A Multicentre Study on the Prevalence of Respiratory Viruses in Children with Community Acquired Pneumonia Requiring Hospitalization in the Setting of the Zero-COVID Policy in China (preprint)

Stringent nonpharmaceutical interventions (NPIs) have been implemented worldwide to combat the COVID-19 pandemic, and the circulation and seasonality of common respiratory viruses have subsequently changed. Multicentre studies and comparisons of the prevalence of respiratory viruses accounting for community-acquired pneumonia (CAP) in hospitalized children between the pre-COVID period and the period after community and school reopening in the setting of the zero-COVID policy are rare. In this study, we included 1543 children with CAP who required hospitalization from November 1st, 2020 to April 30th, 2021 (Period 1) and 629 children with the same conditions from November 1st, 2018 to April 30th, 2019 (Period 2) in our study. All respiratory samples from the included patients were screened for six respiratory viruses (respiratory syncytial virus [RSV], adenovirus [ADV], influenza A virus [Flu A], influenza B virus [Flu B], parainfluenza virus type 1 [PIV1], and parainfluenza virus type 3 [PIV3]) using a multiplex real-time PCR assay. The median ages of enrolled patients at the time of diagnosis were 1.5 years and 1.0 years for period 1 and period 2, respectively. In period 1, viral pathogens were detected in 50.3% (776/1543) of enrolled patients. The most frequently identified viral pathogen was RSV (35.9%, 554/1543), followed by PIV3 (9.6%, 148/1543), PIV1 (3.6%, 56/1543), ADV (3.4%, 52/1543), Flu A (1.0%, 16/1543) and Flu B (0.8%, 13/1543). The total detection rates of these six viruses in the peak season of CAP were at the pre-COVID level. The prevalence of Flu A decreased dramatically and circulation activity was low compared to pre-COVID levels, while the incidence of PIV3 increased significantly. There were no significant differences in the detection rates of RSV, ADV, Flu B and PIV1 between the two periods. Our results showed that respiratory viruses accounted for CAP in hospitalized children at pre-COVID levels as communities and schools reopened within the zero-COVID policy, although the prevalence aetiology spectrum varied.

COVID-19: Short term prediction model using daily incidence data (preprint)

BackgroundPrediction of the dynamics of new SARS-CoV-2 infections during the current COVID-19 pandemic is critical for public health planning of efficient health care allocation and monitoring the effects of policy interventions. We describe a new approach that forecasts the number of incident cases in the near future given past occurrences using only a small number of assumptions. MethodsOur approach to forecasting future COVID-19 cases involves 1) modeling the observed incidence cases using a Poisson distribution for the daily incidence number, and a gamma distribution for the series interval; 2) estimating the effective reproduction number assuming its value stays constant during a short time interval; and 3) drawing future incidence cases from their posterior distributions, assuming that the current transmission rate will stay the same, or change by a certain degree. ResultsWe apply our method to predicting the number of new COVID-19 cases in a single state in the U.S. and for a subset of counties within the state to demonstrate the utility of this method at varying scales of prediction. Our method produces reasonably accurate results when the effective reproduction number is distributed similarly in the future as in the past. Large deviations from the predicted results can imply that a change in policy or some other factors have occurred that have dramatically altered the disease transmission over time. ConclusionWe presented a modelling approach that we believe can be easily adopted by others, and immediately useful for local or state planning.

COVID-19: Short Term Prediction Model Using Daily Incidence Data (preprint)

Background: Prediction of the dynamics of new SARS-CoV-2 infections during the current COVID-19 pandemic is critical for public health planning of efficient health care allocation and monitoring the effects of policy interventions. We describe a new approach that forecasts the number of incident cases in the near future given past occurrences using only a small number of assumptions. Methods: Our approach to forecasting future COVID-19 cases involves 1) modeling the observed incidence cases using a Poisson distribution for the daily incidence number, and a gamma distribution for the series interval; 2) estimating the effective reproduction number assuming its value stays constant during a short time interval; and 3) drawing future incidence cases from their posterior distributions, assuming that the current transmission rate will stay the same, or change by a certain degree.Findings: We apply our method to predicting the number of new COVID-19 cases in a single state in the U.S. and for a subset of counties within the state to demonstrate the utility of this method at varying scales of prediction. Our method produces reasonably accurate results when the effective reproduction number is distributed similarly in the future as in the past. Large deviations from the predicted results can imply that a change in policy or some other factors have occurred that have dramatically altered the disease transmission over time.Interpretation: We presented a modelling approach that we believe can be easily adopted by others, and immediately useful for local or state planning.Funding: No external funding.Declaration of Interests: All authors declare no competing interests.