Last data update: May 06, 2024. (Total: 46732 publications since 2009)
Records 1-3 (of 3 Records) |
Query Trace: Mier YTeran-Romero L[original query] |
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Estimating incidence of infection from diverse data sources: Zika virus in Puerto Rico, 2016 (preprint)
Quandelacy TM , Healy JM , Greening B , Rodriguez DM , Chung KW , Kuehnert MJ , Biggerstaff BJ , Dirlikov E , Mier YTeran-Romero L , Sharp TM , Waterman S , Johansson MA . medRxiv 2020 2020.10.14.20212134 Emerging epidemics are challenging to track. Only a subset of cases is recognized and reported, as seen with the Zika virus (ZIKV) epidemic where large proportions of infection were asymptomatic. However, multiple imperfect indicators of infection provide an opportunity to estimate the underlying incidence of infection. We developed a modeling approach that integrates a generic Time-series Susceptible-Infected-Recovered epidemic model with assumptions about reporting biases in a Bayesian framework and applied it to the 2016 Zika epidemic in Puerto Rico using three indicators: suspected arboviral cases, suspected Zika-associated Guillain-Barré Syndrome cases, and blood bank data. Using this combination of surveillance data, we estimated the peak of the epidemic occurred during the week of August 15, 2016 (the 33rd week of year), and 120 to 140 (50% credible interval [CrI], 95% CrI: 97 to 170) weekly infections per 10,000 population occurred at the peak. By the end of 2016, we estimated that approximately 890,000 (95% CrI: 660,000 to 1,100,000) individuals were infected in 2016 (26%, 95% CrI: 19% to 33%, of the population infected). Utilizing multiple indicators offers the opportunity for real-time and retrospective situational awareness to support epidemic preparedness and response.Competing Interest StatementThe authors have declared no competing interest.Funding StatementThe author(s) received no specific funding for this work.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:Exemption was obtained from the CDC Human Subjects Research Office as the data were collected as part of regular surveillance activities.All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll relevant data are within the manuscript and its Supporting Information files. |
Estimating incidence of infection from diverse data sources: Zika virus in Puerto Rico, 2016
Quandelacy TM , Healy JM , Greening B , Rodriguez DM , Chung KW , Kuehnert MJ , Biggerstaff BJ , Dirlikov E , Mier YTeran-Romero L , Sharp TM , Waterman S , Johansson MA . PLoS Comput Biol 2021 17 (3) e1008812 Emerging epidemics are challenging to track. Only a subset of cases is recognized and reported, as seen with the Zika virus (ZIKV) epidemic where large proportions of infection were asymptomatic. However, multiple imperfect indicators of infection provide an opportunity to estimate the underlying incidence of infection. We developed a modeling approach that integrates a generic Time-series Susceptible-Infected-Recovered epidemic model with assumptions about reporting biases in a Bayesian framework and applied it to the 2016 Zika epidemic in Puerto Rico using three indicators: suspected arboviral cases, suspected Zika-associated Guillain-Barré Syndrome cases, and blood bank data. Using this combination of surveillance data, we estimated the peak of the epidemic occurred during the week of August 15, 2016 (the 33rd week of year), and 120 to 140 (50% credible interval [CrI], 95% CrI: 97 to 170) weekly infections per 10,000 population occurred at the peak. By the end of 2016, we estimated that approximately 890,000 (95% CrI: 660,000 to 1,100,000) individuals were infected in 2016 (26%, 95% CrI: 19% to 33%, of the population infected). Utilizing multiple indicators offers the opportunity for real-time and retrospective situational awareness to support epidemic preparedness and response. |
An open challenge to advance probabilistic forecasting for dengue epidemics.
Johansson MA , Apfeldorf KM , Dobson S , Devita J , Buczak AL , Baugher B , Moniz LJ , Bagley T , Babin SM , Guven E , Yamana TK , Shaman J , Moschou T , Lothian N , Lane A , Osborne G , Jiang G , Brooks LC , Farrow DC , Hyun S , Tibshirani RJ , Rosenfeld R , Lessler J , Reich NG , Cummings DAT , Lauer SA , Moore SM , Clapham HE , Lowe R , Bailey TC , Garcia-Diez M , Carvalho MS , Rodo X , Sardar T , Paul R , Ray EL , Sakrejda K , Brown AC , Meng X , Osoba O , Vardavas R , Manheim D , Moore M , Rao DM , Porco TC , Ackley S , Liu F , Worden L , Convertino M , Liu Y , Reddy A , Ortiz E , Rivero J , Brito H , Juarrero A , Johnson LR , Gramacy RB , Cohen JM , Mordecai EA , Murdock CC , Rohr JR , Ryan SJ , Stewart-Ibarra AM , Weikel DP , Jutla A , Khan R , Poultney M , Colwell RR , Rivera-Garcia B , Barker CM , Bell JE , Biggerstaff M , Swerdlow D , Mier YTeran-Romero L , Forshey BM , Trtanj J , Asher J , Clay M , Margolis HS , Hebbeler AM , George D , Chretien JP . Proc Natl Acad Sci U S A 2019 116 (48) 24268-24274 A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue. |
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