Making sense of consensus: comparative modelling of malaria interventions
Authors: Manoj Gambhir, Chathurika Hettiarachchige
Journal: The Lancet Global Health (2017)

Although the global burden of malaria has been greatly reduced through, for example, vector control,1Fact Sheet: World Malaria Report 2016 http://www.who.int/malaria/media/world-malaria-report-2016/en/ (accessed May 22, 2017).Google Scholar the disease is still a major concern and there is renewed interest in mass drug administration (MDA) as an additional control intervention. A Cochrane review2Poirot E Skarbinski J Sinclair D Kachur SP Slutsker L Hwang J Mass drug administration for malaria.Cochrane Database Syst Rev. 2013; 12: CD008846Google Scholar of MDA for malaria, which included 32 studies, concluded that MDA substantially reduces the risk of infection leading to malaria. However, this study also raised the concern that these reductions are often not sustained. Another review from 20153Newby G Hwang J Koita K et al.Review of mass drug administration for malaria and its operational challenges.Am J Trop Med Hyg. 2015; 93: 125-134Crossref PubMed Scopus (145) Google Scholar suggested that, although MDA is successful in controlling and eliminating disease, there remain substantial knowledge gaps and further studies are essential, particularly on optimal size of the target population, methods to improve coverage, and drug safety. Oliver Brady and colleagues address some of these research gaps on MDA for malaria in The Lancet Global Health (July 2017 issue).4Brady OJ Slater HC Pemberton-Ross P et al.Role of mass drug administration in elimination of Plasmodium falciparum malaria: a consensus modelling study.Lancet Glob Health. 2016; 5: e680-e687Summary Full Text Full Text PDF Scopus (70) Google Scholar Given the relatively small number of studies of the population impact of MDA, computational models that can simulate the underlying biological processes of malaria in human and mosquito populations and also be fitted to the available data represent attractive “what-if?” tools. However, several approaches to modelling malaria epidemiology exist, and Brady and colleagues present a model comparison analysis done by the Bill & Melinda Gates Foundation-funded Malaria Modelling Consortium (MMC) on the effectiveness of MDA in different settings, identifying the most important determinants of MDA effectiveness. They compare a specific model outcome (the Plasmodium falciparum Parasite Rate following 2 years of MDA) generated by four established models, which represent different, yet reasonable, ways of simulating malaria transmission and MDA. The study provides much needed support to researchers responsible for quantitative analysis in the service of malaria control programmes, who are often faced with a plethora of mathematical models, run under non-standardised sets of interventions, and reporting different outcomes. Until recently, these analysts would typically depend on the work of a single modelling group. This approach was clearly less than optimal, since it often failed to take into account the complementary work of several other research groups around the world. Thanks to the leadership of organisations such as The Bill & Melinda Gates Foundation, there has been a shift during the past decade towards modelling consortia (eg, the MMC), in which representatives from several modelling groups meet frequently and evaluate scientific or operational questions collectively. Brady and colleagues, as members of the MMC, compare the quantitative predictions of a set of four malaria computational models. There is no simple recipe for performing such a comparison. Various modelling consortia have had to determine the best way to combine outcomes into consensus reports for diseases such as HIV,5Eaton JW Johnson LF Salomon JA et al.HIV treatment as prevention: systematic comparison of mathematical models of the potential impact of antiretroviral therapy on HIV incidence in South Africa.PLoS Med. 2012; 9: e1001245Crossref PubMed Scopus (287) Google Scholar tuberculosis,6Houben RMGJ Menzies NA Sumner T et al.Feasibility of achieving the 2025 WHO global tuberculosis targets in South Africa, China, and India: a combined analysis of 11 mathematical models.Lancet Glob Health. 2016; 4: e806-e815Summary Full Text Full Text PDF PubMed Scopus (114) Google Scholar and neglected tropical diseases.7Turner HC Truscott JE Fleming FM Hollingsworth TD Brooker SJ Anderson RM Cost-effectiveness of scaling up mass drug administration for the control of soil-transmitted helminths: a comparison of cost function and constant costs analyses.Lancet Infect Dis. 2016; 16: 838-846Summary Full Text Full Text PDF PubMed Scopus (36) Google Scholar In the non-communicable disease domain, the US National Cancer Institute's Cancer Intervention and Surveillance Modeling Network (CISNET) is charged with investigating various questions in a comparative fashion, such as those relating to the potential medical and cost-benefit of cancer screening programmes.8Munoz D Near AM van Ravesteyn NT et al.Effects of screening and systemic adjuvant therapy on ER-specific US breast cancer mortality.J Natl Cancer Inst. 2014; 106Crossref PubMed Scopus (104) Google Scholar The points of agreement of the models in the study by Brady and colleagues4Brady OJ Slater HC Pemberton-Ross P et al.Role of mass drug administration in elimination of Plasmodium falciparum malaria: a consensus modelling study.Lancet Glob Health. 2016; 5: e680-e687Summary Full Text Full Text PDF Scopus (70) Google Scholar make sense—eg, it is intuitive that the post-MDA prevalence of malaria is strongly related to the proportion of individuals in the community missed by an MDA campaign, as well as the pre-MDA endemic prevalence. Although it may be more difficult than discovering and documenting consensus, explaining discrepancy between models is also useful. We quickly move into philosophical territory when we ask questions such as: what does it mean to make a consensus prediction based on model structures that disagree? Or can Modelling Consortia devise experiments to reconcile (or refute) competing model structures? Forecasting and classification based on the averaged outcomes of an ensemble of models has become a standard method in machine learning; but ensemble approaches have not yet been taken up by the infectious disease modelling community, whose models are more mechanistic and therefore have a stronger link to underlying biological processes than machine learning or statistical approaches. However, when more data become available from, and become more consistent between, control programmes, we can expect to see statistical forecasts going head-to-head with mechanistic models in model comparison studies. As the community responsible for the control and elimination of infectious diseases becomes more inclined towards quantitative data, the demand for computational modelling will grow, and this demand will naturally lead to a plurality of approaches. Devising a protocol for aggregating model outcomes and generating a consensus is going to become more important. Brady and colleagues' exemplary approach here is another step in the right direction, and one can hope that it further builds credibility for modelling as a policy analysis tool, and that it finds its way into the debate on malaria control. We declare no competing interests. Role of mass drug administration in elimination of Plasmodium falciparum malaria: a consensus modelling studyMass drug administration has the potential to reduce transmission for a limited time, but is not an effective replacement for existing vector control. Unless elimination is achieved, mass drug administration has to be repeated regularly for sustained effect. Full-Text PDF Open Access