Systematic comparison of epidemic growth patterns using two different estimation approaches

Background: Different estimation approaches are frequently used to calibrate mathematical models to epidemiological data, particularly for analyzing infectious disease outbreaks. Here, we use two common methods to estimate parameters that characterize growth patterns using the generalized growth mod...
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Autor*in:	Yiseul Lee [verfasserIn] Kimberlyn Roosa [verfasserIn] Gerardo Chowell [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Parameter estimation Generalized growth model Least squares estimation Maximum likelihood estimation Epidemiological models

Übergeordnetes Werk:	In: Infectious Disease Modelling - KeAi Communications Co., Ltd., 2017, 6(2021), Seite 5-14
Übergeordnetes Werk:	volume:6 ; year:2021 ; pages:5-14

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.idm.2020.10.005

Katalog-ID:	DOAJ051252406

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245	1	0	\|a Systematic comparison of epidemic growth patterns using two different estimation approaches
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520			\|a Background: Different estimation approaches are frequently used to calibrate mathematical models to epidemiological data, particularly for analyzing infectious disease outbreaks. Here, we use two common methods to estimate parameters that characterize growth patterns using the generalized growth model (GGM) calibrated to real outbreak datasets. Materials and methods: Data from 31 outbreaks are used to fit the GGM to the ascending phase of each outbreak and estimate the parameters using both least squares (LSQ) and maximum likelihood estimation (MLE) methods. We utilize parametric bootstrapping to construct confidence intervals for parameter estimates. We compare the results including RMSE, Anscombe residual, and 95% prediction interval coverage. We also evaluate the correlation between the estimates from both methods. Results: Comparing LSQ and MLE estimates, most outbreaks have similar parameter estimates, RMSE, Anscombe, and 95% prediction interval coverage. Parameter estimates do not differ across methods when the model yields a good fit to the early growth phase. However, for two outbreaks, there are systematic deviations in model fit to the data that explain differences in parameter estimates (e.g., residuals represent random error rather than systematic deviation). Conclusion: Our findings indicate that utilizing LSQ and MLE methods produce similar results in the context of characterizing epidemic growth patterns with the GGM, provided that the model yields a good fit to the data.
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allfields	10.1016/j.idm.2020.10.005 doi (DE-627)DOAJ051252406 (DE-599)DOAJfb2c477e004e4807899cc47e06898725 DE-627 ger DE-627 rakwb eng RC109-216 Yiseul Lee verfasserin aut Systematic comparison of epidemic growth patterns using two different estimation approaches 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Different estimation approaches are frequently used to calibrate mathematical models to epidemiological data, particularly for analyzing infectious disease outbreaks. Here, we use two common methods to estimate parameters that characterize growth patterns using the generalized growth model (GGM) calibrated to real outbreak datasets. Materials and methods: Data from 31 outbreaks are used to fit the GGM to the ascending phase of each outbreak and estimate the parameters using both least squares (LSQ) and maximum likelihood estimation (MLE) methods. We utilize parametric bootstrapping to construct confidence intervals for parameter estimates. We compare the results including RMSE, Anscombe residual, and 95% prediction interval coverage. We also evaluate the correlation between the estimates from both methods. Results: Comparing LSQ and MLE estimates, most outbreaks have similar parameter estimates, RMSE, Anscombe, and 95% prediction interval coverage. Parameter estimates do not differ across methods when the model yields a good fit to the early growth phase. However, for two outbreaks, there are systematic deviations in model fit to the data that explain differences in parameter estimates (e.g., residuals represent random error rather than systematic deviation). Conclusion: Our findings indicate that utilizing LSQ and MLE methods produce similar results in the context of characterizing epidemic growth patterns with the GGM, provided that the model yields a good fit to the data. Parameter estimation Generalized growth model Least squares estimation Maximum likelihood estimation Epidemiological models Infectious and parasitic diseases Kimberlyn Roosa verfasserin aut Gerardo Chowell verfasserin aut In Infectious Disease Modelling KeAi Communications Co., Ltd., 2017 6(2021), Seite 5-14 (DE-627)1694093298 (DE-600)3015225-2 24680427 nnns volume:6 year:2021 pages:5-14 https://doi.org/10.1016/j.idm.2020.10.005 kostenfrei https://doaj.org/article/fb2c477e004e4807899cc47e06898725 kostenfrei http://www.sciencedirect.com/science/article/pii/S2468042720300592 kostenfrei https://doaj.org/toc/2468-0427 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2021 5-14
spelling	10.1016/j.idm.2020.10.005 doi (DE-627)DOAJ051252406 (DE-599)DOAJfb2c477e004e4807899cc47e06898725 DE-627 ger DE-627 rakwb eng RC109-216 Yiseul Lee verfasserin aut Systematic comparison of epidemic growth patterns using two different estimation approaches 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Different estimation approaches are frequently used to calibrate mathematical models to epidemiological data, particularly for analyzing infectious disease outbreaks. Here, we use two common methods to estimate parameters that characterize growth patterns using the generalized growth model (GGM) calibrated to real outbreak datasets. Materials and methods: Data from 31 outbreaks are used to fit the GGM to the ascending phase of each outbreak and estimate the parameters using both least squares (LSQ) and maximum likelihood estimation (MLE) methods. We utilize parametric bootstrapping to construct confidence intervals for parameter estimates. We compare the results including RMSE, Anscombe residual, and 95% prediction interval coverage. We also evaluate the correlation between the estimates from both methods. Results: Comparing LSQ and MLE estimates, most outbreaks have similar parameter estimates, RMSE, Anscombe, and 95% prediction interval coverage. Parameter estimates do not differ across methods when the model yields a good fit to the early growth phase. However, for two outbreaks, there are systematic deviations in model fit to the data that explain differences in parameter estimates (e.g., residuals represent random error rather than systematic deviation). Conclusion: Our findings indicate that utilizing LSQ and MLE methods produce similar results in the context of characterizing epidemic growth patterns with the GGM, provided that the model yields a good fit to the data. Parameter estimation Generalized growth model Least squares estimation Maximum likelihood estimation Epidemiological models Infectious and parasitic diseases Kimberlyn Roosa verfasserin aut Gerardo Chowell verfasserin aut In Infectious Disease Modelling KeAi Communications Co., Ltd., 2017 6(2021), Seite 5-14 (DE-627)1694093298 (DE-600)3015225-2 24680427 nnns volume:6 year:2021 pages:5-14 https://doi.org/10.1016/j.idm.2020.10.005 kostenfrei https://doaj.org/article/fb2c477e004e4807899cc47e06898725 kostenfrei http://www.sciencedirect.com/science/article/pii/S2468042720300592 kostenfrei https://doaj.org/toc/2468-0427 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2021 5-14
allfields_unstemmed	10.1016/j.idm.2020.10.005 doi (DE-627)DOAJ051252406 (DE-599)DOAJfb2c477e004e4807899cc47e06898725 DE-627 ger DE-627 rakwb eng RC109-216 Yiseul Lee verfasserin aut Systematic comparison of epidemic growth patterns using two different estimation approaches 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Different estimation approaches are frequently used to calibrate mathematical models to epidemiological data, particularly for analyzing infectious disease outbreaks. Here, we use two common methods to estimate parameters that characterize growth patterns using the generalized growth model (GGM) calibrated to real outbreak datasets. Materials and methods: Data from 31 outbreaks are used to fit the GGM to the ascending phase of each outbreak and estimate the parameters using both least squares (LSQ) and maximum likelihood estimation (MLE) methods. We utilize parametric bootstrapping to construct confidence intervals for parameter estimates. We compare the results including RMSE, Anscombe residual, and 95% prediction interval coverage. We also evaluate the correlation between the estimates from both methods. Results: Comparing LSQ and MLE estimates, most outbreaks have similar parameter estimates, RMSE, Anscombe, and 95% prediction interval coverage. Parameter estimates do not differ across methods when the model yields a good fit to the early growth phase. However, for two outbreaks, there are systematic deviations in model fit to the data that explain differences in parameter estimates (e.g., residuals represent random error rather than systematic deviation). Conclusion: Our findings indicate that utilizing LSQ and MLE methods produce similar results in the context of characterizing epidemic growth patterns with the GGM, provided that the model yields a good fit to the data. Parameter estimation Generalized growth model Least squares estimation Maximum likelihood estimation Epidemiological models Infectious and parasitic diseases Kimberlyn Roosa verfasserin aut Gerardo Chowell verfasserin aut In Infectious Disease Modelling KeAi Communications Co., Ltd., 2017 6(2021), Seite 5-14 (DE-627)1694093298 (DE-600)3015225-2 24680427 nnns volume:6 year:2021 pages:5-14 https://doi.org/10.1016/j.idm.2020.10.005 kostenfrei https://doaj.org/article/fb2c477e004e4807899cc47e06898725 kostenfrei http://www.sciencedirect.com/science/article/pii/S2468042720300592 kostenfrei https://doaj.org/toc/2468-0427 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2021 5-14
allfieldsGer	10.1016/j.idm.2020.10.005 doi (DE-627)DOAJ051252406 (DE-599)DOAJfb2c477e004e4807899cc47e06898725 DE-627 ger DE-627 rakwb eng RC109-216 Yiseul Lee verfasserin aut Systematic comparison of epidemic growth patterns using two different estimation approaches 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Different estimation approaches are frequently used to calibrate mathematical models to epidemiological data, particularly for analyzing infectious disease outbreaks. Here, we use two common methods to estimate parameters that characterize growth patterns using the generalized growth model (GGM) calibrated to real outbreak datasets. Materials and methods: Data from 31 outbreaks are used to fit the GGM to the ascending phase of each outbreak and estimate the parameters using both least squares (LSQ) and maximum likelihood estimation (MLE) methods. We utilize parametric bootstrapping to construct confidence intervals for parameter estimates. We compare the results including RMSE, Anscombe residual, and 95% prediction interval coverage. We also evaluate the correlation between the estimates from both methods. Results: Comparing LSQ and MLE estimates, most outbreaks have similar parameter estimates, RMSE, Anscombe, and 95% prediction interval coverage. Parameter estimates do not differ across methods when the model yields a good fit to the early growth phase. However, for two outbreaks, there are systematic deviations in model fit to the data that explain differences in parameter estimates (e.g., residuals represent random error rather than systematic deviation). Conclusion: Our findings indicate that utilizing LSQ and MLE methods produce similar results in the context of characterizing epidemic growth patterns with the GGM, provided that the model yields a good fit to the data. Parameter estimation Generalized growth model Least squares estimation Maximum likelihood estimation Epidemiological models Infectious and parasitic diseases Kimberlyn Roosa verfasserin aut Gerardo Chowell verfasserin aut In Infectious Disease Modelling KeAi Communications Co., Ltd., 2017 6(2021), Seite 5-14 (DE-627)1694093298 (DE-600)3015225-2 24680427 nnns volume:6 year:2021 pages:5-14 https://doi.org/10.1016/j.idm.2020.10.005 kostenfrei https://doaj.org/article/fb2c477e004e4807899cc47e06898725 kostenfrei http://www.sciencedirect.com/science/article/pii/S2468042720300592 kostenfrei https://doaj.org/toc/2468-0427 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2021 5-14
allfieldsSound	10.1016/j.idm.2020.10.005 doi (DE-627)DOAJ051252406 (DE-599)DOAJfb2c477e004e4807899cc47e06898725 DE-627 ger DE-627 rakwb eng RC109-216 Yiseul Lee verfasserin aut Systematic comparison of epidemic growth patterns using two different estimation approaches 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Different estimation approaches are frequently used to calibrate mathematical models to epidemiological data, particularly for analyzing infectious disease outbreaks. Here, we use two common methods to estimate parameters that characterize growth patterns using the generalized growth model (GGM) calibrated to real outbreak datasets. Materials and methods: Data from 31 outbreaks are used to fit the GGM to the ascending phase of each outbreak and estimate the parameters using both least squares (LSQ) and maximum likelihood estimation (MLE) methods. We utilize parametric bootstrapping to construct confidence intervals for parameter estimates. We compare the results including RMSE, Anscombe residual, and 95% prediction interval coverage. We also evaluate the correlation between the estimates from both methods. Results: Comparing LSQ and MLE estimates, most outbreaks have similar parameter estimates, RMSE, Anscombe, and 95% prediction interval coverage. Parameter estimates do not differ across methods when the model yields a good fit to the early growth phase. However, for two outbreaks, there are systematic deviations in model fit to the data that explain differences in parameter estimates (e.g., residuals represent random error rather than systematic deviation). Conclusion: Our findings indicate that utilizing LSQ and MLE methods produce similar results in the context of characterizing epidemic growth patterns with the GGM, provided that the model yields a good fit to the data. Parameter estimation Generalized growth model Least squares estimation Maximum likelihood estimation Epidemiological models Infectious and parasitic diseases Kimberlyn Roosa verfasserin aut Gerardo Chowell verfasserin aut In Infectious Disease Modelling KeAi Communications Co., Ltd., 2017 6(2021), Seite 5-14 (DE-627)1694093298 (DE-600)3015225-2 24680427 nnns volume:6 year:2021 pages:5-14 https://doi.org/10.1016/j.idm.2020.10.005 kostenfrei https://doaj.org/article/fb2c477e004e4807899cc47e06898725 kostenfrei http://www.sciencedirect.com/science/article/pii/S2468042720300592 kostenfrei https://doaj.org/toc/2468-0427 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2021 5-14
language	English
source	In Infectious Disease Modelling 6(2021), Seite 5-14 volume:6 year:2021 pages:5-14
sourceStr	In Infectious Disease Modelling 6(2021), Seite 5-14 volume:6 year:2021 pages:5-14
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topic_facet	Parameter estimation Generalized growth model Least squares estimation Maximum likelihood estimation Epidemiological models Infectious and parasitic diseases
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container_title	Infectious Disease Modelling
authorswithroles_txt_mv	Yiseul Lee @@aut@@ Kimberlyn Roosa @@aut@@ Gerardo Chowell @@aut@@
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Here, we use two common methods to estimate parameters that characterize growth patterns using the generalized growth model (GGM) calibrated to real outbreak datasets. Materials and methods: Data from 31 outbreaks are used to fit the GGM to the ascending phase of each outbreak and estimate the parameters using both least squares (LSQ) and maximum likelihood estimation (MLE) methods. We utilize parametric bootstrapping to construct confidence intervals for parameter estimates. We compare the results including RMSE, Anscombe residual, and 95% prediction interval coverage. We also evaluate the correlation between the estimates from both methods. Results: Comparing LSQ and MLE estimates, most outbreaks have similar parameter estimates, RMSE, Anscombe, and 95% prediction interval coverage. Parameter estimates do not differ across methods when the model yields a good fit to the early growth phase. However, for two outbreaks, there are systematic deviations in model fit to the data that explain differences in parameter estimates (e.g., residuals represent random error rather than systematic deviation). 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callnumber-first	R - Medicine
author	Yiseul Lee
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topic_title	RC109-216 Systematic comparison of epidemic growth patterns using two different estimation approaches Parameter estimation Generalized growth model Least squares estimation Maximum likelihood estimation Epidemiological models
topic	misc RC109-216 misc Parameter estimation misc Generalized growth model misc Least squares estimation misc Maximum likelihood estimation misc Epidemiological models misc Infectious and parasitic diseases
topic_unstemmed	misc RC109-216 misc Parameter estimation misc Generalized growth model misc Least squares estimation misc Maximum likelihood estimation misc Epidemiological models misc Infectious and parasitic diseases
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title	Systematic comparison of epidemic growth patterns using two different estimation approaches
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title_full	Systematic comparison of epidemic growth patterns using two different estimation approaches
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title_sort	systematic comparison of epidemic growth patterns using two different estimation approaches
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title_auth	Systematic comparison of epidemic growth patterns using two different estimation approaches
abstract	Background: Different estimation approaches are frequently used to calibrate mathematical models to epidemiological data, particularly for analyzing infectious disease outbreaks. Here, we use two common methods to estimate parameters that characterize growth patterns using the generalized growth model (GGM) calibrated to real outbreak datasets. Materials and methods: Data from 31 outbreaks are used to fit the GGM to the ascending phase of each outbreak and estimate the parameters using both least squares (LSQ) and maximum likelihood estimation (MLE) methods. We utilize parametric bootstrapping to construct confidence intervals for parameter estimates. We compare the results including RMSE, Anscombe residual, and 95% prediction interval coverage. We also evaluate the correlation between the estimates from both methods. Results: Comparing LSQ and MLE estimates, most outbreaks have similar parameter estimates, RMSE, Anscombe, and 95% prediction interval coverage. Parameter estimates do not differ across methods when the model yields a good fit to the early growth phase. However, for two outbreaks, there are systematic deviations in model fit to the data that explain differences in parameter estimates (e.g., residuals represent random error rather than systematic deviation). Conclusion: Our findings indicate that utilizing LSQ and MLE methods produce similar results in the context of characterizing epidemic growth patterns with the GGM, provided that the model yields a good fit to the data.
abstractGer	Background: Different estimation approaches are frequently used to calibrate mathematical models to epidemiological data, particularly for analyzing infectious disease outbreaks. Here, we use two common methods to estimate parameters that characterize growth patterns using the generalized growth model (GGM) calibrated to real outbreak datasets. Materials and methods: Data from 31 outbreaks are used to fit the GGM to the ascending phase of each outbreak and estimate the parameters using both least squares (LSQ) and maximum likelihood estimation (MLE) methods. We utilize parametric bootstrapping to construct confidence intervals for parameter estimates. We compare the results including RMSE, Anscombe residual, and 95% prediction interval coverage. We also evaluate the correlation between the estimates from both methods. Results: Comparing LSQ and MLE estimates, most outbreaks have similar parameter estimates, RMSE, Anscombe, and 95% prediction interval coverage. Parameter estimates do not differ across methods when the model yields a good fit to the early growth phase. However, for two outbreaks, there are systematic deviations in model fit to the data that explain differences in parameter estimates (e.g., residuals represent random error rather than systematic deviation). Conclusion: Our findings indicate that utilizing LSQ and MLE methods produce similar results in the context of characterizing epidemic growth patterns with the GGM, provided that the model yields a good fit to the data.
abstract_unstemmed	Background: Different estimation approaches are frequently used to calibrate mathematical models to epidemiological data, particularly for analyzing infectious disease outbreaks. Here, we use two common methods to estimate parameters that characterize growth patterns using the generalized growth model (GGM) calibrated to real outbreak datasets. Materials and methods: Data from 31 outbreaks are used to fit the GGM to the ascending phase of each outbreak and estimate the parameters using both least squares (LSQ) and maximum likelihood estimation (MLE) methods. We utilize parametric bootstrapping to construct confidence intervals for parameter estimates. We compare the results including RMSE, Anscombe residual, and 95% prediction interval coverage. We also evaluate the correlation between the estimates from both methods. Results: Comparing LSQ and MLE estimates, most outbreaks have similar parameter estimates, RMSE, Anscombe, and 95% prediction interval coverage. Parameter estimates do not differ across methods when the model yields a good fit to the early growth phase. However, for two outbreaks, there are systematic deviations in model fit to the data that explain differences in parameter estimates (e.g., residuals represent random error rather than systematic deviation). Conclusion: Our findings indicate that utilizing LSQ and MLE methods produce similar results in the context of characterizing epidemic growth patterns with the GGM, provided that the model yields a good fit to the data.
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title_short	Systematic comparison of epidemic growth patterns using two different estimation approaches
url	https://doi.org/10.1016/j.idm.2020.10.005 https://doaj.org/article/fb2c477e004e4807899cc47e06898725 http://www.sciencedirect.com/science/article/pii/S2468042720300592 https://doaj.org/toc/2468-0427
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Systematic comparison of epidemic growth patterns using two different estimation approaches

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