Ensemble forecast of human West Nile virus cases and mosquito infection rates

DeFelice, Nicholas; Little, Eliza A.H.; Campbell, Scott R.; Shaman, Jeffrey

doi:10.1038/ncomms14592

Cited by 89 publications

(98 citation statements)

References 33 publications

(59 reference statements)

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“…Similar model-data assimilation frameworks have been successfully used to forecast the local epidemic trajectory for a number of infectious diseases (13)(14)(15)(16)26). During model training, the state variables and parameters in the dynamical model are repeatedly calibrated by available observations; a forecast is then generated by integrating the optimized model into the future.…”

Section: Parameter Inferencementioning

confidence: 99%

Forecasting the spatial transmission of influenza in the United States

Pei

Kandula

Yang

et al. 2018

Proc. Natl. Acad. Sci. U.S.A.

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158

157

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Recurrent outbreaks of seasonal and pandemic influenza create a need for forecasts of the geographic spread of this pathogen. Although it is well established that the spatial progression of infection is largely attributable to human mobility, difficulty obtaining real-time information on human movement has limited its incorporation into existing infectious disease forecasting techniques. In this study, we develop and validate an ensemble forecast system for predicting the spatiotemporal spread of influenza that uses readily accessible human mobility data and a metapopulation model. In retrospective state-level forecasts for 35 US states, the system accurately predicts local influenza outbreak onset,-i.e., spatial spread, defined as the week that local incidence increases above a baseline threshold-up to 6 wk in advance of this event. In addition, the metapopulation prediction system forecasts influenza outbreak onset, peak timing, and peak intensity more accurately than isolated location-specific forecasts. The proposed framework could be applied to emergent respiratory viruses and, with appropriate modifications, other infectious diseases.

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Section: Parameter Inferencementioning

confidence: 99%

Forecasting the spatial transmission of influenza in the United States

Pei

Kandula

Yang

et al. 2018

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

158

157

View full text Add to dashboard Cite

show abstract

“…In our work, we often use a simple ordinary differential equation (ODE) model to depict the underlying dynamics governing the disease transmission process 50 . Similar ODE models have produced satisfactory forecasts for a number of infectious diseases 4 , 5 , 10 , 14 , 15 .…”

Section: Methodsmentioning

confidence: 98%

“…Recent efforts have produced a number of forecasting systems for a range of infectious diseases 2 , 3 , including influenza 4 – 9 , dengue fever 10 , Ebola 11 – 13 , respiratory syncytial virus 14 , and West Nile virus 15 . Validated through retrospective forecasts of historical outbreaks, these techniques have demonstrated the feasibility of epidemiological forecast in real-time.…”

Section: Introductionmentioning

confidence: 99%

Counteracting structural errors in ensemble forecast of influenza outbreaks

Pei

Shaman

2017

Nat Commun

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For influenza forecasts generated using dynamical models, forecast inaccuracy is partly attributable to the nonlinear growth of error. As a consequence, quantification of the nonlinear error structure in current forecast models is needed so that this growth can be corrected and forecast skill improved. Here, we inspect the error growth of a compartmental influenza model and find that a robust error structure arises naturally from the nonlinear model dynamics. By counteracting these structural errors, diagnosed using error breeding, we develop a new forecast approach that combines dynamical error correction and statistical filtering techniques. In retrospective forecasts of historical influenza outbreaks for 95 US cities from 2003 to 2014, overall forecast accuracy for outbreak peak timing, peak intensity and attack rate, are substantially improved for predicted lead times up to 10 weeks. This error growth correction method can be generalized to improve the forecast accuracy of other infectious disease dynamical models.

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“…In a recent study, 12 , 13 we showed that accurate and reliable forecasts of WNV outbreaks can be made using WNV surveillance data and a mathematical model representing the transmission dynamics of WNV among mosquitoes and birds, as well as spillover to humans. This model system was able to retrospectively forecast mosquito infection rates prior to the week of mosquito peak infection and to forecast accurately the seasonal total number of human WNV cases prior to when the majority of cases were reported.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Surveillance of Reporting Delays of Mosquitoes and Humans Infected With West Nile Virus and Associations With Accuracy of West Nile Virus Forecasts

et al. 2019

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Key Points Question What are the operational challenges limiting effective implementation of a real-time West Nile virus (WNV) forecasting system? Findings In this modeling study of historical and real-time mosquito WNV assay results and human medical records, delays in data reporting for both infected mosquitoes and human WNV cases were associated with a reduction in average WNV forecast accuracy. Meaning For public health departments and mosquito abatement districts to integrate forecasting effectively into operational decision making, the relaying of real-time health and environmental surveillance data should be prioritized.

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Ensemble forecast of human West Nile virus cases and mosquito infection rates

Cited by 89 publications

References 33 publications

Forecasting the spatial transmission of influenza in the United States

Forecasting the spatial transmission of influenza in the United States

Counteracting structural errors in ensemble forecast of influenza outbreaks

Modeling and Surveillance of Reporting Delays of Mosquitoes and Humans Infected With West Nile Virus and Associations With Accuracy of West Nile Virus Forecasts

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