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Monitoring Short Term Changes of Infectious Diseases in Uganda with Gaussian Processes

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Book cover Advanced Analysis and Learning on Temporal Data (AALTD 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9785))

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Abstract

A method to monitor infectious diseases based on health records is proposed. Infectious diseases, specially Malaria, are a constant threat for Ugandan public health. The method is applied to health facility records of Malaria in Uganda. The first challenge to overcome is the noise introduced by missing reports of the health facilities. We use Gaussian processes with vector-valued kernels to estimate the missing values in the time series. Later on, for aggregate data at a District level, we use a combination of kernels to decompose the case-counts time series into short and long term components. This method allows not only to remove the effect of specific components, but to study the components of interest with more detail. The short term variations of an infection are divided into four cyclical stages. The progress of an infection across the population can be easily analysed and compared between different Districts. The graphical tool provided can help quick response planning and resources allocation.

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Notes

  1. 1.

    Such a task would require the use of a periodic kernel, which is able to learn a sinusoidal pattern. A periodic kernel does not impose any additional complication for learning the model. Nevertheless, we decided no to use it to show the capabilities of the vector-valued kernel regression.

  2. 2.

    An alternative is to include information about weather conditions in the estimates. That approach deserves a much broader discussion and falls out of the scope of this work.

  3. 3.

    Being more specific, the effect should be similar in those health facilities that are dedicated to treat the disease in question.

  4. 4.

    The number of health facilities was around four thousand in the sample of information used.

  5. 5.

    We did not have the spatial location for some health facilities. These facilities were assigned randomly to different clusters.

  6. 6.

    This model was implemented by using a GP with a bias kernel.

  7. 7.

    Even when we limited the study to health facilities with at least 8 observations, some health facilities did not have enough information to fit a model adequately.

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Author information

Authors and Affiliations

  1. Global Health Group MEI, University of California San Francisco, San Francisco, USA

    Ricardo Andrade-Pacheco

  2. Department of Computer Science, University of Sheffield, Sheffield, UK

    Neil Lawrence

  3. College of Computing and Information Science, Makerere University, Kampala, Uganda

    Martin Mubangizi & John Quinn

  4. UN Global Pulse, Pulse Lab Kampala, Kampala, Uganda

    Martin Mubangizi & John Quinn

Authors
  1. Ricardo Andrade-Pacheco
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  2. Martin Mubangizi
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  3. John Quinn
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  4. Neil Lawrence
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Corresponding author

Correspondence to Ricardo Andrade-Pacheco .

Editor information

Editors and Affiliations

  1. Lab. d'Informatique de Grenoble, Université Grenoble, Grenoble, France

    Ahlame Douzal-Chouakria

  2. Universidade da Coruna, Coruna, Spain

    José A. Vilar

  3. IRISA, Université de Bretagne-Sud, Vannes, France

    Pierre-François Marteau

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Andrade-Pacheco, R., Mubangizi, M., Quinn, J., Lawrence, N. (2016). Monitoring Short Term Changes of Infectious Diseases in Uganda with Gaussian Processes. In: Douzal-Chouakria, A., Vilar, J., Marteau, PF. (eds) Advanced Analysis and Learning on Temporal Data. AALTD 2015. Lecture Notes in Computer Science(), vol 9785. Springer, Cham. https://doi.org/10.1007/978-3-319-44412-3_7

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  • DOI: https://doi.org/10.1007/978-3-319-44412-3_7

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