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CPS1952 Michele N. et al.
we have previously estimated monthly incidence, we can use the monthly
proportion concept and algorithm to derive seasonal statistics.
Centroids of the smallest administrative units were used as point locations
in the LAC data analysis. To avoid this approximation, continuous
autoregressive (CAR) models can be used in place of the geospatial
autoregressive process in our model. However, this has its own drawbacks
(Valle and Lima 2014). The correlation between neighbouring units does not
depend explicitly on the distance between them which is unnatural when we
have units of varying sizes. Furthermore, the relation between malaria
incidence and unit-representative covariates is likely to be weaker than at the
point level.
The monthly proportion model identifies the dominant relationship
between malaria cases and the environment in our study region. A key
assumption is that this and the resultant seasonal pattern remain constant at
least for the time period in our data. Since this may not be the case with
climate change, there is a need to update models and investigate extensions
to deal with varying relations.
As more countries adopt the District Health Information Software 2
(DHIS2) for instant recording of cases, using case data to establish seasonality
patterns will be increasingly feasible and desirable. Currently, work is being
done to apply this methodology to health facility case data from Madagascar.

References
1. Cairns, M., Roca-Feltrer, A., Garske, T., Wilson, A. L., Diallo, D. Milligan, P.
J., Ghani, A. C. & Greenwood, B. M. (2012), ‘Estimating the potential public
impact of seasonal malaria chemoprevention in African children’, Nature
communications 3, 881.
2. Feng, X., Porporato, A. & Rodriguez-Iturbe, I. (2013), ‘Changes in rainfall
seasonality in the tropics’, Nature Climate Change 3(9), 811-815.
3. Gemperli, A., Sogoba, N., Fondjo, E., Mabaso, M., Bagayoko, M., Briët, O. J.
T., Anderegg, D., Liebe, J., Smith, T. & Vounatsou, P. (2006), ‘Mapping
malaria transmission in West and Central Africa’, Tropical Medicine &
International Health 11(7), 1032-1046.
4. Kang, S. Y., Battle, K. E., Gibson, H. S., Ratsimbasoa, A.,
Randrianarivelojosia, M., Ramboarina, S., Zimmerman, P. A., Weiss, D. J.,
Cameron, E., Gething, P. W. & Howes, R. E. (2018), ‘Spatio-temporal
mapping of Madagascar’s Malaria Indicator Survey results to assess
Plasmodium falciparum endemicity trends between 2011 and 2016’, BMC
Medicine 16(1), 71.
5. Lindgren, F., Rue, H. and Lindström, J. (2011), ‘An explicit link between
Gaussian fields and Gaussian Markov random fields: the stochastic partial

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