Disease Diffusion
Bibliographic Data
| ID | 23737336 |
|---|---|
| Authors | Daniel J Exeter, D Exeter (0000-0003-1061-5925, University of Auckland), Clive E Sabel (0000-0001-9180-4861, University of Bristol) |
| Year | 2017 |
| Pages | 1-4 |
| Publication date | 2017-03-06 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | CHAPTER |
| Venue | International Encyclopedia of Geography (SOURCE_BOOK) |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/9781118786352.wbieg0284 |
| OpenAlex | W4231530051 |
| ISBN | 9781118786352 |
| Language | EN |
| References cited | 6 |
The modeling of the spread and incidence of disease has been of longstanding interest to health geographers and epidemiologists. For a number of decades these concerns have been increasingly pursued through quantitative methods. Diffusion studies often focus on the modeling of transmission rates and the spatial spread of infectious disease in order to better understand the underlying processes and mechanisms of disease spread. Epidemiologists and statisticians often analyze the spread of disease in a temporal fashion, focusing on when a proportion of the susceptible population is likely to be in the “infective” stage. Other approaches, typically from a health geography perspective, have tended to look at the spatial extent and dynamics of disease.
Disease · Disease transmission · Environmental health · Geography · Health geography · Infectious disease (medical specialty) · International health · Pathology · Perspective (graphical) · Population · Public health · Sociology · Artificial Intelligence · Computer Science · COVID-19 epidemiological studies · Demography · Health disparities and outcomes · Medicine · Urban, Neighborhood, and Segregation Studies · Virology · Health Policy
| Citation velocity | historical |
|---|---|
| Highly cited | No |