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Article ; Online: A general modeling framework for exploring the impact of individual concern and personal protection on vector-borne disease dynamics.

Roosa, Kimberlyn / Fefferman, Nina H

2022 Volume 15, Issue 1, Page(s) 361

Abstract: Background: As climate variability and extreme weather events associated with climate change become more prevalent, public health authorities can expect to face an expanding spectrum of vector-borne diseases with increasing incidence and geographical ... ...

Abstract	Background: As climate variability and extreme weather events associated with climate change become more prevalent, public health authorities can expect to face an expanding spectrum of vector-borne diseases with increasing incidence and geographical spread. Common interventions include the use of larvicides and adulticides, as well as targeted communications to increase public awareness regarding the need for personal protective measures, such as mosquito repellant, protective clothing, and mosquito nets. Here, we propose a simplified compartmental model of mosquito-borne disease dynamics that incorporates the use of personal protection against mosquito bites influenced by two key individual-level behavioral drivers-concern for being bitten by mosquitos as a nuisance and concern for mosquito-borne disease transmission. Methods: We propose a modified compartmental model that describes the dynamics of vector-borne disease spread in a naïve population while considering the public demand for community-level control and, importantly, the effects of personal-level protection on population-level outbreak dynamics. We consider scenarios at low, medium, and high levels of community-level vector control, and at each level, we consider combinations of low, medium, and high levels of motivation to use personal protection, namely concern for disease transmission and concern for being bitten in general. Results: When there is very little community-level vector control, nearly the entire population is quickly infected, regardless of personal protection use. When vector control is at an intermediate level, both concerns that motivate the use of personal protection play an important role in reducing disease burden. When authorities have the capacity for high-level community vector control through pesticide use, the motivation to use personal protection to reduce disease transmission has little additional effect on the outbreak. Conclusions: While results show that personal-level protection alone is not enough to significantly impact an outbreak, personal protective measures can significantly reduce the severity of an outbreak in conjunction with community-level control. Furthermore, the model provides insight for targeting public health messaging to increase the use of personal protection based on concerns related to being bitten by mosquitos or vector-borne disease transmission.
MeSH term(s)	Aedes ; Animals ; Disease Outbreaks/prevention & control ; Humans ; Mosquito Vectors ; Pesticides ; Public Health ; Vector Borne Diseases/epidemiology ; Vector Borne Diseases/prevention & control ; Zika Virus Infection
Chemical Substances	Pesticides
Language	English
Publishing date	2022-10-08
Publishing country	England
Document type	Journal Article
ZDB-ID	2409480-8
ISSN	1756-3305 ; 1756-3305
ISSN (online)	1756-3305
ISSN	1756-3305
DOI	10.1186/s13071-022-05481-7
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Comparative Assessment of Epidemiological Models for Analyzing and Forecasting Infectious Disease Outbreaks

Roosa, Kimberlyn

Public Health Dissertations

2020

Abstract: Mathematical modeling offers a quantitative framework for analyzing mechanisms underlying infectious disease transmission and explaining patterns in epidemiological data. Models are also commonly applied in outbreak investigations for assessing ... ...

Abstract	Mathematical modeling offers a quantitative framework for analyzing mechanisms underlying infectious disease transmission and explaining patterns in epidemiological data. Models are also commonly applied in outbreak investigations for assessing intervention and control strategies and generating epidemic forecasts in real time. However, successful application of mathematical models depends on the ability to reliably estimate key transmission and severity parameters, which are critical for guiding public health interventions. Overall, the three studies presented provide a thorough guide for assessing and utilizing mathematical models for describing infectious disease outbreak trends. In the first study, we describe the process for analyzing identifiability of parameters of interest in mechanistic disease transmission models. In the second study, we expand this idea to simple phenomenological models and explore the idea of overdispersion in the data and how to determine an appropriate error structure within the analyses. In the third study, we use previously validated phenomenological models to generate short-term forecasts of the ongoing COVID-19 pandemic. During infectious disease epidemics, public health authorities rely on modeling results to inform intervention decisions and resource allocation. Therefore, we highlight the importance of interpreting modeling results with caution, particularly regarding theoretical aspects of mathematical models and parameter estimation methods. Further, results from modeling studies should be presented with quantified uncertainty and interpreted in terms of the assumptions and limitations of the model, methods, and data used. The methodology presented in this dissertation provides a thorough guide for conducting model-based inferences and presenting the uncertainty associated with parameter estimation results.
Keywords	infectious disease epidemiology ; mathematical models ; parameter identifiability ; epidemiological models ; computational biology ; epidemic forecasting ; covid19
Subject code	330
Publishing date	2020-08-11T07:00:00Z
Publisher	ScholarWorks @ Georgia State University
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: GrowthPredict: A toolbox and tutorial-based primer for fitting and forecasting growth trajectories using phenomenological growth models.

Chowell, Gerardo / Bleichrodt, Amanda / Dahal, Sushma / Tariq, Amna / Roosa, Kimberlyn / Hyman, James M / Luo, Ruiyan

Scientific reports

2024 Volume 14, Issue 1, Page(s) 1630

Abstract: Simple dynamic modeling tools can help generate real-time short-term forecasts with quantified uncertainty of the trajectory of diverse growth processes unfolding in nature and society, including disease outbreaks. An easy-to-use and flexible toolbox for ...

Abstract	Simple dynamic modeling tools can help generate real-time short-term forecasts with quantified uncertainty of the trajectory of diverse growth processes unfolding in nature and society, including disease outbreaks. An easy-to-use and flexible toolbox for this purpose is lacking. This tutorial-based primer introduces and illustrates GrowthPredict, a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using phenomenological dynamic growth models based on ordinary differential equations. This toolbox is accessible to a broad audience, including students training in mathematical biology, applied statistics, and infectious disease modeling, as well as researchers and policymakers who need to conduct short-term forecasts in real-time. The models included in the toolbox capture exponential and sub-exponential growth patterns that typically follow a rising pattern followed by a decline phase, a common feature of contagion processes. Models include the 1-parameter exponential growth model and the 2-parameter generalized-growth model, which have proven useful in characterizing and forecasting the ascending phase of epidemic outbreaks. It also includes the 2-parameter Gompertz model, the 3-parameter generalized logistic-growth model, and the 3-parameter Richards model, which have demonstrated competitive performance in forecasting single peak outbreaks. We provide detailed guidance on forecasting time-series trajectories and available software ( https://github.com/gchowell/forecasting_growthmodels ), including the full uncertainty distribution derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance across different models, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score. This tutorial and toolbox can be broadly applied to characterizing and forecasting time-series data using simple phenomenological growth models. As a contagion process takes off, the tools presented in this tutorial can help create forecasts to guide policy regarding implementing control strategies and assess the impact of interventions. The toolbox functionality is demonstrated through various examples, including a tutorial video, and the examples use publicly available data on the monkeypox (mpox) epidemic in the USA.
Language	English
Publishing date	2024-01-18
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-51852-8
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Article: An ensemble

Chowell, Gerardo / Dahal, Sushma / Tariq, Amna / Roosa, Kimberlyn / Hyman, James M / Luo, Ruiyan

medRxiv : the preprint server for health sciences

2022

Abstract: We analyze an ensemble of : Summary: The COVID-19 pandemic has highlighted the urgent need to develop reliable tools to forecast the trajectory of epidemics and pandemics in near real-time. We describe and apply an ... ...

Abstract	We analyze an ensemble of Summary: The COVID-19 pandemic has highlighted the urgent need to develop reliable tools to forecast the trajectory of epidemics and pandemics in near real-time. We describe and apply an ensemble
Language	English
Publishing date	2022-06-21
Publishing country	United States
Document type	Preprint
DOI	10.1101/2022.06.19.22276608
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Article ; Online: Assessing parameter identifiability in compartmental dynamic models using a computational approach: application to infectious disease transmission models.

Roosa, Kimberlyn / Chowell, Gerardo

Theoretical biology & medical modelling

2019 Volume 16, Issue 1, Page(s) 1

Abstract: Background: Mathematical modeling is now frequently used in outbreak investigations to understand underlying mechanisms of infectious disease dynamics, assess patterns in epidemiological data, and forecast the trajectory of epidemics. However, the ... ...

Abstract	Background: Mathematical modeling is now frequently used in outbreak investigations to understand underlying mechanisms of infectious disease dynamics, assess patterns in epidemiological data, and forecast the trajectory of epidemics. However, the successful application of mathematical models to guide public health interventions lies in the ability to reliably estimate model parameters and their corresponding uncertainty. Here, we present and illustrate a simple computational method for assessing parameter identifiability in compartmental epidemic models. Methods: We describe a parametric bootstrap approach to generate simulated data from dynamical systems to quantify parameter uncertainty and identifiability. We calculate confidence intervals and mean squared error of estimated parameter distributions to assess parameter identifiability. To demonstrate this approach, we begin with a low-complexity SEIR model and work through examples of increasingly more complex compartmental models that correspond with applications to pandemic influenza, Ebola, and Zika. Results: Overall, parameter identifiability issues are more likely to arise with more complex models (based on number of equations/states and parameters). As the number of parameters being jointly estimated increases, the uncertainty surrounding estimated parameters tends to increase, on average, as well. We found that, in most cases, R Conclusions: Because public health policies can be influenced by results of mathematical modeling studies, it is important to conduct parameter identifiability analyses prior to fitting the models to available data and to report parameter estimates with quantified uncertainty. The method described is helpful in these regards and enhances the essential toolkit for conducting model-based inferences using compartmental dynamic models.
MeSH term(s)	Animals ; Communicable Diseases/diagnosis ; Communicable Diseases/transmission ; Computer Simulation ; Confidence Intervals ; Culicidae/virology ; Disease Susceptibility ; Hospitalization ; Humans ; Models, Biological ; Zika Virus/physiology ; Zika Virus Infection/epidemiology ; Zika Virus Infection/virology
Language	English
Publishing date	2019-01-14
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2156462-0
ISSN	1742-4682 ; 1742-4682
ISSN (online)	1742-4682
ISSN	1742-4682
DOI	10.1186/s12976-018-0097-6
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Performance of phenomenological models for real-time forecasting the COVID-19 epidemic

Roosa, Kimberlyn / Chowell, Gerardo

Biology and Medicine Through Mathematics Conference

2020

Keywords	Epidemiology ; Life Sciences ; Medicine and Health Sciences ; Physical Sciences and Mathematics ; covid19
Publishing date	2020-05-15T10:57:36Z
Publisher	VCU Scholars Compass
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Assessing parameter identifiability in compartmental dynamic models using a computational approach

Kimberlyn Roosa / Gerardo Chowell

Theoretical Biology and Medical Modelling, Vol 16, Iss 1, Pp 1-

application to infectious disease transmission models

2019 Volume 15

Abstract: Abstract Background Mathematical modeling is now frequently used in outbreak investigations to understand underlying mechanisms of infectious disease dynamics, assess patterns in epidemiological data, and forecast the trajectory of epidemics. However, ... ...

Abstract	Abstract Background Mathematical modeling is now frequently used in outbreak investigations to understand underlying mechanisms of infectious disease dynamics, assess patterns in epidemiological data, and forecast the trajectory of epidemics. However, the successful application of mathematical models to guide public health interventions lies in the ability to reliably estimate model parameters and their corresponding uncertainty. Here, we present and illustrate a simple computational method for assessing parameter identifiability in compartmental epidemic models. Methods We describe a parametric bootstrap approach to generate simulated data from dynamical systems to quantify parameter uncertainty and identifiability. We calculate confidence intervals and mean squared error of estimated parameter distributions to assess parameter identifiability. To demonstrate this approach, we begin with a low-complexity SEIR model and work through examples of increasingly more complex compartmental models that correspond with applications to pandemic influenza, Ebola, and Zika. Results Overall, parameter identifiability issues are more likely to arise with more complex models (based on number of equations/states and parameters). As the number of parameters being jointly estimated increases, the uncertainty surrounding estimated parameters tends to increase, on average, as well. We found that, in most cases, R0 is often robust to parameter identifiability issues affecting individual parameters in the model. Despite large confidence intervals and higher mean squared error of other individual model parameters, R0 can still be estimated with precision and accuracy. Conclusions Because public health policies can be influenced by results of mathematical modeling studies, it is important to conduct parameter identifiability analyses prior to fitting the models to available data and to report parameter estimates with quantified uncertainty. The method described is helpful in these regards and enhances the essential toolkit for conducting model-based inferences using compartmental dynamic models.
Keywords	Compartmental models ; Parameter identifiability ; Uncertainty quantification ; Epidemic models ; Structural parameter identifiability ; Practical parameter identifiability ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
Subject code	310
Language	English
Publishing date	2019-01-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Systematic comparison of epidemic growth patterns using two different estimation approaches.

Lee, Yiseul / Roosa, Kimberlyn / Chowell, Gerardo

Infectious Disease Modelling

2020 Volume 6, Page(s) 5–14

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 ... ...

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.
Language	English
Publishing date	2020-10-24
Publishing country	China
Document type	Journal Article
ZDB-ID	3015225-2
ISSN	2468-0427 ; 2468-2152
ISSN (online)	2468-0427
ISSN	2468-2152
DOI	10.1016/j.idm.2020.10.005
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Article ; Online: An ensemble n-sub-epidemic modeling framework for short-term forecasting epidemic trajectories: Application to the COVID-19 pandemic in the USA.

Chowell, Gerardo / Dahal, Sushma / Tariq, Amna / Roosa, Kimberlyn / Hyman, James M / Luo, Ruiyan

PLoS computational biology

2022 Volume 18, Issue 10, Page(s) e1010602

Abstract: We analyze an ensemble of n-sub-epidemic modeling for forecasting the trajectory of epidemics and pandemics. These ensemble modeling approaches, and models that integrate sub-epidemics to capture complex temporal dynamics, have demonstrated powerful ... ...

Abstract	We analyze an ensemble of n-sub-epidemic modeling for forecasting the trajectory of epidemics and pandemics. These ensemble modeling approaches, and models that integrate sub-epidemics to capture complex temporal dynamics, have demonstrated powerful forecasting capability. This modeling framework can characterize complex epidemic patterns, including plateaus, epidemic resurgences, and epidemic waves characterized by multiple peaks of different sizes. We systematically assess their calibration and short-term forecasting performance in short-term forecasts for the COVID-19 pandemic in the USA from late April 2020 to late February 2022. We compare their performance with two commonly used statistical ARIMA models. The best fit sub-epidemic model and three ensemble models constructed using the top-ranking sub-epidemic models consistently outperformed the ARIMA models in terms of the weighted interval score (WIS) and the coverage of the 95% prediction interval across the 10-, 20-, and 30-day short-term forecasts. In our 30-day forecasts, the average WIS ranged from 377.6 to 421.3 for the sub-epidemic models, whereas it ranged from 439.29 to 767.05 for the ARIMA models. Across 98 short-term forecasts, the ensemble model incorporating the top four ranking sub-epidemic models (Ensemble(4)) outperformed the (log) ARIMA model 66.3% of the time, and the ARIMA model, 69.4% of the time in 30-day ahead forecasts in terms of the WIS. Ensemble(4) consistently yielded the best performance in terms of the metrics that account for the uncertainty of the predictions. This framework can be readily applied to investigate the spread of epidemics and pandemics beyond COVID-19, as well as other dynamic growth processes found in nature and society that would benefit from short-term predictions.
MeSH term(s)	Humans ; United States/epidemiology ; COVID-19/epidemiology ; Pandemics ; Forecasting ; Models, Statistical ; Time
Language	English
Publishing date	2022-10-06
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1010602
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Article: A MATLAB toolbox to fit and forecast growth trajectories using phenomenological growth models: Application to epidemic outbreaks.

Chowell, Gerardo / Bleichrodt, Amanda / Dahal, Sushma / Tariq, Amna / Roosa, Kimberlyn / Hyman, James M / Luo, Ruiyan

Research square

2023

Abstract: Background: Simple dynamic modeling tools can be useful for generating real-time short-term forecasts with quantified uncertainty of the trajectory of diverse growth processes unfolding in nature and society, including disease outbreaks. An easy-to-use ... ...

Abstract	Background: Simple dynamic modeling tools can be useful for generating real-time short-term forecasts with quantified uncertainty of the trajectory of diverse growth processes unfolding in nature and society, including disease outbreaks. An easy-to-use and flexible toolbox for this purpose is lacking. Results: In this tutorial-based primer, we introduce and illustrate a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using phenomenological dynamic growth models based on ordinary differential equations. This toolbox is accessible to various audiences, including students training in time-series forecasting, dynamic growth modeling, parameter estimation, parameter uncertainty and identifiability, model comparison, performance metrics, and forecast evaluation, as well as researchers and policymakers who need to conduct short-term forecasts in real-time. The models included in the toolbox capture exponential and sub-exponential growth patterns that typically follow a rising pattern followed by a decline phase, a common feature of contagion processes. Models include the 2-parameter generalized-growth model, which has proved useful to characterize and forecast the ascending phase of epidemic outbreaks, and the Gompertz model as well as the 3-parameter generalized logistic-growth model and the Richards model, which have demonstrated competitive performance in forecasting single peak outbreaks.The toolbox provides a tutorial for forecasting time-series trajectories that include the full uncertainty distribution, derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance across different models, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score. Conclusions: We have developed the first comprehensive toolbox to characterize and forecast time-series data using simple phenomenological growth models. As a contagion process takes off, the tools presented in this tutorial can facilitate policymaking to guide the implementation of control strategies and assess the impact of interventions. The toolbox functionality is demonstrated through various examples, including a tutorial video, and is illustrated using weekly data on the monkeypox epidemic in the USA.
Language	English
Publishing date	2023-04-21
Publishing country	United States
Document type	Preprint
DOI	10.21203/rs.3.rs-2724940/v2
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