Mathematical modeling of infectious disease dynamics

By Constantinos I. Siettos, Lucia Russo. Virulence, 2013

437 citations recorded by the source corpus.

Over the last years, an intensive worldwide effort is speeding up the developments in the establishment of a global surveillance network for combating pandemics of emergent and re-emergent infectious diseases. Scientists from different fields extending from medicine and molecular biology to computer science and applied mathematics have teamed up for rapid assessment of potentially urgent situations. Toward this aim mathematical modeling plays an important role in efforts that focus on predicting, assessing, and controlling potential outbreaks. To better understand and model the contagious dynamics the impact of numerous variables ranging from the micro host–pathogen level to host-to-host interactions, as well as prevailing ecological, social, economic, and demographic factors across the globe have to be analyzed and thoroughly studied. Here, we present and discuss the main approaches that are used for the surveillance and modeling of infectious disease dynamics. We present the basic concepts underpinning their implementation and practice and for each category we give an annotated list of representative works.

Subfields: Genetics, Modeling and Simulation, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/9cc0534fb1e5181fa20aa2891f5614deab6b87f8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Melissa Tracy
2. C. Anastassopoulou
3. M. Cerdá
4. Katherine M. Keyes
5. A. Tsakris
6. Lucia Russo
7. Constantinos I. Siettos

Citations in the corpus

• Agent-Based Modeling in Public Health: Current Applications and Future Directions (2018, background): “Many ABMs of infectious disease transmission rely on the susceptible-infected-recovered (SIR) framework proposed by Kermack and McKendrick in the 1920s (94), in which the flows between susceptible, infected, and recovered states are governed by differential equations (32).”
• Data-based analysis, modelling and forecasting of the COVID-19 outbreak (2020, background): “Amidst such an important ongoing public health crisis that also has severe economic repercussions, we reverted to mathematical modelling that can shed light to essential epidemiologic parameters that determine the fate of the epidemic [13] .”