Page 91 - Contributed Paper Session (CPS) - Volume 3

CPS1952 Michele N. et al.

A statistical modelling framework for mapping
malaria seasonality
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Michele Nguyen , Jennifer Rozier , Suzanne Keddie , Rosalind E. Howes ,
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Timothy C. D. Lucas , Daniel J. Weiss , Katherine E. Battle , Peter W. Gething ,
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Ewan Cameron , Harry S. Gibson , Mauricette Andriamananjara Nambinisoa
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1 Malaria Atlas Project, Big Data Institute, University of Oxford, Oxford, UK
2 National Malaria Control Programme, Antananarivo, Madagascar

Abstract
Many malaria-endemic areas experience seasonal fluctuations in cases
because the mosquito vector’s life cycle is dependent on the environment.
While most existing maps of malaria seasonality use fixed thresholds of
rainfall, temperature and vegetation indices to find suitable transmission
months, we develop a spatiotemporal statistical model for the seasonal
patterns derived directly from case data.
A log-linear geostatistical model is used to estimate the monthly
proportions of total annual cases and establish a consistent definition of a
transmission season. Two-component von Mises distributions are also fitted
to identify useful characteristics such as the transmission start and end
months, the length of transmission and the associated levels of uncertainty.
To provide a picture of “how seasonal” a location is compared to its
neighbours, we develop a seasonality index which combines the monthly
proportion estimates and existing estimates of annual case incidence. The
methodology is illustrated using administrative level data from the Latin
America and Caribbean region.

Keywords
Seasonality; Spatiotemporal Statistics; Geostatistics; Infectious diseases;
Malaria

1. Introduction
Malaria is a disease caused by the Plasmodium parasite and remains a
major cause of child mortality in sub-Saharan Africa (World Health
Organisation 2018). Like that of many other infectious diseases, malaria
transmission exhibits seasonality across endemic areas. Understanding
location-specific seasonal characteristics is useful for maximising the impact
of interventions, developing early warning systems as well as improving
models relating indicators of transmission and disease (Stuckey et al. 2014).
To this end, maps of malaria seasonality have been developed. By using
thresholds on environmental factors, one can determine the months suitable
for transmission (Cairns et al. 2012, Gemperli et al 2006). Since seasonal
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