Spatial distribution and computational modeling for mapping of tuberculosis in Pakistan.
Background Tuberculosis (TB) like many other infectious diseases has a strong relationship with climatic parameters. Methods The present study has been carried out on the newly diagnosed sputum smear-positive pulmonary TB cases reported to National TB Control Program across Pakistan from 2007 to 202...
| Published in: | Journal of Public Health Vol. 45; no. 2; pp. 338 - 347 |
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| Main Authors: | , , , , , |
| Format: | pictorial research tables/charts Journal Article |
| Published: |
Oxford University Press / USA
Jun2023
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=164395671&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164395671 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17413842 1CA0 jtl: Journal of Public Health issn: 17413842 maglogo: N pubinfo: dt: Jun2023 vid: 45 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 164395671 164395671 164395671 10.1093/pubmed/fdac125 164395671 ppf: 338 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Spatial distribution and computational modeling for mapping of tuberculosis in Pakistan. aug: au: Khaliq, Aasia Ashraf, Uzma Chaudhry, Muhammad N Shahid, Saher Sajid, Muhammad A Javed, Maryam affil: Department of Life Sciences, Lahore University of Management Sciences (LUMS) , Lahore , Pakistan sug: subj: Tuberculosis Epidemiology Disease Hotspot Computer Simulation Concept Mapping Human Pakistan Tuberculosis Risk Factors Incidence Descriptive Statistics Comparative Studies Machine Learning Algorithms Support Vector Machine Risk Assessment ab: Background Tuberculosis (TB) like many other infectious diseases has a strong relationship with climatic parameters. Methods The present study has been carried out on the newly diagnosed sputum smear-positive pulmonary TB cases reported to National TB Control Program across Pakistan from 2007 to 2020. In this study, spatial and temporal distribution of the disease was observed through detailed district wise mapping and clustered regions were also identified. Potential risk factors associated with this disease depending upon population and climatic variables, i.e. temperature and precipitation were also identified. Results Nationwide, the incidence rate of TB was observed to be rising from 7.03% to 11.91% in the years 2007–2018, which then started to decline. However, a declining trend was observed after 2018–2020. The most populous provinces, Punjab and Sindh, have reported maximum number of cases and showed a temporal association as the climatic temperature of these two provinces is higher with comparison to other provinces. Machine learning algorithms Maxent, Support Vector Machine (SVM), Environmental Distance (ED) and Climate Space Model (CSM) predict high risk of the disease with14.02%, 24.75%, 34.81% and 43.89% area, respectively. Conclusion SVM has a higher significant probability of prediction in the diseased area with a 1.86 partial receiver-operating characteristics (ROC) value as compared with other models. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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