Interactions among common non‐SARS‐CoV‐2 respiratory viruses and influence of the COVID‐19 pandemic on their circulation in New York City
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Abstract
Background
Non‐pharmaceutical interventions (NPIs) and voluntary behavioral changes during the COVID‐19 pandemic have influenced the circulation of non‐SARS‐CoV‐2 respiratory infections. We aimed to examine interactions among common non‐SARS‐CoV‐2 respiratory virus and further estimate the impact of the COVID‐19 pandemic on these viruses.
Methods
We analyzed incidence data for seven groups of respiratory viruses in New York City (NYC) during October 2015 to May 2021 (i.e., before and during the COVID‐19 pandemic). We first used elastic net regression to identify potential virus interactions and further examined the robustness of the found interactions by comparing the performance of Seasonal Auto Regressive Integrated Moving Average (SARIMA) models with and without the interactions. We then used the models to compute counterfactual estimates of cumulative incidence and estimate the reduction during the COVID‐19 pandemic period from March 2020 to May 2021, for each virus.
Results
We identified potential interactions for three endemic human coronaviruses (CoV‐NL63, CoV‐HKU, and CoV‐OC43), parainfluenza (PIV)‐1, rhinovirus, and respiratory syncytial virus (RSV). We found significant reductions (by ~70–90%) in cumulative incidence of CoV‐OC43, CoV‐229E, human metapneumovirus, PIV‐2, PIV‐4, RSV, and influenza virus during the COVID‐19 pandemic. In contrast, the circulation of adenovirus and rhinovirus was less affected.
Conclusions
Circulation of several respiratory viruses has been low during the COVID‐19 pandemic, which may lead to increased population susceptibility. It is thus important to enhance monitoring of these viruses and promptly enact measures to mitigate their health impacts (e.g., influenza vaccination campaign and hospital infection prevention) as societies resume normal activities.
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SciScore for 10.1101/2021.10.14.21264861: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has several limitations. First, the data analyzed here are a subset of all tests done in NYC (i.e., only those from laboratories using the expanded respiratory panel tests) and thus may not be fully representative of the entire population. Second, even though the selection criteria have not changed during the study period, …
SciScore for 10.1101/2021.10.14.21264861: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has several limitations. First, the data analyzed here are a subset of all tests done in NYC (i.e., only those from laboratories using the expanded respiratory panel tests) and thus may not be fully representative of the entire population. Second, even though the selection criteria have not changed during the study period, underlying patient characteristics may differ among specimens tested before and during the COVID-19 period, due to changing medical seeking behaviors in response to COVID-19 (e.g., people with mild respiratory symptoms may be more likely to seek testing at the early stage of the pandemic due to concern of COVID-19); this in turn may temporally change the composition of underlying sample population. Third, fewer specimens were tested each week during the early phase of the pandemic due to limited testing supplies and human resources; this reduced sampling likely increased model uncertainty. Fourth, the identified associations (Table 1) were based on population-level epidemic time series and do not imply any causal interactions between each included virus pairs. Future research at the individual level (e.g. frequency of co-infection or subsequent infections by multiple viruses in the same individuals) is warranted to further examine the potential viral interactions reported here. Finally, although we found substantial case reductions during the pandemic for several non-SARS-CoV-2 respiratory viruses, it is difficult to distinguish the impact due to ...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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