Most malaria endemic countries experience seasonal variation in transmission. MAP aims to accurately predict these seasonal malaria transmission patterns (onset, duration, magnitude) in locations where malaria survey data are sparse, using only the environmental covariate data which are available for all locations.
Under the leadership of Suzanne Keddie, MAP is gathering data on the seasonality of malaria to feed into a set of geostatistical models (please see Predicting the effect of seasonality on malaria transmission).
There are three principle sources of seasonality data:
- MAP’s estate of API data derived from routine surveillance reports.
- The PANGAEA – EIR databsae
- An ongoing Seasonality Literature review, a novel factor of which is incorporating anecdotal evidence
The data being gathered covers:
- Vector Density
- Other (‘transmission’)
Data is gathered at both geopoint-level and at administrative unit level. Where data is available at administrative level, it is collected at the lowest level possible. Further details on the collection process can be found in our Seasonality Literature Data Gathering Protocol.
A key challenge in this data-gathering exercise is the transformation of these varied and incompatible data sources into a single canonical set.
A novel aspect of this exercise is the incorporation of anecdotal data by making it comparable to MAP’s malaria time-series data. For peak onset and duration, this is accomplished by a statistical summarisation identifying months exceeding 1 standard deviation from the mean.