Malaria transmission intensity affects almost all aspects of malaria epidemiology, including community prevalence and age-profile of infection, the incidence and type of disease syndromes, and total malaria mortality [1, 2]. It also modulates the expected outcome of malaria control. Because transmission intensity varies geographically, maps that describe this variation are necessary to identify populations at different levels of risk, to compare and interpret malaria interventions conducted in different places, and to evaluate objectively options for disease control.
The most commonly measured metric of malaria transmission is the parasite rate: the proportion of individuals infected at a given point in time. In 2009, the Malaria Atlas Project (MAP) assembled all available data from Plasmodium falciparum parasite rate (PfPR) surveys, and used model-based geostatistics (MBG) to generate a global map of estimated PfPR for the year 2007 . That map provided new insights into global patterns of malaria endemicity and, through the careful handling of uncertainty, a framework for assessing those areas where knowledge of endemicity is inadequate. To remain useful, however, these maps must remain contemporary. The year 2010 has a particular significance as an evaluation milestone for malaria global health policy [4–6] and a huge expansion in the availability of parasite rate surveys since 2007, as well as ongoing refinement in spatial modelling techniques, including the use of environmental covariates, has provided an opportunity to carry out a major revision of the map for this benchmark year.
The global ubiquity of PfPR surveys means that they are the only feasible data source for large-scale malaria mapping [1, 2]. Other metrics of malaria transmission, however, have distinct and crucial roles in informing control decisions. The basic reproductive number for malaria, PfR
0, quantifies the potential for the disease to spread within a naive population [7, 8]. The same metric for scenarios moderated by malaria control has been termed PfR
c . These metrics underpin mathematical models of transmission that are central to contemporary questions in malaria control : identifying optimal intervention suites and coverage levels, predicting timelines of declining endemicity, and assessing the regional feasibility of elimination [2, 11–17]. If these values exceed one, infection prevalence increases to a steady state, and if less than one, prevalence declines. Thus, if sustained disease control reduces transmission intensity by a factor that exceeds PfR
0, the parasite will eventually be eliminated. PfR
0 is, therefore, an index of both how well malaria spreads and the effort required to eliminate it.
Although central to epidemiological theory, PfR
0 is almost impossible to measure directly [8, 9]. When mathematical models of malaria are fitted to real data, this is generally via a third metric of transmission: the entomological inoculation rate (EIR) which describes the number of expected bites from infected mosquitoes per person per unit time and can be measured in the field, albeit laboriously [18–20]. EIR has, therefore, become a key metric for modelling interactions between transmission intensity and, for example, intervention impact [21–25], acquired immunity [26, 27], and morbidity and mortality [28–31]. The causal relationships between PfPR, PfR
0 and PfEIR formed the basis of the earliest malaria transmission models [32, 33]. These models have subsequently been augmented and diversified to capture greater complexity in the transmission system, and such refined models provide a mechanism to estimate PfR
0 and PfEIR based on the more readily measured PfPR [9, 20].
Here, a suite of transmission models are presented that link these three fundamental metrics of malaria transmission. They include the key mechanisms of super-infection and heterogeneous biting  and are validated with existing data. These models are used in conjunction with an updated 2010 PfPR map to create new global predictions of both PfEIR and PfR
C [12, 14, 34] that include an enumeration of the uncertainty in the underlying prevalence map and in the relationships between the different transmission metrics. The suite of maps presented here provide a rich landscape of data that can be used to help address some of the urgent needs for planning malaria control and elimination defined by the international community [11–15, 35].
This study also marks a landmark release of malariometric data into the public domain, via the MAP website . Along with all the modelling output presented here, the underlying MAP database of PfPR surveys is made public for the first time. It is hoped that the open access release of this major malariometric dataset, via a low-bandwidth and user-friendly interface, will enhance malaria research and control worldwide.