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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/308| Accurate influenza forecasts using type-specific incidence data for small geographical units | |
| Steven Riley Michal Ben-Nun Pete Riley James Turtle | |
| Novel Coronavirus | |
| Acceso Abierto | |
| Atribución | |
| 10.1101/19012807 | |
| Influenza incidence forecasting is used to facilitate better health system planning and could potentially be used to allow at-risk individuals to modify their behavior. For example, the US Centers for Disease Control and Prevention (CDC) runs an annual competition to forecast influenza-like illness (ILI) at the regional and national levels in the US, based on a standard discretized incidence scale. Here, we use a suite of forecasting models to analyze type-specific incidence at the smaller spatial scale of clusters of nearby counties. We used data from point-of-care (POC) diagnostic machines over three seasons, in 10 clusters, capturing: 57 counties; 1,061,891 total specimens; and 173,909 specimens positive for Influenza A. Total specimens were closely correlated with comparable CDC ILI data. Mechanistic models were substantially more accurate when forecasting influenza A positive POC data than total specimen POC data, especially at longer lead times. Also, models that fit individual counties separately were better able to forecast clusters than were models that directly fit to aggregated cluster data. Public health authorities may wish to consider developing forecasting pipelines for type-specific POC data in addition to ILI data. Simple mechanistic models will likely improve forecast accuracy when applied at small spatial scales to pathogen-specific data before being scaled to larger geographical units and broader syndromic data. Highly local forecasts may enable new public health messaging to encourage at-risk individuals to temporarily reduce their social mixing. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement No specific funding. ### Author Declarations All relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript. Yes All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I 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). Yes I 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. Yes All weekly incidence data for the smallest spatial unit (counties) are available immediately from the authors for non-commercial use in a format that can be used immediately with the forecasting tools used to generate these results. The forecasting tools are available with documentation and examples from https://github.com/predsci/DICE_quidel_manuscript. | |
| Cold Spring Harbor Laboratory Press | |
| 2019 | |
| Preimpreso | |
| https://www.medrxiv.org/content/10.1101/19012807v1 | |
| Inglés | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Artículos científicos |
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| Fichero | Tamaño | Formato | |
|---|---|---|---|
| Accurate influenza forecasts.pdf | 4.75 MB | Adobe PDF | Visualizar/Abrir |