Modeling in Real Time During the Ebola Response

Meltzer, Martin I.; Santibañez, Scott; Fischer, Leah S.; Merlin, Toby L.; Adhikari, Bishwa B.; Atkins, Charisma Y.; Campbell, Caresse; Fung, Isaac Chun-Hai; Gambhir, Manoj; Gift, Thomas; Greening, Bradford; Gu, Weidong; Jacobson, Evin Uzun; Kahn, Emily B.; Carias, Cristina; Nerlander, Lina; Rainisch, Gabriel; Shankar, Manjunath; Wong, Karen K.; Washington, Michael L.

doi:10.15585/mmwr.su6503a12

Cited by 20 publications

(24 citation statements)

References 7 publications

(6 reference statements)

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“…During outbreak responses, modelers are asked to estimate the size of populations at risk for disease or death and the potential impact of interventions on both the timing and public health burden of an outbreak (Figure). By facilitating dialogue about what data are available and what data are needed to answer these questions, modelers can aid decision-makers as an outbreak situation evolves (11). Framing and addressing such questions via models helps leadership understand the appropriate size, type, time frame, and scale of resources needed to deploy interventions to maximize their impact.…”

Section: Providing Real-time Insight During Public Health Emergenciesmentioning

confidence: 99%

CDC Grand Rounds: Modeling and Public Health Decision-Making

Fischer¹,

Santibañez²,

Hatchett³

et al. 2016

MMWR Morb. Mortal. Wkly. Rep.

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Mathematical models incorporate various data sources and advanced computational techniques to portray real-world disease transmission and translate the basic science of infectious diseases into decision-support tools for public health. Unlike standard epidemiologic methods that rely on complete data, modeling is needed when there are gaps in data. By combining diverse data sources, models can fill gaps when critical decisions must be made using incomplete or limited information. They can be used to assess the effect and feasibility of different scenarios and provide insight into the emergence, spread, and control of disease. During the past decade, models have been used to predict the likelihood and magnitude of infectious disease outbreaks, inform emergency response activities in real time (1), and develop plans and preparedness strategies for future events, the latter of which proved invaluable during outbreaks such as severe acute respiratory syndrome and pandemic influenza (2-6). Ideally, modeling is a multistep process that involves communication between modelers and decision-makers, allowing them to gain a mutual understanding of the problem to be addressed, the type of estimates that can be reliably generated, and the limitations of the data. As models become more detailed and relevant to real-time threats, the importance of modeling in public health decision-making continues to grow. Predicting the Likelihood, Timing, and Magnitude of Infectious Disease OutbreaksFederal agencies and academic partners are working to produce models with short-and long-term projections of when and where outbreaks will occur (7). For example, the "Predict the Influenza Season" challenge, started in 2013, moved influenza forecasting forward by engaging the scientific community to develop innovative and cost-effective methods to predict influenza activity and to more clearly identify areas of uncertainty in forecasting flu activity (8). This ongoing project encourages participants to predict the timing, peak, and intensity of influenza seasons by combining social media data (e.g., Twitter, internet search data, web surveys, etc.) and data from CDC's routine influenza surveillance systems (9). As part of the Influenza Virologic Surveillance Right Size project, a public health-academic partnership developed models that determine the minimum weekly number of specimens to be screened per public health laboratory to efficiently detect emerging viruses and select strains for inclusion in the next seasonal influenza vaccine (10). Providing Real-Time Insight During Public Health EmergenciesDuring public health emergencies, decision-makers need to quantify the risk to the public, delineate priorities with a clear and narrow focus, and maintain flexibility in considering options. During outbreak responses, modelers are asked to estimate the size of populations at risk for disease or death and the potential impact of interventions on both the timing and public health burden of an outbreak (Figure). By facilitating dialogue about what dat...

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Section: Providing Real-time Insight During Public Health Emergenciesmentioning

confidence: 99%

CDC Grand Rounds: Modeling and Public Health Decision-Making

Fischer¹,

Santibañez²,

Hatchett³

et al. 2016

MMWR Morb. Mortal. Wkly. Rep.

Self Cite

View full text Add to dashboard Cite

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“…Inadequate diagnostics contribute to significant delays in Ebola patient identification and isolation, giving the virus an opportunity to spread quickly and impacting how quickly and effectively an outbreak is controlled [8,9]. During the 2014/15 outbreak, the World Health Organization (WHO) recognized the need for a simple, point-of-care (POC) Ebola diagnostic and published target product profiles (TPPs) to encourage test development [10,11].…”

Section: Introductionmentioning

confidence: 99%

Comparative performance study of three Ebola rapid diagnostic tests in Guinea

et al. 2020

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Background The 2014/15 Ebola outbreak in West Africa resulted in 11,000 deaths and massive strain on local health systems, and the ongoing outbreak in Democratic Republic of Congo has afflicted more than 3000 people. Accurate, rapid Ebola diagnostics suitable for field deployment would enable prompt identification and effective response to future outbreaks, yet remain largely unavailable. The purpose of this study was to assess the accuracy of three novel rapid diagnostic tests (RDTs): an Ebola, an Ebola-Malaria, and a Fever Panel test that includes Ebola, all from a single manufacturer. Methods We evaluated the three RDTs in 109 Ebola-positive and 96 Ebola-negative stored serum samples collected during the outbreak in Guinea in 2014/15, and tested by real-time polymerase chain reaction (RT-PCR). Sensitivity, specificity, and overall percent agreement were calculated for each RDT using RT-PCR as a reference standard, stratified by Ct value ranges. Results All tests performed with high accuracy on samples with low Ct value (high viral load). The Fever Panel test performed with the highest accuracy, with a sensitivity of 89.9% and specificity of 90.6%. The Ebola and Ebola-Malaria tests performed comparably to each other: sensitivity was 77.1 and 78% respectively, and specificity was 91.7% for the Ebola test and 95.8% for the Ebola-Malaria test. Conclusions This study evaluated the accuracy of three novel rapid diagnostic tests for Ebola. The tests may have significant public health relevance, particularly the Fever Panel test, which detects seven pathogens including Ebola. Given limitations to the study resulting from uncertain sample quality, further evaluation is warranted. All tests performed with highest accuracy on samples with low Ct value (high viral load), and the data presented here suggests that these RDTs may be useful for point-of-care diagnosis of cases in the context of an outbreak. Restrictions to their use in non-severe Ebola cases or for longitudinal monitoring, when viral loads are lower, may be appropriate. Highlighting the challenge in developing and evaluating Ebola RDTs, there were concerns regarding sample integrity and reference testing, and there is a need for additional research to validate these assays.

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“…Several methods have been used to make epidemic predictions, including exponential growth models [5,16], sigmoid-based extrapolations [17], SIR-type models [18] and more realistic model accounting for spatial and population structure [19]. But in addition to specifying a model, selecting good parameter values is also essential to obtain good predictions.…”

Section: Introductionmentioning

confidence: 99%

Improving early epidemiological assessment of emerging Aedes-transmitted epidemics using historical data

2018

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Model-based epidemiological assessment is useful to support decision-making at the beginning of an emerging Aedes-transmitted outbreak. However, early forecasts are generally unreliable as little information is available in the first few incidence data points. Here, we show how past Aedes-transmitted epidemics help improve these predictions. The approach was applied to the 2015–2017 Zika virus epidemics in three islands of the French West Indies, with historical data including other Aedes-transmitted diseases (chikungunya and Zika) in the same and other locations. Hierarchical models were used to build informative a priori distributions on the reproduction ratio and the reporting rates. The accuracy and sharpness of forecasts improved substantially when these a priori distributions were used in models for prediction. For example, early forecasts of final epidemic size obtained without historical information were 3.3 times too high on average (range: 0.2 to 5.8) with respect to the eventual size, but were far closer (1.1 times the real value on average, range: 0.4 to 1.5) using information on past CHIKV epidemics in the same places. Likewise, the 97.5% upper bound for maximal incidence was 15.3 times (range: 2.0 to 63.1) the actual peak incidence, and became much sharper at 2.4 times (range: 1.3 to 3.9) the actual peak incidence with informative a priori distributions. Improvements were more limited for the date of peak incidence and the total duration of the epidemic. The framework can adapt to all forecasting models at the early stages of emerging Aedes-transmitted outbreaks.

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Modeling in Real Time During the Ebola Response

Cited by 20 publications

References 7 publications

CDC Grand Rounds: Modeling and Public Health Decision-Making

CDC Grand Rounds: Modeling and Public Health Decision-Making

Comparative performance study of three Ebola rapid diagnostic tests in Guinea

Improving early epidemiological assessment of emerging Aedes-transmitted epidemics using historical data

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