Epidemic tracking using network-based scaling - An Innovative Approach for Real-time Epidemic Surveillance and Control in East
| dc.contributor.author | Murgolo , Daniele | |
| dc.contributor.author | Vu, Tomas | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för data och informationsteknik | sv |
| dc.contributor.department | Chalmers University of Technology / Department of Computer Science and Engineering | en |
| dc.contributor.examiner | Papatriantafilou, Marina | |
| dc.contributor.supervisor | Tsigas, Philippas | |
| dc.date.accessioned | 2024-09-13T11:05:58Z | |
| dc.date.available | 2024-09-13T11:05:58Z | |
| dc.date.issued | 2024 | |
| dc.date.submitted | ||
| dc.description.abstract | Estimating the sizes of subpopulations enables the potential to optimally allocate resources and funding for people in need, e.g., subpopulations that are affected by epidemics such as HIV/AIDS. We propose a method based on related literature on networking to accomplish this, namely the survey-based Network scale-up method (NSUM). This is done by collecting aggregated relational data (ARD) by asking participants “How many X do you know?”, where X can be any subpopulation, such as people named Michael or doctors. The internal workings behind NSUM is that with the use of participants’ social networks, the sizes of hard-to-reach subpopulations can be estimated by extrapolating and scaling up the total population. The aim of this thesis is to build a system based on a Web questionnaire and the use of NSUM in hopes of estimating the sizes of hard-to-reach subpopulations. Different models within NSUM such as the random degree model (RD-model), barrier effect model (BE-model), and transmission bias model (TB-model) which account for various errors and biases, were also explored where comparisons and analysis of their performance were conducted. Data sets from Uganda, pertaining to occupational distribution, and Rwanda, focusing on the age distribution were used. The results from the two data sets are presented in a forest plot. The metric of choice is the difference in magnitude between the true value and the estimated values. The RD-model produces estimates that are close to the true value with small variances whereas the TB-model overestimates with a large dispersion for both datasets. Lastly, the BE-model produces conflicting estimates between the two datasets. Even though it is difficult to affirm the estimated value, we conclude that, while these estimates are consistent to a certain degree, the various biases and errors may produce less than satisfactory results. | |
| dc.identifier.coursecode | DATX05 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12380/308593 | |
| dc.language.iso | eng | |
| dc.setspec.uppsok | Technology | |
| dc.title | Epidemic tracking using network-based scaling - An Innovative Approach for Real-time Epidemic Surveillance and Control in East | |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.degree | Master's Thesis | en |
| dc.type.uppsok | H | |
| local.programme | Data science and AI (MPDSC), MSc |