Temporal and long-term trend analysis of class C notifiable diseases in China from 2009 to 2014

Abstract

ObjectivesTime series models are effective tools for disease forecasting. This study aims to explore the time series behaviour of 11 notifiable diseases in China and to predict their incidence through effective models.Settings and participantsThe Chinese Ministry of Health started to publish class C notifiable diseases in 2009. The monthly reported case time series of 11 infectious diseases from the surveillance system between 2009 and 2014 was collected.MethodsWe performed a descriptive and a time series study using the surveillance data. Decomposition methods were used to explore (1) their seasonality expressed in the form of seasonal indices and (2) their long-term trend in the form of a linear regression model. Autoregressive integrated moving average (ARIMA) models have been established for each disease.ResultsThe number of cases and deaths caused by hand, foot and mouth disease ranks number 1 among the detected diseases. It occurred most often in May and July and increased, on average, by 0.14126/100 000 per month. The remaining incidence models show good fit except the influenza and hydatid disease models. Both the hydatid disease and influenza series become white noise after differencing, so no available ARIMA model can be fitted for these two diseases.ConclusionTime series analysis of effective surveillance time series is useful for better understanding the occurrence of the 11 types of infectious disease.

Forecasting incidence of infectious diarrhea using Random forest in Jiangsu province, China

“…Mathematical models for predicting are urgently needed to reinforce an integrated management for monitoring, control and prevention of infectious diarrhea. Some researchers have introduced different prediction methods for this purpose [23][24][25]. In this study, a RF model with meteorological factors was constructed.…”

Spatial distribution of 12 class B notifiable infectious diseases in China: A retrospective study

“…Second, among sexually transmitted diseases (AIDS, gonorrhea, syphilis), the values of global Moran’s I for AIDS and gonorrhea are higher than 0.3, indicating that it is more challenging and less optimistic for the prevention and control of these two diseases. According to the trend analysis of Zhang and Wilson [ 12 ], the incidence rates of these kinds of diseases have continued to rise since the 1990s. It is important to note that in 1985, the first case of AIDS in China was discovered in Yunnan province, which is situated in the southwest border of China [ 21 ].…”

“…Class B (infectious diseases that require strict control and prevention measures) includes 26 diseases, which feature relatively strong infectivity, e.g., acquired immunodeficiency syndrome (AIDS), measles, and malaria. Class C (infectious diseases that require proper surveillance) includes less severe and endemic infectious diseases, such as influenza and typhus [ 12 ].…”

The utility of LASSO-based models for real time forecasts of endemic infectious diseases: A cross country comparison

“…A further limitation is that, for simplicity, only weather information from the capitals were used in the study, but for countries like Japan, Thailand and Taiwan, there are great variation between climatic conditions in the North and the South. Prediction accuracy might improve if [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] incidence and weather information can be collected at a finer resolution. We suspect that even the accuracy of short term forecasts may be reduced should new epidemiological conditions replace those that the model was trained on.…”

“…If accurate forecasts were available in both the near and far future, effective policies could then be targeted to the expected future needs. Existing approaches to real-time forecasting include generalized linear regression, seasonal autoregressive integrated moving average (SARIMA) model or a simpler ARIMA form of it, phenomenological models like the logistic growth model and Richards model, and mechanistic models like the SIR models [19][20][21][22][23][24]. Often such approaches involve the challenge of integrating environmental factors including temperature, humidity and rainfall, which may influence pathogen transmission directly or affect the vector activities (for vector borne diseases), especially in temperate regions [25][26][27][28][29].…”