Disaster Survival Game?

By having the students learn about these topics by playing Plague Inc. they almost forget that they are learning about them because they are an important part of their curriculum, not just a way to beat the game. After playing the game, most students self-reported a greater understanding of microbial evolution, epidemiology and pathogenesis than they had before. My anecdotal evidence, based on their ability to discuss these topics after playing, supports this.

Many of my students continue to play outside of class and have recruited their friends to play as well. But most importantly - as a teacher it is exciting to use a tool that generates so much excitement and interest amongst my students.

James Vaughan, founder of Ndemic Creations, spoke to CDC staff on March 20, 2013 about his new mobile app, Plague Inc. Within the game, players select a pathogen and strategize how to evolve symptoms, transmit the disease, and counter actions taken by world governments and scientists. With a successful disease, players can watch as governments fall and humanity is wiped out.

I became interested in Vaughan’s game as a tool to teach the public about outbreaks and disease transmission because of how it uses a non-traditional route to raise public awareness on epidemiology, disease transmission, and diseases/pandemic information. The game creates a compelling world that engages the public on serious public health topics.

Plague Inc. has been downloaded over 10 million times worldwide and over 200 million games have been played to date. As an intelligent and sophisticated strategy game, I think Plague Inc. appeals to people looking for something more meaningful and substantial than the majority of mobile games. It makes people think about infectious disease in a new light – helping them realize the threats that we face every day.

An interesting fact is that it has also become an educational tool – teachers and professors often get in touch to let me know how they used Plague Inc. to illustrate biological and economical concepts to their students.

In the 2011 film “Contagion” (Warner Bros. Pictures), a new and unknown disease spreads like wildfire, starting in Hong Kong and eventually decimating the world’s population before a vaccine can be developed and disseminated. The film focuses on the U.S. Centers for Disease Control and Prevention’s (CDC) efforts to track individuals exposed to the disease and to determine the best mitigation strategies to implement to prevent further disease spread. “Contagion” has received praise from the CDC for being much more realistic than similarly-themed movies, but like most Hollywood tales, Contagion takes some liberties, notably with the CDC’s ease and accuracy in predicting disease spread.

Intuitively, predicting the spread of a disease is the first, and arguably most important, step in designing effective and efficient mitigation strategies. For many years, the most common form of disease spread model was a homogeneous mixing model, which is based on the premise that all members of a population can be treated identically, and everyone is equally likely to come into contact with anyone else. Each infected person in the population will infect the same number of people; this number is called the basic reproduction number (R ), and it is a simple function of a person’s number of contacts, probability of disease transmission per contact and duration of contagious period.

In the “Contagion” film, scientists at one point observed that the disease mutated, and then calculated an R increase from 2 to 4. Unfortunately for the CDC, R as of now can only be calculated retrospectively; further, R is known to be different in rural vs. urban settings, where individuals’ behaviors and contacts are different, so even if R was known in densely populated Hong Kong, that may not be useful for Minneapolis.

Agent-based simulation models (where people are agents) have recently become popular tools to address population heterogeneities in predicting pandemic disease spread. Simulations are especially adept at handling the uncertainties inherently present in modeling disease transmission, individual behaviors and public health interventions.