The base model is a comprehensive, individual-based model of malaria and vaccination in humans that has been previously published in a supplement to the American Journal of Tropical Medicine and Hygiene[5] ,[30] (link)–[34] (link) Briefly, a simulated population of humans is updated at each 5-d time step via components representing new infections, parasite densities, acquired immunity, uncomplicated and severe malaria episodes (including severe malarial anemia), direct and indirect mortality, infectiousness to mosquitoes, case management [35] (link), and vaccination with a pre-erythrocytic vaccine [36] (link). The simulated malaria infections each have distinct parasite densities that vary by time step, while the level of malaria transmission is assumed to vary seasonally.
The models are constructed in a modular way, with distinct components that represent infection of humans, blood-stage parasite densities, infectiousness of humans to mosquitoes, incidence of morbidity, and mortality. Each of the components aims to capture the relevant biology, while at the same time fitting available data. Simulated immunity acts mainly by controlling parasite densities [30] (link). In turn, the simulated incidence of clinical malaria is a function of parasite density [32] (link), as are the incidences of severe disease and malaria-related mortality [31] (link). Natural immunity to infection without vaccination is acquired only after considerable exposure to Plasmodium falciparum malaria parasites [33] (link).
The ensemble was constructed by varying different modular components of the base model. A total of 30 models, each constructed by substituting different versions of one or more components, were investigated. Sixteen of these models were excluded from the ensemble, either because they were very similar to other models in the ensemble, or because the model-fitting algorithm did not find any sets of parameter values that provided an adequate fit to the data (see “Model Fitting” below). Fourteen models were retained. The modifications of the base model that resulted in inclusion of these 14 models are summarized inTable 2 and described in detail in Text S1 . Each of these models was assigned the identifier used for the fitting process. Each specific parameterization evaluated in the fitting process (several thousands for each model; see Text S1 ) was also assigned a unique identifier. The models were programmed in C++ as part of the open source software platform OpenMalaria (http://code.google.com/p/openmalaria/ ).
The models are constructed in a modular way, with distinct components that represent infection of humans, blood-stage parasite densities, infectiousness of humans to mosquitoes, incidence of morbidity, and mortality. Each of the components aims to capture the relevant biology, while at the same time fitting available data. Simulated immunity acts mainly by controlling parasite densities [30] (link). In turn, the simulated incidence of clinical malaria is a function of parasite density [32] (link), as are the incidences of severe disease and malaria-related mortality [31] (link). Natural immunity to infection without vaccination is acquired only after considerable exposure to Plasmodium falciparum malaria parasites [33] (link).
The ensemble was constructed by varying different modular components of the base model. A total of 30 models, each constructed by substituting different versions of one or more components, were investigated. Sixteen of these models were excluded from the ensemble, either because they were very similar to other models in the ensemble, or because the model-fitting algorithm did not find any sets of parameter values that provided an adequate fit to the data (see “Model Fitting” below). Fourteen models were retained. The modifications of the base model that resulted in inclusion of these 14 models are summarized in
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