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Infectious diseases are still deadly in developing countries; in developed ones, epidemics thought to be under control are resurging. These great challenges call for new approaches. Can we use the deluge of data on human movements and behavior to prevent epidemics, and inform policymaking? Can mathematics tell if an epidemic could be prevented by voluntary vaccination?
Can big data predict the next epidemic?
Epidemics of infectious diseases pose a grave burden on human health, and hinder development. Luckily, as we face new challenges, new tools become available in our ongoing fight against them. Computer models of disease spread, informed by the increasingly available data on human movements and behavior, are one of them. What happens when a new pathogen appears? Will it go extinct on its own? Will it cause a widespread epidemic? What is the best way to intervene? New answers to these questions are coming up, based on mathematical modeling, and informed by real-world data.
Can you prevent an epidemic by getting vaccinated?
When facing an ongoing epidemic, you get to decide whether or not to adopt a prevention method against the infection: “Do I want to vaccinate my kid against measles?” “What’s the worst that could happen if I don’t?” “Measles is just a rash, right?”. One way to make that decision is by weighing all the pros and cons, and choosing what you believe you benefit the most from. Mathematical tools may be used to understand the impact of every decision on the course of the epidemic, at the population level. Could the voluntary use of prevention prevent an epidemic?