[Application of ARIMA model in predicting the incidence of tuberculosis in China from 2018 to 2019]

Tuberculosis Prevalence Trends from a Predictive Modelling Study - 10 High-Burden Countries, 1980-2035

What is already known about this topic?

Given the challenges presented by drug-resistant strains of tuberculosis (TB) and the rising mobility of the population, achieving the objective of eradicating TB appears uncertain.

What is added by this report?

The examination of TB incidence trends in 10 high-burden countries (HBCs) indicated a steady rise in cases, with India and China jointly accounting for nearly 70% of the burden. Projections for the future show diverse trajectories in these countries, with potential difficulties in reaching the TB elimination target, especially in Nigeria, Congo, and South Africa.

What are the implications for public health practice?

The number of TB cases is on the rise. It is crucial to learn from successful strategies to improve TB prevention and control worldwide through collaborative efforts.

Interrupted time series analysis using the ARIMA model of the impact of COVID-19 on the incidence rate of notifiable communicable diseases in China

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic in China is ongoing. Some studies have shown that the incidence of respiratory and intestinal infectious diseases in 2020 decreased significantly compared with previous years. Interrupted time series (ITS) is a time series analysis method that evaluates the impact of intervention measures on outcomes and can control the original regression trend of outcomes before and after the intervention. This study aimed to analyse the impact of COVID-19 on the incidence rate of notifiable communicable diseases using ITS in China.

METHODS

National data on the incidence rate of communicable diseases in 2009-2021 were obtained from the National Health Commission website. Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models was used to analyse the changes in the incidence rate of infectious diseases before and after the COVID-19 epidemic.

RESULTS

There was a significant short-term decline in the incidence rates of respiratory infectious diseases and enteric infectious diseases (step values of -29.828 and - 8.237, respectively), which remained at a low level for a long time after the decline. There was a short-term decline in the incidence rates of blood-borne and sexually transmitted infectious diseases (step = -3.638), which tended to recover to previous levels in the long term (ramp = 0.172). There was no significant change in the incidence rate of natural focus diseases or arboviral diseases before and after the epidemic.

CONCLUSION

The COVID-19 epidemic had strong short-term and long-term effects on respiratory and intestinal infectious diseases and short-term control effects on blood-borne and sexually transmitted infectious diseases. Our methods for the prevention and control of COVID-19 can be used for the prevention and control of other notifiable communicable diseases, especially respiratory and intestinal infectious diseases.

Predicting the Number of Reported Pulmonary Tuberculosis in Guiyang, China, Based on Time Series Analysis Techniques

Tuberculosis (TB) is one of the world's deadliest infectious disease killers today, and despite China's increasing efforts to prevent and control TB, the TB epidemic is still very serious. In the context of the COVID-19 pandemic, if reliable forecasts of TB epidemic trends can be made, they can help policymakers with early warning and contribute to the prevention and control of TB. In this study, we collected monthly reports of pulmonary tuberculosis (PTB) in Guiyang, China, from January 1, 2010 to December 31, 2020, and monthly meteorological data for the same period, and used LASSO regression to screen four meteorological factors that had an influence on the monthly reports of PTB in Guiyang, including sunshine hours, relative humidity, average atmospheric pressure, and annual highest temperature, of which relative humidity (6-month lag) and average atmospheric pressure (7-month lag) have a lagging effect with the number of TB reports in Guiyang. Based on these data, we constructed ARIMA, Holt-Winters (additive and multiplicative), ARIMAX (with meteorological factors), LSTM, and multivariable LSTM (with meteorological factors). We found that the addition of meteorological factors significantly improved the performance of the time series prediction model, which, after comprehensive consideration, included the ARIMAX (1,1,1) (0,1,2)₁₂ model with a lag of 7 months at the average atmospheric pressure, outperforms the other models in terms of both fit (RMSE = 37.570, MAPE = 10.164%, MAE = 28.511) and forecast sensitivity (RMSE = 20.724, MAPE = 6.901%, MAE = 17.306), so the ARIMAX (1,1,1) (0,1,2)₁₂ model with a lag of 7 months can be used as a predictor tool for predicting the number of monthly reports of PTB in Guiyang, China.

Application of three prediction models in pesticide poisoning

To establish a reasonable prediction model of pesticide poisoning and predict the future trend of pesticide poisoning in Jiangsu Province, so as to provide the basis for rational allocation of public health resources and formulation of prevention and control strategies, the number of pesticide poisoning in Jiangsu province from 2006 to 2020 was collected. Grey model (GM(1,1)) model, autoregressive integrated moving average model (ARIMA) model and exponential smoothing model were used for prediction and comparative analysis. Finally, the model with the best fitting effect was selected. The average relative errors of ARIMA(0,1,1)(0,1,0)₁₂ model, Holt-Winters multiplicative model and GM(1,1) were 0.096, 0.058 and 0.274 separately. The fitting effect of GM model is the worst, while the fitting effect of ARIMA(0,1,1) (0,1,0)₁₂ model and Holt-Winters multiplication model is relatively good, which can be basically used for prediction. Holt-Winters multiplicative model has the best fitting effect and the highest accuracy in predicting the number of pesticide poisoning. The numbers of pesticide poisonings in the next 3 years are 454, 410 and 368, with a total of 1232, according to the Holt-Winters multiplicative model. Through the prediction of the number of pesticide poisoning in the next 3 years, this paper also provides a basis for the formulation of pesticide-related policies in the future.

Time series analysis of cumulative incidences of typhoid and paratyphoid fevers in China using both Grey and SARIMA models

Typhoid and paratyphoid fevers are common enteric diseases causing disability and death in China. Incidence data of typhoid and paratyphoid between 2004 and 2016 in China were analyzed descriptively to explore the epidemiological features such as age-specific and geographical distribution. Cumulative incidence of both fevers displayed significant decrease nationally, displaying a drop of 73.9% for typhoid and 86.6% for paratyphoid in 2016 compared to 2004. Cumulative incidence fell in all age subgroups and the 0–4 years-old children were the most susceptible ones in recent years. A cluster of three southwestern provinces (Yunnan, Guizhou, and Guangxi) were the top high-incidence regions. Grey model GM (1,1) and seasonal autoregressive integrated moving average (SARIMA) model were employed to extract the long-term trends of the diseases. Annual cumulative incidence for typhoid and paratyphoid were formulated by GM (1,1) as x^(t)=−14.98(e−0.10(t−2004)−e−0.10(t−2005)) and x^(t)=−4.96(e−0.19(t−2004)−e−0.19(t−2005)) respectively. SARIMA (0,1,7) × (1,0,1)₁₂ was selected among a collection of constructed models for high R² and low errors. The predictive models for both fevers forecasted cumulative incidence to continue the slightly downward trend and maintain the cyclical seasonality in near future years. Such data-driven insights are informative and actionable for the prevention and control of typhoid and paratyphoid fevers as serious infectious diseases.