Short description
: Predicting
epidemiological spreads in space and time is challenging because official
health data is oftentimes not publicly available and partly outdated and often
limited in its spatial and temporal resolution. Thus, this thesis will develop
methods for identifying spatio-temporal patterns in COVID-19, dengue and chikungunya
cases. The developed methodology will identify hot spots in space and time based
on user-generated data (e.g., Twitter) and correlate the results with official and
ancillary data (e.g. socio-economic & environmental data). Depending on the
progress of the thesis, additional goals comprise the implementation of a tool
for dynamic visualisation and the development of a method for forecasting the
further spread of a disease, and investigating relationships with
socio-economic information. The results can potentially support health
institutions in planning vaccination and preparing for a rapidly spreading epidemic.
The thesis has extraordinary practical relevance due to the COVID-19 pandemic and because chikungunya is
currently spreading over South, Middle and North America, and Dengue fever is
prevalent in many tropical and sub-tropical regions across the globe and
increasingly impacts urban agglomerations.
The master thesis will be carried out together with Harvard University’s School of Public Health (HSPH) and the Center for Geographic Analysis (CGA).
: Predicting
epidemiological spreads in space and time is challenging because official
health data is oftentimes not publicly available and partly outdated and often
limited in its spatial and temporal resolution. Thus, this thesis will develop
methods for identifying spatio-temporal patterns in COVID-19, dengue and chikungunya
cases. The developed methodology will identify hot spots in space and time based
on user-generated data (e.g., Twitter) and correlate the results with official and
ancillary data (e.g. socio-economic & environmental data). Depending on the
progress of the thesis, additional goals comprise the implementation of a tool
for dynamic visualisation and the development of a method for forecasting the
further spread of a disease, and investigating relationships with
socio-economic information. The results can potentially support health
institutions in planning vaccination and preparing for a rapidly spreading epidemic.
The thesis has extraordinary practical relevance due to the COVID-19 pandemic and because chikungunya is
currently spreading over South, Middle and North America, and Dengue fever is
prevalent in many tropical and sub-tropical regions across the globe and
increasingly impacts urban agglomerations.The master thesis will be carried out together with Harvard University’s School of Public Health (HSPH) and the Center for Geographic Analysis (CGA).
Literature:
Hagenlocher,
M., Delmelle, E., Casas, I., and Kienberger, S. (2013) Assessing socioeconomic
vulnerability to dengue fever in Cali, Colombia: statistical vs expert-based
modeling. International journal of health geographics, 12(1), 36.
Jaenisch, T.
and Patz, J. (2002) Assessment of Associations between Climate and Infectious
Diseases; a Comparison of the Reports of the Intergovernmental Panel on Climate
Change (IPCC), the National Research Council (NRC), and United States Global
Change Research Program (USGCRP). Global Change & Human Health 2002, Volume
3(1), 2-7.
Resch, B.
(2013) People as Sensors and Collective Sensing - Contextual Observations
Complementing Geo-Sensor Network Measurements. In: Krisp, J. (2013) Advances in
Location-Based Services, ISBN 978-3-642-34202-8, Springer, Berlin Heidelberg,
pp. 391-406.
Start date: ASAP
Prerequisites/qualifications: experience with analysing VGI, data visualisation, and
spatio-temporal pattern detection
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