Summary results of the 2014-2015 DARPA Chikungunya challenge.

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chikungunya 479 (pmc-release): 5/2018Publication date (collection): /2018AbstractBackground: Emerging pathogens such as Zika, chikungunya , Ebola, and dengue viruses are serious threats to national and global health security. Accurate forecasts
chikungunya 744 critical to minimizing subsequent mortality, morbidity, and economic loss. The recent introduction of chikungunya and Zika virus to the Americas underscores the need for better methods for disease surveillance and
chikungunya 1113 launched the 2014–2015 DARPA Chikungunya Challenge to forecast the number of cases and spread of chikungunya disease in the Americas. Challenge participants (n=38 during final evaluation) provided predictions
chikungunya 1228 disease in the Americas. Challenge participants (n=38 during final evaluation) provided predictions of chikungunya epidemics across the Americas for a six-month period, from September 1, 2014 to February 16, 2015, to
chikungunya 5836 variety of participants to foster innovation and advance the state of the art by attempting to predict chikungunya incidence across the Americas [[20]].Nonetheless, significant challenges remain for the development
chikungunya 6737 methodologies.Chikungunya challengeChikungunya is a mosquito-borne viral infection of humans. Although rarely fatal, chikungunya is an emerging, debilitating viral disease that is transmitted among humans by mosquitoes [[22]]. There
chikungunya 6976 treatment for the disease, although palliative care has been shown to reduce its severity and duration. The chikungunya virus (CHIKV) was originally detected in Tanzania in 1952, with the name meaning ‘to become contorted’
chikungunya 11649 experts in infectious disease modeling, CHIKV, and other vector-borne diseases. Fig. 1Weekly incidence of chikungunya cases, aggregated by region from PAHO reports (symbols) and smoothed epidemic curves (lines). The two
chikungunya 13820 forecasts. The following are descriptions of their overall approach, methodologies to forecast the spread of chikungunya in the Americas, and a brief summary of their results.First place submission (henceforth participant
chikungunya 13978 results.First place submission (henceforth participant 1)A simple model for the recent outbreaks of chikungunya in the AmericasModeling Approach: Participant 1 relied on estimating the growth rate G(N) of the outbreak
chikungunya 16709 are shown as blue circlesSecond place submission (henceforth participant 2)Predicting the spread of chikungunya using a logistic S-curveModeling Approach: Participant 2 used a Bounded Geometric Growth approach (shown
chikungunya 17560 incidence than for countries with low incidence.Honorable mention #1 (henceforth participant 3)Forecasting chikungunya feverModeling Approach: Participant 3 implemented three different predictive models for each country,
chikungunya 21703 Participant 5 used a stochastic, mechanistic model of transmission dynamics in each locality to forecast chikungunya epidemics for each country and territory in the PAHO data. A susceptible-exposed-infectious-recovered
chikungunya 24249 were severely (> 50%) underestimated in the five-month forecast. Fig. 6Weekly simulation of reported chikungunya cases in (a) Puerto Rico and (b) Saint Barthelemy from Participant 5. Simulations are one-month forecasts
chikungunya 24642 dashed lines are 95% prediction intervalsHonorable mention #4 (henceforth participant 6)Modeling the chikungunya epidemic in the Americas: Distributional ecology and population dynamicsModeling Approach: Participant
chikungunya 27159 description of the model and methodology please refer to [[45]].ResultsReported PAHO dataThe distribution of chikungunya cases across the 50 participating PAHO countries, at three times during the Challenge is shown in Fig. 8,
chikungunya 29458 with a reported incidence of 0.16%.The 20 most-affected countries accounted for 98% of all reported chikungunya cases. The Dominican Republic reported the most cases, followed by El Salvador and Colombia. Both delayed
infectious disease 2521 data in order to more accurately predict the course of epidemics.BackgroundMathematical models for infectious disease s have been used to gain insight into disease dynamics for more than a century [[1]–[4]]. However,
infectious disease 2782 begun to be designed specifically for the task of providing regularly updated quantitative forecasts of infectious disease spread that are analogous to those available for weather prediction. Forecasting approaches vary substantially
infectious disease 3246 [[5]–[8]].In parallel, recent experiences responding to outbreaks have highlighted the significant utility of infectious disease forecasts to support decision-making [[9], [10]]. Models provide critical insight in the face of limited
infectious disease 5019 United States (US) Government agencies have conducted challenge and prize competitions that involved infectious disease forecasting in an effort to help mature operational forecasting technologies. The Center for Disease
infectious disease 8052 incorporated into these models. The Challenge provided a baseline of current forecasting capabilities for infectious disease s and their applicability for vector-borne infectious diseases.Design and execution of the DARPA Chikungunya
infectious disease 8113 of current forecasting capabilities for infectious diseases and their applicability for vector-borne infectious disease s.Design and execution of the DARPA Chikungunya challengeThe introduction of CHIKV into the Western Hemisphere
infectious disease 10057 this, there needs to be a proactive approach to anticipating the geographic and temporal trajectory of infectious disease outbreaks.Mathematical and statistical models (grouped under the morphological category in this manuscript)
infectious disease 11098 encourage non-traditional participants, forecasting approaches, and data sources to improve overall infectious disease forecasting capabilities. The forecast submissions were evaluated and scored on a weighted basis (Table 1).
infectious disease 11554 Evaluation of methodology was performed by a panel of non-competing government subject matter experts in infectious disease modeling, CHIKV, and other vector-borne diseases. Fig. 1Weekly incidence of chikungunya cases, aggregated
infectious disease 38396 Challenge) attempted to address this gap by promoting innovation in data collection techniques and infectious disease modeling and prediction. The Challenge also aimed to identify and characterize methodologies, data streams,
infectious disease 38933 natural biological events. In order to accomplish this, proactive anticipation of the trajectory of infectious disease s outbreaks is required for public health planning. The results from this Challenge may inform future
infectious disease 41342 and quality of reporting and the associated response to an outbreak, making the dream of an effective infectious disease forecasting architecture a reality

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