| Title |
State estimators for some epidemiological systems
|
|---|---|
| Published in |
Journal of Mathematical Biology, July 2018
|
| DOI | 10.1007/s00285-018-1273-3 |
| Pubmed ID | |
| Authors |
A. Iggidr, M. O. Souza |
| Abstract |
We consider a class of epidemiological models that includes most well-known dynamics for directly transmitted diseases, and some reduced models for indirectly transmitted diseases. We then propose a simple observer that can be applied to models in this class. The error analysis of this observer leads to a non-autonomous error equation, and a new bound for fundamental matrices is also presented. We analyse and implement this observer in two examples: the classical SIR model, and a reduced Bailey-Dietz model for vector-borne diseases. In both cases we obtain arbitrary exponential convergence of the observer. For the latter model, we also applied the observer to recover the number of susceptible using dengue infection data from a district in the city of Rio de Janeiro. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 19 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 5 | 26% |
| Student > Master | 5 | 26% |
| Student > Ph. D. Student | 2 | 11% |
| Student > Doctoral Student | 2 | 11% |
| Professor | 1 | 5% |
| Other | 2 | 11% |
| Unknown | 2 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Mathematics | 3 | 16% |
| Medicine and Dentistry | 3 | 16% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 11% |
| Biochemistry, Genetics and Molecular Biology | 2 | 11% |
| Agricultural and Biological Sciences | 1 | 5% |
| Other | 3 | 16% |
| Unknown | 5 | 26% |
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 23 June 2020.
All research outputs
#18,643,992
of 23,096,849 outputs
Outputs from Journal of Mathematical Biology
#451
of 665 outputs
Outputs of similar age
#253,265
of 329,030 outputs
Outputs of similar age from Journal of Mathematical Biology
#14
of 20 outputs
Altmetric has tracked 23,096,849 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 665 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 17th percentile – i.e., 17% 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 329,030 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.