The estimates shown are our best current assessment of movements, given the considerations described above regarding having to use last year's data. However there are a number of uncertainties, some of which may be addressed in later analyses. The numbers given should not be interpreted as the truth and should be interpreted with other available evidence, notably derived from field surveys and reports.
CDR data has a number of inherent limitations, particularly the resolution and representativeness of the data.
Geographic variations in network coverage and phone use activity impede spatial and temporal comparisons
The spatial resolution of the data is limited by the density of cell towers. This means that the resolution of the data varies between different areas, particularly urban areas with a very high density of towers and rural areas with a low density. As a result, relatively small changes in location observable in urban areas may not be observed in rural areas, which could be interpreted as lower mobility, meaning areas with different coverage are not directly comparable. Furthermore, no observation can be made outside of the network coverage, though under normal conditions coverage in Haiti is high.
The temporal resolution of CDR data is dependent on the frequency with which subscribers use their mobile devices. Changes in subscribers’ locations will therefore only be observed if the subscribers use their mobile device while in each location. Variation in mobile device usage can therefore also be interpreted as a change in mobility and needs to be adjusted for. How active subscribers are may also vary regionally, as a result mobility may appear larger in a given region only because subscribers are more active there.
Our data in this report reflect the mobility of Digicel subscribers - not the mobility of the population
CDR datasets include a non-random sample of the population of interest. It is therefore important to assess biases in the representativity of the mobility of this sample compared to that of the population as a whole. In order to be included in a CDR dataset, an individual must therefore first own a mobile device and second subscribe to the mobile network operator(s) whose CDR data are being processed. Furthermore, a subscriber must use their mobile device often enough to generate sufficient calls for analysis.
As a result, there are several layers of filters affecting the sample of the population included in the dataset: mobile phone ownership, subscription to a participating mobile network operator, sufficient usage of the mobile device during the study period. For each of these filters, factors such as age, gender and socio-economic status may affect whether an individual is included in the dataset. Representativity of the sample may vary regionally, this means that a larger number of travelling subscribers in a specific region compared to another may not correspond to a larger number of travelling people, if the representativity of the mobility of subscribers varies between the two regions .
Variation in surveillance and reporting may affect the geographic distribution of reported cholera cases
There is also uncertainty in the case data provided by MSPP. No surveillance system is perfect and many infectious individuals are asymptomatic and not picked up by any reporting system. There is variation in the surveillance, diagnosis and reporting of cholera cases between different areas which may result in cases being more likely to be reported in some areas than others.
While these analyses replicate our earlier research, which were predictive of (with a level of uncertainty) where new outbreaks occurred in the 2010 outbreak, responders should also consider that communes across the country differ in access to water and sanitation as well as to other risk factors, which will influence the risk of new outbreaks occurring.
We welcome sharing of alternative aggregated case data to be shared with us for comparative analyses.
Not all observed trips are substantial opportunities for the transmission of cholera
Mobility aggregates derived from CDR data provide an estimate of how many subscribers were present in an area having previously been present in another area. However, the aggregates used in this report do not assess the length of time subscribers were in any given area. We therefore do not differentiate between subscribers who remain in an area for a sufficient period of time to facilitate the transmission of cholera and those who pass through an area en route to another destination without stopping. This limitation introduces a further source of uncertainty in our estimation of infectious pressure.
As a result, our results may overestimate the infectious pressure experienced by some communes, especially those along important transport corridors such as major highways. However, communes which are well-connected to areas with substantial cholera cases would be expected to be at higher risk of cholera spreading to the area, and we have observed such a pattern evolve as the outbreak has continued.
We hope to better evaluate the types of trips which are more likely to provide sufficient opportunity for infection in order to address this source of uncertainty, but this remains our best interpretation of the data at this time.