Influence of Media on Seasonal Influenza Epidemic Curves

Abstract

Education through media has an effect on the epidemic curve of seasonal influenza.

A description of ‘Australian Lyme disease’ epidemiology and impact: an analysis of submissions to an Australian senate inquiry

“…Almost 10% of submissions reported self‐diagnosis after viewing a media report. Media influence on increasing diagnosis and clinical presentations has been described in influenza and in particular mental health, where media coverage of suicides can lead to increased at‐risk presentations and suicides . A patient who feels let‐down by mainstream medicine may personally identify with the generally sensationalised, but ultimately hopeful, tone of many of these media reports, and then seek a similar diagnosis.…”

The effect of media coverage on threshold dynamics for a stochastic SIS epidemic model

“…The emerging and reemerging diseases have led to an extensive interest in investigation of infectious disease [40]. The medica coverage as an effective preventive measure plays an important role in controlling the disease spread [4]. Dynamical modeling of infectious disease has become a powerful tool to improve our understanding of the pattern of epidemic spread and develop better controlling strategies [41].…”

“…Once an infectious disease appears and outbreaks in one area, the department for disease control and prevention will take some actions to prevent the epidemic spreading. One strategy is to disseminate the correct preventive knowledge of diseases through the media with the least delay possible [3][4][5]. The mass media developing rapidly, especially, internetbased is becoming very popular, also take the profound influence to the information dissemination way as well as people's life style.…”

“…The sample paths of stochastic model(1.3) and its corresponding deterministic model (1.1) when R s0 < 1: (a) S(t), (b) I(t).The parameter values are the same as in Example 5.1 and the initial value is(21,4).…”

Hospital variations during the first year of the COVID-19 pandemic in a referral hospital in a low-to-middle-income country: A large single-center cohort study

“…We considered a total period of 12 months for data extraction. We described the epidemic curves, including periods and peaks, using four indices: the duration (weeks) of the onset (or new onset in the case of the second period) to peak (DOP), the duration from the onset to the end (DOE), the DOP/DOE ratio, and the steepness of epidemic curves, expressed as steepness index (SI) (peak number/DOE) (7) We divided patients into two groups (survivors and non-survivors) according to the presence of the outcome (weekly in-hospital mortality at 30 days of follow-up). Other outcomes of interest were demographic changes in patients' characteristics (gender, RT-PCR results, and disease awareness, de ned as patient recognition of close contact, and knowledge of COVID-19, comprising associated conditions or treatments).…”