A comparative study of three models to analyze the impact of air pollutants on the number of pulmonary tuberculosis cases in Urumqi, Xinjiang

Wang, Ying-Dan; Gao, Chunjie; Zhao, Tiantian; Haiyan, Jiao; Liao, Ying; Hu, Zengyun; Wang, Lei

doi:10.1371/journal.pone.0277314

Cited by 1 publication

(1 citation statement)

References 34 publications

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“…To assess the model's performance, the correlation coefficient (CC), absolute error (AE), root mean square error (RMSE) and distance between indices of simulation and observation (DISO) are employed as in previous studies (7,13,26,35,36). The DISO index is a comprehensive assessment of multiple models, which is a merge of different statistical metrics with dimensions from 1 to n (37)(38)(39).…”

Section: Methodsmentioning

confidence: 99%

Dynamic variations in and prediction of COVID-19 with omicron in the four first-tier cities of mainland China, Hong Kong, and Singapore

Ni,

Sun,

et al. 2023

Front. Public Health

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BackgroundThe COVID-19 pandemic, which began in late 2019, has resulted in the devastating collapse of the social economy and more than 10 million deaths worldwide. A recent study suggests that the pattern of COVID-19 cases will resemble a mini-wave rather than a seasonal surge. In general, COVID-19 has more severe impacts on cities than on rural areas, especially in cities with high population density.MethodsIn this study, the background situation of COVID-19 transmission is discussed, including the population number and population density. Moreover, a widely used time series autoregressive integrated moving average (ARIMA) model is applied to simulate and forecast the COVID-19 variations in the six cities. We comprehensively analyze the dynamic variations in COVID-19 in the four first-tier cities of mainland China (BJ: Beijing, SH: Shanghai, GZ: Guangzhou and SZ: Shenzhen), Hong Kong (HK), China and Singapore (SG) from 2020 to 2022.ResultsThe major results show that the six cities have their own temporal characteristics, which are determined by the different control and prevention measures. The four first-tier cities of mainland China (i.e., BJ, SH, GZ, and SZ) have similar variations with one wave because of their identical “Dynamic COVID-19 Zero” strategy and strict Non-Pharmaceutical Interventions (NPIs). HK and SG have multiple waves primarily caused by the input cases. The ARIMA model has the ability to provide an accurate forecast of the COVID-19 pandemic trend for the six cities, which could provide a useful approach for predicting the short-term variations in infectious diseases.Accurate forecasting has significant value for implementing reasonable control and prevention measures.ConclusionsOur main conclusions show that control and prevention measures should be dynamically adjusted and organically integrated for the COVID-19 pandemic. Moreover, the mathematical models are proven again to provide an important scientific basis for disease control.

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