Search PubMed⌕ Search

PubMed · 16647969

Determining global population distribution: methods, applications and data.

Abstract

Evaluating the total numbers of people at risk from infectious disease in the world requires not just tabular population data, but data that are spatially explicit and global in extent at a moderate resolution. This review describes the basic methods for constructing estimates of global population distribution with attention to recent advances in improving both spatial and temporal resolution. To evaluate the optimal resolution for the study of disease, the native resolution of the data inputs as well as that of the resulting outputs are discussed. Assumptions used to produce different population data sets are also described, with their implications for the study of infectious disease. Lastly, the application of these population data sets in studies to assess disease distribution and health impacts is reviewed. The data described in this review are distributed in the accompanying DVD.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

D L Balk, U Deichmann, G Yetman, F Pozzi, S I Hay, A Nelson. 2006. Determining global population distribution: methods, applications and data.. https://doi.org/10.1016/s0065-308x(05)62004-0

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Application of the promotion time cure model with time-changing exposure to the study of HIV/AIDS and other infectious diseases.

Infectious diseases are caused by single or successive contacts with pathogens. Nevertheless, contacts with pathogens do not implicate infection. In 1993, Yakovlev et al. proposed a model to study a population of cancer patients with a cured fraction, a well adapted model to describe an infectious disease with a unique infection occasion. Extensions of this model have been proposed in the recent years. We present a mechanistic formulation in the context of infectious diseases with multiple infection occasions. It is a mixture model that enables to study risk factors associated with infection intensity at each infection occasion and factors shortening the delay from exposure to clinical event. Simulations are performed to evaluate the model fitting and two examples are presented for illustration: an analysis of an HIV-1 mother-to-child transmission data set and an analysis of nosocomial urinary tract infections data set.

Communicable Diseases↗

On observing and analyzing disease versus signals.

The immune system has co-evolved with microbes that cause acute infectious disease. Immune responses must be appropriate to allow survival of both the individual and the species. These responses involve complex interactions that often go unmeasured.

Communicable Diseases↗