A1 Journal article (refereed), original research

Agent-based modelling of complex factors impacting malaria prevalence

Open Access publication

Publication Details

Authors: Amadi Miracle, Shcherbacheva Anna, Haario Heikki

Publisher: BioMed Central

Publication year: 2021

Language: English

Related journal or series: Malaria Journal

Volume number: 20

ISSN: 1475-2875

JUFO level of this publication: 1

Digital Object Identifier (DOI): http://dx.doi.org/10.1186/s12936-021-03721-2

Permanent website address: https://malariajournal.biomedcentral.com/articles/10.1186/s12936-021-03721-2

Open Access: Open Access publication



complex models have been developed to characterize the transmission
dynamics of malaria. The multiplicity of malaria transmission factors
calls for a realistic modelling approach that incorporates various
complex factors such as the effect of control measures, behavioural
impacts of the parasites to the vector, or socio-economic variables.
Indeed, the crucial impact of household size in eliminating malaria has
been emphasized in previous studies. However, increasing complexity also
increases the difficulty of calibrating model parameters. Moreover,
despite the availability of much field data, a common pitfall in malaria
transmission modelling is to obtain data that could be directly used
for model calibration.


this work, an approach that provides a way to combine in situ field
data with the parameters of malaria transmission models is presented.
This is achieved by agent-based stochastic simulations, initially
calibrated with hut-level experimental data. The simulation results
provide synthetic data for regression analysis that enable the
calibration of key parameters of classical models, such as biting rates
and vector mortality. In lieu of developing complex dynamical models,
the approach is demonstrated using most classical malaria models, but
with the model parameters calibrated to account for such complex
factors. The performance of the approach is tested against a wide range
of field data for Entomological Inoculation Rate (EIR) values.


overall transmission characteristics can be estimated by including
various features that impact EIR and malaria incidence, for instance by
reducing the mosquito–human contact rates and increasing the mortality
through control measures or socio-economic factors.


phenomena such as the impact of the coverage of the population with
long-lasting insecticidal nets (LLINs), changes in behaviour of the
infected vector and the impact of socio-economic factors can be included
in continuous level modelling. Though the present work should be
interpreted as a proof of concept, based on one set of field data only,
certain interesting conclusions can already be drawn. While the present
work focuses on malaria, the computational approach is generic, and can
be applied to other cases where suitable in situ data is available.

Last updated on 2021-27-04 at 13:18