AI & Data science in public health

About this session

Key takeaways

• AI & Data science in public health
• It is important that African authorities and government build on the experience of Covid 19, the lessons learn and best practices to ensure a better care and more resilient healthcare systems, especially in the wake of pandemics
• COVID-19 has pushed a lot of countries to have more of an integrated, while decentralised, approach regarding diagnostic, surveillance & care not only for Covid-19 but for all diseases of a public health importance
• Diagnostics and health monitoring need to remain a top priority agenda for authorities as this supports advanced industrial health coverage, address health emergencies and promote healthier populations
• While African countries are in the midst of their epidemiological transition, communicable diseases remain important in some regions and epidemics are not to be neglected, therefore health authorities in coordination and cooperation with all stakeholders from all industries need to have a preparedness plan to make sure to be prepared and ready for any type of health crisis scenario as Covid has highlighted that while it was a public health emergency it affected all sectors.
• Covid- 19 highlighted the weak diagnostic infrastructures, laboratories capacities and human resources capacities for testing on the continent and pushed governments to invest in them quickly to be able to answer to the health emergency of covid-19 and its best management