Challenges and Opportunities in Disease Forecasting in Outbreak Settings: A Case Study of Measles in Lola Prefecture, Guinea

Graham, Matthew M.; Suk, Jonathan E.; Takahashi, Saki; Metcalf, C. Jessica E.; Jimenez, A Paez; Príkazský, Vladimír; Ferrari, Matthew J.; Lessler, Justin

doi:10.4269/ajtmh.17-0218

Cited by 12 publications

(7 citation statements)

References 19 publications

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“…While the best choice of models for short-term infectious disease forecasts is an ongoing topic of research, there is some evidence that introducing mechanistic assumptions does not necessarily improve short-term predictive performance compared to statistical models that have no specific assumptions related to the disease transmission process 4 . While short-term forecasts are most prominent for seasonal influenza, more recently they have also been made for outbreaks such as Ebola, measles, Zika, and diphtheria [5][6][7][8][9] . Developing accurate and reliable short-term forecasts in real time for novel infectious agents such as SARS-CoV-2 in early 2020 is particularly challenging because of uncertainty about modes of transmission, severity profiles and other relevant parameters 7,10 .…”

Section: Introductionmentioning

confidence: 99%

“…While short-term forecasts are most prominent for seasonal influenza, more recently they have also been made for outbreaks such as Ebola, measles, Zika, and diphtheria [5][6][7][8][9] . Developing accurate and reliable short-term forecasts in real time for novel infectious agents such as SARS-CoV-2 in early 2020 is particularly challenging because of uncertainty about modes of transmission, severity profiles and other relevant parameters 7,10 .…”

Section: Introductionmentioning

confidence: 99%

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Short-term forecasts to inform the response to the Covid-19 epidemic in the UK

Funk

Abbott

Atkins

et al. 2020

Preprint

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BackgroundShort-term forecasts of infectious disease can aid situational awareness and planning for outbreak response. Here, we report on multi-model forecasts of Covid-19 in the UK that were generated at regular intervals starting at the end of March 2020, in order to monitor expected healthcare utilisation and population impacts in real time.MethodsWe evaluated the performance of individual model forecasts generated between 24 March and 14 July 2020, using a variety of metrics including the weighted interval score as well as metrics that assess the calibration, sharpness, bias and absolute error of forecasts separately. We further combined the predictions from individual models into ensemble forecasts using a simple mean as well as a quantile regression average that aimed to maximise performance. We compared model performance to a null model of no change.ResultsIn most cases, individual models performed better than the null model, and ensembles models were well calibrated and performed comparatively to the best individual models. The quantile regression average did not noticeably outperform the mean ensemble.ConclusionsEnsembles of multi-model forecasts can inform the policy response to the Covid-19 pandemic by assessing future resource needs and expected population impact of morbidity and mortality.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Short-term forecasts to inform the response to the Covid-19 epidemic in the UK

Funk

Abbott

Atkins

et al. 2020

Preprint

View full text Add to dashboard Cite

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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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“…In the early stages of an infectious disease outbreak, it is crucial to understand the epidemiology of the infection. By quantifying transmission dynamics, it is possible to produce forecasts of future incidence [9,10] and evaluate the potential impact of control measures [11,12] .…”

Section: Introductionmentioning

confidence: 99%