About: A geographic analysis of population density thresholds in the influenza pandemic of 1918

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: A geographic analysis of population density thresholds in the influenza pandemic of 1918–19 Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	A geographic analysis of population density thresholds in the influenza pandemic of 1918–19
Creator	Chandra, Siddharth Kassens-Noor, Eva Kuljanin, Goran Vertalka, Joshua
Source	Medline; PMC
abstract	BACKGROUND: Geographic variables play an important role in the study of epidemics. The role of one such variable, population density, in the spread of influenza is controversial. Prior studies have tested for such a role using arbitrary thresholds for population density above or below which places are hypothesized to have higher or lower mortality. The results of such studies are mixed. The objective of this study is to estimate, rather than assume, a threshold level of population density that separates low-density regions from high-density regions on the basis of population loss during an influenza pandemic. We study the case of the influenza pandemic of 1918–19 in India, where over 15 million people died in the short span of less than one year. METHODS: Using data from six censuses for 199 districts of India (n=1194), the country with the largest number of deaths from the influenza of 1918–19, we use a sample-splitting method embedded within a population growth model that explicitly quantifies population loss from the pandemic to estimate a threshold level of population density that separates low-density districts from high-density districts. RESULTS: The results demonstrate a threshold level of population density of 175 people per square mile. A concurrent finding is that districts on the low side of the threshold experienced rates of population loss (3.72%) that were lower than districts on the high side of the threshold (4.69%). CONCLUSIONS: This paper introduces a useful analytic tool to the health geographic literature. It illustrates an application of the tool to demonstrate that it can be useful for pandemic awareness and preparedness efforts. Specifically, it estimates a level of population density above which policies to socially distance, redistribute or quarantine populations are likely to be more effective than they are for areas with population densities that lie below the threshold.
has issue date	2013-02-20(xsd:dateTime)
bibo:doi	10.1186/1476-072x-12-9
bibo:pmid	23425498
has license	cc-by
sha1sum (hex)	ac379a37c74e9e9ea13ad9813f46f53dbab62b66
schema:url	https://doi.org/10.1186/1476-072x-12-9
resource representing a document's title	A geographic analysis of population density thresholds in the influenza pandemic of 1918–19
has PubMed Central identifier	PMC3641965
has PubMed identifier	23425498
schema:publication	Int J Health Geogr
resource representing a document's body	covid:ac379a37c74e9e9ea13ad9813f46f53dbab62b66#body_text
is schema:about of	named entity 'regions' named entity 'study' named entity 'India' named entity 'embedded' named entity 'analysis' named entity '1918' named entity 'THRESHOLDS' named entity 'LEVEL' named entity 'SHORT' named entity 'POPULATION' named entity 'LOWER' named entity 'STUDIES' named entity 'THRESHOLDS' named entity 'USE' named entity 'PLAY' named entity 'LARGEST' named entity 'MILLION' named entity 'ESTIMATE' named entity 'VARIABLES' named entity 'CENSUSES' named entity 'PLACES' named entity '1918' named entity 'TESTED FOR' named entity 'LESS THAN ONE YEAR' named entity 'RATHER' named entity 'POPULATION GROWTH' named entity 'PANDEMIC' named entity 'DIED' named entity 'GROWTH MODEL' named entity 'EPIDEMICS' named entity 'HIGHER' named entity 'LOW-DENSITY' named entity 'ROLE' named entity 'VARIABLE' named entity 'SPAN' named entity 'PEOPLE' named entity 'WHERE' named entity 'METHOD' named entity 'METHODS' named entity 'GEOGRAPHIC' named entity 'ARBITRARY' named entity 'REGIONS' named entity 'CASE' named entity 'INFLUENZA PANDEMIC' named entity 'GEOGRAPHIC' named entity 'ANALYSIS' named entity 'POPULATION DENSITY' named entity 'MORTALITY' named entity 'STUDY' named entity 'IMPORTANT' named entity 'SPLITTING' named entity 'EMBEDDED' named entity 'USING' named entity 'INDIA' named entity 'SPREAD' named entity 'BASIS' named entity 'INFLUENZA PANDEMIC' named entity 'HAVE' named entity 'INFLUENZA' named entity 'OBJECTIVE' named entity 'RESULTS' named entity 'SAMPLE' named entity 'LOSS' named entity 'MIXED' named entity 'DATA' named entity 'THRESHOLD' named entity 'BACKGROUND' named entity 'HIGH-DENSITY' named entity 'POPULATION DENSITY' named entity 'COUNTRY' named entity 'PRIOR' covid:arg/ac379a37c74e9e9ea13ad9813f46f53dbab62b66 named entity 'thresholds' named entity 'districts' named entity 'population density' named entity 'died' named entity 'threshold' named entity 'population density'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software