Dataset Information

Rhinovirus infections in western Sweden: a four-year molecular epidemiology study comparing local and globally appearing types.

ABSTRACT: Human rhinovirus (HRV) is a highly prevalent pathogen and a major cause of acute respiratory tract infection (ARTI). HRV express less seasonality than other viral ARTIs, which typically appear as seasonal epidemics lasting for 1-2 months. The aim of this study was to investigate the seasonal patterns of HRV types over four consecutive years in one geographic region. HRV identified in respiratory samples from 114 patients over a four-year period were analysed by VP4/VP2 sequencing. HRV-A was found in 64, HRV-B in 11 and HRV-C in 37 cases. Overall, 33 different HRV-A types, nine B types and 21 C types were found. As many as 21 of the HRV types appeared during several seasons, with a maximum time-span of four years. Some types appeared during successive seasons and, in some cases, phylogenetic analysis indicated extended periods of circulation locally. Most of the strains were closely related to HRV identified in other parts of the world during the same time period. HRV strains that circulate locally represent many types and seem to reflect that HRV infections are highly globalised. The existence of simultaneous or successive epidemics with different HRV types in combination with the ability of each type to remain in the local population over extended periods of time may contribute to explaining the high rate of HRV infections.

SUBMITTER: Sansone M

PROVIDER: S-EPMC7087832 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

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Rhinovirus infections in western Sweden: a four-year molecular epidemiology study comparing local and globally appearing types.

Sansone M M Andersson M M Brittain-Long R R Andersson L-M LM Olofsson S S Westin J J Lindh M M

European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology 20130223 7

Human rhinovirus (HRV) is a highly prevalent pathogen and a major cause of acute respiratory tract infection (ARTI). HRV express less seasonality than other viral ARTIs, which typically appear as seasonal epidemics lasting for 1-2 months. The aim of this study was to investigate the seasonal patterns of HRV types over four consecutive years in one geographic region. HRV identified in respiratory samples from 114 patients over a four-year period were analysed by VP4/VP2 sequencing. HRV-A was foun ...[more]

PMID: 23435753

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Project description:BackgroundAdvances in molecular diagnostics have implicated newly-discovered respiratory viruses in the pathogenesis of pneumonia. We aimed to determine the prevalence and clinical characteristics of human bocavirus (hBoV), human rhinovirus (hRV), polyomavirus-WU (WUPyV) and -KI (KIPyV) and human coronaviruses (CoV)-OC43, -NL63, -HKU1 and -229E among children hospitalized with lower respiratory tract infections (LRTI).MethodsMultiplex real-time reverse-transcriptase polymerase chain reaction was undertaken on archived nasopharyngeal aspirates from HIV-infected and -uninfected children (<2 years age) hospitalized for LRTI, who had been previously investigated for respiratory syncytial virus, human metapneumovirus, parainfluenza I-III, adenovirus and influenza A/B.ResultsAt least one of these viruses were identified in 274 (53.0%) of 517 and in 509 (54.0%) of 943 LRTI-episodes in HIV-infected and -uninfected children, respectively. Human rhinovirus was the most prevalent in HIV-infected (31.7%) and -uninfected children (32.0%), followed by CoV-OC43 (12.2%) and hBoV (9.5%) in HIV-infected; and by hBoV (13.3%) and WUPyV (11.9%) in HIV-uninfected children. Polyomavirus-KI (8.9% vs. 4.8%; p = 0.002) and CoV-OC43 (12.2% vs. 3.6%; p<0.001) were more prevalent in HIV-infected than -uninfected children. Combined with previously-tested viruses, respiratory viruses were identified in 60.9% of HIV-infected and 78.3% of HIV-uninfected children. The newly tested viruses were detected at high frequency in association with other respiratory viruses, including previously-investigated viruses (22.8% in HIV-infected and 28.5% in HIV-uninfected children).ConclusionsWe established that combined with previously-investigated viruses, at least one respiratory virus was identified in the majority of HIV-infected and HIV-uninfected children hospitalized for LRTI. The high frequency of viral co-infections illustrates the complexities in attributing causality to specific viruses in the aetiology of LRTI and may indicate a synergetic role of viral co-infections in the pathogenesis of childhood LRTI.

Project description:ImportanceAssumption of a well-mixed population during modeling is often erroneously made without due analysis of its validity. Ignoring the importance of the geo-spatial granularity at which the data is collected could have significant implications on the quality of forecasts and the actionable clinical recommendations that are based on it.ObjectiveThis paper's primary objective is to test the hypothesis that the characteristic dynamics defining the trajectory of the pandemic in a region is lost when the data is aggregated and modeled at higher geo-spatial levels.DesignWe use publicly available confirmed SARS-CoV-2 cases and deaths from January 1st, 2020 to August 3rd, 2020 in the United States at different geo-spatial granularities to conduct our experiments. To understand the impact of this hypothesis, the output of this study was implemented in Tampa General Hospital (TGH) to provide resource demand forecast.ResultsThe Mean Absolute Percentage Error (MAPE) in the forecast confirmed cases can be 30% higher for modeling at the state-level than aggregating model results at the scale of counties or clusters of counties. Similarly, modeling at a state-level and crafting policy decisions based on them may not be effective - county-level forecasts made by partitioning state-level forecasts are 3x worse for confirmed cases and 20x worse for deaths relative to the same model at the county level. By leveraging these results, TGH was able to accurately allocate clinical resources to tackle COVID-19 cases, continue elective surgical procedures largely uninterrupted and avoid costly construction of overflow capacity in the first two epidemic waves.Conclusions and relevanceAccurate forecasting at the county level requires hyper-local modeling with county resolution. State-level modeling does not accurately predict community spread in smaller sub-regions because state populations are not well mixed, resulting in large prediction errors. Actionable decisions such as deciding whether to cancel planned surgeries or construct overflow capacity require models with local specificity.

Dataset Information

Rhinovirus infections in western Sweden: a four-year molecular epidemiology study comparing local and globally appearing types.

Publications

Rhinovirus infections in western Sweden: a four-year molecular epidemiology study comparing local and globally appearing types.

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