Epidemiological Models Lacking Process Noise Can Be Overconfident

Lavi Shpigelman; Michal Chorev; Zeev Waks; Ya'Ara Goldschmidt; Edwin Michael

doi:10.3233/978-1-61499-753-5-136

Epidemiological Models Lacking Process Noise Can Be Overconfident

Lavi Shpigelman^*
, Michal Chorev
, Zeev Waks
, Ya'Ara Goldschmidt
, Edwin Michael

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

Mathematic models of epidemics are the key tool for predicting future course of disease in a population and analyzing the effects of possible intervention policies. Typically, models that produce deterministic are applied for making predictions and reaching decisions. Stochastic modeling methods present an alternative. Here, we demonstrate by example why it is important that stochastic modeling be used in population health decision support systems.

Original language	English
Title of host publication	Informatics for Health
Subtitle of host publication	Connected Citizen-Led Wellness and Population Health
Editors	Rebecca Randell, Ronald Cornet, Philip J. Scott, Ronald Cornet, Niels Peek, Colin McCowan
Publisher	IOS Press
Pages	136-140
Number of pages	5
ISBN (Electronic)	9781614997528
DOIs	https://doi.org/10.3233/978-1-61499-753-5-136
State	Published - 2017
Externally published	Yes

Publication series

Name	Studies in Health Technology and Informatics
Volume	235
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Bibliographical note

Publisher Copyright:
© 2017 European Federation for Medical Informatics (EFMI) and IOS Press.

Keywords

Epidemiological models
Stochastic processes

Access to Document

10.3233/978-1-61499-753-5-136

Cite this

Shpigelman, L., Chorev, M., Waks, Z., Goldschmidt, YA., & Michael, E. (2017). Epidemiological Models Lacking Process Noise Can Be Overconfident. In R. Randell, R. Cornet, P. J. Scott, R. Cornet, N. Peek, & C. McCowan (Eds.), Informatics for Health: Connected Citizen-Led Wellness and Population Health (pp. 136-140). (Studies in Health Technology and Informatics; Vol. 235). IOS Press. https://doi.org/10.3233/978-1-61499-753-5-136

Shpigelman, Lavi ; Chorev, Michal ; Waks, Zeev et al. / Epidemiological Models Lacking Process Noise Can Be Overconfident. Informatics for Health: Connected Citizen-Led Wellness and Population Health. editor / Rebecca Randell ; Ronald Cornet ; Philip J. Scott ; Ronald Cornet ; Niels Peek ; Colin McCowan. IOS Press, 2017. pp. 136-140 (Studies in Health Technology and Informatics).

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year = "2017",

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language = "אנגלית",

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Shpigelman, L, Chorev, M, Waks, Z, Goldschmidt, YA & Michael, E 2017, Epidemiological Models Lacking Process Noise Can Be Overconfident. in R Randell, R Cornet, PJ Scott, R Cornet, N Peek & C McCowan (eds), Informatics for Health: Connected Citizen-Led Wellness and Population Health. Studies in Health Technology and Informatics, vol. 235, IOS Press, pp. 136-140. https://doi.org/10.3233/978-1-61499-753-5-136

Epidemiological Models Lacking Process Noise Can Be Overconfident. / Shpigelman, Lavi; Chorev, Michal; Waks, Zeev et al.
Informatics for Health: Connected Citizen-Led Wellness and Population Health. ed. / Rebecca Randell; Ronald Cornet; Philip J. Scott; Ronald Cornet; Niels Peek; Colin McCowan. IOS Press, 2017. p. 136-140 (Studies in Health Technology and Informatics; Vol. 235).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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T1 - Epidemiological Models Lacking Process Noise Can Be Overconfident

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AU - Chorev, Michal

AU - Waks, Zeev

AU - Goldschmidt, Ya'Ara

AU - Michael, Edwin

PY - 2017

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AB - Mathematic models of epidemics are the key tool for predicting future course of disease in a population and analyzing the effects of possible intervention policies. Typically, models that produce deterministic are applied for making predictions and reaching decisions. Stochastic modeling methods present an alternative. Here, we demonstrate by example why it is important that stochastic modeling be used in population health decision support systems.

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KW - Stochastic processes

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BT - Informatics for Health

A2 - Randell, Rebecca

A2 - Cornet, Ronald

A2 - Scott, Philip J.

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A2 - McCowan, Colin

PB - IOS Press

ER -

Shpigelman L, Chorev M, Waks Z, Goldschmidt YA, Michael E. Epidemiological Models Lacking Process Noise Can Be Overconfident. In Randell R, Cornet R, Scott PJ, Cornet R, Peek N, McCowan C, editors, Informatics for Health: Connected Citizen-Led Wellness and Population Health. IOS Press. 2017. p. 136-140. (Studies in Health Technology and Informatics). doi: 10.3233/978-1-61499-753-5-136

Epidemiological Models Lacking Process Noise Can Be Overconfident

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