up the angular momentum in its pattern to make the core, the formation epoch is rich in accretion events to recreate or trigger a classic stellar bar. The evolution of the cuspy inner halo by the first-generation bar paves the way for a long-lived subsequent bar with low torque and a stable pattern speed.

Inverse Problem for Identification of Infectivity and Recovery Rates in SIR Epidemic Models as Functions of Time Illustrated  with Corona Virus Dynamics  up to July 09, 2020

2020 ◽  
Author(s):  
Tchavdar Marinov ◽  
Rossitza Marinova
Keyword(s):  
Inverse Problem ◽  
United States Of America ◽  
Real Data ◽  
The United States ◽  
Sir Model ◽  
Recovery Rates ◽  
Sir Epidemic ◽  
Term Evolution ◽  
Sir Epidemic Models

Abstract This work deals with the inverse problem in epidemiology based on a SIR model with time-dependent infectivity and recovery rates, allowing for a better prediction of the long term evolution of a pandemic. The method is used for investigating the COVID-19 spread by first solving an inverse problem for estimating the infectivity and recovery rates from real data. Then, the estimated rates are used to compute the evolution of the disease. The time-depended parameters are estimated for the World and several countries (The United States of America, Canada, Italy, France, Germany, Sweden, Russia, Brazil, Bulgaria, Japan, South Korea, New Zealand) and used for investigating the COVID-19 spread in these countries.

scholarly journals Predicting the cumulative medical load of COVID-19 outbreaks after the peak in daily fatalities

2020 ◽  
Author(s):  
Claudius Gros ◽  
Roser Valenti ◽  
Lukas Schneider ◽  
Benedikt Gutsche ◽  
Dimitrije Markovic
Keyword(s):  
Sir Model ◽  
Cumulative Number ◽  
Hospitalised Patients ◽  
Before And After ◽  
The Status ◽  
Containment Measures ◽  
Short And Long Term ◽  
The Relationship ◽  
Fixed Period

The distinct ways the COVID-19 pandemics has been unfolding in different countries and regions suggest that local societal and governmental structures play an essential role both for the baseline infection rate and the short-term and long-term reaction to the outbreak. Here we investigate how societies as a whole, and governments, in particular, modulate the dynamics of a novel epidemic using a generalisation of the SIR model, the controlled SIR model. We posit that containment measures correspond to feedback between the status of the outbreak (the daily or the cumulative number of cases and fatalities) and the reproduction factor. We present the exact phase space solution of the controlled SIR model and use it to quantify containment policies for a large number of countries in terms of short- and long-term control parameters. Furthermore, we identified for numerous countries a relationship between the number of fatalities within a fixed period before and after the peak in daily fatalities. As the number of fatalities corresponds to the number of hospitalised patients, the relationship can be used to predict the cumulative medical load, once the effectiveness of outbreak suppression policies is established with sufficient certainty.

Computer Analyses in Preventive Health Research

1967 ◽  
Vol 06 (01) ◽  
pp. 8-14 ◽  
Author(s):  
M. F. Collen
Keyword(s):  
Medical Research ◽  
Preventive Medicine ◽  
Health Research ◽  
Large Scale ◽  
Preventive Health ◽  
New Era ◽  
Large Numbers ◽  
Normal Test ◽  
Computer Analyses

The utilization of an automated multitest laboratory as a data acquisition center and of a computer for trie data processing and analysis permits large scale preventive medical research previously not feasible. Normal test values are easily generated for the particular population studied. Long-term epidemiological research on large numbers of persons becomes practical. It is our belief that the advent of automation and computers has introduced a new era of preventive medicine.

Link-Level Simulator for Wireless local Area Network

2018 ◽  
Vol 6 (7) ◽  
pp. 314
Author(s):  
Chaithra. H. U ◽  
Vani H.R
Keyword(s):  
Wireless Local Area Network ◽  
Local Area Network ◽  
Local Area Networks ◽  
Local Area ◽  
Long Term Evolution ◽  
Wireless Local Area Networks ◽  
Area Network ◽  
Network Simulator ◽  
Term Evolution

Now a days in Wireless Local Area Networks (WLANs) used in different fields because its well-suited simulator and higher flexibility. The concept of WLAN  with  advanced 5th Generation technologies, related to a Internet-of-Thing (IOT). In this project, representing the Network Simulator (NS-2) used linked-level simulators for Wireless Local Area Networks and still utilized IEEE 802.11g/n/ac with advanced IEEE 802.11ah/af technology. Realization of the whole Wireless Local Area Networking linked-level simulators inspired by the recognized Vienna Long Term Evolution- simulators. As a outcome, this is achieved to link together that simulator to detailed performances of Wireless Local Area Networking with Long Term Evolution, operated in the similar RF bands. From the advanced 5th Generation support cellular networking, such explore is main because different coexistences scenario can arise linking wireless communicating system to the ISM and UHF bands.

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