Collateral effects of COVID-19 countermeasures on hepatitis E incidence pattern: a case study of china based on time series models

Background There are abundant studies on COVID-19 but few on its impact on hepatitis E. We aimed to assess the effect of the COVID-19 countermeasures on the pattern of hepatitis E incidence and explore the application of time series models in analyzing this pattern. Methods Our pivotal idea was to fit a pre-COVID-19 model with data from before the COVID-19 outbreak and use the deviation between forecast values and actual values to reflect the effect of COVID-19 countermeasures. We analyzed the pattern of hepatitis E incidence in China from 2013 to 2018. We evaluated the fitting and forecasting capability of 3 methods before the COVID-19 outbreak. Furthermore, we employed these methods to construct pre-COVID-19 incidence models and compare post-COVID-19 forecasts with reality. Results Before the COVID-19 outbreak, the Chinese hepatitis E incidence pattern was overall stationary and seasonal, with a peak in March, a trough in October, and higher levels in winter and spring than in summer and autumn, annually. Nevertheless, post-COVID-19 forecasts from pre-COVID-19 models were extremely different from reality in sectional periods but congruous in others. Conclusions Since the COVID-19 pandemic, the Chinese hepatitis E incidence pattern has altered substantially, and the incidence has greatly decreased. The effect of the COVID-19 countermeasures on the pattern of hepatitis E incidence was temporary. The incidence of hepatitis E was anticipated to gradually revert to its pre-COVID-19 pattern..

Medienart:

E-Artikel

Erscheinungsjahr:

2024

Erschienen:

2024

Enthalten in:

Zur Gesamtaufnahme - volume:24

Enthalten in:

BMC infectious diseases - 24(2024), 1 vom: 27. März

Sprache:

Englisch

Beteiligte Personen:

Qin, Yajun [VerfasserIn]
Peng, Haiyang [VerfasserIn]
Li, Jinhao [VerfasserIn]
Gong, Jianping [VerfasserIn]

Links:

Volltext [kostenfrei]

BKL:

44.00

Themen:

COVID-19
Computer neural networks
Forecasting
Hepatitis E
Incidence

Anmerkungen:

© The Author(s) 2024

doi:

10.1186/s12879-024-09243-x

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

SPR055326250