Showing 7 results for Public Health Surveillance
M Karami, H Soori, Y Mehrabi, Aa Haghdoost, Mm Gouya,
Volume 8, Issue 3 (12-2012)
Abstract
Background & Objectives: Knowledge of the presence of seasonal trends and other explainable patterns in the prediagnostic data sources and removing such patterns before applying outbreak detection methods seem very important. This study aimed to detect and remove the explainable patterns such as seasonality, day-of-week (DOW) and holiday effects of the daily counts of suspected cases of measles in Iran.Methods: Data on daily counts of suspected cases of measles as a pre-diagnostic data source were obtained from Iranian national surveillance system between 21 March 2008 and 20 March 2011. We used lines plot, moving average chart, autocorrelation and partial autocorrelation functions for detecting explainable patterns. Moving average (MA) and Holt- Winters (HW) exponential smoothing method are used for removing explainable patterns.
Results: Our findings indicate the presence of seasonality, DOW effect, holidays and weekend effects in the daily counts of suspected cases of measles. The good performance of HW exponential smoothing technique in removing seasonal patterns is evident. MA technique showed better performance regarding assumption violation on outbreak detection methods.
Conclusion: Because of the presence of explainable patterns in the daily counts of suspected cases of measles, considering such patterns before applying outbreak detection algorithms is very important. Implementing both MA (7 days) techniques for its simplicity as a pre- processing method and HW method for its efficacy in removing seasonal patterns is recommended.
M Karami, H Soori, Y Mehrabi, Aa Haghdoost, Mm Gouya,
Volume 8, Issue 3 (12-2012)
Abstract
Background & Objectives: Evaluating the performance of outbreak detection methods using real data testing provide the highest degree of validity. The aim of this study was to determine the performance of the Exponentially Weighted Moving Average (EWMA) in real time detection of two local outbreaks in Iran.
Methods: The EWMA algorithm (both ƛ= 0.3 and 0.6) applied on daily counts of suspected cases of measles to detect local outbreaks which had been occurred in Mashhad and Bandar Abbas cities during 2010. The performance of The EWMA algorithms were evaluated using real data testing approach and reported by correlation analysis.
Results: Mashhad outbreak was detected with a delay of about 2 to 7 days using EWMA algorithms as outbreak detection method while the utility of EWMA algorithms in real time detection of Bandar Abbas’ outbreak were on time good optimal. Maximum correlation value for EWMA 2 in relation to Mashhad outbreak was 0.60 at lag 2.
Conclusion: Applying the EWMA algorithm as an outbreak detection method at local levels is not suggested. However the characteristics of data are determinant of the performance of such detection methods.
B Lotfi, M Karami, A Soltanian, J Poorolajal, M Mirzaee,
Volume 11, Issue 2 (9-2015)
Abstract
Background & Objectives: This study was conducted to evaluate the completeness and geographical representativeness of the hepatitis B surveillance system between 2007 and 2013 in Hamadan Province.
Methods: In this descriptive study, all reported cases in Hamadan Province were extracted. The analytical literature review method was used to examine completeness. We used studies on the prevalence and incidence of hepatitis in Iran published between 2007 and 2013. Sensitivity analysis of the results was performed by simulation program using the R software. Geographical representativeness was assessed and plotted by the ArcGIS software, as well.
Results: Totally, 1378 cases were reported to the hepatitis B surveillance system in Hamadan Province. Most cases were from urban areas and were men, married, and housewives mostly in the age group 20 to 29 and 30 to 39 years. Completeness of the hepatitis B surveillance system was 77%. The hepatitis B surveillance system was not representative in terms of occupation but was representative in terms of age, sex, marital status, and place of living.
Conclusion: We concluded that the hepatitis B surveillance system had a relatively good performance. Moreover, findings of the analytical literature review method are affected by the included studies and interpretation of the results should be performed with caution.
K Jafari, M Karami, A Soltanian, N Esmailnasab,
Volume 12, Issue 2 (8-2016)
Abstract
Background and Objectives: Syndromic surveillance systems are used to early detection of outbreaks. The purpose of this study was to determine the feasibility of clinical and non-clinical data sources used in influenza syndromic surveillance in Zanjan.
Methods: In this time series study, clinical and non-clinical data related to influenza like illness (ILI) as a potential data source of syndromic surveillance systems, including the number of missed school days collected from 12 schools and the data of over the counter (OTC) drug sale obtained from 15 pharmacies selected randomly in Zanjan during 2014 were used. We used the line plot and moving average chart to explore trends and detect potential explainable patterns of data sources. The autocorrelation function and cross correlation function besides corresponding graphs were used to assess the feasibility of school absenteeism and OTC sale in timely detection of influenza outbreaks.
Results: Line plots indicated the presence of explainable patterns and the effect of the day of the week. The cross correlation value was 0.5 and cross correlogram revealed the similarity of both data sources in this study.
Conclusion: Our findings indicated the feasibility of influenza data sources, including school absenteeism and OTC, as potential data sources of syndromic surveillance systems.
E Ghaderi, M Salehi Vaziri , E Mostafavi, Gh Moradi, Kh Rahmani, M Zeinali, Mr Shirzadi, H Erfani, Sh Afrasiabian, S Eybpoosh,
Volume 15, Issue 3 (11-2019)
Abstract
Background and Objectives: To provide an overview of the national program of Crimean-Congo hemorrhagic fever surveillance in Iran, its current achievements, and challenges.
Methods: In this mixed method study, the relevant reports, documents, and guidelines, as well as published literature and surveillance data were gathered and critically reviewed. The opinions of the key informants at local and governmental levels were assessed through structured interviews.
Results: The program was integrated into Iran’s primary healthcare (PHC) network in 1999. The involved organizations include CDC, medical universities, Pasteur Institute of Iran (PII), and Veterinary Organization. Case finding is based on standard definitions of suspected, probable, and confirmed cases. Laboratory confirmation is necessary for diagnosis and is provided within 48 hours after receipt of the specimen by the National Reference Laboratory of PII. CCHF treatment is primarily supportive. Antiviral therapy with ribavirin is also considered. Both therapeutic services are free. Education mainly focuses on high-risk groups and healthcare workers. Major achievements of the program include rapid diagnosis and treatment of cases, prevention of nosocomial transmission, identification of high-risk provinces and major transmission routes, improved outbreak preparedness, development of laboratory tests for detection of other arboviruses, and reduction of CCHF case fatality rate.
Conclusion: Program implementation has had a positive impact on early detection and proper control of annual outbreaks. However, some aspects of the program still need improvement, including promotion of the general and high-risk populations’ awareness and regional collaborations (especially among neighboring countries) for infection control in humans, livestock, and vectors.
Fatemeh Ershadinia, Elham Rahimi, Bushra Zareie, Hadi Pashapoor, Manoochehr Karami,
Volume 19, Issue 2 (9-2023)
Abstract
Background and Objectives: The disease surveillance system provides essential information about the population at risk and the disease pattern. This review aimed to describe the experiences of countries in establishing COVID-19 school-based surveillance systems.
Methods: We conducted a systematic review. Four databases were searched between January 2019 and December 2022 using relevant keywords. The studies were screened by two people according to the inclusion and exclusion criteria. The findings were extracted using a standard form and aligned to the objectives of the review.
Results: The data from 12 studies were extracted using the standard form. All studies related to the school-based surveillance system of COVID-19. Most of studies were conducted in the United States of America and England. The reports did not conform to the standard. The number of schools covered in surveillance systems ranged from 2 to more than 6000 schools. The age group in these studies was 0 to 19 years. Schools submitted data daily or weekly.
Conclusion: The results of the COVID-19 surveillance systems in schools should be reported according to standard Instructions. This is considered a necessity to monitor and evaluate the surveillance system. It also allows other countries and researchers to share and use the results. In addition, sensitivity, timeliness, and positive predictive value were not reported in implemented surveillance systems.
Manoochehr Karami,
Volume 20, Issue 3 (12-2024)
Abstract
Artificial intelligence (AI) refers to the process in which computers, rather than human intelligence, perform tasks, such as early warning of an epidemic. This editorial aimed to describe the potential applications of digital health and the challenges faced by the health system of Iran concerning the application of artificial intelligence and innovative technology in public health surveillance and early warning of epidemics. The use of new technologies at national and subnational levels for early warning of public health threats requires a suitable platform within the context of disease surveillance systems. The Iran health system currently utilizes a syndromic approach and event-based surveillance to monitor acute respiratory infections. However, the structure of Iran's national communicable disease surveillance system has faced challenges due to the inability to share and exchange data at the level of primary health care data sources. Accordingly, application and integration of AI should be considered as Iran’s health priority to promote infrastructure and technology requirements, including compatibility, interoperability, and strategies for ethical and responsible use by public health authorities. Since pandemics and epidemics have not been limited to the previous ones, such as COVID-19, influenza, SARS, dengue fever, and similar threats, operations planning is required for the integration of artificial intelligence tools to prepare and respond to biological threats promptly by the Iranian Ministry of Health, stakeholders, and other parties.
