Parameter Estimation and Forecasting Strategies for Cholera Dynamics: Insights from the 1991–1997 Peruvian Epidemic
Environmental transmission is a critical driver of cholera dynamics and a key factor influencing model-based inference and forecasting. This study focuses on stable parameter estimation and forecasting of cholera outbreaks using a compartmental SIRB model informed by three formulations of the enviro...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/10/1692 |
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| author | Hamed Karami Gerardo Chowell Oscar J. Mujica Alexandra Smirnova |
| author_facet | Hamed Karami Gerardo Chowell Oscar J. Mujica Alexandra Smirnova |
| author_sort | Hamed Karami |
| collection | DOAJ |
| description | Environmental transmission is a critical driver of cholera dynamics and a key factor influencing model-based inference and forecasting. This study focuses on stable parameter estimation and forecasting of cholera outbreaks using a compartmental SIRB model informed by three formulations of the environmental transmission rate: (1) a pre-parameterized periodic function, (2) a temperature-driven function, and (3) a flexible, data-driven time-dependent function. We apply these methods to the 1991–1997 cholera epidemic in Peru, estimating key parameters; these include the case reporting rate and human-to-human transmission rate. We assess practical identifiability via parametric bootstrapping and compare the performance of each transmission formulation in fitting epidemic data and forecasting short-term incidence. Our results demonstrate that while the data-driven approach achieves superior in-sample fit, the temperature-dependent model offers better forecasting performance due to its ability to incorporate seasonal trends. The study highlights trade-offs between model flexibility and parameter identifiability and provides a framework for evaluating cholera transmission models under data limitations. These insights can inform public health strategies for outbreak preparedness and response. |
| format | Article |
| id | doaj-art-b6c9ef614ac24f41bd5dfbc8a4130948 |
| institution | DOAJ |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-b6c9ef614ac24f41bd5dfbc8a41309482025-08-20T03:14:31ZengMDPI AGMathematics2227-73902025-05-011310169210.3390/math13101692Parameter Estimation and Forecasting Strategies for Cholera Dynamics: Insights from the 1991–1997 Peruvian EpidemicHamed Karami0Gerardo Chowell1Oscar J. Mujica2Alexandra Smirnova3Department of Mathematics & Statistics, Georgia State University, Atlanta, GA 30303, USADepartment of Population Health Sciences, Georgia State University, Atlanta, GA 30303, USADepartment of Evidence and Intelligence for Action in Health, Pan American Health Organization, Washington, DC 20037, USADepartment of Mathematics & Statistics, Georgia State University, Atlanta, GA 30303, USAEnvironmental transmission is a critical driver of cholera dynamics and a key factor influencing model-based inference and forecasting. This study focuses on stable parameter estimation and forecasting of cholera outbreaks using a compartmental SIRB model informed by three formulations of the environmental transmission rate: (1) a pre-parameterized periodic function, (2) a temperature-driven function, and (3) a flexible, data-driven time-dependent function. We apply these methods to the 1991–1997 cholera epidemic in Peru, estimating key parameters; these include the case reporting rate and human-to-human transmission rate. We assess practical identifiability via parametric bootstrapping and compare the performance of each transmission formulation in fitting epidemic data and forecasting short-term incidence. Our results demonstrate that while the data-driven approach achieves superior in-sample fit, the temperature-dependent model offers better forecasting performance due to its ability to incorporate seasonal trends. The study highlights trade-offs between model flexibility and parameter identifiability and provides a framework for evaluating cholera transmission models under data limitations. These insights can inform public health strategies for outbreak preparedness and response.https://www.mdpi.com/2227-7390/13/10/1692infectious diseasesparameter estimationcholera transmissionforecasting |
| spellingShingle | Hamed Karami Gerardo Chowell Oscar J. Mujica Alexandra Smirnova Parameter Estimation and Forecasting Strategies for Cholera Dynamics: Insights from the 1991–1997 Peruvian Epidemic Mathematics infectious diseases parameter estimation cholera transmission forecasting |
| title | Parameter Estimation and Forecasting Strategies for Cholera Dynamics: Insights from the 1991–1997 Peruvian Epidemic |
| title_full | Parameter Estimation and Forecasting Strategies for Cholera Dynamics: Insights from the 1991–1997 Peruvian Epidemic |
| title_fullStr | Parameter Estimation and Forecasting Strategies for Cholera Dynamics: Insights from the 1991–1997 Peruvian Epidemic |
| title_full_unstemmed | Parameter Estimation and Forecasting Strategies for Cholera Dynamics: Insights from the 1991–1997 Peruvian Epidemic |
| title_short | Parameter Estimation and Forecasting Strategies for Cholera Dynamics: Insights from the 1991–1997 Peruvian Epidemic |
| title_sort | parameter estimation and forecasting strategies for cholera dynamics insights from the 1991 1997 peruvian epidemic |
| topic | infectious diseases parameter estimation cholera transmission forecasting |
| url | https://www.mdpi.com/2227-7390/13/10/1692 |
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