Trade-offs between individual and ensemble forecasts of an emerging infectious disease

Oidtman, Rachel J.; Omodei, Elisa; Kraemer, Moritz U. G.; Castañeda-Orjuela, Carlos; Cruz-Rivera, Erica; Misnaza, Sandra Patricia; Cifuentes, Myriam Patricia; Rincón, Luz Emilse; Cañon, Viviana; Alarcón, Pedro A. de; España, Guido; Huber, John H.; Hill, Sarah C.; Barker, Christopher M.; Johansson, Michael A.; Manore, Carrie; Reiner, Robert C.; Rodríguez-Barraquer, Isabel; Siraj, Amir; Frı́as-Martı́nez, Enrique; García-Herranz, Manuel; Perkins, T. Alex

doi:10.1038/s41467-021-25695-0

Cited by 25 publications

(22 citation statements)

References 63 publications

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“…Our findings are in line with a recent study on the 2015–2016 Zika virus epidemic in Colombia showing that an ensemble modelling approach integrating multiple data sources for human mobility, including CDR-derived mobility, is prominent to forecast an emerging infectious disease like Zika [ 24 ]. Human mobility is in fact a key driver of ZIKV spread and integrating this variable into spatial models can provide valuable insights for epidemic preparedness and response [ 11 ].…”

Section: Discussionsupporting

confidence: 90%

“…phone calls, text messages and data connections), CDRs represents a relatively low-cost resource to draw a high-level picture of human mobility patterns at an unprecedented scale [12]. The availability of aggregated CDR-derived mobility has impacted several research fields [16], with significant applications to the spatial modelling of many infectious diseases, such as malaria [17,18], dengue [19], cholera [20], rubella [21], Ebola [22,23], ZIKV [24], and COVID-19 [25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

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Comparing sources of mobility for modelling the epidemic spread of Zika virus in Colombia

et al. 2022

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Timely, accurate, and comparative data on human mobility is of paramount importance for epidemic preparedness and response, but generally not available or easily accessible. Mobile phone metadata, typically in the form of Call Detail Records (CDRs), represents a powerful source of information on human movements at an unprecedented scale. In this work, we investigate the potential benefits of harnessing aggregated CDR-derived mobility to predict the 2015-2016 Zika virus (ZIKV) outbreak in Colombia, when compared to other traditional data sources. To simulate the spread of ZIKV at sub-national level in Colombia, we employ a stochastic metapopulation epidemic model for vector-borne diseases. Our model integrates detailed data on the key drivers of ZIKV spread, including the spatial heterogeneity of the mosquito abundance, and the exposure of the population to the virus due to environmental and socio-economic factors. Given the same modelling settings (i.e. initial conditions and epidemiological parameters), we perform in-silico simulations for each mobility network and assess their ability in reproducing the local outbreak as reported by the official surveillance data. We assess the performance of our epidemic modelling approach in capturing the ZIKV outbreak both nationally and sub-nationally. Our model estimates are strongly correlated with the surveillance data at the country level (Pearson’s r = 0.92 for the CDR-informed network). Moreover, we found strong performance of the model estimates generated by the CDR-informed mobility networks in reproducing the local outbreak observed at the sub-national level. Compared to the CDR-informed networks, the performance of the other mobility networks is either comparatively similar or substantially lower, with no added value in predicting the local epidemic. This suggests that mobile phone data captures a better picture of human mobility patterns. This work contributes to the ongoing discussion on the value of aggregated mobility estimates from CDRs data that, with appropriate data protection and privacy safeguards, can be used for social impact applications and humanitarian action.

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Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Comparing sources of mobility for modelling the epidemic spread of Zika virus in Colombia

et al. 2022

Self Cite

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“…Relying on an ensemble model is appealing because it acknowledges that each model has limitations and imperfectly captures the complex reality of this pandemic. Although individual models may perform better in some situations, forecasts that build on an ensemble of models are less likely to be overly influenced by the assumptions of a specific model ( 15 ). The benefits are confirmed in practice, with the ensemble model performing best on average.…”

Section: Discussionmentioning

confidence: 99%

An ensemble model based on early predictors to forecast COVID-19 health care demand in France

Paireau

Andronico

Hozé

et al. 2022

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

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Significance The COVID-19 pandemic is inducing significant stress on health care structures, which can be quickly saturated with negative consequences for patients. As hospitalization comes late in the infection history of a patient, early predictors—such as the number of cases, mobility, climate, and vaccine coverage—could improve forecasts of health care demand. Predictive models taken individually have their pros and cons, and it is advantageous to combine the predictions in an ensemble model. Here, we design an ensemble that combines several models to anticipate French COVID-19 health care needs up to 14 days ahead. We retrospectively test this model, identify the best predictors of the growth rate of hospital admissions, and propose a promising approach to facilitate the planning of hospital activity.

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“…Data mining scientists recommend using diverse and independent models for ensemble modeling [29,37]. Most medical and biological application studies have not mentioned how individual models were selected for ensemble modeling [20,21,24–26,32,60]. However, model selection may affect the eventual predictions of the ensembled model.…”

Section: Discussionmentioning

confidence: 99%

“…The reasons for employing ensemble methods in building a model are to enhance the overall performance of the model, minimize the error rate that can be caused by using individual models, and reduce the overall uncertainty of predictions [22,29,30]. There are different ways to ensemble the models, including most-votes, simple average, weighted average (linear or nonlinear), boosting, and stacking [22,24,27,[31][32][33]. In mosquito studies, ensemble modeling has been used to predict the global expansion of Aedes mosquitoes and the invasion of Anopheles stephensi in Africa [26,34,35].…”

Section: Introductionmentioning

confidence: 99%

Mapping potential malaria vector larval habitats for larval source management: Introduction to multi-model ensembling approaches

Zhou

Lee

Wang

et al. 2022

Preprint

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Mosquito larval source management (LSM) is a viable supplement to the currently implemented first-line malaria control tools for use under certain conditions for malaria control and elimination. Implementation of larval source management requires a carefully designed strategy and effective planning. Identification and mapping of larval sources is a prerequisite. Ensemble modeling is increasingly used for prediction modeling, but it lacks standard procedures. We proposed a detailed framework to predict potential malaria vector larval habitats using ensemble modeling, which includes selection of models, ensembling method and predictors; evaluation of variable importance; prediction of potential larval habitats; and assessment of prediction uncertainty. The models were built and validated based on multi-site, multi-year field observations and climatic/environmental variables. Model performance was tested using independent multi-site, multi-year field observations. Overall, we found that the ensembled model predicted larval habitats with about 20% more accuracy than the average of the individual models ensembled. Key larval habitat predictors were elevation, geomorphon class, and precipitation 2 months prior. Mapped distributions of potential malaria vector larval habitats showed different prediction errors in different ecological settings. This is the first study to provide a detailed framework for the process of multi-model ensemble modeling. Mapping of potential habitats will be helpful in LSM planning.

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Trade-offs between individual and ensemble forecasts of an emerging infectious disease

Cited by 25 publications

References 63 publications

Comparing sources of mobility for modelling the epidemic spread of Zika virus in Colombia

Comparing sources of mobility for modelling the epidemic spread of Zika virus in Colombia

An ensemble model based on early predictors to forecast COVID-19 health care demand in France

Mapping potential malaria vector larval habitats for larval source management: Introduction to multi-model ensembling approaches

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