Forecasting methods and models of disease spread

The number of papers addressing the forecasting of the infectious disease morbidity is rapidly growing due to accumulation of available statistical data. This article surveys the major approaches for the shortterm and the long-term morbidity forecasting. Their limitations and the practical applicati...

Full description

Bibliographic Details
Main Author:	Mikhail Alexandrovich Kondratyev
Format:	Article
Language:	Russian
Published:	Institute of Computer Science 2013-10-01
Series:	Компьютерные исследования и моделирование
Subjects:	morbidity forecasting point-to-point estimates regression models ARIMA hidden Markov models method of analogues exponential smoothing SIR Rvachev–Baroyan model cellular automata populationbased models agent-based models
Online Access:	http://crm.ics.org.ru/uploads/crmissues/crm_2013_5/13509.pdf

id	doaj-d057861e8d474304a7d9951452d1008d
record_format	Article
spelling	doaj-d057861e8d474304a7d9951452d1008d2020-11-25T01:54:35ZrusInstitute of Computer ScienceКомпьютерные исследования и моделирование2076-76332077-68532013-10-015586388210.20537/2076-7633-2013-5-5-863-8822089Forecasting methods and models of disease spreadMikhail Alexandrovich KondratyevThe number of papers addressing the forecasting of the infectious disease morbidity is rapidly growing due to accumulation of available statistical data. This article surveys the major approaches for the shortterm and the long-term morbidity forecasting. Their limitations and the practical application possibilities are pointed out. The paper presents the conventional time series analysis methods - regression and autoregressive models; machine learning-based approaches - Bayesian networks and artificial neural networks; case-based reasoning; filtration-based techniques. The most known mathematical models of infectious diseases are mentioned: classical equation-based models (deterministic and stochastic), modern simulation models (network and agent-based).http://crm.ics.org.ru/uploads/crmissues/crm_2013_5/13509.pdfmorbidity forecastingpoint-to-point estimatesregression modelsARIMAhidden Markov modelsmethod of analoguesexponential smoothingSIRRvachev–Baroyan modelcellular automatapopulationbased modelsagent-based models
collection	DOAJ
language	Russian
format	Article
sources	DOAJ
author	Mikhail Alexandrovich Kondratyev
spellingShingle	Mikhail Alexandrovich Kondratyev Forecasting methods and models of disease spread Компьютерные исследования и моделирование morbidity forecasting point-to-point estimates regression models ARIMA hidden Markov models method of analogues exponential smoothing SIR Rvachev–Baroyan model cellular automata populationbased models agent-based models
author_facet	Mikhail Alexandrovich Kondratyev
author_sort	Mikhail Alexandrovich Kondratyev
title	Forecasting methods and models of disease spread
title_short	Forecasting methods and models of disease spread
title_full	Forecasting methods and models of disease spread
title_fullStr	Forecasting methods and models of disease spread
title_full_unstemmed	Forecasting methods and models of disease spread
title_sort	forecasting methods and models of disease spread
publisher	Institute of Computer Science
series	Компьютерные исследования и моделирование
issn	2076-7633 2077-6853
publishDate	2013-10-01
description	The number of papers addressing the forecasting of the infectious disease morbidity is rapidly growing due to accumulation of available statistical data. This article surveys the major approaches for the shortterm and the long-term morbidity forecasting. Their limitations and the practical application possibilities are pointed out. The paper presents the conventional time series analysis methods - regression and autoregressive models; machine learning-based approaches - Bayesian networks and artificial neural networks; case-based reasoning; filtration-based techniques. The most known mathematical models of infectious diseases are mentioned: classical equation-based models (deterministic and stochastic), modern simulation models (network and agent-based).
topic	morbidity forecasting point-to-point estimates regression models ARIMA hidden Markov models method of analogues exponential smoothing SIR Rvachev–Baroyan model cellular automata populationbased models agent-based models
url	http://crm.ics.org.ru/uploads/crmissues/crm_2013_5/13509.pdf
work_keys_str_mv	AT mikhailalexandrovichkondratyev forecastingmethodsandmodelsofdiseasespread
_version_	1724986539204673536

Forecasting methods and models of disease spread

Similar Items