Defining the relationship between infection prevalence and clinical incidence of Plasmodium falciparum malaria

Abstract

In many countries health system data remain too weak to accurately enumerate Plasmodium falciparum malaria cases. In response, cartographic approaches have been developed that link maps of infection prevalence with mathematical relationships to predict the incidence rate of clinical malaria. Microsimulation (or ‘agent-based') models represent a powerful new paradigm for defining such relationships; however, differences in model structure and calibration data mean that no consensus yet exists on the optimal form for use in disease-burden estimation. Here we develop a Bayesian statistical procedure combining functional regression-based model emulation with Markov Chain Monte Carlo sampling to calibrate three selected microsimulation models against a purpose-built data set of age-structured prevalence and incidence counts. This allows the generation of ensemble forecasts of the prevalence–incidence relationship stratified by age, transmission seasonality, treatment level and exposure history, from which we predict accelerating returns on investments in large-scale intervention campaigns as transmission and prevalence are progressively reduced.

Impact of mosquito gene drive on malaria elimination in a computational model with explicit spatial and temporal dynamics

“…The rollout of LLINs has been responsible for dramatic declines in malaria burden and prevalence (2), but pyrethroid resistance has aggressively spread across Africa, raising the question of whether these gains can be maintained (33). In general, the lower the vectorial capacity, the lower the resulting endemic EIR, and the lower the resulting burden of malaria (50). The potential reductions in vectorial capacity demonstrated in these simulations across a wide representative set of epidemiological conditions covered by parameter combinations shows the potential not only to prevent loss of achieved gains due to pyrethroid resistance, but to decrease vectorial capacity, and thus malaria, even further than has been presently achieved.…”

Global Epidemiology of Plasmodium vivax

“…This metric was used to map the global prevalence of P. vivax endemicity (Figure 1). 2 Case estimates can then be derived from the prevalence map by using a model of the relationship between prevalence of infection and clinical incidence 128,129,133–135Figure 11 represents such a model developed for P. vivax that will be integrated into a cartographic approach to derive estimates of P. vivax clinical cases 134,136.…”

Challenges of malaria diagnosis in clinical settings and disease surveillance under reduced malaria burden in Tanzania

“…Since time immemorial, the diagnosis of malaria has had difficulties because of the reliance on the fever syndrome as the entry point in the clinical algorithms for malaria diagnosis [68,69]. The situation is likely to be more worse when malaria transmission and prevalence are progressively reduced because clients would continue to attend to health facilities on account of non-malarial causes of febrile illnesses [70]. While the adoption of mRDTs has significantly reduced the syndromic management of malaria, the mRDTs may not be sensitive and robust enough to detect low level and asymptomatic infections as malaria declines to low endemicity [41], hence the need to invest in the more robust and sensitive tests such as the NATs [51].…”