Superensemble forecasts of dengue outbreaks

Yamana, Teresa K.; Kandula, Sasikiran; Shaman, Jeffrey

doi:10.1098/rsif.2016.0410

Cited by 88 publications

(119 citation statements)

References 34 publications

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“…During an Ebola outbreak, real-time forecasting has the potential to support decision-making and allocation of resources, but highly accurate forecasts have proven difficult for Ebola [8,9] as well as other diseases [10][11][12][13]. Highly accurate forecasts of small, noisy outbreaks may be a fundamentally elusive ideal [14].…”

Section: Introductionmentioning

confidence: 99%

Projections of epidemic transmission and estimation of vaccination impact during an ongoing Ebola virus disease outbreak in Northeastern Democratic Republic of Congo, as of Feb. 25, 2019

et al. 2019

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Background As of February 25, 2019, 875 cases of Ebola virus disease (EVD) were reported in North Kivu and Ituri Provinces, Democratic Republic of Congo. Since the beginning of October 2018, the outbreak has largely shifted into regions in which active armed conflict has occurred, and in which EVD cases and their contacts have been difficult for health workers to reach. We used available data on the current outbreak, with case-count time series from prior outbreaks, to project the short-term and long-term course of the outbreak. Methods For short- and long-term projections, we modeled Ebola virus transmission using a stochastic branching process that assumes gradually quenching transmission rates estimated from past EVD outbreaks, with outbreak trajectories conditioned on agreement with the course of the current outbreak, and with multiple levels of vaccination coverage. We used two regression models to estimate similar projection periods. Short- and long-term projections were estimated using negative binomial autoregression and Theil-Sen regression, respectively. We also used Gott’s rule to estimate a baseline minimum-information projection. We then constructed an ensemble of forecasts to be compared and recorded for future evaluation against final outcomes. From August 20, 2018 to February 25, 2019, short-term model projections were validated against known case counts. Results During validation of short-term projections, from one week to four weeks, we found models consistently scored higher on shorter-term forecasts. Based on case counts as of February 25, the stochastic model projected a median case count of 933 cases by February 18 (95% prediction interval: 872–1054) and 955 cases by March 4 (95% prediction interval: 874–1105), while the auto-regression model projects median case counts of 889 (95% prediction interval: 876–933) and 898 (95% prediction interval: 877–983) cases for those dates, respectively. Projected median final counts range from 953 to 1,749. Although the outbreak is already larger than all past Ebola outbreaks other than the 2013–2016 outbreak of over 26,000 cases, our models do not project that it is likely to grow to that scale. The stochastic model estimates that vaccination coverage in this outbreak is lower than reported in its trial setting in Sierra Leone. Conclusions Our projections are concentrated in a range up to about 300 cases beyond those already reported. While a catastrophic outbreak is not projected, it is not ruled out, and prevention and vigilance are warranted. Prospective validation of our models in real time allowed us to generate more accurate short-term forecasts, and this process may prove useful for future real-time short-term forecasting. We estimate that transmission rates are higher than would be seen under target levels of 62% coverage due to contact tracing and vaccination, and this model estimate may offer a surrogate indicator for the outbreak response c...

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Section: Introductionmentioning

confidence: 99%

Projections of epidemic transmission and estimation of vaccination impact during an ongoing Ebola virus disease outbreak in Northeastern Democratic Republic of Congo, as of Feb. 25, 2019

et al. 2019

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“…For Riyadh, Macca and Madina prediction periods were, respectively, from weeks 105-156 (7 th June, 2015 -11 th June, 2016), weeks 65-116 (11 th July, 2015 -2 nd July, 2016), and weeks 63-114 (11 th July, 2015 -2 nd July, 2016)( Fig 3). We generated predictions for three major characteristics of the epidemiological cycle similar to previous attempts made for cholera, dengue and influenza [15,[41][42][43] namely: (a) peak week: the week during which the maximum number of clinical cases occurred in a season (comprising 52 weeks); (b) peak maximum: the number of cases occurring at the peak week, and (c) season totals: the total number of cases in the entire season. Prediction for each target variable was made every 4 weeks (i.e.…”

Section: B) Super-spreaders and 1-strain Vs 2-strain Modelsmentioning

confidence: 99%

“…The timing and severity of infectious disease outbreaks, two matters of considerable public-health relevance, are the main challenges when attempting to predict disease outbreaks [11][12][13][14]. Attempts to set up prediction frameworks for anticipating epidemics for other diseases such as dengue and influenza were pursued in the recent past with different degree of success, but clear added value [11][12][13][14][15][16]. These systems proved effective to better anticipate future outbreaks and increase our understanding on mechanisms driving disease variability.…”

Section: Introductionmentioning

confidence: 99%

A realistic two-strain model for MERS-CoV infection uncovers the high risk for epidemic propagation

Sardar¹,

Ghosh

Rodó

et al. 2020

PLoS Negl Trop Dis

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Middle East Respiratory Syndrome Coronavirus (MERS-CoV) causes severe acute respiratory illness with a case fatality rate (CFR) of 35,5%. The highest number of MERS-CoV cases are from Saudi-Arabia, the major worldwide hotspot for this disease. In the absence of neither effective treatment nor a ready-to-use vaccine and with yet an incomplete understanding of its epidemiological cycle, prevention and containment measures can be derived from mathematical models of disease epidemiology. We constructed 2-strain models to predict past outbreaks in the interval 2012-2016 and derive key epidemiological information for Macca, Madina and Riyadh. We approached variability in infection through three different disease incidence functions capturing social behavior in response to an epidemic (e.g. Bilinear, BL; Non-monotone, NM; and Saturated, SAT models). The best model combination successfully anticipated the total number of MERS-CoV clinical cases for the 2015-2016 season and accurately predicted both the number of cases at the peak of seasonal incidence and the overall shape of the epidemic cycle. The evolution in the basic reproduction number (R 0 ) warns that MERS-CoV may easily take an epidemic form. The best model correctly captures this feature, indicating a high epidemic risk (1�R 0 �2,5) in Riyadh and Macca and confirming the alleged co-circulation of more than one strain. Accurate predictions of the future MERS-CoV peak week, as well as the number of cases at the peak are now possible. These results indicate public health agencies should be aware that measures for strict containment are urgently needed before new epidemics take off in the region. Author summaryThere is currently no way to anticipate MERS-CoV epidemic outbreaks and strategies for disease prediction and containment are largely undermined by the limited knowledge of its epidemiological cycle. Not an effective treatment nor a vaccine for MERS-CoV exist to date. Instead, using three two-strain mathematical models that incorporate human social behavior as different disease incidence functions (e.g. bilinear, non-monotone and PLOS Neglected Tropical Diseases | https://doi.

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“…Among the models described in this work, one component model (Delphi-Stat) is a multi-model ensemble and all of the FluSight Network models are also multi-model ensembles (Table 1). Third, the term superensemble has been used for models that combine components that are themselves ensembles (either multimodel or single-model) [16,41]. Since not all of the components in our approach are ensembles themselves, we chose the term multi-model ensemble to refer to our approach.…”

Section: Ensemble Nomenclaturementioning

confidence: 99%

A Collaborative Multi-Model Ensemble for Real-Time Influenza Season Forecasting in the U.S

Reich

McGowan

Yamana

et al. 2019

Preprint

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Seasonal influenza results in substantial annual morbidity and mortality in the United States and worldwide. Accurate forecasts of key features of influenza epidemics, such as the timing and severity of the peak incidence in a given season, can inform public health response to outbreaks. As part of ongoing efforts to incorporate data and advanced analytical methods into public health decision-making, the United States Centers for Disease Control and Prevention (CDC) has organized seasonal influenza forecasting challenges since the 2013/2014 season. In the 2017/2018 season, 22 teams participated. A subset of four teams created a research consortium called the FluSight Network in early 2017. During the 2017/2018 season they worked together to produce a collaborative multi-model ensemble that combined 21 separate component models into a single model using a machine learning technique called stacking. This approach creates a weighted average of predictive densities where the weight for each component is based on that component's forecast accuracy in past seasons. In the 2017/2018 influenza season, one of the largest seasonal outbreaks in the last 15 years, this multi-model ensemble performed better on average than all individual component models and placed second overall in the CDC challenge. It also outperformed the baseline multi-model ensemble created by the CDC that took a simple average of all models submitted to the forecasting challenge. This project shows that collaborative efforts between research teams to develop ensemble forecasting approaches can bring measurable improvements in forecast accuracy and important reductions in the variability of performance from year to year. Efforts such asthis, that emphasize real-time testing and evaluation of forecasting models and facilitate the close collaboration between public health officials and modeling researchers, are essential to improving our understanding of how best to use forecasts to improve public health response to seasonal and emerging epidemic threats.

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Superensemble forecasts of dengue outbreaks

Cited by 88 publications

References 34 publications

Projections of epidemic transmission and estimation of vaccination impact during an ongoing Ebola virus disease outbreak in Northeastern Democratic Republic of Congo, as of Feb. 25, 2019

Projections of epidemic transmission and estimation of vaccination impact during an ongoing Ebola virus disease outbreak in Northeastern Democratic Republic of Congo, as of Feb. 25, 2019

A realistic two-strain model for MERS-CoV infection uncovers the high risk for epidemic propagation

A Collaborative Multi-Model Ensemble for Real-Time Influenza Season Forecasting in the U.S

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