We combined machine learning and epidemiological modelling to help public authorities manage critical healthcare resources, deliver targeted interventions and track disease spread within communities.
Covid’s impact has been enough to ravage healthcare systems all over the world. In densely populated areas with limited health resources and long lead times for meeting the demands of increased case numbers, there was a need to efficiently test at-risk patients, quarantine infected people and treat those with severe symptoms.
To reduce deaths, ensuring adequate capacity – such as ventilator-equipped hospital beds and oxygen supplies – and availability of critical healthcare infrastructure is critical. Local authorities had to adopt a proactive stance so that the disease would not overwhelm public health facilities. There was a need for forecasting reported infections at a local level to inform capacity planning, model the effects of policy changes and prepare for potential scenarios.
We are grateful that our work has been supported by donors such as USAID, Bill & Melinda Gates Foundation, and Fondation Botnar.