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Mass Gatherings

Description

In spite of the fact that mass gatherings are an undeniably regular element of our society attended by huge crowds yet such occasions are not very well understood. Even though such gatherings are accumulations of "well people", vast number of people associated with mass gatherings can put a serious strain on the entire health care system [1].The public health implications of mass gathering events include a potential increased risk for disease transmission because of the variability and mobility of those attending the event and increased media attention. Risk assessment for mass gathering events is crucial to identify the potential health hazards which aids in planning and response activities specific to the event [2]. Preparing for mass gatherings offer an opportunity to improve health service delivery, enhance health promotion and strengthen public health systems [3]. In India, many of the religious festivals are observed with mass gatherings and prayers. Large crowd participate in such festivals as participants to observe the unique rituals and also as spectators. Literature indicates that in India, we might be well equipped for response activities but the scientific concept of risk assessment i.e., to understand the existing risks, identify the risks, characterize the risks and plan for risk reduction strategies accordingly are at an infant stage .The little that has been done in the field of mass gatherings has generally focused on description of preparedness activities of single event, crowd control, prevention of stampedes with little attention to public health preparedness. The present project is an attempt to systemize the process of risk assessment by developing a risk assessment tool consisting of characteristics peculiar to planned religious mass gatherings of Indian context.

Objective: To develop a risk assessment tool to assess the public health and environmental risks associated with religious mass gathering events of Tamil Nadu, a state in the southern part of India

Submitted by elamb on
Description

Influenza viral infection is contentious, has a short incubation period, yet preventable if multiple barriers are employed. At some extend school holidays and travel restrictions serve as a socially accepted control measure. A study of a spatiotemporal spread of influenza among school-aged children in Belgium illustrated that changes in mixing patterns are responsible for altering disease seasonality3. Stochastic numerical simulations suggested that weekends and holidays can delay disease seasonal peaks, mitigate the spread of infection, and slow down the epidemic by periodically dampening transmission. While Christmas holidays had the largest impact on transmission, other school breaks may also help in reducing an epidemic size. Contrary to events reducing social mixing, sporting events and mass gatherings facilitate the spread of infections. A study on county-level vital statistics of the US from 1974-2009 showed that Super Bowl social mixing affects influenza dissemination by decreasing mortality rates in older adults in Bowl-participating counties. The effect is most pronounced for highly virulent influenza strains and when the Super Bowl occurs closer to the influenza seasonal peak. Simulation studies exploring how social mixing affects influenza spread demonstrated that impact of the public gathering on prevalence of influenza depends on time proximity to epidemic peak. While the effects of holidays and social events on seasonal influenza have been explored in surveillance time series and agent-based modeling studies, the understanding of the differential effects across age groups is incomplete.

Objective: In the presented study, we examined the impact of school holidays (Autumn, Winter, Summer, and Spring Breaks) and social events (Super Bowl, NBA Finals, World Series, and Black Friday) for five age groups (<4, 5-24, 25-44, 45-64, >65 years) on four health outcomes of influenza (total tested, all influenza positives, positives for influenza A, and B) in Milwaukee, WI, in 2004-2009 using routine surveillance.

Submitted by elamb on