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AI-GEOSTATS: Summary: Interpolation techniques for disease study

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    Dear list members, Here is a summary of the answers I received concerning my question about interpolation techniques for malaria vector data. Many thanks once
    Message 1 of 1 , May 17, 2001
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      Dear list members,

      Here is a summary of the answers I received concerning my question about interpolation techniques for malaria vector data.
      Many thanks once more for the helpful suggestions.


      Hugo Pilkington
      EuroHIV - European Centre for the Epidemiological Monitoring of AIDS
      Institut de Veille Sanitaire (InVS)
      12, rue du Val d'Osne
      94415 Saint-Maurice cedex

      Tel: +33 (0)141 79 68 68 http://www.ceses.org
      Fax: +33 (0)141 79 68 02 http://www.invs.sante.fr

      I've little background about how to study malaria distribution using
      geostatsitics. But I'd like to mention you that, there is a program (called
      SaTScan) that can be used for studying cluster analysis of disease spread
      like malaria. It provides you locations of clusters and their significance.
      It is free for download from this site


      I hope this will help you in your study.
      Thank you

      Isam Salih
      Linkoping University, SWEDEN.

      You should better compute the correlations on the 21 informed
      houses than perform an interpolation, especially for only
      21 samples that are generally not sufficient for an accurate
      spatial analysis and the interpolated field will not be very obective.
      The best thing you can do is make sure
      that the 21 exposure rates are equally certain (if they are not,
      i.e. if some houses were sampled during one year only instead of two,
      then you should account for this in the following step giving
      different weights to your data),
      then compute a simple omni-directional variogram (for 21 values,
      the variographic cloud will be enough) and hope
      a structure will come out. If any sample has an exceptionnally
      high or low rate value, then you should discard it from the
      dataset and redraw the variogram to catch the hidden structures.

      If the exposure rate has rather local variations
      and, as you wrote in your message, the 21 houses are
      well spread all over the village, then probably this
      variogram will look completely flat (pure nugget effect).
      In that case you can rely on classical statistics and
      compute the correlations between malaria and exposure rates
      on the 21 samples.
      If a structured variogram appears then you can compute cross-
      covariances between the exposure rate and the other covariates
      and use them for co-kriging to see if it brings some improvements
      compared to ordinary monovariate kriging.

      Interpolating should always take place after data analysis, I guess.

      I would be interested in the results of this study
      please send me a copy of the published works,

      Yours sincerely

      Laurent Bertino

      I too work on modeling mosquito counts. I am the lead statistician at the
      Center for Disease Control who supports all of our malaria projects. I have
      used kriging as a fast, rough method to interpolate mosquito counts to all
      houses in our region of study - Asembo Bay(90 villages) near Kisumu, Kenya.
      There are definitely problems with this approach and I don't plan to publish
      using regular kriging.

      Kriging really isn't the way to go because the number of mosquitoes is count
      data with excess zeros and over-dispersion (many high counts). Some houses
      just seem to be mosquito houses; Others have none. One way to consider
      modeling this is to use a mixture model where you have a binomial model
      which determines whether or not there are mosquitoes in the house and an
      over-dispersed Poisson to estimate the number of mosquitoes in the mosquito
      houses. Of course you have to work in the spatial correlation matrix. As far
      as I know, no one has implemented this procedure now. A colleague and I are
      planning to work on this as soon as I can complete the analysis of our
      long-awaited bed net study. Hopefully we will start on this by next fall.

      Also you could consider (as we are) some type of Bayesian analysis. Tom
      Smith, head of the Biometrics Unit of the Swiss Tropical Institute, works on
      malaria issues on the Kenyan coast. This is the units website:
      I don't think they have solved the exact problem that the two of us seem to
      be working on, but they are working on very similar issues. Actually I am
      planning to use some of the software they have developed to calibrate the
      results of our bed net traps. We used three collection methods in a sample
      of our household, but only bed net traps in the majority. You may want to
      contact Tom Smith directly as I'm not sure that he monitors the AI-GEOSTATS

      Could you please forward any solutions you receive to me? I would be
      extremely happy to have someone else solve this problem. If someone else
      doesn't have a solution, I will be happy to share what we come up with. It
      will probably be too late for your current project, but may help with future


      Margarette Smith Kolczak, Ph.D.

      Madelaine Thomson, Peter Diggle and collaborators have been publishing
      results on a study of Malaria in Gambia using geostatistical models

      Concerning modelling strategies there are two basic approaches discussed
      in the following papers:

      1) using linear mixed models + GEE (generalised estimating equations) to
      account for the spatial variation.
      Basically, residuals from a GLM (generalised linear model) are used to
      find a variogram model which is them used to correct std. errors for the
      covariates coefficients.

      Thomson, M.C., Connor, S.J., D'Alessandro, U., Rowlingson, B.S., Diggle,
      P.J., Cresswell, M. and Greenwood, B.M. (2000). A spatial model of malaria
      risk: satellite data used to predict the impact of bednets in The
      Gambia. American Journal of Tropical Medicine and Hygiene. To appear.

      2) Using a generalised spatial linear mixed model with inference via MCMC
      (Markov Chain Monte Carlo).
      The basic paper is:
      Diggle, P.J., Moyeed, R.A. and Tawn, J.A. (1998). Model-based
      geostatistics (with Discussion). Applied Statistics, 47, 299-350.


      Dear Hugo,
      I'd use the kernel estimation technique. It is very sound and reliable, and
      easier to perform than bayesian modelling techniques. I've used it in
      producing an oncological mortality atlas in my region, but I think you can
      easily adopt it also in case of infective pathologies.
      Moreover, you can find a freeware called "Crimestat", very nice and
      performing the kernel estimates.
      You can find a description at
      where you can redirect to
      to download it.
      I'd be interested also in other replies you had.
      Let me know.
      My best regards

      Dr. Alberto Zucchi
      MD, Epidemiologist
      Resp. Epidemiology Unit
      Local Health Authority of Bergamo Province

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