Malaria Modeling in the Era of Eradication

Abstract

Mathematical models provide the essential basis of rational research and development strategies in malaria, informing the choice of which technologies to target, which deployment strategies to consider, and which populations to focus on. The Internet and remote sensing technologies also enable assembly of ever more relevant field data. Together with supercomputing technology, this has made available timely descriptions of the geography of malaria transmission and disease across the world and made it possible for policy and planning to be informed by detailed simulations of the potential impact of intervention programs. These information technology advances do not replace the basic understanding of the dynamics of malaria transmission that should be embedded in the thinking of anyone planning malaria interventions. The appropriate use of modeling may determine whether we are living in an era of hubris or indeed in an age of eradication.

Entomological Surveillance as a Cornerstone of Malaria Elimination: A Critical Appraisal

“…….assessment of the consequences of uncertainties in parameter values, are generally much more timeconsuming and challenging than the modelling itself [23].…”

“…… fitting complex models to multiple types of data is challenging, and model predictions are always likely to be unreliable at very high spatial resolution. The twin objectives of understanding the dynamics and making quantitative predictions can also be in conflict, because the push to include all relevant factors in a locally calibrated predictive model rapidly leads to complex behaviour that can no longer be explained [23].…”

“…' … it is challenging for a non-specialist to distinguish modelling that is useful from poor quality modelling that may support misguided policies' [23].…”

Modelling the epidemiology of residual Plasmodium vivax malaria in a heterogeneous host population: a case study in the Amazon Basin

“…A quantitative understanding of malaria transmission dynamics is required for planning, monitoring, and evaluating interventions aimed at its elimination [6]. However, classical susceptible-infected-susceptible (SIS) malaria models often disregard, totally or partially, risk heterogeneity at the community level and classify hosts as more uniformly susceptible or infectious than they actually are.…”

“…However, classical susceptible-infected-susceptible (SIS) malaria models often disregard, totally or partially, risk heterogeneity at the community level and classify hosts as more uniformly susceptible or infectious than they actually are. Models that take insufficient account of real-world heterogeneities may not properly recapitulate the transmission dynamics of malaria in endemic settings, in addition to not providing insights into the impact of targeting control interventions to high-risk groups [1, 6]. SIS models of infectious diseases may incorporate risk heterogeneity among hosts as, for example, a continuous distribution of hosts’ susceptibility to infection, which can be determined empirically from the proportions of hosts that are experimentally infected at different pathogen challenge doses [7–9].…”