Despite great strides in medication, sanitation, hygiene and in animal and pest control, infectious diseases remain an enormous threat to human and animal health, as COVID-19 demonstrated to the world.
Deeper dive
How these infectious diseases spread and become epidemics depends on a range of interconnected dynamics of pathogens, people, and animals. Some microbes are transmitted between people, or between people and animals; some circulate among multiple hosts before they are transmitted, and others must be carried in an insect vector before spreading. Many factors including, increasing antibiotic resistance, human connectivity and behavior, population growth, climate change, land-use change, farming, urbanization, and global travel, also impact the emergence and spread of infectious disease, as well as pose challenges for prevention and control.
Given these huge complexities, scientists have increasingly turned to mathematical models to understand epidemiological patterns to target public health decisions. These models hark back to Isaac Newton, who had the fundamental insight that there are unchanging universal laws that govern the actions of natural phenomena. The hope is that with more data and more computing power, the most complex outcomes can be predicted.
In the past 20 years, growing computing power and infectious surveillance has enabled scientists to gather more data for developing these models. Researchers are collecting volumes of data from epidemiology, evolutionary biology, immunology, sociology, climate, and public health resources to develop models that mimic how infections might evolve and spread.
The challenge with modeling infectious disease, however, is that pathogens, the environment, the rate of contagiousness, the rate of transmission, the availability of vaccines, and the climate are ever changing. But most models rely on data from past events to predict the future.
In 2021, the Centers for Disease Control and Prevention launched the Center for Forecasting and Outbreak Analysis to make use of data modeling to forecast and respond to future public health outbreaks.