| Chapter title |
Systems Biology Approaches to Understanding COVID-19 Spread in the Population.
|
|---|---|
| Chapter number | 15 |
| Book title |
Systems Biology
|
| Published in |
Methods in molecular biology, January 2024
|
| DOI | 10.1007/978-1-0716-3577-3_15 |
| Pubmed ID | |
| Book ISBNs |
978-1-07-163576-6, 978-1-07-163577-3
|
| Authors |
Marković, Sofija, Salom, Igor, Djordjevic, Marko |
| Abstract |
In essence, the COVID-19 pandemic can be regarded as a systems biology problem, with the entire world as the system, and the human population as the element transitioning from one state to another with certain transition rates. While capturing all the relevant features of such a complex system is hardly possible, compartmental epidemiological models can be used as an appropriate simplification to model the system's dynamics and infer its important characteristics, such as basic and effective reproductive numbers of the virus. These measures can later be used as response variables in feature selection methods to uncover the main factors contributing to disease transmissibility. We here demonstrate that a combination of dynamic modeling and machine learning approaches can represent a powerful tool in understanding the spread, not only of COVID-19, but of any infectious disease of epidemiological proportions. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 3 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Lecturer | 1 | 100% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 1 | 100% |