Austerity might have affected the capacity of public hospitals in Greece to diagnose salmonellosis (laboratory capacity) over the period 2010–2016, as well as the performance of the existing surveillance systems. The scope of this paper is to present data on laboratory capacity over these years, as well as the results of a two-source capture-recapture study (data from Mandatory Notification System and National Reference Laboratory System for Salmonella). The main findings were that: (a) laboratory capacity was high and steady besides the financial crisis, (b) the estimated number of laboratory-confirmed cases (n = 6017, 95% CI 5892–6142) resulted in an incidence rate (7.9 cases/100 000 population) almost twice than that reported by the two systems Mandatory Notification System (MNS); 4.1 and National Reference Laboratory System (NRLS); 4.5 cases/100 000 population, (c) underreporting was high for both systems (MNS; 47.5% and NRLS; 42.8%) and (d) differences by geographical region, size and type of hospital were identified. We suggest that (a) specific interventions are needed to increase completeness of the systems by type of hospital and geographical region, (b) record linkage can help in estimating the disease burden in a more valid way than each system separately and (c) a common electronic database in order to feed one system to the other could significantly increase completeness of both systems.

Seasonal autoregressive integrated moving average (SARIMA) has been used to model nationwide tuberculosis (TB) incidence in other countries. This study aimed to characterise monthly TB notification rate in China. Monthly TB notification rate from 2005 to 2017 was used. Time-series analysis was based on a SARIMA model and a hybrid model of SARIMA-generalised regression neural network (GRNN) model. A decreasing trend (3.17% per years, P < 0.01) and seasonal variation of TB notification rate were found from 2005 to 2016 in China, with a predominant peak in spring. A SARIMA model of ARIMA (0,1,1) (0,1,1)12 was identified. The mean error rate of the single SARIMA model and the SARIMA–GRNN combination model was 6.07% and 2.56%, and the determination coefficient was 0.73 and 0.94, respectively. The better performance of the SARIMA–GRNN combination model was further confirmed with the forecasting dataset (2017). TB is a seasonal disease in China, with a predominant peak in spring, and the trend of TB decreased by 3.17% per year. The SARIMA–GRNN model was more effective than the widely used SARIMA model at predicting TB incidence.