Editing Pathogen transmission (section)

==Tracking==
{{See also|Mathematical modelling of infectious disease}}
Tracking the transmission of infectious diseases is called [[disease surveillance]]. Surveillance of infectious diseases in the public realm traditionally has been the responsibility of [[public health]] agencies, on an international, national, or local level. Public health staff relies on health care workers and microbiology laboratories to report cases of [[reportable disease]]s to them. The analysis of [[aggregate data]] can show the spread of a disease and is at the core of the specialty of [[epidemiology]].
To understand the spread of the vast majority of non-notifiable diseases, data either need to be collected in a particular study, or existing data collections can be mined, such as insurance company data or antimicrobial drug sales for example.{{cn|date=June 2021}}

For diseases transmitted within an institution, such as a hospital, prison, nursing home, boarding school, orphanage, refugee camp, etc., [[infection control]] specialists are employed, who will review medical records to analyze transmission as part of a hospital epidemiology program, for example.{{cn|date=June 2021}}

Because these traditional methods are slow, time-consuming, and labor-intensive, [[proxy (statistics)|proxies]] of transmission have been sought. One proxy in the case of influenza is tracking of [[influenza-like illness]] at certain sentinel sites of health care practitioners within a state, for example.<ref>{{cite journal | vauthors = Polgreen PM, Chen Z, Segre AM, Harris ML, Pentella MA, Rushton G | title = Optimizing influenza sentinel surveillance at the state level | journal = American Journal of Epidemiology | volume = 170 | issue = 10 | pages = 1300–1306 | date = November 2009 | pmid = 19822570 | pmc = 2800268 | doi = 10.1093/aje/kwp270 }}</ref> Tools have been developed to help track influenza [[epidemic]]s by finding patterns in certain [[web search query]] activity. It was found that the frequency of influenza-related web searches as a whole rises as the number of people sick with influenza rises. Examining space-time relationships of web queries has been shown to approximate the spread of influenza<ref name=gins>{{cite journal | vauthors = Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L | title = Detecting influenza epidemics using search engine query data | journal = Nature | volume = 457 | issue = 7232 | pages = 1012–1014 | date = February 2009 | pmid = 19020500 | doi = 10.1038/nature07634 | bibcode = 2009Natur.457.1012G | url = http://li.mit.edu/Stuff/CNSE/Paper/Ginsberg09Mohebbi.pdf | url-status = dead | s2cid = 125775 | archive-url = https://web.archive.org/web/20181024023011/http://li.mit.edu/Stuff/CNSE/Paper/Ginsberg09Mohebbi.pdf | archive-date = 2018-10-24 }}</ref> and [[dengue]].<ref name=chan>{{cite journal | vauthors = Chan EH, Sahai V, Conrad C, Brownstein JS | title = Using web search query data to monitor dengue epidemics: a new model for neglected tropical disease surveillance | journal = PLOS Neglected Tropical Diseases | volume = 5 | issue = 5 | pages = e1206 | date = May 2011 | pmid = 21647308 | pmc = 3104029 | doi = 10.1371/journal.pntd.0001206 | doi-access = free }}</ref>

[[Computer simulation]]s of infectious disease spread have been used.<ref name=math>{{cite journal | vauthors = Siettos CI, Russo L | title = Mathematical modeling of infectious disease dynamics | journal = Virulence | volume = 4 | issue = 4 | pages = 295–306 | date = May 2013 | pmid = 23552814 | pmc = 3710332 | doi = 10.4161/viru.24041 }}</ref>
Human aggregation can drive transmission, [[seasonal variation]] and [[epidemic|outbreaks]] of infectious diseases, such as the annual start of school, bootcamp, the annual [[Hajj]] etc. Most recently, data from cell phones have been shown to be able to capture population movements well enough to predict the transmission of certain infectious diseases, like rubella.<ref name=pnas>{{cite journal | vauthors = Wesolowski A, Metcalf CJ, Eagle N, Kombich J, Grenfell BT, Bjørnstad ON, Lessler J, Tatem AJ, Buckee CO | display-authors = 6 | title = Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data | journal = Proceedings of the National Academy of Sciences of the United States of America | volume = 112 | issue = 35 | pages = 11114–11119 | date = September 2015 | pmid = 26283349 | pmc = 4568255 | doi = 10.1073/pnas.1423542112 | doi-access = free | bibcode = 2015PNAS..11211114W }}</ref>