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Pandemics

Pandemics are widespread outbreaks of infectious diseases that affect large populations across multiple countries or continents.
These global health emergencies can cause significant morbidity, mortality, and socioeconomic disruption.
Pandeimcs are typically caused by novel or mutated pathogens, such as viruses, that can spread rapidly through human-to-human transmission.
Effective pandemic preparedness, prevention, and response strategies are critical to mitigate the impact of these public health crises.
Understanding the epidemiology, virology, and clinical characteristics of pandemics is essential for developing targeted interventions, improving disease surveillance, and coordinating international cooperation.

Most cited protocols related to «Pandemics»

1
Age-Structured SIR Modeling for Influenza Scenarios
To demonstrate the application of the projected contact matrices, we performed age-structured Susceptible-Infected-Removed (SIR) modelling [39 (link)] using the derived age-specific contact matrices for countries of different levels of development. For two pandemic influenza scenarios (R0 = 1.2 and 1.5), the age-specific final epidemic size and the percent reduction in infection were calculated for three scenarios: No intervention (total contacts calculated as a sum of contacts made at home, work, school and other), School closure and social distancing of younger individuals (zero contribution from school contacts and reduction in contacts at other locations with individuals below 20 years and a small increase in contacts made at home) and Workplace distancing (reduction of work contacts by a half). These were obtained by scaling the contact matrices to obtain the scenario’s R0, setting the removal rate without loss of generality to one. We initialize the SIR model with starting immunity levels derived from age-specific susceptibility data from the study by Miller et al. [40 ]. More details are in the S1 Text.
Prem K., Cook A.R, & Jit M. (2017). Projecting social contact matrices in 152 countries using contact surveys and demographic data. PLoS Computational Biology, 13(9), e1005697.
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Publication 2017
Epidemics Infection Influenza Pandemics Response, Immune Susceptibility, Disease Youth
2
Tracking COVID-19 Conversations on Twitter
By continuously monitoring Twitter's trending topics, keywords, and sources associated with COVID-19, we did our best to capture conversations related to the outbreak.
Twitter's streaming API returns any tweet containing the keyword(s) in the text of the tweet, as well as in its metadata; therefore, it is not always necessary to have each permutation of a specific keyword in the tracking list. For example, the keyword “Covid” will return tweets that contain both “Covid19” and “Covid-19.” We list a subset of the keywords and accounts that we are following in Tables 1 and 2, respectively, along with the date we began tracking them. There are some keywords that overlap due to an included keyword being a substring of another, but we included both for good measure. The keyword choices in the current data set are all in English, so there is a heavy bias toward English tweets and events related to English-speaking countries. Due to the evolving nature of the pandemic and online conversations, these tables will expand as we continue to monitor Twitter for additional keywords and accounts to add to our tracking list.
Chen E., Lerman K, & Ferrara E. (2020). Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set. JMIR Public Health and Surveillance, 6(2), e19273.
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Publication 2020
COVID 19 Pandemics
3
Longitudinal Survey of COVID-19 Behaviors in the UK
We commissioned the market research company Ipsos to conduct a survey of UK adults (referred to here as the CoMix survey). Adults (≥ 18 years) were recruited into the survey by sending email invitations to existing members of their online panel. Representativeness of the general UK population was ensured by setting quotas on age, gender, geographical location, and socioeconomic status. This cohort of individuals will be requested to answer the survey every 2 weeks for a total of 16 weeks to track changes in their self-reported behaviour. The first surveys were sent on Tuesday, 24 March, 1 day after a lockdown was announced for the UK.
Participants were asked about their attitudes towards COVID-19 and the effect of physical distancing interventions, whether they or any of their household members experienced any recent symptoms, whether they were tested for COVID-19, whether they had had any contact with known COVID-19 cases, and whether they were affected by physical distancing measures.
Participants reported (i) if any person in their household were advised to quarantine, isolate, or limit time in their workplace or educational facility in the preceding 7 days due to COVID-19 and (ii) if they heeded the advice and isolated, quarantined, or stayed away from their workplace or educational facility. In the survey, we defined quarantine as limiting contacts and staying at home, with restricted allowance for movement outside the home after a potential exposure with a COVID-19 case. We defined isolation as completely separating from uninfected contacts, including household members, either in the home or in a health facility. To assess the impact of advice and policy changes regarding physical distancing, we asked participants to indicate if they had planned to participate in a set of events in the preceding week. For each event type, they reported (i) whether they proceeded with their plan, or (ii) if it was cancelled or they decided not to go, and (iii) the frequency of the event type in the previous 7 days. Additional questions were asked about preventive behaviours, such as handwashing or wearing masks, and about the use of public transport in the previous 7 days.
In addition, we asked participants to record all direct contacts made between 5 am the day preceding the survey and 5 am the day of the survey. A direct contact was defined as anyone who was met in person and with whom at least a few words were exchanged, or anyone with whom the participants had any sort of skin-to-skin contact. We were unable to ask parents to provide contact information for their children due to lack of ethical approval; however, participants were able to list contacts who were under 18.
For every recorded contact, participants documented the age and gender of the contact, relationship to the contact, the frequency with which they usually contact this person, whether contact was physical (skin to skin) or not, and the setting where the contact occurred (e.g. at home, work, school, or whilst undertaking leisure activities), including whether contact occurred in- or outside an enclosed building. Questions on social contacts were consistent with those from the UK arm of the POLYMOD survey [14 (link)], which was used as the baseline pre-pandemic comparison dataset. Details on survey methodology, the study protocol, and a copy of the questionnaire used are provided in Additional files 1 and 2.
Jarvis C.I., Van Zandvoort K., Gimma A., Prem K., Klepac P., Rubin G.J, & Edmunds W.J. (2020). Quantifying the impact of physical distance measures on the transmission of COVID-19 in the UK. BMC Medicine, 18, 124.
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Publication 2020
Adult Child COVID 19 Households isolation Movement Pandemics Parent Physical Examination Quarantine Skin
4
Psychosocial Impacts of COVID-19
Downstream and subjective impacts of structural changes, such as changes in social contacts, effects on family relationships, changes in living situation, food insecurity, and stressors associated with these changes (14 items). Participants were also asked about job loss and school closure due to the pandemic; these items were used as internal validators.
Nikolaidis A., Paksarian D., Alexander L., Derosa J., Dunn J., Nielson D.M., Droney I., Kang M., Douka I., Bromet E., Milham M., Stringaris A, & Merikangas K.R. (2021). The Coronavirus Health and Impact Survey (CRISIS) reveals reproducible correlates of pandemic-related mood states across the Atlantic. Scientific Reports, 11, 8139.
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Publication 2021
Pandemics
5
Influenza Intensity Thresholds in Europe
Weekly ILI and ARI incidence rates or proportions of primary care consultations from the 1996/1997 to the 2013/2014 influenza season (from week 40 to week 20 of the next year) were taken from the EuroFlu database (WHO Regional Office for Europe). Countries included in the study were selected among the 50 participating Member States in the WHO European Region, according to the following criteria: data available for at least six consecutive seasons, excluding the pandemic season 2009/2010 (a minimum of five seasons for the calculations and the target season), and no major changes in the surveillance systems during the reporting period. Countries fulfilling these criteria were invited to participate.
Data were checked for inconsistencies, such as abnormal weekly estimates or missing values during the surveillance period, and sent to the country representatives for validation and updating when necessary.
A sequential analysis using the R Language implementation of MEM (package “mem” [Internet]. Available from: http://cran.r-project.org/web/packages/mem/index.html) was carried out for each country to calculate the epidemic threshold (level of influenza activity that signals the start and end of the annual epidemic wave) and the three intensity thresholds (medium, high, and very high) for each season. The number of seasons included in each analysis ranged from five to 10 (training period).
Moving epidemic method has three main steps, which have been previously described.14 (link) In the first step, for each season separately, the length of the epidemic period is estimated as the minimum number of consecutive weeks with the maximum accumulated percentage rates, splitting the season in three periods: a pre-epidemic, an epidemic, and a post-epidemic period. In the second step, MEM calculates the epidemic threshold as the upper limit of the 95% one-sided confidence interval of 30 highest pre-epidemic weekly rates, the n highest for each season taking the whole training period, was n = 30/number of seasons. In the third step, medium, high, and very high intensity thresholds were estimated as the upper limits of the 40%, 90%, and 97·5% one-sided confidence intervals of the geometric mean of 30 highest epidemic weekly rates, the n highest for each season taking the whole training period, were n = 30/number of seasons. For the purposes of this work, if the medium intensity threshold is lower than the epidemic threshold, the epidemic threshold is used for both.
The intensity levels were defined as follows (Figure1):
For the analysis, at least 5 years of consecutive data were required to calculate the threshold and intensity levels for the next season; that is, the analysis started with data from seasons 1996/1997 to 2000/2001 to estimate the thresholds for season 2001/2002, or from the first season available in the country to the fifth one, to estimate the thresholds for the sixth season. From then on, calculations for each subsequent season included one more season of data (to a maximum of 10) to estimate the thresholds. The last step of analysis used data from a maximum of 10 seasons if available (2002/2003 to 2012/2013, excluding the 2009 A(H1N1) pandemic) or at least five seasons (2007/2008 to 2012/2013, excluding the pandemic) to make estimations for season 2013/2014. As we excluded the pandemic season from MEM calculations, estimations for the 2009/2010 and 2010/2011 season thresholds are the same.
For each country, the highest weekly rate per season (the season peak) was compared to the intensity thresholds and described for each of the countries over time. Furthermore, a log scale of the weekly incidence rates and percentage of consultations was used to graphically compare and discuss the season 2013/2014 country intensity levels in Europe.
Finally, weekly maps were drawn to show the spread of the 2013/2014 season intensity in Europe.
Complementary figures in Appendix S1 show the historical data included in this study, the threshold trend over the years, and the season 2013/2014 surveillance. Appendix S2 is an animated gif of the evolution of the 2013/2014 intensity levels by country.
The R Language (v3.2.0) mem library (v1.4) was used for calculations of the thresholds and graphic output.
Vega T., Lozano J.E., Meerhoff T., Snacken R., Beauté J., Jorgensen P., Ortiz de Lejarazu R., Domegan L., Mossong J., Nielsen J., Born R., Larrauri A, & Brown C. (2015). Influenza surveillance in Europe: comparing intensity levels calculated using the moving epidemic method. Influenza and Other Respiratory Viruses, 9(5), 234-246.
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Publication 2015
Biological Evolution cDNA Library Epidemics Europeans Influenza Menstruation Disturbances Microtubule-Associated Proteins Pandemics Primary Health Care

Most recents protocols related to «Pandemics»

1
Exploring Pandemic Information Behaviors
We iteratively developed a semi-structured interview guide to explore three broad topics with participants: (1) access and evaluation of information, (2) perspectives on mainstream and social media coverage of COVID-19, and (3) influences of messaging on individual and community perceptions of the pandemic and intentions to comply with public health measures (Supplemental Table 2). The interview guide was pilot tested with four members of the public for clarity and consistency with the study aim. Interviews were conducted in English and French by two researchers (SJMi, ML) experienced in qualitative methods; interviews occurred via Zoom (https://www.zoom.us/) or telephone, dependent on participant preference. The interview guide was developed to be 30 min; interviews lasted on average 27 min. Participants were asked 12 open-ended interview questions followed by 12 demographic questions at the conclusion of the interview. Interviews were digitally recorded to produce verbatim transcripts. English audio files were sent to a transcription company (www.rev.com/); French audio files were transcribed using NVivo 12 (QSR International, Melbourne, Australia), corrected by a fluent research team member (SJMi), and then translated by an artificial intelligence software (Sonix; https://sonix.ai/). French-to-English transcripts were reviewed a final time by the same fluent researcher (SJMi) to ensure accuracy. All textual data was reviewed, cleaned, and de-identified before analysis. Participants were given the chance to review their transcripts as a form of member-checking; however, none elected to do so.
Parsons Leigh J., Moss S.J., Mizen S.J., FitzGerald E.A., Brundin-Mather R., de Grood C., Dodds A., Stelfox H.T, & Fiest K.M. (2023). Exploring the influence of behavioural, normative and control beliefs on intentions to adhere to public health guidelines during the COVID-19 pandemic: a qualitative interview based study. BMC Public Health, 23, 464.
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Publication 2023
COVID 19 Mainstreaming, Education Pandemics Transcription, Genetic
2
COVID-19 Pandemic Family Impacts
As the starting point of the COVID-19 pandemic in Germany, we chose March 15, 2020. Notably, schools closed on March 16, 2020, and the first economic shutdown was on March 22, 2020. We assumed an ongoing pandemic throughout April 2, 2022, as throughout the whole time public health measures were established to mitigate the spread of the COVID-19 pandemic. Those measures ranged from mask mandates (also in schools and public), home office regulations for parents, limited leisure time activities, travel restrictions, partial home-schooling, limited possibilities for social contacts (also in schools) and unusual structure of school-days (e.g., ventilation, split schoolyards to prevent transmission of the virus, daily fast-testing routines). In this analysis, April 2, 2022 marks the “last day of the pandemic” (i.e. last longitudinal data point considered) since almost all public health measures to mitigate the spread of the COVID-19 pandemic were discontinued in the study region Baden-Württemberg at that time [38 ]. The pre-pandemic family situation was taken into account between T5 and T9 to minimize reverse causality. In particular, since it is possible that parental outcomes during the pandemic are influenced by changing offspring outcomes during the pandemic, the results of this approach can be clearly interpreted based on family situation before the pandemic.
Kurz D., Braig S., Genuneit J, & Rothenbacher D. (2023). Trajectories of child mental health, physical activity and screen-time during the COVID-19 pandemic considering different family situations: results from a longitudinal birth cohort. Child and Adolescent Psychiatry and Mental Health, 17, 36.
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Publication 2023
COVID 19 Pandemics Parent Transmission, Communicable Disease Virus
3
Identifying Children at Risk from Pandemic Family Situations
We used maternal questionnaire data of the FU waves T5-T9 to define three different groups of pre-pandemic family situations to allow simple identification of children possibly at risk. A child belonged to a specific group if maternal criteria for the group were fulfilled. Table 1 shows how groups of different pre-pandemic family situations were defined.

Groups of considered pre-pandemic family situations based on maternal questionnaire data of follow-up waves T5-T9

Group #Group nameDefinitionCommentN girlsN boys
Overall300288
#1Depression or anxiety symptoms of motherMother has at least two times HADS_D score ≥ 8 or two times HADS_A score ≥ 8 in FU-waves T5-T8Group of children in which mother has either symptoms of depression or anxiety6467
#2Housing densityHigh housing density for at least two years between T5-T9Living space per person (m2/person) < median of specific FU-wave: T5 < 32.5, T6 < 32.5, T7 < 32.0, T8 < 32.5, T9 < 33.5148131
#3Working days of motherMother works ≥ 5 days per week for at least one year in FU-waves T6-T9–150115

FU follow-up

Maternal pre-pandemic mental health was indexed by the Hospital Anxiety and Depression (HADS)-scale. The HADS questionnaire [44 (link), 45 (link)] is a 14-item screening measure with two subscales assessing symptoms of anxiety and depression. Scores on each subscale range from 0 to 21. A score between 8 and 10 indicates moderate levels of symptoms, and a score between 11 and 21 indicates severe levels of symptoms [46 (link)]. The questionnaire is also validated in the German language and can be used in the general population [46 (link)].
Kurz D., Braig S., Genuneit J, & Rothenbacher D. (2023). Trajectories of child mental health, physical activity and screen-time during the COVID-19 pandemic considering different family situations: results from a longitudinal birth cohort. Child and Adolescent Psychiatry and Mental Health, 17, 36.
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Publication 2023
4-amino-4'-hydroxylaminodiphenylsulfone Anxiety Child Depressive Symptoms Mental Health Mothers Pandemics
4
Trajectories of Child Health Amid COVID-19
Children aged 5–9 years who completed at least one questionnaire in the SPATZ study were included in this analysis. COVID-19 pandemic started in the first or second year of school (children aged 6 or 7 years). Data for the pre-pandemic family situation were taken from the follow-up waves T5-T9. See Fig. 1 for details of the study design and data considered.

Study design of trajectories of child health in light of the COVID-19 pandemic (T7-T11) taking family situation into account

Kurz D., Braig S., Genuneit J, & Rothenbacher D. (2023). Trajectories of child mental health, physical activity and screen-time during the COVID-19 pandemic considering different family situations: results from a longitudinal birth cohort. Child and Adolescent Psychiatry and Mental Health, 17, 36.
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Publication 2023
Child Children's Health COVID 19 Pandemics
5
Pandemic-Related Outcomes in Children
We conducted descriptive statistics and estimated the trajectories of outcomes between T7 and T11 (i.e. child age 5–9 years). The maximum number of measurement points used per individual was five, and the minimum number of measurement points per individual to be included in the analysis was one.
We used mixed models, assuming an unstructured covariance matrix, to estimate differences in means associated with the time during the pandemic vs. before the pandemic. Adjustment variables were child age, maternal educational attainment (duration of school education < 12 years/duration of school education ≥ 12 years) and maternal nationality (German/Non-German). The intercept represents the individual score at baseline. The analysis was stratified by gender and was performed using SAS® 9.4 (The SAS Institute, Cary, NC, USA).
Kurz D., Braig S., Genuneit J, & Rothenbacher D. (2023). Trajectories of child mental health, physical activity and screen-time during the COVID-19 pandemic considering different family situations: results from a longitudinal birth cohort. Child and Adolescent Psychiatry and Mental Health, 17, 36.
Full text: Click here
Publication 2023
Child Mothers Pandemics

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More about "Pandemics"

Pandemics are widespread outbreaks of infectious diseases that affect large populations across multiple countries or continents, also known as global health emergencies or epidemics.
These public health crises can cause significant illness, death, and socioeconomic disruption.
Pandemics are typically driven by novel or mutated pathogens, often viruses, that can spread rapidly through human-to-human transmission.
Understanding the epidemiology, virology, and clinical characteristics of pandemics is crucial for developing targeted interventions, improving disease surveillance, and coordinating international cooperation.
Effective pandemic preparedness, prevention, and response strategies are critical to mitigate the impact of these public health emergencies.
Leveraging statistical software like SAS version 9.4, SPSS version 26, Stata 16, and SPSS Statistics for Windows can provide valuable insights for pandemic research and analysis.
These tools can help researchers and public health professionals analyze epidemiological data, model disease patterns, and evaluate the effectiveness of interventions.
By optimizing pandemic research protocols with AI-driven insights from PubCompare.ai, researchers can locate the best protocols and products from literature, pre-prints, and patents, streamlining their research efforts.
This cutting-edge tool can help identify the most relevant and effective strategies for pandemic preparedness, prevention, and response.