Group | Analytics and Intervention Modelling

The research group Analytics and Intervention Modelling (AIM) contributes to build a data-driven and evidence-based process for decision-making and advocacy by the National Malaria Control Programmes in countries using ad hoc infectious disease transmission models, health economics and geospatial tools. Our focus is malaria but we are able to model also other infectious disease such as neglected tropical diseases (NTDs).

The group is formed by experts in disease modelling, epidemiology, statistics, computer science, health economics and project management.

Thanks to its strong global network, including the Clinton Health Access Initiative (CHAI), the Bill and Melinda Gate Foundation (BMGF), the President’s Malaria Initiative (PMI) and Malaria Modelling Consortium (MMC), the group can secure support from local collaborators on the ground.

Specificity

The group develops and applies disease transmission models in countries with different transmission intensities, vectors and agents such as different species of anopheles mosquitoes and plasmodium parasites (Plasmodium falciparum and P. vivax). As transmission decreases, heterogeneity increases such that applying the same strategy across a country becomes inefficient. The group is specialised in assessing the most efficient stratification of interventions to reduce transmission and burden. We design and analyse field studies to ensure that our models relate to real-world situations.

Country Modelling

Thanks to a detailed understanding of the epidemiological profile, we are able to model  malaria dynamics in different countries. The geographical unit considered in the model 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. The models can serve to simulate the impact of national strategic plans on malaria burden and risk, identify the most impactful intervention stratification strategies, and provide guidance regarding intervention prioritisation or targeting.

Cost effectiveness analysis

Thanks to health economics tools developed in-house, cost data can be attached 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.

Geospatial

The group provides geospatial analyses and modelling to determine operational units for the implementation of malaria interventions in various countries. Geospatial analyses were conducted to simulate the optimal placement of community health workers and inform programmatic decisions related to catchment areas while accounting for pre-defined operational constraints. These types of analyses also helps to identify coverage gaps and priority areas for scale-up according to national objectives for universal coverage or burden reduction.

Capacity Building

The modelling group contributes to in-country/regional capacity building on the application of epidemiological impact models and costing tools. We strongly believe that this is essential to ensure long-term sustainability of health service projects, transfer knowledge and support decolonization of global health.

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

Building on previous literature (White et al. 2016), 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 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 - 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. Read more

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Selected projects at this location:

Cameron E et al. Mapping the endemicity and seasonality of clinical malaria for intervention targeting in Haiti using routine case data. Elife. 2021;10:e62122. DOI: 10.7554/eLife.62122

Cazelles B, Champagne C, Nguyen-Van-Yen B, Comiskey C, Vergu E, Roche B. A mechanistic and data-driven reconstruction of the time-varying reproduction number: application to the COVID-19 epidemic. PLoS Comput Biol. 2021;17(7):e1009211. DOI: 10.1371/journal.pcbi.1009211

Cazelles B, Comiskey C, Nguyen-Van-Yen B, Champagne C, Roche B. Parallel trends in the transmission of SARS-CoV-2 and retail/recreation and public transport mobility during non-lockdown periods. Int J Infect Dis. 2021;104:693-695. DOI: 10.1016/j.ijid.2021.01.067

Denz A et al. Predicting the impact of outdoor vector control interventions on malaria transmission intensity from semi-field studies. Parasit Vectors. 2021;14:64. DOI: 10.1186/s13071-020-04560-x

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. 2021;116:e200460. DOI: 10.1590/0074-02760200460