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10.1371/journal.pcbi.1008301 http://scihub22266oqcxt.onion/10.1371/journal.pcbi.1008301 33090997!7608986!33090997     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33090997&cmd=llinks): Failed to open stream: HTTP request failed! 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Aggregating forecasts of multiple respiratory pathogens supports more accurate forecasting of influenza-like illness |pdf=|usr=}}{{33090997}} gab.com Text Aggregating forecasts of multiple respiratory pathogens supports more accurate forecasting of influenza-like illness JO: PLoS Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=33090997 #FOAvip Influenza-like illness (ILI) is a commonly measured syndromic signal representative of a range of acute respiratory infections. Reliable forecasts of ILI can support better preparation for patient surges in healthcare systems. Although ILI is an amalgamation of multiple pathogens with variable seasonal phasing and attack rates, most existing process-based forecasting systems treat ILI as a single infectious agent. Here, using ILI records and virologic surveillance data, we show that ILI signal can be disaggregated into distinct viral components. We generate separate predictions for six contributing pathogens (influenza A/H1, A/H3, B, respiratory syncytial virus, and human parainfluenza virus types 1-2 and 3), and develop a method to forecast ILI by aggregating these predictions. The relative contribution of each pathogen to the total ILI signal is estimated using a Markov Chain Monte Carlo (MCMC) method upon forecast aggregation. We find highly variable overall contributions from influenza type A viruses across seasons, but relatively stable contributions for the other pathogens. Using historical data from 1997 to 2014 at US national and regional levels, the proposed forecasting system generates improved predictions of both seasonal and near-term targets relative to a baseline method that simulates ILI as a single pathogen. The hierarchical forecasting system can generate predictions for each viral component, as well as infer and predict their contributions to ILI, which may additionally help physicians determine the etiological causes of ILI in clinical settings. Twit Text FOAVip Aggregating forecasts of multiple respiratory pathogens supports more accurate forecasting of influenza-like illness ????PLoS Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=33090997 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33090997 #FOAvip English Wikipedia <ref>{{Cite pmid|33090997}}</ref> Aggregating forecasts of multiple respiratory pathogens supports more accurate forecasting of influenza-like illness #MMPMID33090997 Pei S; Shaman J PLoS Comput Biol 2020[Oct]; 16 (10): e1008301 PMID33090997show ga Influenza-like illness (ILI) is a commonly measured syndromic signal representative of a range of acute respiratory infections. Reliable forecasts of ILI can support better preparation for patient surges in healthcare systems. Although ILI is an amalgamation of multiple pathogens with variable seasonal phasing and attack rates, most existing process-based forecasting systems treat ILI as a single infectious agent. Here, using ILI records and virologic surveillance data, we show that ILI signal can be disaggregated into distinct viral components. We generate separate predictions for six contributing pathogens (influenza A/H1, A/H3, B, respiratory syncytial virus, and human parainfluenza virus types 1-2 and 3), and develop a method to forecast ILI by aggregating these predictions. The relative contribution of each pathogen to the total ILI signal is estimated using a Markov Chain Monte Carlo (MCMC) method upon forecast aggregation. We find highly variable overall contributions from influenza type A viruses across seasons, but relatively stable contributions for the other pathogens. Using historical data from 1997 to 2014 at US national and regional levels, the proposed forecasting system generates improved predictions of both seasonal and near-term targets relative to a baseline method that simulates ILI as a single pathogen. The hierarchical forecasting system can generate predictions for each viral component, as well as infer and predict their contributions to ILI, which may additionally help physicians determine the etiological causes of ILI in clinical settings. |*Models, Statistical[MESH] |Computational Biology/*methods[MESH] |Forecasting[MESH] |Humans[MESH] |Influenza, Human/*epidemiology[MESH] |Respiratory Tract Infections/epidemiology[MESH]Aggregating forecasts of multiple respiratory pathogens supports more (epmc).pdf DeepDyve Pubget Overpricing