### Abstract

Mathematical models have the potential to be useful to forecast the course of epidemics. In this chapter, a family of logistic patch models are preliminarily evaluated for use in disease modeling and forecasting. Here we also derive the logistic equation in an infectious disease transmission context based on population behavior and used it for forecasting the trajectories of the 2013-2015 Ebola epidemic inWest Africa. The logistic model is then extended to include spatial population heterogeneity by using multi-patch models that incorporate migration between patches and logistic growth within each patch. Each model’s ability to forecast epidemic data was assessed by comparing model forecasting error, parameter distributions and parameter confidence intervals as functions of the number of data points used to calibrate the models. The patch models show an improvement over the logistic model in short-term forecasting, but naturally require the estimation of more parameters from limited data.

Original language | English (US) |
---|---|

Title of host publication | Mathematical and Statistical Modeling for Emerging and Re-emerging Infectious Diseases |

Publisher | Springer International Publishing |

Pages | 147-167 |

Number of pages | 21 |

ISBN (Electronic) | 9783319404134 |

ISBN (Print) | 9783319404110 |

DOIs | |

State | Published - Jan 1 2016 |

### Fingerprint

### Keywords

- Behavior change
- Bootstrap
- Ebola
- Infectious disease forecasting
- Logistic equation
- Patchmodel

### ASJC Scopus subject areas

- Mathematics(all)
- Medicine(all)

### Cite this

*Mathematical and Statistical Modeling for Emerging and Re-emerging Infectious Diseases*(pp. 147-167). Springer International Publishing. https://doi.org/10.1007/978-3-319-40413-4_10

**Patch models of EVD transmission dynamics.** / Pell, Bruce; Baez, Javier; Phan, Tin; Gao, Daozhou; Chowell, Gerardo; Kuang, Yang.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

*Mathematical and Statistical Modeling for Emerging and Re-emerging Infectious Diseases.*Springer International Publishing, pp. 147-167. https://doi.org/10.1007/978-3-319-40413-4_10

}

TY - CHAP

T1 - Patch models of EVD transmission dynamics

AU - Pell, Bruce

AU - Baez, Javier

AU - Phan, Tin

AU - Gao, Daozhou

AU - Chowell, Gerardo

AU - Kuang, Yang

PY - 2016/1/1

Y1 - 2016/1/1

N2 - Mathematical models have the potential to be useful to forecast the course of epidemics. In this chapter, a family of logistic patch models are preliminarily evaluated for use in disease modeling and forecasting. Here we also derive the logistic equation in an infectious disease transmission context based on population behavior and used it for forecasting the trajectories of the 2013-2015 Ebola epidemic inWest Africa. The logistic model is then extended to include spatial population heterogeneity by using multi-patch models that incorporate migration between patches and logistic growth within each patch. Each model’s ability to forecast epidemic data was assessed by comparing model forecasting error, parameter distributions and parameter confidence intervals as functions of the number of data points used to calibrate the models. The patch models show an improvement over the logistic model in short-term forecasting, but naturally require the estimation of more parameters from limited data.

AB - Mathematical models have the potential to be useful to forecast the course of epidemics. In this chapter, a family of logistic patch models are preliminarily evaluated for use in disease modeling and forecasting. Here we also derive the logistic equation in an infectious disease transmission context based on population behavior and used it for forecasting the trajectories of the 2013-2015 Ebola epidemic inWest Africa. The logistic model is then extended to include spatial population heterogeneity by using multi-patch models that incorporate migration between patches and logistic growth within each patch. Each model’s ability to forecast epidemic data was assessed by comparing model forecasting error, parameter distributions and parameter confidence intervals as functions of the number of data points used to calibrate the models. The patch models show an improvement over the logistic model in short-term forecasting, but naturally require the estimation of more parameters from limited data.

KW - Behavior change

KW - Bootstrap

KW - Ebola

KW - Infectious disease forecasting

KW - Logistic equation

KW - Patchmodel

UR - http://www.scopus.com/inward/record.url?scp=85018227307&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85018227307&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-40413-4_10

DO - 10.1007/978-3-319-40413-4_10

M3 - Chapter

AN - SCOPUS:85018227307

SN - 9783319404110

SP - 147

EP - 167

BT - Mathematical and Statistical Modeling for Emerging and Re-emerging Infectious Diseases

PB - Springer International Publishing

ER -