Agent-based models of malaria transmission: a systematic review

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Plasmodium falciparum malaria 1 infectiousdiseases
infectious disease 4 infectiousdiseases
malaria 69 infectiousdiseases
tuberculosis 1 infectiousdiseases

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Plasmodium falciparum malaria 3091 infectious diseases of humans. The global response to malaria transmission has been significant, with Plasmodium falciparum malaria eliminated from 79 countries from 1979 to 2010 [[1]]. Modelling suggests that 70% of the reduction in
infectious disease 2989 authorized users.BackgroundMalaria, alongside HIV and tuberculosis, is considered one of the “big three” infectious disease s of humans. The global response to malaria transmission has been significant, with Plasmodium falciparum
infectious disease 8073 individual medications), and relevant Plasmodium speciesTerms relating to epidemiology, demography, infectious disease outbreaks or transmission, epidemics and key outbreak model parameters (e.g. basic reproduction number)Terms
infectious disease 49758 [[108], [116], [118]] and parameter estimation by Griffin et al. [[14]], as well as in modelling of other infectious disease s [[124], [125]]. Approaches such as MCMC and approximate Bayesian computation are increasing in popularity
infectious disease 51469 As is likely the case regarding spatial methods, optimization of malaria transmission modelling (and infectious disease simulation more broadly) may benefit from adapting approaches outside the field to a new context.Increased
malaria 44 Title: Malaria JournalAgent-based models of malaria transmission: a systematic reviewNeal R. SmithJames M. TrauerManoj GambhirJack S. RichardsRichard J.
malaria 372 date (collection): /2018AbstractBackgroundMuch of the extensive research regarding transmission of malaria is underpinned by mathematical modelling. Compartmental models, which focus on interactions and transitions
malaria 1113 published between 1998 and May 2018 was performed, characterizing agent-based models (ABMs) relevant to malaria transmission. The review provides an overview of approaches used to date, determines the advantages
malaria 1728 of these approaches provides avenues for further exploration of agent-based techniques for modelling malaria transmission. Potential extensions include varying elimination strategies across spatial landscapes,
malaria 2263 these elements together under a common framework may enhance knowledge of, and guide policies towards, malaria elimination. However, because of the diversity of available models, endorsing a standardized approach
malaria 3043 tuberculosis, is considered one of the “big three” infectious diseases of humans. The global response to malaria transmission has been significant, with Plasmodium falciparum malaria eliminated from 79 countries from
malaria 3113 humans. The global response to malaria transmission has been significant, with Plasmodium falciparum malaria eliminated from 79 countries from 1979 to 2010 [[1]]. Modelling suggests that 70% of the reduction in
malaria 3223 eliminated from 79 countries from 1979 to 2010 [[1]]. Modelling suggests that 70% of the reduction in malaria cases in sub-Saharan Africa (SSA) between 2000 and 2015 was attributable to the implementation of intervention
malaria 3823 mathematical simulation is increasingly used to provide further insights.Infectious disease modelling of malaria has existed for over a century [[3]], with the dominant paradigm being the Ross–Macdonald models used
malaria 4289 “susceptible”, “exposed”, “infectious” and “recovered”. More recent compartmental models of malaria provide insights into risk-stratification of populations, multiple mosquito populations, and waning
malaria 5454 variability in time of infection, time to recovery, and location of infection). Compartmental models of malaria transmission do exist that incorporate either stochasticity of individual infections [[8]] or spatial
malaria 6229 spatial variation can help fill knowledge gaps [[7]] about transmission heterogeneities important in malaria elimination strategies.The flexibility of agent-based approaches also allows models to be constructed
malaria 6382 agent-based approaches also allows models to be constructed to address practical questions relating to malaria control and elimination in specific local contexts [[7], [10]]. This is advantageous because identifying
malaria 6844 attributes to reflect local individual characteristics and geographical factors.As more is learned about malaria transmission, the complexity of the questions asked increases, which in turn calls for more nuanced
malaria 7124 technical expertise and computing power increase. With the increasing capacity for modelling to assist in malaria elimination programmes, a review of the published literature for ABMs of malaria transmission was performed.
malaria 7205 modelling to assist in malaria elimination programmes, a review of the published literature for ABMs of malaria transmission was performed. Analysis included characterization of the structure of existing models,
malaria 7337 performed. Analysis included characterization of the structure of existing models, the factors influencing malaria transmission modelled, and the methods of data use and output analysis. The approaches used were highlighted
malaria 7920 following three concepts in their subject heading, keywords list, title or abstract:Terms relating to malaria , malaria vaccines, anti-malarials (including individual medications), and relevant Plasmodium speciesTerms
malaria 7929 three concepts in their subject heading, keywords list, title or abstract:Terms relating to malaria, malaria vaccines, anti-malarials (including individual medications), and relevant Plasmodium speciesTerms relating
malaria 7952 subject heading, keywords list, title or abstract:Terms relating to malaria, malaria vaccines, anti- malaria ls (including individual medications), and relevant Plasmodium speciesTerms relating to epidemiology,
malaria 8705 suitability, with those that mentioned an agent-based, individual-based, or microsimulation model of malaria transmission selected for full-text review. Full-text articles were excluded if no agent-based model
malaria 8880 excluded if no agent-based model of hosts or vectors was described or used, or if no components of malaria transmission (such as human disease, vector biting, or interventions) were explicitly modelled. Models
malaria 9032 biting, or interventions) were explicitly modelled. Models of vector life cycles or ecology without malaria -specific elements were excluded. Articles that described comparison or ensemble modelling of pre-existing
malaria 12017 agent, and interactions of interest. Certain ABM frameworks naturally arise from the above concepts; in malaria modelling, key considerations include the choice of agent and whether to focus on disease states or
malaria 14837 spatial ABM to investigate the impact of the location of food, hosts, and resting and breeding sites on malaria transmission. This required simulation of a physical landscape, with both mosquito and human locations
malaria 16006 tracking vectors as frequently as each second [[21], [31]].Agency and elements modelledThe complexity of malaria transmission prevents any one model simulating all transmission factors in depth. In practice, each
malaria 16663 scenarios, and therefore may be suited to modelling with an ABM.Fig. 3Diagram outlining factors influencing malaria transmission that have been modelled by ABMs. Factors pertaining to humans and mosquitoes are in red
malaria 18879 have been simulated for both patients [[47]] and carers [[28]]. One model simulated the impact of anti- malaria l use on HIV-positive pregnant women [[48]], including disease severity and improvements in birth weight.The
malaria 21695 interventions for vector control [[21]]. Human agents were often included to relate Anopheles populations and malaria transmission, in models with vector dynamics as a core component. When models also included a spatial
malaria 21816 in models with vector dynamics as a core component. When models also included a spatial component, malaria transmission generally required vector and host to be co-located. These simulations used a ‘decision
malaria 22183 individual level was regularly used to assess interventions directed at mosquitoes and their effects on malaria transmission [[19], [72]].ParasiteOf 54 models that specified a malaria parasite (see Additional file
malaria 22255 mosquitoes and their effects on malaria transmission [[19], [72]].ParasiteOf 54 models that specified a malaria parasite (see Additional file 2, column five), 51 modelled the dynamics of Plasmodium falciparum, two
malaria 23239 considered, including parasite strains [[17], [74]], PfHRP2 status [[75], [76]] and recrudescence of P. vivax malaria [[30]]; the latter model considered the disease to be a submodule of the overall simulation framework.
malaria 23513 [77]], and four allowed antigenic parasite variation [[15], [78]–[82]], particularly to capture anti malaria l resistance.The choice of parasite, much like the Anopheles species, was often based on the dominant
malaria 23685 species, was often based on the dominant species in the target location. The dominance of P. falciparum malaria simulation reflects the attention paid to it, which is largely due to the historic relative burden of
malaria 24117 model structure, for example to account for the recrudescence seen in P. vivax but not P. falciparum malaria . Pizzitutti et al. [[30], [37]] incorporated these differences by adding parameters governing recurrence
malaria 25140 Additional file 2, column 11; explored further in Additional file 4). Seventeen models investigated malaria transmission in Africa, by simulating a specific location (e.g. [[20], [85]]) or using a hypothetical
malaria 29266 locations [[61]].InterventionsFifty-eight studies assessed the impact of at least one intervention on malaria transmission, mosquito prevalence or EIR (Additional file 2, column 6). The majority of papers assessed
malaria 29488 interventions, 21 assessed interventions in combination, and one investigated the removal of current malaria strategies [[52]]. Interventions could broadly be divided into those targeted at the human host (e.g.
malaria 31095 populations. Third, hypothetical interventions were described by their impacts, to target a specific aspect of malaria transmission [[65]]. The hypothetical impact may approximate a pre-existing intervention, such as halving
malaria 31743 densities in oviposition sites [[59], [65]], and the pharmacokinetics and pharmacodynamics of anti- malaria ls [[75], [80], [91], [96]].Data use, model outputs and analysisParameter estimation and robustness of
malaria 38164 outputs from model variants, to investigate interventions such as vaccination [[108], [116]], seasonal malaria chemoprevention [[115]], mass test-and-treat strategies [[109]], and long-lasting insecticidal nets
malaria 38940 approaches, the consensus modelling largely drew consistent findings on the relationship between available malaria prevalence data and clinical incidence [[118]], and on the impacts of vaccination [[119]] and mass drug
malaria 39141 mass drug administration (MDA) [[120]].DiscussionMathematical modelling plays an important role in malaria elimination, and agent-based approaches make a major contribution to these efforts. The extension of
malaria 39336 extension of compartmental models to their early ABM equivalents arose from the need to understand malaria transmission at the individual level. The result is a rich array of model families and simulation techniques,
malaria 40974 times, [[25], [38], [39]]. The OpenMalaria models progressed from assessing the force of infection of malaria transmission [[32]], to estimating cost-effectiveness of a vaccination programme [[27]]. Given the similarities
malaria 41121 cost-effectiveness of a vaccination programme [[27]]. Given the similarities of compartmental models of malaria to the original Ross–McDonald framework [[6]], it is clear that the depth and flexibility of agent-based
malaria 41275 [[6]], it is clear that the depth and flexibility of agent-based methods are allowing new insights into malaria transmission and prevention.The variation in the models described above highlights the difficulties
malaria 41436 models described above highlights the difficulties in developing a standardized style of ABM for use in malaria epidemiology. However, this is arguably a major advantage, with the abundance of techniques allowing
malaria 42221 to assess interventions [[62], [85]]. Therefore, while not every model incorporated every aspect of malaria epidemiology, each was tailored to the research question at hand.Conversely, if modelling groups are
malaria 43496 effects of climate on larval habitats, anthropophily, ITN, IRS, larval habitat removal, vaccination, anti- malaria l use, attractive toxic sugar baits, and rates of human disease. As each framework provides insights
malaria 43627 sugar baits, and rates of human disease. As each framework provides insights into key components of malaria transmission, all of which are important in guiding elimination strategies.To some extent, combining
malaria 44957 with local knowledge of physical characteristics (such as host/vector movement patterns) to simulate malaria transmission, ecology, and the impact of interventions based on their location. These insights include
malaria 45139 location. These insights include the distances between larval habitats and houses to effectively reduce malaria transmission [[53]], and the impact on systematic versus random location of attractive toxic sugar baits
malaria 46043 larval source management at the local water sources, whilst increasing access to vaccines and anti- malaria ls in the healthcare centre. Human movement dynamics [[121]] could be incorporated to assess the relative
malaria 46896 habitats, which would be collected in a similar manner over larger areas. If it is deemed useful to model malaria over a wider area, techniques from other fields may be used, such as probability modelling of invasive
malaria 47108 species, which has been performed for an area of over 35,000 km2 [[122]].Regarding the locations of malaria modelling, there is an understandable focus on SSA, which was responsible for 88% of the global malaria
malaria 47212 malaria modelling, there is an understandable focus on SSA, which was responsible for 88% of the global malaria burden in 2015 [[2]]. However, there has also been a recent increase in attention on South-East Asia
malaria 47579 and insecticide resistance are more prominent in these areas. Despite this, approximately half of all malaria cases outside Africa in 2015 were due to P. vivax [[2]], while Plasmodium knowlesi malaria transmission
malaria 47670 half of all malaria cases outside Africa in 2015 were due to P. vivax [[2]], while Plasmodium knowlesi malaria transmission is increasing in locations, such as Malaysia [[123]]. The methods of ABM construction used
malaria 47820 such as Malaysia [[123]]. The methods of ABM construction used in SSA and SEA, and for P. falciparum malaria , suggest transferability to other regions and Plasmodium species, which will be important as data availability
malaria 49625 part of parameter space that has non-negligible posterior probability. MCMC has already been used in malaria ensemble modelling [[108], [116], [118]] and parameter estimation by Griffin et al. [[14]], as well
malaria 51433 and transportation and logistics. As is likely the case regarding spatial methods, optimization of malaria transmission modelling (and infectious disease simulation more broadly) may benefit from adapting approaches
malaria 52476 individual models, ensemble modelling is an important tool for generating robust conclusions about malaria transmission. A review of ebola models advocated for an ensemble modelling approach that adequately
malaria 52667 that adequately compares state-of-the-art models, but also allows for model diversity [[131]]. For malaria models built for similar purposes (for example, to estimate certain parameters of interest, or to predict
malaria 53243 models are most appropriate when ensemble members differ in parameter estimates or outputs.ConclusionsAs malaria transmission continues to decline and interventions become more nuanced, agent-based modelling will
malaria 54851 ever-increasing computing power available to researchers, detailed ABMs that accurately reflect the biology of malaria transmission are increasingly feasible on a fine spatial resolution over large geographical regions.
malaria 55039 geographical regions. As such, agent-based modelling will be an important tool for helping to inform malaria elimination strategies over the coming years.Additional filesAdditional file 1. Systematic review search
malaria 55232 Systematic review search strategies.Additional file 2. Key characteristics of individual-based models of malaria .Additional file 3. Adapted PRISMA search flow diagram of study selection.Additional file 4. Overview
tuberculosis 2934 supplementary material, which is available to authorized users.BackgroundMalaria, alongside HIV and tuberculosis , is considered one of the “big three” infectious diseases of humans. The global response to malaria

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