Events Calendar

This presentation is in French, but participants can enable the CAPTION feature on Zoom to receive real-time translation into the language of choice. This presentation analyzes the impact of the introduction of the malaria vaccine (MV) on the incidence of severe malaria among children under five in three health districts of Cameroon (Edea, Japoma, and Mbanga) between 2020 and 2024. Based on a retrospective analysis of surveillance data, it highlights a strong negative correlation (r = -0.86) between third-dose vaccine coverage and trends in severe cases, suggesting that higher complete vaccination coverage is associated with a lower incidence of severe malaria.

Join us for the next AMMnet Hackathon, a two-hour, hands-on learning session where you will learn how to build, simulate, and infer epidemiological models using Python, Google Colab, and ChatGPT. No prior experience with epidemiological modeling is required. All experience levels are welcome! By the end of this hackathon, you will be able to: -Understand the SIR (Susceptible–Infected–Recovered) epidemic model -Simulate disease outbreaks using ordinary differential equations (ODEs) -Estimate key epidemiological parameters such as: Transmission rate (β), Recovery rate (γ) & Basic reproduction number (R₀) -Fit models to real outbreak data using: Least squares, Poisson likelihood -Use ChatGPT effectively for: Writing scientific Python code, Debugging models, Explaining results & Generating clean reports All work is done in Google Colab — no local installation required.

The March 3rd AMMnet seminar was given by Dr. Michael C. Wimberly, who presented on "Incorporating Environmental Determinants of Malaria into Predictive Models for Decision Support." His seminar described the development and implementation of the EPIDEMIA (Epidemic Prognosis Incorporating Disease and Environmental Monitoring for Integrated Assessment) system in Ethiopia. Building on research into environmental modeling of malaria across different regions of Ethiopia, the EPIDEMIA system uses a range of data sources, including temperature, precipitation, vegetation indices, and epidemiological surveillance data, to forecast whether malaria epidemics are likely to occur across Ethiopia's districts. The system aims to address the resurgence of malaria by providing public health officials with both early detection of an epidemic (based on observed cases exceeding a threshold) and early warning (based on forecasted values exceeding a threshold). EPIDEMIA automatically generates weekly reports to help Ethiopia's public health sector target interventions and allocate resources. Moving forward, the project is evolving into EPIDEMIA 2.0 with support from NASA, focusing on containerized software, dynamic dashboards, and the inclusion of other climate-sensitive diseases.

This webinar is available in French only. The World Malaria Report 2025 highlights that, despite significant progress that has prevented 2.3 billion cases and 14 million deaths between 2000 and 2024, the fight against the disease faces major challenges. In 2024, there was an increase in cases to 282 million and 610,000 deaths, mainly in Africa, far from the targets set by the WHO. The main threats include growing resistance to drugs and insecticides, a funding gap of £5.4 billion, and weakened health systems. However, concrete measures are in place, such as improved case management, the deployment of new-generation mosquito nets, and the introduction of two promising vaccines (RTS,S and R21) that have already reached millions of children. The report concludes that renewed political commitment, sustainable funding, and community mobilisation are needed to overcome these obstacles and restart progress towards malaria elimination.