ArticleScientific reports2023
Impact of COVID-19 prevention and control on tuberculosis and scarlet fever in China's Guizhou.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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4 citing papers in PubMed, 17 citations in OpenAlex.
- Impact of the COVID-19 Pandemic on Vaccine-Preventable Diseases in Mexico: A Time Series Analysis (2014-2024).Epidemiologia (Basel, Switzerland) · 2026Article
- Epidemiological changes of scarlet fever before, during and after the COVID-19 pandemic in Chongqing, China: a 19-year surveillance and prediction study.BMC public health · 2024Article
- Collateral effects of COVID-19 countermeasures on hepatitis E incidence pattern: a case study of china based on time series models.BMC infectious diseases · 2024Article
- Temporal disruption in tuberculosis incidence patterns during COVID-19: a time series analysis in China.PeerJ · 2024Article
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6 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
China has implemented a series of long-term measures to control the spread of COVID-19, however, the effects of these measures on other chronic and acute respiratory infectious diseases remain unclear. Tuberculosis (TB) and scarlet fever (SF) serve as representatives of chronic and acute respiratory infectious diseases, respectively. In China's Guizhou province, an area with a high prevalence of TB and SF, approximately 40,000 TB cases and hundreds of SF cases are reported annually. To assess the impact of COVID-19 prevention and control on TB and SF in Guizhou, the exponential smoothing method was employed to establish a prediction model for analyzing the influence of COVID-19 prevention and control on the number of TB and SF cases. Additionally, spatial aggregation analysis was utilized to describe spatial changes in TB and SF before and after the COVID-19 outbreak. The parameters of the TB and SF prediction models are R
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