tetano
Editor, Senior Moderator
BMC Res Notes
. 2025 Feb 17;18(1):72.
doi: 10.1186/s13104-025-07139-w. Nationwide spatial epidemiological dataset of over 100,000 influenza-like illness notifications in Iran by county (2015-2019)
Atieh Sedghian[SUP] 1 2 [/SUP], Shahab MohammadEbrahimi[SUP] 3 4 [/SUP], Benn Sartorius[SUP] 5 6 7 [/SUP], Behzad Kiani[SUP] 5 [/SUP]
Affiliations
Objectives: This data note documents influenza-like illness (ILI) notifications in Iran by county from 2015 to 2019 as a pre-COVID-19 dataset, providing individual and spatial data for further comprehensive spatiotemporal analysis. Due to the high contagion rate of ILI and global health impact, precise geographic mapping serves as a critical tool for public health officials and researchers to monitor, mitigate, and predict epidemics. By utilizing advanced spatial-temporal epidemiological analysis to study disease occurrence patterns, this geodatabase can enable a better understanding and more effective management of ILIs in the future.
Data description: This is the most comprehensive dataset of all individual ILI notifications in Iran between 2015 and 2019 by date of notification and county (398 counties). The database includes two data files, a help file, and a data usage agreement: Data File 1 is an Excel (.xlsx) file detailing demographic and clinical information from 109,919 ILI notifications nationwide, covering county and date of notification, patient demographics, admission details, sample types, differential diagnosis, medical history, mortality details, test results, and symptoms. Data File 2 contains spatiotemporal information in polygon shapefiles (.shp), mapping ILI notification locations by county with data on case counts for each year, total population, gender distribution, and geographic coordinates.
Keywords: Big data; Influenza-like illness; Iran; Public health; Space-time; Spatiotemporal.
. 2025 Feb 17;18(1):72.
doi: 10.1186/s13104-025-07139-w. Nationwide spatial epidemiological dataset of over 100,000 influenza-like illness notifications in Iran by county (2015-2019)
Atieh Sedghian[SUP] 1 2 [/SUP], Shahab MohammadEbrahimi[SUP] 3 4 [/SUP], Benn Sartorius[SUP] 5 6 7 [/SUP], Behzad Kiani[SUP] 5 [/SUP]
Affiliations
- PMID: 39962562
- DOI: 10.1186/s13104-025-07139-w
Objectives: This data note documents influenza-like illness (ILI) notifications in Iran by county from 2015 to 2019 as a pre-COVID-19 dataset, providing individual and spatial data for further comprehensive spatiotemporal analysis. Due to the high contagion rate of ILI and global health impact, precise geographic mapping serves as a critical tool for public health officials and researchers to monitor, mitigate, and predict epidemics. By utilizing advanced spatial-temporal epidemiological analysis to study disease occurrence patterns, this geodatabase can enable a better understanding and more effective management of ILIs in the future.
Data description: This is the most comprehensive dataset of all individual ILI notifications in Iran between 2015 and 2019 by date of notification and county (398 counties). The database includes two data files, a help file, and a data usage agreement: Data File 1 is an Excel (.xlsx) file detailing demographic and clinical information from 109,919 ILI notifications nationwide, covering county and date of notification, patient demographics, admission details, sample types, differential diagnosis, medical history, mortality details, test results, and symptoms. Data File 2 contains spatiotemporal information in polygon shapefiles (.shp), mapping ILI notification locations by county with data on case counts for each year, total population, gender distribution, and geographic coordinates.
Keywords: Big data; Influenza-like illness; Iran; Public health; Space-time; Spatiotemporal.