Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/149787
Title: MODELLING GEOGRAPHICALLY-DEFINED EXPOSURES TO INFECTIOUS DISEASE
Authors: KIESHA PREM
ORCID iD:   orcid.org/0000-0003-0528-798X
Keywords: infectious disease modelling, Bayesian data augmentation, spatial modelling, temporal projections
Issue Date: 13-Sep-2018
Citation: KIESHA PREM (2018-09-13). MODELLING GEOGRAPHICALLY-DEFINED EXPOSURES TO INFECTIOUS DISEASE. ScholarBank@NUS Repository.
Abstract: Statistical and epidemiological methods help to investigate the spread of infectious diseases. Modelling facilitates informed public health response during infectious diseases outbreaks; such interventions may be spatial in nature. This thesis will discuss methods to incorporate location-stratified social contact and spatial data to study geographical-related exposures for contact-transmissible infectious diseases and vector-borne diseases, to evaluate and inform location-specific interventions such as school closure, workplace distancing, and intensive national vector control programmes. This thesis contains three case studies which focus on understanding how prevalence of infectious diseases may vary across geographical regions of a country that differ demographically; how social contact which may lead to infection of a contact-transmissible infectious disease varying across locations and between countries at different stages of demographic and economic development; and finally how human mobility across homes and workplaces can advance the transmission of a geographically structured infectious disease.
URI: http://scholarbank.nus.edu.sg/handle/10635/149787
Appears in Collections:Ph.D Theses (Open)

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