Group | Analytics and Intervention Modelling

The Analytics and Intervention Modelling (AIM) group contributes to build a data-driven and evidence-based recommendations to support National Malaria Control Programmes in their fight against malaria. This is possible by using state of the art infectious disease modelling, health economics frameworks and the growing evidence generated by real-world data.

Together with local partners, we co-create action plans to prioritize the most impactful interventions in countries around the Globe to fight malaria. We strongly believe that collaborations are essential to ensure long-term sustainability of health services and also believe that sharing knowledge with in-country training and staff exchanges contributes to capacity strengthening.

We bring together experts in disease modelling, epidemiology, statistics, computer science, health economics and project management.

The geographical unit considered in the models could vary from national to local scales such as districts, depending on the data available as well as the country-specific malaria program operational units.

We are able to attach cost data to simulation outputs to predict the costs and the cost-effectiveness of different strategies, and compare the relative efficiency of the plans. Such applications of cost-effectiveness analysis ensure that the highest health benefit is achieved for a given resource constraint.

As a designated WHO Collaborating Centre for Modelling, Monitoring and Training for Malaria Control and Elimination, the group supports the High Burden to High Impact initiative to implement subnational tailoring of interventions using local data to develop sound national strategic plans and optimized resource prioritization.

Our focus is malaria but we are able to model also other infectious disease such as neglected tropical diseases (NTDs).

We are also the co-funder of the Applied Malaria Modelling Network (AMMnet), a community of applied malaria modellers, data analysts, and partners with the common goal of improving the quality of analytical support offered to malaria programs.

Our work is mainly funded by the Clinton Health Access Initiative (CHAI), the Bill and Melinda Gate Foundation (BMGF), the President’s Malaria Initiative (PMI) and The Global Fund.

Have look at some of our projects in the below sections to learn more about our work. You can find here some of our open access tools.

High Burden to High Impact

Through the High Burden to High Impact (HBHI) response launched by the World Health Organization (WHO) and the Roll Back Malaria partnership (RBM), public health modelers have been invited by the Global Fund to Fight AIDS, Tuberculosis and Malaria (“the Global Fund”) to support the national malaria programmes on the understanding of the current malaria profile in priority countries by conducting intervention mix analyses and prioritization to inform countries’ stratification plans. The aim of the work is to inform the elaboration of country malaria strategic plans and provide insight to the coming Global Fund funding requests. Read more

Plasmodium vivax Transmission Model

Swiss TPH has developed a model for Plasmodium vivax transmission to support decision-making and advocacy in countries where this parasite is dominant. This compartmental model accounts specifically for the liver stages of P. vivax malaria and it has recently been refined to include imported infections as well as treatment of blood and/or liver stages parasites. Additional ongoing development of the model include the account for delay in treatment, the inclusion of additional interventions and extensions to the stochastic framework for estimating probabilities of elimination under different scenarios. Read more

Country modelling to Support National Malaria Control Programme in Benin

The Programme National de Lutte Contre le Paludisme (PNLP) of Benin is updating the National Strategic Plan (NSP) to achieve specific reductions in malaria burden over the coming decade, and specifically to reduce incidence to 25% relative to the current Business as Usual strategy by 2023. OpenMalaria, a malaria transmission simulation platform developed by Swiss TPH in 2006, is used to simulate the impact of interventions in Benin. The collection of information and data on the epidemiological situation of malaria in Benin has made it possible to estimate the parameters required for the model and to simulate the transmission dynamics of infection for each of Benin’s communes since 2005. Read more

Community Health Workers Geographical Placement in Haiti

The demand for geographic information systems (GIS) and related tools is increasing among public health decision-makers, as the utility is being recognized and both methodological innovations and computational capacity evolve. Simulation studies of optimal geographical placement scenarios for health services, including community health workers, have been developed to inform programmatic decisions related to the size of catchment areas and to identify where there are coverage gaps and/or priority areas for scale-up. In 2019-2020, the AIM group provided support to the Haitian strategic plan on community health by providing scenarios for optimized geographical community health worker placement, accounting for travel time, population density and health facility locations. Read more

{"locations":{"0":{"title":"Papua New Guinea","description":"<p><\/p>","latitude":"-6.2454379","longitude":"144.4646209","projects":{"0":{"uid":38,"title":"Evaluation of the Papua New Guinea National Malaria Control Program, 2015-2017","detailUrl":"https:\/\/www.swisstph.ch\/en\/projects\/project-detail\/project\/evaluation-of-the-papua-new-guinea-national-malaria-control-program-2015-2017"},"1":{"uid":273,"title":"Evaluation of the Papua New Guinea National Malaria Control Program, 2018-2020","detailUrl":"https:\/\/www.swisstph.ch\/en\/projects\/project-detail\/project\/evaluation-of-the-papua-new-guinea-national-malaria-control-program-2018-2020"},"2":{"uid":30,"title":"Understanding heterogeneities in the epidemiology of malaria in Papua New Guinea","detailUrl":"https:\/\/www.swisstph.ch\/en\/projects\/project-detail\/project\/understanding-heterogeneities-in-the-epidemiology-of-malaria-in-papua-new-guinea"},"3":{"uid":33,"title":"Understanding the human, parasite, vector and environmental interactions driving residual malaria transmission in Papua New Guinea","detailUrl":"https:\/\/www.swisstph.ch\/en\/projects\/project-detail\/project\/understanding-the-human-parasite-vector-and-environmental-interactions-driving-residual-malaria-transmission-in-papua-new-guinea"}}},"1":{"title":"Uganda","description":"<p><\/p>","latitude":"1.3733","longitude":"32.2903","projects":{}},"2":{"title":"Haiti","description":"<p><\/p>","latitude":"19.0558462","longitude":"-73.0513321","projects":{}},"3":{"title":"Ghana","description":"<p><\/p>","latitude":"7.9527706","longitude":"-1.0307118","projects":{}},"4":{"title":"Tanzania","description":"<p><\/p>","latitude":"-6.3728253","longitude":"34.8924826","projects":{}},"5":{"title":"Zanzibar","description":"<p><\/p>","latitude":"-6.09994","longitude":"39.32094","projects":{}},"6":{"title":"Mozambique","description":"<p><\/p>","latitude":"-18.6696553","longitude":"35.5273354","projects":{}},"7":{"title":"Laos","description":"<p><\/p>","latitude":"19.8563","longitude":"102.4955","projects":{}},"8":{"title":"Panama","description":"<p><\/p>","latitude":"8.4255193","longitude":"-80.1053645","projects":{}},"9":{"title":"Cameroon","description":"<p><\/p>","latitude":"7.3697","longitude":"12.3547","projects":{}},"10":{"title":"Benin","description":"<p><\/p>","latitude":"9.3077","longitude":"2.3158","projects":{}},"11":{"title":"Ethiopia","description":"<p><\/p>","latitude":"9.1450","longitude":"40.4897","projects":{}}}}
Selected projects at this location:

Champagne C et al. Using observed incidence to calibrate the transmission level of a mathematical model for Plasmodium vivax dynamics including case management and importation. Math Biosci. 2022;343:108750. DOI: 10.1016/j.mbs.2021.108750

Champagne C et al. Improving access to care and community health in Haiti with optimized community health worker placement. PLOS Glob Public Health. 2022;2(5):e0000167. DOI: 10.1371/journal.pgph.0000167

Owen B.N et al. Dynamical malaria modeling as a tool for bold policy-making. Nat Med. 2022;28(4):610-611. DOI: 10.1038/s41591-022-01756-9

Runge M et al. Sub-national tailoring of malaria interventions in Mainland Tanzania: simulation of the impact of strata-specific intervention combinations using modelling. Malar J. 2022;21:92. DOI: 10.1186/s12936-022-04099-5

Sanmartino M, Forsyth C.J, Avaria A, Velarde-Rodriguez M, Gomez i Prat J, Albajar-Viñas P. The multidimensional comprehension of Chagas disease. Contributions, approaches, challenges and opportunities from and beyond the information, education and communication field. Mem Inst Oswaldo Cruz. 2022;117:e200460. DOI: 10.1590/0074-02760200460