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> Phenomena > Phenomenon or Process > Epidemics

Epidemics

Epidemics refer to the rapid spread of infectious diseases across a population, often exceeding expected levels.
These outbreaks can have significant public health implications, impacting communities and healthcare systems.
Epidemiological research is crucial for understanding the causes, transmission dynamics, and effective control measures for epidemics.
PubCompare.ai, an AI-driven platform, enhances the reproducibility and accuracy of epidemics research by helping researchers locate relevant protocols from literature, preprints, and patents, while using AI-driven comparisons to identify the best protocols and products for their needs.
This platform can be a valuable tool for epidemiology studies, contributing to improved understanding and management of disease outbreaks.

Most cited protocols related to «Epidemics»

1
Early COVID-19 Epidemiological Insights
The epidemic curve was constructed by date of illness onset, and key dates relating to epidemic identification and control measures were overlaid to aid interpretation. Case characteristics were described, including demographic characteristics, exposures, and health care worker status. The incubation period distribution (i.e., the time delay from infection to illness onset) was estimated by fitting a log-normal distribution to data on exposure histories and onset dates in a subset of cases with detailed information available. Onset-to-first-medical-visit and onset-to-admission distributions were estimated by fitting a Weibull distribution on the dates of illness onset, first medical visit, and hospital admission in a subset of cases with detailed information available. We fitted a gamma distribution to data from cluster investigations to estimate the serial interval distribution, defined as the delay between illness onset dates in successive cases in chains of transmission.
We estimated the epidemic growth rate by analyzing data on the cases with illness onset between December 10 and January 4, because we expected the proportion of infections identified would increase soon after the formal announcement of the outbreak in Wuhan on December 31. We fitted a transmission model (formulated with the use of renewal equations) with zoonotic infections to onset dates that were not linked to the Huanan Seafood Wholesale Market, and we used this model to derive the epidemic growth rate, the epidemic doubling time, and the basic reproductive number (R0), which is defined as the expected number of additional cases that one case will generate, on average, over the course of its infectious period in an otherwise uninfected population. We used an informative prior distribution for the serial interval based on the serial interval of SARS with a mean of 8.4 and a standard deviation of 3.8.11 (link)Analyses of the incubation period, serial interval, growth rate, and R0 were performed with the use of MATLAB software (MathWorks). Other analyses were performed with the use of SAS software (SAS Institute) and R software (R Foundation for Statistical Computing).
Li Q., Guan X., Wu P., Wang X., Zhou L., Tong Y., Ren R., Leung K.S., Lau E.H., Wong J.Y., Xing X., Xiang N., Wu Y., Li C., Chen Q., Li D., Liu T., Zhao J., Liu M., Tu W., Chen C., Jin L., Yang R., Wang Q., Zhou S., Wang R., Liu H., Luo Y., Liu Y., Shao G., Li H., Tao Z., Yang Y., Deng Z., Liu B., Ma Z., Zhang Y., Shi G., Lam T.T., Wu J.T., Gao G.F., Cowling B.J., Yang B., Leung G.M, & Feng Z. (2020). Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia. The New England Journal of Medicine, 382(13), 1199-1207.
Publication 2020
Epidemics Gamma Rays Infection Seafood Severe Acute Respiratory Syndrome Transmission, Communicable Disease Workers Zoonoses
2
ZIKV Surveillance Protocol in Brazil
Biological samples of patients were obtained and processed in the context of the emergency definition of the Ministry of Health during surveillance activities of the ZIKV epidemic in Brazil. This study was approved (opinion number 1.888.946) by the Research Ethics Committee (CEP) of the Evandro Chagas Institute (IEC). All steps and methods of the study followed the recommendations established by legislation in force in Brazil.
Azevedo R.S., de Sousa J.R., Araujo M.T., Martins Filho A.J., de Alcantara B.N., Araujo F.M., Queiroz M.G., Cruz A.C., Vasconcelos B.H., Chiang J.O., Martins L.C., Casseb L.M., da Silva E.V., Carvalho V.L., Vasconcelos B.C., Rodrigues S.G., Oliveira C.S., Quaresma J.A, & Vasconcelos P.F. (2018). In situ immune response and mechanisms of cell damage in central nervous system of fatal cases microcephaly by Zika virus. Scientific Reports, 8, 1.
Publication 2018
Biopharmaceuticals Emergencies Epidemics Ethics Committees, Research Patients Zika Virus
3
ZIKV 2007 Strain Real-Time PCR Assay
Two real-time primer/probe sets specific for the ZIKV 2007 strain were designed by using ZIKV 2007 nucleotide sequence data in the PrimerExpress software package (Applied Biosystems, Foster City, CA, USA). Primers were synthesized by Operon Biotechnologies (Huntsville, AL, USA) with 5-FAM as the reporter dye for the probe (Table 3). All real-time assays were performed by using the QuantiTect Probe RT-PCR Kit (QIAGEN, Valencia, CA, USA) with amplification in the iCycler instrument (Bio-Rad, Hercules, CA, USA) following the manufacturer’s protocol. Specificity of the ZIKV primers was evaluated by testing the following viral RNAs, all of which yielded negative results: DENV-1, DENV-2, DENV-3, DENV-4, WNV, St. Louis encephalitis virus, YFV, Powassan virus, Semliki Forest virus, o’nyong-nyong virus, chikungunya virus, and Spondweni virus (SPOV).
Sensitivity of the ZIKV real-time assay was evaluated by testing dilutions of known copy numbers of an RNA transcript copy of the ZIKV 2007 sequence. Copy numbers of RNA were determined by using the Ribogreen RNA-specific Quantitiation Kit (Invitrogen) and the TBE-380 mini-fluorometer (Turner Biosystems, Sunnyvale, CA, USA). RNA transcripts ranging from 16,000 to 0.2 copies were tested in quadruplicate to determine the sensitivity limit and to construct a standard curve for estimating the genome copy number of ZIKV in patient samples. All serum samples obtained during the epidemic were tested for ZIKV RNA by using this newly designed real-time RT-PCR. Concentration of viral RNA (copies/milliliter) was estimated in ZIKV-positive patients by using the standard curve calculated by the iCycler instrument (Table 4). All RT-PCR–positive specimens were placed on monolayers of Vero, LLC-MK2, and C6/36 cells to isolate virus; no specimens showed virus replication.
Lanciotti R.S., Kosoy O.L., Laven J.J., Velez J.O., Lambert A.J., Johnson A.J., Stanfield S.M, & Duffy M.R. (2008). Genetic and Serologic Properties of Zika Virus Associated with an Epidemic, Yap State, Micronesia, 2007. Emerging Infectious Diseases, 14(8), 1232-1239.
Publication 2008
Base Sequence Biological Assay Cells Chikungunya virus Encephalitis Viruses Epidemics Genome Hypersensitivity Oligonucleotide Primers Operon Patients Powassan virus Real-Time Polymerase Chain Reaction Reverse Transcriptase Polymerase Chain Reaction RNA, Viral RNA Sequence Semliki forest virus Serum Strains Technique, Dilution Virus Virus Replication Zika Virus
4
Age-Specific Incidence of Epidemic Spread
We explore the age-specific incidence of infection during the initial phase of an epidemic of an emerging infectious disease agent that spreads in a completely susceptible population. We focus on the generic features of epidemic spread along the transmission route that is specified by physical and nonphysical contacts as defined here. We partition the population into 5 y age bands, and we group all individuals aged 70 y and older together. This process results in 15 age classes. We denote the number of at-risk contacts of an individual in age class j with individuals in age class i by kij. We take kij as proportional to the observed number of contacts (both physical and nonphysical) that a respondent in age band j makes with other individuals in age band i. The matrix with elements kij is known in infectious disease epidemiology as the next generation matrix K [32 ]. The next generation matrix can be used to calculate the distribution of numbers of new cases in each generation of infection from any arbitrary initial number of introduced infections. For example, when infection is introduced by one single 65-y-old infected individual into a completely susceptible population, we can denote the number of initial cases in generation 0 by the vector x0 = (0,0,0,0,0,0,0,0,0,0,0,0,0,1,0)T. The expected numbers of new cases in the ith generation are denoted by the vector xi, and this vector is calculated by applying the next generation matrix K i times to the initial numbers of individuals x0, that is, xi = Ki x0. For large i, the vector xi will be proportional to the leading eigenvector of K. We find that, in practice, the distribution of new cases is stable after five generations; that is, the distribution no longer depends on the precise age of the initial case. The incidence of new infections per age band is obtained by dividing the expected number of new cases per age class by the number of individuals in each age class. To facilitate comparison among countries, we normalized the distribution of incidence over age classes such that for each country the age-specific incidences sum to one.
Mossong J., Hens N., Jit M., Beutels P., Auranen K., Mikolajczyk R., Massari M., Salmaso S., Tomba G.S., Wallinga J., Heijne J., Sadkowska-Todys M., Rosinska M, & Edmunds W.J. (2008). Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases. PLoS Medicine, 5(3), e74.
Publication 2008
Age Groups Cloning Vectors Communicable Diseases Communicable Diseases, Emerging Epidemics Generic Drugs Infection Maritally Unattached Physical Examination Transmission, Communicable Disease
5
Estimating COVID-19 Serial Interval

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Publication 2020
COVID 19 Epidemics Gamma Rays Human Body Transmission, Communicable Disease

Most recents protocols related to «Epidemics»

1
Longitudinal Evaluation of HIV Cohort
The ‘historical cohort’ of Hospital Clínic includes all PLWH who visited Hospital Clínic since the HIV epidemic started. The ‘active cohort’ of Hospital Clínic consists of PLWH who are currently in follow-up, i.e. HIV patients who had at least one laboratory test 12 months before the data extraction was performed (December 2020).
The historical cohort was used to describe all patients with a new HIV diagnosis who ever visited our hospital. These were described according to each year of the study and stratified according to sex.
The active cohort served as a base for more extensive epidemiological, immunovirological and clinical analyses. Data in the active cohort were stratified by sex and retrospectively analysed according to successive periods within the study. The time intervals of these periods (all starting on 1 January and ending on 31 December of the given years) were determined by considering the introduction of combined ART, commercialisation of newer drugs, and changes in policies on when to start ART. Two periods pre-introduction of combined ART and four periods post-introduction of combined ART were established: period 1, from January 1982 to December 1989; period 2, from January 1990 to December 1996; period 3, from January 1997 to December 2004; period 4, from January 2005 to December 2009; period 5, from January 2010 to December 2014; and period 6, from January 2015 to December 2020.
Toyos S., Berrocal L., González-Cordón A., Inciarte A., de la Mora L., Martínez-Rebollar M., Laguno M., Fernández E., Ambrosioni J., Chivite I., de Lazzari E., Blanco J.L., Martínez E., Miró J.M., Mallolas J, & Torres B. (2023). Sex-based epidemiological and immunovirological characteristics of people living with HIV in current follow-up at a tertiary hospital: a comparative retrospective study, Catalonia, Spain, 1982 to 2020. Eurosurveillance, 28(10), 2200317.
Publication 2023
Diagnosis Epidemics Patients Pharmaceutical Preparations
2
Epidemiologic Trends of PLWH
This was a descriptive, retrospective and comparative observational study of PLWH who were followed-up in the HIV unit of Hospital Clínic from the beginning of the HIV epidemic until December 2020, when data extraction was performed.
Toyos S., Berrocal L., González-Cordón A., Inciarte A., de la Mora L., Martínez-Rebollar M., Laguno M., Fernández E., Ambrosioni J., Chivite I., de Lazzari E., Blanco J.L., Martínez E., Miró J.M., Mallolas J, & Torres B. (2023). Sex-based epidemiological and immunovirological characteristics of people living with HIV in current follow-up at a tertiary hospital: a comparative retrospective study, Catalonia, Spain, 1982 to 2020. Eurosurveillance, 28(10), 2200317.
Publication 2023
Epidemics
3
Microbial Survey of SCTLD Outbreak in Corals
To acquire small subunit (SSU) 16S rRNA datasets for this meta-analysis, an email was sent on July 14, 2020, and July 23, 2020, to the hosts of the coral-list listserv and the SCTLD Disease Advisory Committee (DAC) email list, respectively, requesting scientists to share unpublished SCTLD-associated microbiome datasets. In addition, to allow for comparisons of microbiomes between a past Caribbean coral disease to the novel SCTLD outbreak, a rapid tissue loss (RTL) disease study in Acropora palmata (APAL) and Acropora cervicornis (ACER) comprising apparently healthy (AH) samples, inoculated AH samples, and inoculated diseased samples [61 ], also was included in some analyses. This particular study was selected because Acropora spp. reportedly are not susceptible to SCTLD and the study used V4 primers [3 ]. In total, 17 studies were analyzed, 16 from SCTLD and one from an Acropora spp. RTL study (Supplementary Table 1).
Study authors were requested to complete a preformatted metadata file to facilitate comparisons of data across studies. Requested metadata included sample handling information (e.g., source laboratory, and sample collector) and ecological information (e.g., source reef name, coordinates, zone, water temperature, and coral colony measurements). SCTLD zones included vulnerable (i.e., locations where the disease had not been observed/reported), endemic (i.e., locations where the initial outbreak of the disease had moved through and no or few active lesions were observed on colonies), and epidemic (i.e., locations where the outbreak was active and prevalent across colonies of multiple species). Invasion zone sites, where the disease was newly arrived but not yet prevalent, were grouped within the epidemic zone for consistency across studies and simplicity of analysis. Some metadata required standardization of units and not all metadata were available across all studies. However, in all field-collected samples, all sampling dates and site information were available, enabling the completion of SCTLD disease zone metadata for Florida studies by referencing the Coral Reef Evaluation and Monitoring Project, Disturbance Response Monitoring, and SCTLD boundary reconnaissance databases. For USVI, zones were assigned based on the USVI Department of Planning and Natural Resources SCTLD database (https://dpnr.vi.gov/czm/sctld/).
Rosales S.M., Huebner L.K., Evans J.S., Apprill A., Baker A.C., Becker C.C., Bellantuono A.J., Brandt M.E., Clark A.S., del Campo J., Dennison C.E., Eaton K.R., Huntley N.E., Kellogg C.A., Medina M., Meyer J.L., Muller E.M., Rodriguez-Lanetty M., Salerno J.L., Schill W.B., Shilling E.N., Stewart J.M, & Voss J.D. (2023). A meta-analysis of the stony coral tissue loss disease microbiome finds key bacteria in unaffected and lesion tissue in diseased colonies. ISME Communications, 3, 19.
Publication 2023
Caribbean People Coral Coral Reefs Epidemics Microbiome Oligonucleotide Primers Protein Subunits RNA, Ribosomal, 16S Specimen Collection Tissues
4
Microbiome Composition Analysis with ANCOM-BC
The program Analysis of Compositions of Microbiomes with Bias Correction (ANCOM_BC) was used to identify differentially abundant microbial taxa [75 ]. ANCOM_BC was used with the global test option and the results were considered significant if the false discovery rate adjusted p-value (Padj) was <0.001 and if the W statistic was >90. Field-sourced AH samples were tested for differential abundance among zones (vulnerable, endemic, and epidemic), and SCTLD-susceptible coral samples (without Acropora spp.) were evaluated for differences in disease state (AH, DU, and DL). For SCTLD-susceptible corals, the data were parsed by the three coral compartments (mucus, tissue slurry, and tissue slurry skeleton). ANCOM_BC analyses were run for each compartment due to the relatively low sample size of tissue slurry skeleton samples compared to the two other compartment types. The taxa were further evaluated if they had a log-fold change between −1.5< and >1.5. The ASVs that were significantly enriched were used to identify the relative abundance of the ASVs across sample types and zones. In addition, those enriched only in either DU or DL were used to identify the presence or absence of each ASV in coral species and study per biome. The same ANCOM_BC analysis was repeated without MCAV and OFAV to evaluate if the two dominant coral species in our meta-analysis were driving the enriched bacteria.
Rosales S.M., Huebner L.K., Evans J.S., Apprill A., Baker A.C., Becker C.C., Bellantuono A.J., Brandt M.E., Clark A.S., del Campo J., Dennison C.E., Eaton K.R., Huntley N.E., Kellogg C.A., Medina M., Meyer J.L., Muller E.M., Rodriguez-Lanetty M., Salerno J.L., Schill W.B., Shilling E.N., Stewart J.M, & Voss J.D. (2023). A meta-analysis of the stony coral tissue loss disease microbiome finds key bacteria in unaffected and lesion tissue in diseased colonies. ISME Communications, 3, 19.
Publication 2023
Bacteria Biome Coral Epidemics Microbiome Mucus Skeleton Tissues
5
Evaluating Coral Diversity Across Zones
Shannon diversity metrics were generated with the phyloseq function rarefy_even_depth with option replace = TRUE, and a minimum sequence depth for a sample of 1000. Prior to rarefaction, taxa with a sum of zero across the subsetted data were removed. Two sets of alpha-diversity analyses were run: [1 (link)] evaluated differences across the three zones (vulnerable, endemic, and epidemic) in field-sourced apparently healthy (AH) corals, and [2 ] evaluated differences across disease states (AH, unaffected tissue [DU], and lesion tissue [DL] on a diseased colony) in SCTLD-susceptible corals (i.e., without Acropora spp.). Significance was tested with linear mixed models with the R packages lme4 v1.1.21 [68 ], and emmeans v1.4.3.1 [69 (link)], and Tukey’s HSD was used for pairwise comparisons. For zones and disease states, coral species was used as a random effect.
Rosales S.M., Huebner L.K., Evans J.S., Apprill A., Baker A.C., Becker C.C., Bellantuono A.J., Brandt M.E., Clark A.S., del Campo J., Dennison C.E., Eaton K.R., Huntley N.E., Kellogg C.A., Medina M., Meyer J.L., Muller E.M., Rodriguez-Lanetty M., Salerno J.L., Schill W.B., Shilling E.N., Stewart J.M, & Voss J.D. (2023). A meta-analysis of the stony coral tissue loss disease microbiome finds key bacteria in unaffected and lesion tissue in diseased colonies. ISME Communications, 3, 19.
Publication 2023
Coral Epidemics Tissues

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More about "Epidemics"

Epidemics, infectious disease outbreaks, disease spread, public health crisis, epidemiology research, data analysis tools, statistical software, PubCompare.ai, protocol identification, FBS, SAS, Stata, R, SPSS.
Epidemics refer to the rapid and widespread dissemination of contagious illnesses across a population, frequently surpassing anticipated levels.
These outbreaks can have significant public health ramifications, impacting communities and healthcare systems.
Epidemiological studies are crucial for comprehending the causes, transmission dynamics, and effective control measures for epidemics.
PubCompare.ai, an AI-driven platform, enhances the reproducibility and accuracy of epidemics research by assisting researchers in locating relevant protocols from literature, preprints, and patents, while utilizing AI-driven comparisons to identify the best protocols and products for their needs.
This platform can be a valuable tool for epidemiology studies, contributing to improved understanding and management of disease outbreaks.
Statistical software like FBS, SAS, Stata, R, and SPSS can be leveraged in epidemics research to analyze data, model disease spread, and evaluate the effectiveness of interventions.
These tools offer a wide range of analytical capabilities, from descriptive statistics to advanced modeling techniques, which can aid researchers in uncovering insights and informing public health decision-making.