This study aimed to predict seasonal influenza activity and detection of influenza outbreaks. Data of all registered cases (n = 53,526) of influenza-like illnesses (ILIs) from sentinel sites of healthcare centers in Iran were obtained from the FluNet web-based tool, World Health Organization (WHO), from 2010 to 2015. The status of the ILI activity was obtained from the FluNet and considered as the gold standard of the seasonal activity of influenza during the study period. The cumulative sum (CUSUM) as an outbreak detection method was used to predict the seasonal activity of influenza. Also, time series similarity between the ILI trend and CUSUM was assessed using the cross-correlogram. Of 7684 (14%) positive cases of influenza, about 71% were type A virus and 28% were type B virus. The majority of the outbreaks occurred in winter and autumn. Results of the cross-correlogram showed that there was a considerable similarity between time series graphs of the ILI cases and CUSUM values. However, the CUSUM algorithm did not have a good performance in the timely detection of influenza activity. Despite a considerable similarity between time series of the ILI cases and CUSUM algorithm in weekly lag, the seasonal activity of influenza in Iran could not be predicted by the CUSUM algorithm. H I G H L I G H T S • Activity of influenza in Iran could not be predicted by CUSUM algorithm. • The majority of the outbreaks occurred in winter and autumn. • CUSUM algorithm did not have a good performance in detection of influenza outbreaks.

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be imaged only once each day. To identify cells and track cell fate in these less frequent images, we need alternate methods that reliably capture single-cell information over the course of the study. Molecular and instrumental advances in cell imaging have provided elegant experimental approaches to barcoding individual cells. However, these techniques require additional reagents, time, and channels. Here, we present a complementary technique to identify cells computationally. We transfected primary neurons with a fluorescent morphology marker, imaged the samples daily for 1 week, and extracted unique cellular parameters. We will summarize our progress with different approaches for their ability to collect features from individual cells and to determine cell type, vitality, and identify.

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