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Malaria

CRID malaria model

Brief description of model:

This model is currently in development. Gaussian process-based models (with a negative binomial distribution) or Fourrier functions shall be used to characterise changes in mosquito abundance over time. Using datasets and statistical methods, we shall generate parameter sets that can be used to explore the impact of climate change in Cameroon. More precisely, we shall use CHIRPS data (Climate Hazards Group Infrared Precipitation with Station data) to explore how well rainfall can explain the variability in the entomological parameters on different sites in Cameroon.  This will allow better parameterisation of a mechanistic mathematical model of malaria transmission. These results shall be combined with machine learning methods (such as random forest) to explore spatial and temporal drivers in the number of the different mosquito species.

Environmental monitoring stations which collect hourly temperature and humidity information (as a minimum) will be placed within experimental hut structures to explore how local weather station information and ERA5 data compare to conditions experienced by mosquitoes. Model parameterisation will be compared to those generated by the wider VIMC-Wellcome project. The work will provide a high-quality entomological dataset which can be used to support VIMC-Wellcome model predictions.

Link to publication(s):

C. Whittaker et al., A novel statistical framework for exploring the population dynamics and seasonality of mosquito populations. Proc. Biol. Sci. 289, 20220089 (2022).

Link to publicly available code (where available):

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UAC-LABEF malaria model

Lead modeller:  Romain Glèlè Kakaï

Link to all modelling group members

Institution(s): Universite D'Abomey-Calavi (UAC) and Mountain Top University

Brief description of model:

An age-structure mathematical stochastic model is developed to analyze the impacts of vaccination on malaria transmission and burden in endemic countries. The model stratifies the human population into three subgroups: vaccinated with first three doses (age cohort 0), vaccinated with a booster dose (age cohort 1) and unvaccinated (age cohorts 2 to 100). Each subgroup is split into six compartments: Susceptible, Exposed, Asymptomatic, Uncomplicated malaria, Severe malaria, Hospitalized and Recovered. The model incorporates other interventions, such as the use of Long-lasting Insecticide-treated bed Nets (LLINs) and access to treatment for uncomplicated and severe malaria. Two malaria vaccines are considered: RTS,S/AS01E and R21. The model’s parameters are estimated using a nonlinear least squares method with the built-in function fminsearchbnd of Matlab2023a. The model is driven by the Entomological Inoculation Rate (EIR) and first estimates the initial vaccine efficacy and the decay rate before predicting the mean number of malaria cases, deaths, number of years of life lost YLL), and disability-adjusted life-years (DALYs) in each human age cohort for several years under various vaccination scenarios.

Graph_UAC_LABEF_Model.png

Key publication(s)

 

Model code (where available):

https://github.com/ProfGLELE/Malaria-model-code-.git

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Telethon Kids Institute (TKI) malaria model

Lead modeller:  Melissa Penny

Link to all modelling group members

Institution(s): Telethon Kids Institute (TKI)

Brief description of model:

OpenMalaria is a stochastic, individual-based, single location simulation model of malaria in humans (Reiker et al., 2021; Smith et al., 2008) linked to a deterministic model of malaria in mosquitoes (Chitnis et al., 2012). The simulation model includes sub-models of infection in humans, blood-stage parasite densities, infectiousness to mosquitoes as a lagged function of asexual parasite density, incidence of morbidity including severe malaria and hospitalization, and mortality. Pre-erythrocytic and blood stage immunity comprise separate sub-models, with blood-stage immunity predominating as infection-blocking immunity which occurs only in those with very high cumulative exposure. An ensemble of 14 model variants with varying assumptions is available (Smith et al., 2012). These models include different assumptions for decay of natural immunity, greater within host variability between infection and entomological exposure, heterogeneity in transmission and heterogeneity in susceptibility to co-morbidities. Our model predictions for the Vaccine Impact Modelling Consortium (VIMC) predominantly utilize the base mode and transmission is modelled through periodically varying vectorial capacity driven by the entomological inoculation rate (EIR). The model also accounts for genetic diversity, enabling the study of drug resistance, vaccine insensitivity, and vector susceptibility (Masserey et al., 2022). By integrating clinical, epidemiological and entomological data, OpenMalaria facilitates the evaluation of existing and new malaria interventions by predicting their potential public health impact and providing insights on intervention mixes.

OpenMalaria was initially developed in 2003-2006 to simulate the population-level impact of the RTS,S malaria vaccine using Phase 2 clinical trial data. Since 2006, OpenMalaria has been an open-source model with ongoing development to address various questions, including supporting GAVI, the vaccine alliance investment strategies and the World Health Organization (WHO) policy recommendations of RTS,S from 2013 to 2023, and more recently to support R21 analysis (Penny et al., 2015, 2016). The modelling supporting VIMC is led by the Intervention and Infectious Disease Modelling team. OpenMalaria model is developed by Telethon Kids Institute and Swiss Tropical and Public Health Institute.

Malaria Atlas Project (MAP) - In addition to the OpenMalaria modelling team at Telethon Kids Institute, the Malaria Atlas Project (MAP) group supports and collaborates with the VIMC malaria modelling teams. MAP are a World Health Organization Collaborating Centre in Geospatial Disease Modelling and work with the Bill and Melinda Gates Foundation, The Global Fund, USAID President’s Malaria Initiative, research institutions and National Malaria Programs. MAP’s research outputs directly inform malaria policies and program implementation in about 20 Sub-Saharan Africa countries across six thematic areas of risk mapping, burden estimation, interventions tracking, commodities planning, impact evaluation and skills strengthening (Bertozzi-Villa et al., 2021; Bhatt et al., 2015; Weiss et al., 2019). With more than 40 integrated projects, examples include: (i) providing estimation of malaria disease burden and interventions coverage to the annual WHO World Malaria Report (ii)  developing bespoke analytical frameworks addressing country-specific malaria research questions including sub-national tailoring (risk stratification and intervention targeting) as part of the High Burden to High Impact (HBHI) country-led approach and other projects, (iii) malaria commodities forecasting and case management dashboards, (iv) providing district level prevalence estimates for use by countries for targeting areas to deploy or scale-up malaria vaccines according to the WHO-GAVI malaria vaccine allocation framework. Since August 2023, MAP has also commenced operations from the Ifakara Health Institute in Dar es Salam, Tanzania as part of our decentralization vision.

Key publication(s):


Masserey T, Lee T, Golumbeanu M, Shattock AJ, Kelly SL, Hastings IM, Penny MA. The influence of biological, epidemiological, and treatment factors on the establishment and spread of drug-resistant Plasmodium falciparum. Elife. 2022

Reiker T, Golumbeanu M, Shattock AJ, Burgert L, Smith TA, Filippi S, Cameron E, Penny MA. Emulator-based Bayesian optimization for efficient multi-objective calibration of an individual-based model of malaria. Nature communications. 2021

Penny, M. A., Verity, R., Bever, C. A., Sauboin, C., Galactionova, K., Flasche, S., White, M. T., Wenger, E. A., Van de Velde, N., Pemberton-Ross, P., Griffin, J. T., Smith, T. A., Eckhoff, P. A., Muhib, F., Jit, M., & Ghani, A. C. (2016). Public health impact and cost-effectiveness of the RTS,S/AS01 malaria vaccine: A systematic comparison of predictions from four mathematical models. Lancet (London, England), 387(10016), Article 10016.

Penny, M. A., Pemberton-Ross, P., & Smith, T. A. (2015). The time-course of protection of the RTS,S vaccine against malaria infections and clinical disease. Malaria Journal, 14(1), Article 1.

Smith, T., Ross, A., Maire, N., Chitnis, N., Studer, A., Hardy, D., Brooks, A., Penny, M., & Tanner, M. (2012). Ensemble Modeling of the Likely Public Health Impact of a Pre-Erythrocytic Malaria Vaccine. PLOS Medicine, 9(1), Article 1.

Chitnis, N., Hardy, D., & Smith, T. (2012). A Periodically-Forced Mathematical Model for the Seasonal Dynamics of Malaria in Mosquitoes. Bulletin of Mathematical Biology, 74(5), 1098–1124.

Smith, T., Maire, N., Ross, A., Penny, M., Chitnis, N., Schapira, A., Studer, A., Genton, B., Lengeler, C., Tediosi, F., Savigny, D. D., & Tanner, M. (2008). Towards a comprehensive simulation model of malaria epidemiology and control. Parasitology, 135(13), 1507–1516.

Malaria Atlas Project (MAP)

Bertozzi-Villa, A., Bever, C. A., Koenker, H., Weiss, D. J., Vargas-Ruiz, C., Nandi, A. K., Gibson, H. S., Harris, J., Battle, K. E., Rumisha, S. F., Keddie, S., Amratia, P., Arambepola, R., Cameron, E., Chestnutt, E. G., Collins, E. L., Millar, J., Mishra, S., Rozier, J., … Bhatt, S. (2021). Maps and metrics of insecticide-treated net access, use, and nets-per-capita in Africa from 2000-2020. Nature Communications, 12(1), 3589.

Weiss, D. J., Lucas, T. C. D., Nguyen, M., Nandi, A. K., Bisanzio, D., Battle, K. E., Cameron, E., Twohig, K. A., Pfeffer, D. A., Rozier, J. A., Gibson, H. S., Rao, P. C., Casey, D., Bertozzi-Villa, A., Collins, E. L., Dalrymple, U., Gray, N., Harris, J. R., Howes, R. E., … Gething, P. W. (2019). Mapping the global prevalence, incidence, and mortality of Plasmodium falciparum, 2000-17: A spatial and temporal modelling study. Lancet (London, England), 394(10195), 322–331.

Bhatt, S., Weiss, D. J., Cameron, E., Bisanzio, D., Mappin, B., Dalrymple, U., Battle, K. E., Moyes, C. L., Henry, A., Eckhoff, P. A., Wenger, E. A., Briët, O., Penny, M. A., Smith, T. A., Bennett, A., Yukich, J., Eisele, T. P., Griffin, J. T., Fergus, C. A., … Gething, P. W. (2015). The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature, 526(7572), 207–211.

Model code (where available):

https://github.com/SwissTPH/openmalaria

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Imperial malaria model

Brief description of model:

To be updated.

Key publication(s)

 

Model code (where available):

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