Predicting temporal propagation of seasonal influenza using improved gaussian process model.

Influenza rapidly spreads in seasonal epidemics and imposes a considerable economic burden on hospitals and other healthcare costs. Thus, predicting the propagation of influenza accurately is crucial in preventing influenza outbreaks and protecting public health. Most current studies focus on the spread simulation of influenza. However, few studies have investigated the dependencies between meteorological variables and influenza activity. This study develops a non-parametric model based on Gaussian process regression for influenza prediction considering meteorological effect to capture temporal dependencies hidden in influenza time series. To identify the most explanatory external variables, L1-regularization is applied to identify meteorology factor subsets, and three types of covariance functions are designed to characterize non-stationary and periodic behavior in influenza activity. The dependencies of diseases and meteorology are modeled through the designed cross-covariance function. A real case in Shenzhen, China was studied to validate our proposed model along with comparisons to recently developed multivariate statistical models for influenza prediction. Results show that our proposed influenza prediction approach achieves superior performance in terms of one-week-ahead prediction of influenza-like illness.

A Regularization-Based eXtreme Gradient Boosting Approach in Foodborne Disease Trend Forecasting.

Foodborne disease is a growing public health problem worldwide and imposes a considerable economic burden on hospitals and other healthcare costs. Thus, accurately predicting the propagation of foodborne disease is crucial in preventing foodborne disease outbreaks. Few studies have investigated the dependencies between environmental variables and foodborne disease activity. This study develops a regularization-based eXtreme gradient boosting approach for foodborne disease trend forecasting considering environmental effects to capture dependencies hidden in foodborne disease time series. A real case in Shanghai, China was studied to validate our proposed model along with comparisons to traditional and benchmark algorithms for foodborne disease prediction. Results show that the foodborne disease prediction approach we propose achieves slightly superior performance in terms of one-day-ahead prediction of foodborne disease, and presents more robust prediction for 2-7 days ahead prediction.

The Association between Air Pollution and Outpatient and Inpatient Visits in Shenzhen, China.

Nowadays, air pollution is a severe environmental problem in China. To investigate the effects of ambient air pollution on health, a time series analysis of daily outpatient and inpatient visits in 2015 were conducted in Shenzhen (China). Generalized additive model was employed to analyze associations between six air pollutants (namely SO2, CO, NO2, O3, PM10, and PM2.5) and daily outpatient and inpatient visits after adjusting confounding meteorological factors, time and day of the week effects. Significant associations between air pollutants and two types of hospital visits were observed. The estimated increase in overall outpatient visits associated with each 10 µg/m³ increase in air pollutant concentration ranged from 0.48% (O3 at lag 2) to 11.48% (SO2 with 2-day moving average); for overall inpatient visits ranged from 0.73% (O3 at lag 7) to 17.13% (SO2 with 8-day moving average). Our results also suggested a heterogeneity of the health effects across different outcomes and in different populations. The findings in present study indicate that even in Shenzhen, a less polluted area in China, significant associations exist between air pollution and daily number of overall outpatient and inpatient visits.