Improved autoregressive integrated moving average model for COVID-19 prediction by using statistical significance and clustering techniques

Exploring the influence of environmental indicators and forecasting influenza incidence using ARIMAX models

Background

Influenza is a respiratory infection that poses a significant health burden worldwide. Environmental indicators, such as air pollutants and meteorological factors, play a role in the onset and propagation of influenza. Accurate predictions of influenza incidence and understanding the factors influencing it are crucial for public health interventions. Our study aims to investigate the impact of various environmental indicators on influenza incidence and apply the ARIMAX model to integrate these exogenous variables to enhance the accuracy of influenza incidence predictions.

Method

Descriptive statistics and time series analysis were employed to illustrate changes in influenza incidence, air pollutants, and meteorological indicators. Cross correlation function (CCF) was used to evaluate the correlation between environmental indicators and the influenza incidence. We used ARIMA and ARIMAX models to perform predictive analysis of influenza incidence.

Results

From January 2014 to September 2023, a total of 21,573 cases of influenza were reported in Fuzhou, with a noticeable year-by-year increase in incidence. The peak of influenza typically occurred around January each year. The results of CCF analysis showed that all 10 environmental indicators had a significant impact on the incidence of influenza. The ARIMAX(0, 0, 1) (1, 0, 0)12 with PM10(lag5) model exhibited the best prediction performance, as indicated by the lowest AIC, AICc, and BIC values, which were 529.740, 530.360, and 542.910, respectively. The model achieved a fitting RMSE of 2.999 and a predicting RMSE of 12.033.

Conclusion

This study provides insights into the impact of environmental indicators on influenza incidence in Fuzhou. The ARIMAX(0, 0, 1) (1, 0, 0)12 with PM10(lag5) model could provide a scientific basis for formulating influenza control policies and public health interventions. Timely prediction of influenza incidence is essential for effective epidemic control strategies and minimizing disease transmission risks.

Impact of the COVID-19 pandemic on cardiovascular mortality and contrast analysis within subgroups

Background

An increase in deaths has been perceived during the pandemic, which cannot be explained only by COVID-19. The actual number of deaths far exceeds the recorded data on deaths directly related to SARS-CoV-2 infection. Data from early and short-lived pandemic studies show a dramatic shift in cardiovascular mortality. Grounded in the post-pandemic era, macroscopic big data on cardiovascular mortality during the pandemic need to be further reviewed and studied, which is crucial for cardiovascular disease prevention and control.

Methods

We retrieved and collected data associated with cardiovascular disease mortality from the National Vital Statistic System from the Center for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) platform based on the ICD-10 codes. We applied regression analysis to characterize overall cardiovascular disease mortality trends from 2010 to 2023 and built a time series model to predict mortality for 2020-2023 based on mortality data from 2010 to 2019 in order to affirm the existence of the excess deaths by evaluating observed vs. predicted mortality. We also conducted subgroup analyses by sex, age and race/ethnicity for the purpose of obtaining more specific sociodemographic information.

Results

All-cause age-standardised mortality rates (ASMRs) for CVD dramatically increased between 2019 and 2021[annual percentage change (APC) 11.27%, p < 0.01], and then decreased in the following 2021-2023(APC: -7.0%, p < 0.01). Subgroup analyses found that the ASMR change was most pronounced in Alaska Indians/Native American people (APC: 16.5% in 2019-2021, -12.5% in 2021-2023, both p < 0.01), Hispanics (APC: 12.1% in 2019-2021, -12.2% in 2021-2023, both p < 0.05) and non-Hispanic Black people (APC:11.8% in 2019-2021, -10.3% in 2021-2023, both p < 0.01)whether during the increasing or declining phase. Similarly, the ASMR change was particularly dramatic for the 25-44 age group (APC:19.8% in 2019-2021, -15.4% in 2021-2023, both p < 0.01) and males (APC: 11.5% in 2019-2021, -7.6% in 2021-2023, both p < 0.01). By the end of 2023, the proportion of COVID-related excess death remained high among the elderly (22.4%), males (42.8%) and Alaska Indians/Native American people(39.7%). In addition, we did not find the presence of excess deaths in the young (25-44) and middle-aged cohort (45-64) in 2023, while excess deaths remained persistent in the elderly.

Conclusions

All-cause ASMRs for CVD increased notably during the initial two years of the COVID-19 pandemic and then witnessed a decline in 2021-2023. The cohorts (the young, males and minorities) with the steepest rise in mortality decreased at the fastest rate instead. Previous initiatives to promote cardiovascular health were effective, but further research on cardiovascular healthcare for the elderly and racial disparities should be attached to priority considering the presence of sociodemographic differences in CVD death.