Group Title: Malaria Journal 2009, 8:287
Title: The use of mobile phone data for the estimation of the travel patterns and imported Plasmodium falciparum rates among Zanzibar residents
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Title: The use of mobile phone data for the estimation of the travel patterns and imported Plasmodium falciparum rates among Zanzibar residents
Series Title: Malaria Journal 2009, 8:287
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Creator: Tatem AJ
Qiu Y
Smith DL
Sabot O
Ali AS
Moonen B
Publication Date: 40157
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Volume ID: VID00001
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Research

The use of mobile phone data for the estimation of the travel
patterns and imported Plasmodium falciparum rates among
Zanzibar residents
Andrew J Tatem*1,2, Youliang Qiu1, David L Smith2,3, Oliver Sabot4,
Abdullah S Ali5 and Bruno Moonen4


Address: 'Department of Geography, 3141 Turlington Hall, University of Florida, Gainesville, Florida, 32611-7315, USA, 2Emerging Pathogens
Institute, University of Florida, Gainesville, Florida, 32610-0009, USA, 3Department of Biology, Bartram-Carr Hall, University of Florida,
Gainesville, Florida, 32611, USA, 4The William J Clinton Foundation, 383 Dorchester Avenue, Suite 400, Boston, Massachusetts, 02127, USA and
5Zanzibar Malaria Control Programme (ZMCP), Zanzibar Ministry of Health and Social Welfare, P. O. Box 236, Zanzibar
Email: Andrew J Tatem* Andy.Tatem@gmail.com; Youliang Qiu youliang@gmail.com; David L Smith smitdave@gmail.com;
Oliver Sabot osabot@clintonfoundation.org; Abdullah S Ali abdullahsuleimanali@yahoo.com;
Bruno Moonen bmoonen@clintonfoundation.org
* Corresponding author


Published: 10 December 2009
Malaria journal 2009, 8:287 doi:10. 1186/1475-2875-8-287


Received: 17 September 2009
Accepted: 10 December 2009


This article is available from: http://www.malariajournal.com/content/8/1/287
2009 Tatem et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.



Abstract
Background: Malaria endemicity in Zanzibar has reached historically low levels, and the
epidemiology of malaria transmission is in transition. To capitalize on these gains, Zanzibar has
commissioned a feasibility assessment to help inform on whether to move to an elimination
campaign. Declining local transmission has refocused attention on imported malaria. Recent studies
have shown that anonimized mobile phone records provide a valuable data source for
characterizing human movements without compromizing the privacy of phone users. Such
movement data in combination with spatial data on P. falciparum endemicity provide a way of
characterizing the patterns of parasite carrier movements and the rates of malaria importation,
which have been used as part of the malaria elimination feasibility assessment for the islands of
Zanzibar.
Data and Methods: Records encompassing three months of complete mobile phone usage for
the period October-December 2008 were obtained from the Zanzibar Telecom (Zantel) mobile
phone network company, the principal provider on the islands of Zanzibar. The data included the
dates of all phone usage by 770,369 individual anonymous users. Each individual call and message
was spatially referenced to one of six areas: Zanzibar and five mainland Tanzania regions.
Information on the numbers of Zanzibar residents travelling to the mainland, locations visited and
lengths of stay were extracted. Spatial and temporal data on P. falciparum transmission intensity and
seasonality enabled linkage of this information to endemicity exposure and, motivated by malaria
transmission models, estimates of the expected patterns of parasite importation to be made.
Results: Over the three month period studied, 88% of users made calls that were routed only
through masts on Zanzibar, suggesting that no long distance travel was undertaken by this group.
Of those who made calls routed through mainland masts the vast majority of trips were estimated
to be of less than five days in length, and to the Dar Es Salaam Zantel-defined region. Though this
region covered a wide range of transmission intensities, data on total infection numbers in Zanzibar

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combined with mathematical models enabled informed estimation of transmission exposure and
imported infection numbers. These showed that the majority of trips made posed a relatively low
risk for parasite importation, but risk groups visiting higher transmission regions for extended
periods of time could be identified.
Conclusion: Anonymous mobile phone records provide valuable information on human
movement patterns in areas that are typically data-sparse. Estimates of human movement patterns
from Zanzibar to mainland Tanzania suggest that imported malaria risk from this group is
heterogeneously distributed; a few people account for most of the risk for imported malaria. In
combination with spatial data on malaria endemicity and transmission models, movement patterns
derived from phone records can inform on the likely sources and rates of malaria importation. Such
information is important for assessing the feasibility of malaria elimination and planning an
elimination campaign.


Background
Many countries are committing to nationwide malaria
elimination and global eradication is once more back on
the international agenda [1-3]. Historically, the technical
feasibility of achieving malaria elimination in a region has
been conceptualized as being composed of 'receptivity'
and 'vulnerability' [4,5]. Receptivity represents the
strength of transmission in an area, while vulnerability is
the risk of malaria importation [6]. While both have been
regularly discussed theoretically, neither have been quan-
tified, nor methods for their quantification ever defined.

Quantifying imported malaria risk represents a central
component for not only assessing the feasibility of
malaria elimination from a region, but for planning the
implementation of an elimination campaign. Malaria is
constantly being exported and imported around the
World, and in areas of high transmission, malaria impor-
tation is generally a minor concern. As local transmission
is reduced and after malaria has been eliminated from a
region, however, importation becomes a primary concern.

Zanzibar, an island group of the coast of Tanzania, is one
of the territories in sub-Saharan Africa that has recently
expressed its willingness to move from control towards
elimination. Since 2003, the introduction of artemisinin-
based combination therapy (ACT) and high coverages of
long-lasting insecticide treated nets and indoor residual
spraying, has reduced malaria prevalence to just 0.8%
[7,8]. These efforts have resulted in the government of
Zanzibar considering an elimination campaign and
undertaking an elimination feasibility assessment. Never-
theless, proximity and high connectivity to the mainland
where transmission levels remain substantially higher in
many places [9] implies that imported malaria will be a
constant problem [10].

In general, parasites can be imported into Zanzibar in one
of three ways: (i) the migration of an infected mosquito,
(ii) infected humans visiting or migrating from the main-


land, (iii) residents visiting the mainland and becoming
infected, then returning. While mosquitoes may occasion-
ally arrive though wind-blown or accidental aircraft or
ship transport, typically they will only fly short distances.
Human carriage of parasites, therefore, represents the
principal risk, and is to blame in many past instances else-
where where malaria has resurged [11-14]. Quantifying
such movements both temporally and spatially, and the
resulting imported infection risks, represents an impor-
tant task if effective, evidence-based planning for elimina-
tion is to be undertaken.

Recent approaches to quantifying human mobility pat-
terns point the way to novel insights from new data
[15,16], especially through the analysis of mobile phone
records [17-19]. Anonimized phone call record data that
has both the time each call was made and the location of
the nearest mast that each call was routed through can be
used to construct trajectories of the movements of individ-
uals over time [19]. Here, the potential of such data for
estimating importation risk in the malaria elimination
feasibility assessment for the islands of Zanzibar is dem-
onstrated. The low market share on the mainland for the
network provider restricts the focus here to those infec-
tions brought in by residents returning from mainland
travel. However, the approaches put forward are suffi-
ciently generic to be applied to alternative regions, elimi-
nation settings and phone network provider data.
Moreover, this exercise aims to present the first explora-
tion of mobile phone based approaches to the quantifica-
tion of vulnerability to inform malaria elimination
decisions and planning.

Methods
Study area
Like other areas of sub-Saharan Africa, the islands of Zan-
zibar, off the coast of Tanzania in East Africa (Figure 1),
have falciparum malaria and efficient vectors, including
Anopheles gambiae, and at many points in the past, malaria
in Zanzibar would have been called hyperendemic (PfPR


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Malaria Journal 2009, 8:287


Arusha
SDar Es Salaam
Dodoma
Mbeya
Mwanza
Zanzibar
\ No Coverage

Figure I
Zantel coverage regions in Tanzania.


in the 2-10 age group ~50-75%). Recent control efforts
[8], possibly combined with socioeconomic changes,
have pushed Plasmodium falciparum prevalences down to
0.3% for the southern island of Unguja, and 1.4% for the
northern island of Pemba [7], meaning approximately
8,500 infected people at any one time; 3,000 on Unguja
and 5,500 on Pemba.

Zanzibar, however, has strong transport connections to
the mainland where transmission levels are higher, result-


ing in concerns about achieving and sustaining elimina-
tion being raised [10], and making the quantification of
human movement patterns and ultimately, imported
infection rates, a critical aspect of elimination feasibility.
While daily flights bring in around 10,000 people a
month [20], these are mainly tourists from non-endemic
regions, who will likely be taking prophylaxis, and thus
represent a low risk in terms of imported infections and
onward transmission. Ferry services have capacity to move
up to 1,800 people daily between Zanzibar and the main-


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land. This route, as well as informal movements such as
small fishing and trading vessels, likely represent the high-
est risk pathways for any imported infections. Figure 2
shows the recorded total numbers of ferry passengers each
month for 2007, with these numbers likely split equally
between visitors from the mainland and Zanzibar resi-
dents [21].

Plasmodium falciparum malaria endemicity data
A new global map of P. falciparum malaria endemicity for
2007 has now been published [9]. This provides a contin-
uous prediction of prevalence (P. falciparum parasite rate
in the two up to ten year old age group, PfPR2-10,
between 0-100%) for every 5 x 5 km pixel within the sta-
ble limits of P. falciparum malaria transmission [22]. It
represents a contemporary measure of global malaria
endemicity, based on evidence in a huge repository of par-
asite rate surveys [23]. Using mathematical models
described previously [24-26], the parasite rate map was
converted to a map of the daily Entomological Inocula-
tion Rate (dEIR), a more relevant measure for assessing
the risk of infection acquisition in an area. The dEIR data
for the study area was extracted and is shown in Figure
3(a). Maps showing the start and end months of the prin-
cipal (Figures 3(c) and 3(d)) and secondary P. falciparum
transmission seasons [27] extracted for Tanzania were


also obtained to enable spatial refinement of transmission
levels during the study period.

Population distribution data
Population distribution maps for 2002 at 100 m spatial
resolution, as described in Tatem et al [28] and available
through the AfriPop project [29], were obtained for the
study area. These were projected forward to 2008 to match
the mobile phone data by applying national, medium var-
iant, inter-censal growth rates [30] using methods
described previously [31] and are shown in Figure 3(b).

Mobile phone data
The Zanzibar Telecom (Zantel) mobile phone operator
has approximately a 10% share of the Tanzanian market
[32]. While nine out of ten Tanzanians are reported to
have 'access' to a mobile phone, what these Figures mean
in terms of ownership and usage are subject to debate and
uncertainty [33,34]. However, while the 10% share Zantel
has likely represents an unrepresentative sample of Tanza-
nia as a whole, Zantel does have a 99% market share on
Zanzibar. With over 330,000 individual users apparently
resident on Zanzibar (see later analyses) out of a total
population of just over a million, this suggests that a sub-
stantial sample of Zanzibar phone users is covered by the
dataset. Analyses here were, therefore, focused on Zanzi-
bar residents only, though information derived from


Figure 2
Total ferry passenger numbers between Dar Es Salaam and Zanzibar for 2007.


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45,000



35.000


30.000

25.000

10.000



10,000

5.000

0


<6 <-


p9~1~ce~t\P ~9 P.~~~e e0


Malaria Journal 2009, 8:287








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i I


J
-
- I


'


I -
^L., *_.-


03


(c) (d)


Figure 3
Zantel coverage regions for Tanzania overlaid on (a) Daily Entomological Inoculation Rate (dEIR); (b) Popula-
tion distribution; (c) Month of start of principal malaria transmission season; (d) Last month of principal
malaria transmission season. Secondary transmission season maps shown in Additional File I: supplemental information.


mainland resident users is presented in Additional file 1:
supplemental information.

Records encompassing three months of complete mobile
phone usage for the period October-December 2008 were
obtained from Zantel. This represents the limit of availa-


ble Zantel data, since the company only keeps the preced-
ing three months of records. Nevertheless, this covers the
busiest period in terms of travel to and from Zanzibar
(Figure 2), and, therefore, enables a conservative upper
limit on infection importation risk to be estimated. The
data included the dates of all phone usage by 770,369


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individual users, making a total of 21,053,198 calls and
text messages. Prior to receiving the data, Zantel assigned
each individual user a unique code to ensure that the ano-
nymity of users was maintained and that the data could
only be used for studying general patterns of mobility.
Each individual call and message was spatially referenced
to one of six areas: Arusha, Dar Es Salaam, Dodoma,
Mbeya, Mwanza and Zanzibar (Figure 1). Any individual
that made just four or less calls in any one month (an
average of one per week) was removed from further anal-
yses to ensure that sufficient temporal resolution existed
in the remainder of the dataset for trajectory analysis.

Estimating exposure to transmission levels
For each of the three months in the study period, and for
each Zantel region, the areas within their principal (Fig-
ures 3(c) and 3(d)) or secondary transmission seasons
were identified and overlaid onto the dEIR map (Figure
3(a)), with non-transmission season areas masked out.
The minimum, mean and maximum dEIR values for each
Zantel region and month were then calculated, and the
gridded population data and dEIR data were combined to
calculate population weighted mean dEIRs for the entire
regions, and their principal cities (Table 1). To examine
the ranges of possible results, should for instance the
unlikely case of all visitors travelling to the highest trans-
mission part of each Zantel region be reality, analyses
were undertaken with the extreme conditions of mini-
mum and maximum possible dEIR exposure per region.
With Zantel coverage principally available in the major
populated areas, and travellers more likely to visit heavily
populated regions than empty rural areas, it was assumed
however that the population-weighted measures likely
represented the more realistic range of estimates for dEIR
exposure within each coverage region. Moreover, with a
high percentage of travellers likely visiting just the princi-
pal cities when travelling to each region, the population
weighted mean dEIR within the city limits of Arusha, Dar
Es Salaam, Dodoma, Mbeya and Mwanza, as defined by
the global rural-urban mapping project urban extent map
[35], were also calculated. These scenarios and assump-


Table I: Monthly estimates of dEIR for each Zantel region.


tions were tested through comparing estimated imported
infection numbers (see the quantifying imported malaria
risk section) to the known numbers of infections present
at any one time on the islands of Unguja and Pemba.

Quantifying imported malaria risk from returning residents
Malaria importation risk or vulnerability have been dis-
cussed in relation to malaria elimination for decades (e.g.
[4,5,12]), but never quantified. In simple terms, malaria
importation risk as a measurable quantity in a focal coun-
try or area is the product of human immigration rates
from other malaria endemic countries or areas and their
corresponding level of endemicity. However, it may not
be sufficient to estimate the number of people who cross
the borders of a country or region infected with malaria
elsewhere; it also matters how long they stayed in
endemic regions, how long they remain infected and
infectious in the country or area of interest, as well as
where they stay. Thus, the risks deriving from visitors from
the mainland (see Additional file 1: supplemental infor-
mation) and returning residents should be quantified dif-
ferently.

For Zanzibar residents visiting the mainland on day t,
their length of stay, L, and dEIR in the area of stay are the
key factors. Recent research efforts have provided spatial
quantification of P. falciparum endemicity [9] enabling
estimation of the dEIR at the locations that Zanzibar resi-
dents are visiting. Motivated by malaria transmission
models [36], the probability of obtaining an infection, P,
is thus:


P = 1 e =0dE)
The total number of imported infections, I, over all N trips
made in the three month study period is therefore:


I (-I -=dEIR)
l= 1-e t 0


Population weighted mean Pop weighted principal
city mean


Oct Nov Dec Oct Nov Dec Oct Nov Dec Oct


0.0 0.00005 0.00021 0.00063 0.00068
0.0 0.00104 0.00840 0.04049 0.00285
0.0 0.00032 0.00099 0.00792 0.00332
0.0 0.00625 0.00974 0.02517 0.00995
0.0 0.00031 0.00066 0.00846 0.00221


Nov Dec Oct


0.00154 0.00341 0.00001 0.00008 0.00014
0.05654 0.21508 0.00082 0.00213 0.00855
0.0322 0.05258 0.00021 0.00084 0.00785
0.06526 0.29468 0.00444 0.00848 0.03133
0.00887 0.03671 0.00009 0.00032 0.0169


0
0.00023
0
0
0


Nov Dec


0.00006 0.00006
0.00023 0.00023
0.00199 0.00199
0.00512 0.00512
0.00492 0.00492


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Minimum


Zantel
region


Mean


Maximum


Arusha
Dar Es Salaam
Dodoma
Mbeya
Mwanza


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Given the estimates of trip length, range of estimates of
dEIR and proportion of travellers captured in the dataset,
the total number of infections brought into Zanzibar by
returning residents were estimated, as well as the distribu-
tion of infection origins. With only around 8,500 infec-
tions on the islands at any one time, and just 3,000 on
Unguja, where the majority of movements to and from
the mainland derive from, this places a realistic limit on
the estimates of imported infection numbers, and thus, a
guide to the likely dEIR visitor exposure for each Zantel
region.

Results
Identifying travellers
Of the 770,369 individual phone users in the Zantel data-
set, 24,625 (3.2%) made four calls or less per month in
the three month study period, and were thus removed
from further analysis. Of the remaining users, 335,621
made the majority of their calls on Zanzibar. From here
on, we assume that these represent Zanzibar residents,
since the majority of calls by a customer are most likely to
be made in their home region. There will of course be
exceptions to this, for instance, if a mobile phone is prin-
cipally used for business use when travelling, but in the
absence of further information, this represents a reasona-
ble assumption to make. Of the 335,621 Zanzibar resi-
dent users, just 12.08% of them (40,543 users) made calls
from the mainland. Thus, the vast majority of users only
made calls from Zanzibar, indicating a lack of travel.

Locations visited
Figure 4 shows, of those Zanzibar residents who travelled
in the study period, the proportions that made the major-
ity of their non-home calls at each other mast location. It
is clear that of those who travelled to the mainland, a sub-
stantial proportion made the majority of their non-Zanzi-
bar calls in the Dar Es Salaam region, with only a small
proportion making the majority of their non-Zanzibar
calls at the other four mast locations.

Trip lengths
To estimate the lengths of trips made by those making
calls from more than one location, it was assumed that the
date of the first mainland call made represented the start
of a trip. The end of this trip was estimated as the date
when the first Zanzibar-based call was made again. For
each user, the start and end dates of each individual trip
made were estimated in this way and the trip lengths
quantified and recorded. A total of 73,095 trips were
made, with 12,584 residents travelling in October making
a total of 24,439 trips, 11,947 in November making
24,335 trips and 12,882 in December making 24,321
trips. These figures correspond well with the ferry passen-
ger numbers (Figure 1) and, assuming residents made up
around half of ferry passengers [21], suggest that around


95% of all trips made by Zanzibar residents to the main-
land were captured in the dataset.

Figure 5 shows the distribution of trip lengths made by
Zanzibar residents. As shown in Figure 4, the vast majority
of trips made were to the Dar Es Salaam region. What is
clear from Figure 5 is that the majority of trips made to the
mainland were of less than five days long. In fact, 17.4%
of all trips to the Dar region were estimated to be of just
one day in length, while 29% were of two days in length
or less. A similar pattern is shown for the other regions,
though with substantially fewer visits made, and a higher
proportion of longer (10-30 days) trips made by those
travelling further, e.g. Mbeya or Mwanza.

Estimating imported malaria risk
To provide estimates of imported case numbers from
returning Zanzibar residents and likely origins of infec-
tions, the data on dEIR scenarios for each Zantel region
were combined with the trip length estimates using equa-
tion (2). Table 2 shows that only the results from the pop-
ulation weighted region and city scenarios fall under the
realistic limits of total infections on the islands, given that
imported infections will also be brought in by visitors
from the mainland and that the majority of travel is to
Unguja. Realistically, while a significant majority of visi-
tors to each region will visit the principal cities, others will
travel to alternative population centres, thus the regional
population weighted mean dEIR (upper) and principal
city population weighted mean dEIR (lower) scenarios
represent credible limits for estimating the likely number
of imported infections per month arising from returning
residents. Thus, converting these to annualized measures,
estimates of between one and 12 imported infections per
1,000 people per year from returning residents represent
realistic limits. Given increased travel in October-Decem-
ber (Figure 2), these also likely represent conservative
overestimates.

Figure 6 shows the distribution of trips by probability of
infection acquisition, P, under the scenarios of exposure
to regional population weighted mean dEIR and principal
city population weighted mean dEIR. Each scenario high-
lights that the majority of trips made entailed a probabil-
ity of infection acquisition of less than 0.05. Figure 7
shows the regional composition of these distributions,
illustrating that under both scenarios, the trips made by
residents to Dodoma, Mbeya and Mwanza provided
greater risks of infection acquisition, due to a higher pro-
portion of longer stays in these regions typically, com-
bined with overall high levels of transmission.

Discussion
Results here show that, despite data limitations, spatially
and temporally referenced mobile phone usage data can


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As described in the methods section, the data used here
have specific limitations that prevent more comprehen-
sive analysis. With just a 10% share of the market on the
mainland and Zantel subscribers more likely to travel to
Zanzibar than non-subscribers, detailed analyses were not
E Arusha presented based on visitors from the mainland, since the
Dodoma data probably exhibits significant biases. In addition, the
Da-r activities of visitors to high transmission areas are
unknown in extreme scenarios, some may sleep under
*Mbeya bed nets in air-conditioned hotels, while others may
SMwanza spend the night outdoors. Further, those travelling to or
from further afield than Tanzania are not captured by this
dataset, nor are those who switch to an alternative net-
work provider on the mainland, nor are trips longer than
three months captured. Finally, information on move-
ment patterns on Zanzibar are also lacking, preventing an
Figure 4 understanding of the likelihood of onwards transmission,
The proportions of Zanzibar resident users that since imported cases may play a key role in sustaining
made the majority of their non-home calls at each local transmission in some parts of Zanzibar. Previous
location, work has shown however, that many mobile phone com-
panies often have the ability to provide more precise spa-
tial locations on data (e.g[19]), potentially improving
provide valuable information on human movement pat- upon conclusions made, should similar malaria-related
terns. In combination with spatial data on malaria ende- studies be undertaken. Moreover, additional studies are
micity, derived movement patterns can inform on the planned and should be encouraged to test the approaches
likely sources, risks and case numbers of imported presented here further and help to arrive at a clear meth-
malaria. The estimates presented represent the first quan- odology for the quantification of vulnerability. The
tification of the vulnerability of an area to imported importance of preserving the anonymity of phone users
malaria, a necessary quantity in determining the feasibil- should remain the utmost priority though.
ity of achieving and sustaining elimination.
The information derived from these analyses can be used
According to the Zantel data, of the 770,369 users in the to guide strategic planning for elimination, should the
entire dataset (made up of Zanzibar and mainland resi- Ministry of Health decide to pursue such a campaign. Typ-
dents), only just over 100,000 travelled anywhere during ically, three principal means of reducing imported infec-
the three-month study period. Of those Zanzibar resi- tion risk are considered: (i) Identify infected individuals
dents that travelled, the overwhelming majority went and treat them promptly, ideally before or upon entry,
solely to the Dar Es Salaam region (and likely to Dar Es before they can infect competent local vectors and lead to
Salaam city itself), where the population weighted average secondary cases and sustained foci of indigenous trans-
dEIR is relatively low. The majority of these trips were for mission; (ii) address the source of infection by directly
just one to two days, thus posing a relatively low risk of reducing transmission in all regions that are primary
acquiring an infection and again confirming that most sources of infected travellers; (iii) provide prophylaxis to
trips could not have involved travel to much further residents visiting endemic areas. While the second
beyond Dar Es Salaam city itself. If malaria prevalence lev- method is being addressed indirectly through the scaling
els continue to fall on the nearby mainland [37,38], there up of control on the mainland [37,38], these analyses pro-
is reason to believe that importation risk on Zanzibar will vide baseline data to inform on the first and third
fall simultaneously. There do however exist small mobile approaches. Screening with rapid diagnostic tests (RDTs)
groups that (i) travel for extended periods to the mainland or microscopy at the ports of entry and providing follow-
from Zanzibar (ii) travel to higher transmission areas up treatment of infected individuals may play an impor-
from Zanzibar. These represent the risk groups contribut- tant role in reducing imported case numbers and out-
ing most to the imported infection numbers brought in by breaks. Such an approach is being used for all individuals
residents visiting the mainland. Moreover, basic analyses entering the island of Aneityum in Vanuatu [39], while
on mainland resident movement patterns (Additional File visitors from Africa were tested at the airports of Oman
1: supplemental information), suggest that similar risk during its elimination campaign. Moreover, the details of
groups exist among visitors to Zanzibar. all visitors to Mauritius from endemic regions are
recorded and follow-up is undertaken by health surveil-


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All regions


Arusha


I I I I I

0 20 40 60 80

Trip length (days)


Dodoma


I I I I I

0 20 40 60 80

Trip length (days)


Mbeya


LL..
a-
S-


o g
CD

3
CL CD

0 -

0 -


I I I I II

0 20 40 60 80

Trip length (days)


Dar


C Cm
o
, -
r- CD -
CD


C -


I I I I I

0 20 40 600


Trip length (days)




Mwanza


I I I 60 8

0 20 40 60 80


Trip length (days)


I I 4I I I

0 20 40 60 80


Trip length (days)


Figure 5
The distributions of trip lengths made by Zanzibar residents to the mainland, overall and by Zantel region.
Note differing y-axis limits.





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Malaria Journal 2009, 8:287








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Table 2: Estimated average monthly numbers of imported infections under the differing dEIR scenarios outlined in table I.


Minimum Mean Maximum Pop weighted mean


1.84063
3541.63122
22.60743
19.62360
6.23628


11.18779
8518.20389
127.97949
53.46253
22.12087


0.0 3591.93917 8732.95458


0.47931
1191.30602
21.75056
19.67336
9.59984

1242.80909


Pop weighted principal city mean

0.25034
81.11407
10.77893
6.03524
6.88978


105.06836


lance officers [40]. When movement rates are high and
resources are limited however, as in the case of Zanzibar,
screening all visitors at the ports or providing follow-up
may be prohibitively expensive and inefficient due to the
large number of low-risk trips undertaken (Figure 6).

Modelling work on achieving and maintaining elimina-
tion done for the Zanzibar malaria elimination feasibility
assessment suggests that as long as effective coverage with
vector control measures is higher than 80%, elimination
will be achieved and can be maintained. However, once
transmission is reduced to very low levels, scaling down
prevention without risking resurgence will only be possi-
ble if the importation levels estimated here are lowered
considerably [Moonen B, Cohen J, Smith DL, Tatem AJ,
Sabot O, Msellem M, Le Menach A, Randell H, Bjorkman


A, Ali A: Malaria elimination feasibility assessment in
Zanzibar I: Technical feasibility. Malar Journal 2009, in
preparation]. Prophylaxis for Zanzibari travellers is
unlikely to be cost-effective or even practical given the
high frequency of travel to mainly low risk regions.
Screening on the ferries, especially of high risk groups dur-
ing high risk periods of the year, might be a simpler and
more cost-effective option compared to screening at the
port of entry. Passengers are on the slow and fast ferries for
six and two hours, respectively; enough time to adminis-
ter a short questionnaire a rapid diagnostic test and treat-
ment if necessary. However, better data are necessary to
determine the PfPR in ferry travellers to appreciate the
operational consequences of such an approach.

Future work will aim to link the findings here to GIS data
on travel networks in the region, and build these into sto-
chastic metapopulation models of transmission, provid-
ing flexible tools for elimination planning. Moreover,
retrospective analyses of health facility records at Zanzibar
malaria early epidemic detection system sites are being
undertaken at present, while surveys on the ferries are
planned to corroborate and compliment findings here.
This work also links into and is complemented by other
datasets being gathered and analysed as part of a new
research agenda initiated by the Malaria Atlas Project [41]
to quantify human movement patterns in relation to
assessment of malaria elimination feasibility.


Malaria elimination requires a significant investment of
resources and capacity and, as has been demonstrated
twice before on Zanzibar, failure to achieve this ambitious
1. 'S-^,_-_ __._ __ _ _ _. __ target can lead to fatigue among donors and policymakers
I i i and subsequent devastating resurgence of malaria. As
oo 02 04 o0 0o 10 more countries across the world make progress toward
Prcabilit o acquiring mfdn malaria elimination, there is a need for evidence based
and locally-tailored assessments of the feasibility of mak-
Figure 6 ing the final step in initiating an elimination campaign.
All trips made by Zanzibar residents plotted by prob- With mobile phone uptake continuing to grow around
ability of infection acquisition, based on region popu- the world, this novel data source has the potential to play
lation weighted mean dEIR (red line) and population a key role in providing such valuable evidence. While 'vul-
weighted principal city mean dEIR (blue line), nerability' has been discussed in relation to malaria elim-
ination for decades, the approaches outlined here


Page 10 of 12
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Zantel region

Arusha
Dar Es Salaam
Dodoma
Mbeya
Mwanza


Malaria Journal 2009, 8:287








http://www.malariajournal.com/content/8/1/287


m1.

Arusha Daf Dodoma Mbeya Mwanza


Arusha Dar Dodoma Mbeya Mwanza

(b)


Figure 7
Boxplots of trip probabilities of infection acquisition by Zantel region under scenarios of (a) region population
weighted mean dEIR; (b) population weighted principal city mean dEIR. The central dark line in each box shows the
median value, the box size shows the interquartile range, while the whiskers extend to the most extreme datapoints that are
no more than 1.5 times the interquartile range from the box.


represent a first step towards finally quantifying it. Repli-
cating and refining these approaches in other areas will
enable the development of a standardized methodology
for malaria importation risk assessment to aid countries
that are considering and planning elimination.

Conflicts of interests
The authors declare that they have no competing interests.

Authors' contributions
AJT conceived, designed and implemented the research
and wrote the paper. BM, DS, OS and YQ aided with ideas,
methodological and editorial input. BM and AA provided
support in data compilation. The final version of the man-
uscript was seen and approved by all authors.

Additional material


Additional file 1
Supplemental Information. Analyses of movement patterns of mainland
residents based on mobile phone data.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1475-
2875-8-287-S1.PDF]


Acknowledgements
The authors are grateful to Bob Snow and Simon Hay for comments on ear-
lier versions of this manuscript, to the Clinton Foundation for the financial
support that facilitated this work, and to Noel Herrity and Shinuna Kassim


at Zantel for supply of the data used in the research. AJT and DLS are sup-
ported by a grant from the Bill and Melinda Gates Foundation (#49446).
This work forms part of the output of the Malaria Atlas Project (MAP, http:
/www.map.ox.ac.uk), principally funded by the Wellcome Trust, U.K.

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