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Mendeley readers
Attention Score in Context
| Title |
Pathogen seasonality and links with weather in England and Wales: a big data time series analysis
|
|---|---|
| Published in |
BMC Public Health, August 2018
|
| DOI | 10.1186/s12889-018-5931-6 |
| Pubmed ID | |
| Authors |
Mark P. C. Cherrie, Gordon Nichols, Gianni Lo Iacono, Christophe Sarran, Shakoor Hajat, Lora E. Fleming |
| Abstract |
Many infectious diseases of public health importance display annual seasonal patterns in their incidence. We aimed to systematically document the seasonality of several human infectious disease pathogens in England and Wales, highlighting those organisms that appear weather-sensitive and therefore may be influenced by climate change in the future. Data on infections in England and Wales from 1989 to 2014 were extracted from the Public Health England (PHE) SGSS surveillance database. We conducted a weekly, monthly and quarterly time series analysis of 277 pathogen serotypes. Each organism's time series was forecasted using the TBATS package in R, with seasonality detected using model fit statistics. Meteorological data hosted on the MEDMI Platform were extracted at a monthly resolution for 2001-2011. The organisms were then clustered by K-means into two groups based on cross correlation coefficients with the weather variables. Examination of 12.9 million infection episodes found seasonal components in 91/277 (33%) organism serotypes. Salmonella showed seasonal and non-seasonal serotypes. These results were visualised in an online Rshiny application. Seasonal organisms were then clustered into two groups based on their correlations with weather. Group 1 had positive correlations with temperature (max, mean and min), sunshine and vapour pressure and inverse correlations with mean wind speed, relative humidity, ground frost and air frost. Group 2 had the opposite but also slight positive correlations with rainfall (mm, > 1 mm, > 10 mm). The detection of seasonality in pathogen time series data and the identification of relevant weather predictors can improve forecasting and public health planning. Big data analytics and online visualisation allow the relationship between pathogen incidence and weather patterns to be clarified. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 38 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 24% |
| Student > Master | 7 | 18% |
| Student > Bachelor | 4 | 11% |
| Student > Ph. D. Student | 2 | 5% |
| Other | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 14 | 37% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 4 | 11% |
| Biochemistry, Genetics and Molecular Biology | 3 | 8% |
| Agricultural and Biological Sciences | 3 | 8% |
| Veterinary Science and Veterinary Medicine | 3 | 8% |
| Computer Science | 2 | 5% |
| Other | 10 | 26% |
| Unknown | 13 | 34% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 December 2022.
All research outputs
#15,708,506
of 23,342,092 outputs
Outputs from BMC Public Health
#11,602
of 15,202 outputs
Outputs of similar age
#212,836
of 335,477 outputs
Outputs of similar age from BMC Public Health
#216
of 257 outputs
Altmetric has tracked 23,342,092 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 15,202 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.0. This one is in the 16th percentile – i.e., 16% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 335,477 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 257 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.