MALARIA ELIMINATION IN ZANZIBAR - Soper Strategies

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USING MOBILE PHONE DATA TO ASSESS IMPORTATION BY ZANZIBARI RESIDENTS Recent approaches to quantifying human mobility patterns point the way to novel insights from new data (Brockmann et al., 2006; Brockmann and Theis, 2008), especially through the analysis of mobile phone records (Candia et al., 2008; Gonzalez et al., 2008). Anonymised 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 individuals over time (Gonzalez and Hidalgo et al., 2008). The low market share on the mainland for the network provider restricts our focus here to those infections brought in by residents returning from mainland travel. Data The Zanzibar Telecom (Zantel) mobile phone operator has approximately a 10% share of the Tanzanian market (Tanzania Communications Regulatory Authority, 2009). 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 (Vodafone, 2005; James and Versteeg, 2007). However, while the 10% share Zantel has likely represents an unrepresentative sample of Tanzania 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 substantial sample of Zanzibar phone users is covered by the dataset. Analyses here were therefore focused on Zanzibar residents only, though information derived from mainland resident users is presented in supplemental information. Records encompassing 3 months of complete mobile phone usage for the period October-December 2008 were obtained from Zantel. This represents the limit of available Zantel data, since the company only keeps the preceding three months of records. Nevertheless, this covers the busiest period in terms of travel to and from Zanzibar, 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 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 anonymity 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 12(a)). Any individual that made just four or less calls in any one month (an average of 1 per week) was removed from further analyses to ensure that sufficient temporal resolution existed in the remainder of the dataset for trajectory analysis. For each of the three months in the study period, and for each Zantel region, the areas within their principal or secondary transmission seasons were identified and overlaid onto a map of daily Entomological Inoculation Rate (dEIR)(Figure 12(b)), a measure calculated from the parasite rate (Figure 9(b)) using mathematical models (Smith and McKenzie, 2004; Smith et al., 2005; Smith et al., 2007). Motivated by malaria transmission 22 FIGURE: 12 (A) ZANTEL COVERAGE ON MAINLAND TANZANIA AND ZANZIBAR (B) DAILY ENTOMOLOGICAL INOCULATION RATE (dEIR) models (Smith and Hay, 2009), the probability of each individual becoming infected was then calculated based upon the length of time they spent in these regions before returning to Zanzibar. With dEIR varying within regions, the full range of possible transmission levels that a traveler could be exposed to were tested. However, with travelers more likely to visit heavily populated regions than empty rural areas, it was assumed that population distribution-weighted measures of dEIR likely represented the more realistic average levels of traveler exposure. Realistically, while a significant majority of visitors 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 are presented here as credible limits for estimating the likely number of imported infections per month arising from returning residents. Full analyses are presented in Tatem et al. (2009). 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. To provide estimates of imported case numbers from returning Zanzibar residents and likely origins of infections, the data on dEIR scenarios for each Zantel region were combined with the trip length estimates. Estimates suggest that imported malaria risk from Zanzibar residents is heterogeneously distributed; a few people account for most of the risk for imported malaria. Figure 13 shows the distribution of trips by probability of infection acquisition under the scenarios of exposure to regional population weighted mean dEIR and principal city population weighted mean dEIR. Each scenario highlights that the majority of trips made entailed a very low probability of infection acquisition.
PROBABILITY OF ACQUIRING INFECTION FIGURE 13: ALL TRIPS MADE BY ZANZIBAR RESIDENTS PLOTTED BY PROBABILITY OF INFECTION ACQUISITION, BASED ON REGION POPULATION WEIGHTED MEAN dEIR (RED LINE) AND POPULATION WEIGHTED PRINCIPAL CITY MEAN dEIR (BLUE LINE) 250000 250000 NUMBER NUMBER OR OR TRIPS 100000 150000 200000 100000 150000 200000 50000 50000 00 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 PROBABILITY OF AQUIRING INFECTION PROBABILITY OF ACQUIRING INFECTION FIGURE 14: 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 0.7 (A) 0.7 (B) PROBABILITY OF ACQUIRING INFECTION 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0.0 0.0 ARUSHA DAR DODOMA MBEYA MWANZA ARUSHA DAR DODOMA MBEYA MWANZA Figure 14 shows the regional composition of the distributions in Figure 13, 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 proportion of longer stays in these regions typically, combined with overall high levels of transmission. By summing the probabilities of infection for all the trips made, estimates of imported infection numbers from returning residents can be made. Under the two dEIR transmission level scenarios presented here, this resulted in estimates of between 105 and 1,243 imported cases per month on average (though according to Figure 8, the months used are higher travel months on average), equating to a range of 1-12 cases per 1,000 residents per year. OPPORTUNITY FOR ONWARD TRANSMISSION The above calculations provide estimates of the number of infected individuals traveling to Zanzibar. However, if those individuals spend a single afternoon in a Stonetown hotel, they are very unlikely to infect a mosquito during their time in Zanzibar. A person who brings malaria into the country and stays for a week 1 | Technical Feasibility is equivalent to seven people who stay overnight. The destination of travelers, their length of stay, and their lodging and activities while there will all greatly influence the overall importation risk. As such, the importation risk is a priori very different for different groups of people. Zanzibar residents, immigrants, and migrant workers are likely to stay on the islands the longest after acquiring an infection. They are also most likely to travel to areas of the islands with high transmission risk and to engage in activities that are conducive to transmission (sleeping in open areas rather than air conditioned rooms). If an infected individual is treated promptly, their infection will be cleared and they will be less likely to spread the infection. However, treatment seeking behavior with all of these groups will be an added challenge; in many cases, these people will have acquired malaria immunity, so they have mild or no symptoms and will be less likely to seek treatment. Treatment-seeking practices are known to vary by socioeconomic status (McCombie, 1996), so it is likely that migrant individuals with lower incomes will be less likely to be treated with effective ACTs than other migrant groups. DISCUSSION The calculations performed for migrant ICR in this section have relied upon data on travelers entering Zanzibar by ferry. While it is reasonable to assume that air passengers contribute little to importation risk, it is possible that informal traffic, including traders moving between the mainland and islands on small boats, could present a significant risk. Little information is available on this informal traffic but assuming that these informal travelers have a similar mobile phone use profile compared to the general population, the mobile phone analyses likely incorporate informal travel. Assuming that returning Zanzibar residents represent half of all travelers to the island, and that visitors from the mainland have similar travel patterns (evidence from the mobile phone data suggests that this may be true), estimates from the mobile phone data analyses provide a likely range of 2-24 infections per 1000 inhabitants per year. However, actual importation risk will likely be towards the lower end of this range due to many individuals not spending sustained time on the islands, not traveling to areas of high transmission risk (urban areas), or not coming into contact with vectors. Moreover, residents traveling to the mainland may sleep under a net. For the modeling components below, we thus estimate importation to range from 2-8 infections per 1000 inhabitants per year. However, improved information on all these factors will be essential to designing an effective program to prevent reintroduction of malaria following elimination. In particular, data must be collected on: �� How many individuals travel to Zanzibar by ferry or informal boats (including visitors and returning residents)? �� Where do these travelers originate? �� Where is their destination in Zanzibar? �� How long are they likely to stay there? �� What is their socioeconomic status and lodging while on the islands? 23
Page 1: PHOTO: © SASJA VAN VECHGEL MALARIA
Page 5: ACKNOWLEDGEMENTS The feasibility as
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Page 13 and 14: INTRODUCTION BACKGROUND In October
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analysis be conducted. Moreover, gi
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FIGURE 30: AVERAGE COST PER YEAR FO
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which are being explored by the glo
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portion of those costs). Before thi
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Jacobs, Lesley A (9/2007). Rights a
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Sandberg, Ingstad K, Bjune G (2007)
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World Health Organization, Global M
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