In this paper we demonstrate a clear link between vaccination coverage and population movement from endemic regions and the risk of outbreaks of poliomyelitis in Africa. Significant temporal and geographic variation in the number of reported outbreaks is explained by changes in population immunity and exposure to wild poliovirus imported by travellers from affected countries. Notably, a model incorporating population movement proportional to the number of permanent migrants was found to fit the data better than models based on physical distance, tourism, or flight data. The identified risk factors are sufficient to describe the scale and geographic distribution polio outbreaks in Africa 6 mo in advance with a predictive ability of 82%. Given the resource constraints frequently faced by the eradication programme, planning SIAs on the basis of a statistical model that describes evolving risk is therefore a useful supplement to expert review of epidemiological and vaccination data. Indeed, the statistical model presented here has been used to help plan SIAs in Africa that are described in the GPEI Strategic Plan for 2010–2012 
. Furthermore, a 6-mo forecast based on current AFP data can be a helpful aid to the planning of preventive SIAs despite the time taken to analyse the data and the 6-wk lead time required to ensure that sufficient vaccine stocks are available from pharmaceutical companies (personal communication, M. Shirely, United Nations Children's Fund). As an example, AFP data up to 31 December 2010 were available for analysis on 25 January 2011 and were used to predict likely outbreaks from 1 January–30 June 2011. The results from the predictive risk model were communicated to WHO immunisation planners on 16 February 2011.
Our analysis highlights how the geographical risk of poliomyelitis outbreaks has changed over time, particularly in 2010 and 2011, moving from countries surrounding Nigeria to countries bordering those with re-established transmission. This predicted change in risk has been followed by outbreaks in the Congo and Uganda during the period 1 July–31 December 2010. In order to prevent future outbreaks, it is vital that poliovirus transmission is halted in these reinfected countries, as well as in Nigeria. Predictions in 2011 have overestimated the risk in countries south of DRC; with additional longitudinal data the apparent reduced risk may adjust.
Poliovirus exposure estimated from migration and polio incidence accounted for much of the spatial heterogeneity in the number of expected outbreaks per country. In addition, countries bordering Nigeria had a higher number of outbreaks than those not sharing a border. Much of this increased risk will be due to additional local population movement that occurs in addition to that captured by the migration database. We also examined alternative measures of population movement, including international flight and tourism data. However, we found movement that was assumed to be proportional to the number of permanent migrants provided the best fit to the data, presumably because permanent migration is related to broader patterns of cross-border and international travel that is not captured by these other measures. Inclusion of a variable describing changes in exposure to poliovirus over the preceding 18 mo further improved the model fit, presumably capturing aspects of wild-type poliovirus dynamics that are not described by other variables. This may include naturally acquired immunity from the many asymptomatic infections that occur during an outbreak but are not detected using AFP surveillance.
The ability of our study to estimate the impact of routine immunisation and SIAs on population immunity was limited because estimates of routine coverage are known to be measured with error 
, and new methods for surveillance of SIA coverage were only introduced in 2009 
. Estimates of trivalent and monovalent OPV effectiveness against poliomyelitis in Nigeria have been used to examine the level and trends in immunity against each poliovirus serotype 
. Using estimates of immunity derived in this way was not possible here because of the absence of accurate estimates of vaccine effectiveness for most countries in Africa.
An association of <5-y mortality greater than 150 deaths per 1,000 live births with a higher risk of poliomyelitis outbreaks is likely to be indicative of poor sanitary conditions, poverty, high population density, and poor access to health care and nutrition. These factors contribute both to high childhood mortality and poliomyelitis susceptibility 
. Many countries in Africa have reported an improvement in childhood mortality rates 
, and so susceptibility to outbreaks may be further limited through investment in health care and living conditions.
A limitation of our study is the use of a statistical model that may not capture some of the important non-linearities of poliovirus transmission as eradication is approached. For example, increasing population immunity above a critical vaccination threshold results in herd immunity and could eliminate the risk of polio outbreaks. The log-linear form of the Poisson regression model does not capture these kinds of thresholds. However, no such thresholds were apparent in the data at a country level. The analysis relies on relatively consistent reporting rates for non-polio AFP, as this appears to provide the best estimate of population immunity. Unfortunately, not all countries reported a consistently high AFP rate, which will introduce uncertainties in the analysis. In addition, estimates of human migration and poliovirus exposure across Africa were shown to be associated with polio outbreaks, but there is likely to be much temporal variation in human movement patterns that was not captured in the available data. This could explain why the magnitudes of the polio outbreaks in mid-2004 and mid-2008 were not fully captured by the model, and why the westward spread of polio from Nigeria in 2008 and 2009 was not completely replicated in the model. Other sources of movement patterns within Africa are required to fully capture this important determinant of disease transmission.
Countries bordering Nigeria experienced more frequent outbreaks, but in general these were reduced in duration and size compared with outbreaks in other countries in Africa. This association could be explained by unmeasured increases in population immunity following exposure to wild-type poliovirus. If natural exposure to poliovirus is the cause of the reduced risk, outbreak size in these countries should be carefully monitored as the incidence in Nigeria reduces. Serological surveys for antibodies to poliovirus within populations in Africa would also supplement current surveillance for AFP and poliovirus in understanding polio epidemiology.
Although there is always an element of uncertainty and chance in the distribution of infectious disease outbreaks, this study highlights that poliomyelitis outbreaks in Africa are largely governed by the extent of immunity in the population, population movement, and exposure to infection. Planning SIA campaigns based on evolving risk may reduce the number of outbreaks by responding to increased risk prior to an outbreak occurring. As the incidence of polio in Nigeria has remained very low in 2010 and 2011, there may be a unique opportunity to eliminate polio from Africa in the near-term through targeted vaccination informed by appropriate predictive models.