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The purpose of the current study was to determine the upper threshold number of cases for which pertussis infection would reach an outbreak level nationally in Iran.
Data on suspected cases of pertussis from the 25th February 2012 to the 23rd March 2018 from the Center for Disease Control and Prevention in Iran was used. The national upper threshold level was estimated using the exponentially weighted moving average (EWMA) method and the Poisson regression method.
In total, 2,577 (33.6%) and 1,714 (22.3%) cases were reported in the Spring and Summer respectively. There were 1,417 (18.5%) and 1,971 (25.6%) cases reported in the Autumn and Winter, respectively. The overall upper threshold using the EWMA and the Poisson regression methods, was estimated as a daily occurrence of 8 (7.55) and 7.50 (4.48–11.06) suspected cases per 10,000,000 people, respectively. The daily seasonal thresholds estimated by the EWMA and the Poisson regression methods were 10, 7, 6, 8 cases and 10, 7, 7, 9 cases for the Spring, Summer, Autumn, and Winter, respectively.
The overall and seasonal estimated thresholds by the 2 methods were similar. Therefore, the estimated thresholds of 6–10 cases in a day, per 10,000,000 people could be used to detect pertussis outbreaks and epidemics by health policymakers.
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In this pandemic situation caused by a novel coronavirus disease in 2019 (COVID-19), an electronic support system that can rapidly and accurately perform epidemic investigations, is needed. It would systematically secure and analyze patients’ data (who have been confirmed to have the infection), location information, and credit card usage.
The “Infectious Disease Prevention and Control Act” in South Korea, established a legal basis for the securement, handling procedure, and disclosure of information required for epidemic investigations. The Epidemic Investigation Support System (EISS) was developed as an application platform on the Smart City data platform.
The EISS performed the function of inter-institutional communication which reduced the processing period of patients’ data in comparison to other methods. This system automatically marked confirmed cases’ tracking data on a map and hot-spot analysis which lead to the prediction of areas where people may be vulnerable to infection.
The EISS was designed and implemented for use during an epidemic investigation to prevent the spread of an infectious disease, by specifically tracking confirmed cases of infection.
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