S. Jane Flint - Principles of Virology, Volume 2

An epidemiologist investigates outbreaks by undertaking careful data collection in the field (that is, where the infections occur) and performing statistical analyses. Questions often asked include “How might the symptoms observed in an infected individual implicate one mode of viral transmission over another?” or “Can a timeline be established to trace back the origins of an epidemic to a single event?” The goal is to learn more about the pathogen and how it caused the epidemic. Individual differences among prospective hosts, group dynamics and behaviors, geography, and climate all influence how efficiently a virus can establish infection within a population. Epidemiologists lack the luxury of performing controlled experiments, in which only one variable is manipulated. Instead, they must consider many parameters simultaneously to identify the source and transmission potential of a viral pathogen within a host community. Many of these variables are captured in various video games and mobile phone apps that simulate outbreaks ( Box 1.4). In the next section, we identify some crucial terms and concepts used in this field. (For commentary and a personal account related to the topic, see the interview with Dr. Thomas London: http://bit.ly/Virology_London.)

Determining the number of infected individuals in a population is a primary goal of epidemiological studies. This information is required to establish both the incidenceand the prevalenceof infection. Incidence is defined as the number of new cases within a population in a specified period. Some epidemiologists use this term to determine the number of new cases in a community during a particular period, while others use incidence to indicate the number of new disease cases per unit of population per period. For example, the incidence of influenza can be stated as the number of reported cases in New York City per year or the number of new cases/1,000 people/year. Disease prevalence, on the other hand, is a measure of the number of infected individuals at one moment in time divided by an appropriate measure of the population. A highly infectious and lethal disease (such as the 1793 epidemic of yellow fever in Philadelphia) may have a high incidence but a low prevalence, because many of the infected individuals either died or cleared the infection. In contrast, a virus that can persist in a host for decades is likely to have high prevalence. An example of high prevalence is provided by hepatitis B virus; of the 300 million to 400 million people infected globally, one-third live in China, with 130 million carriers. For this reason, incidence is an in formative measure for acute or highly lethal infections, whereas prevalence is often used to describe long-lasting or persistent infections.

The hugely popular online video game World of Warcraft became a model for the transmission of viral infections. In 2005, a dungeon was added to the fantasy world in which players could confront a powerful creature called Hakkar. In his death throes, Hakkar hit foes with “corrupted blood,” infected with a virus that killed the virtual player. The infection was meant to affect only those in the immediate vicinity of Hakkar’s corpse, but the virus spread as players and their virtual pets traveled to other cities in the game. Within hours after the software update, a full-blown virtual epidemic ensued as millions of characters became infected.

Although such games are meant only for entertainment, they do model disease spread in a mostly realistic manner. For example, as in real life, the spread of the virus in Hakkar’s blood depended on the ease of travel within the game, zoonotic transmission by pets, and transmission via asymptomatic carriers. Moreover, such games have a large number of participants, at one point more than 10 million for World of Warcraft , creating an excellent community for experimental study of infectious diseases. The players’ responses to dangerous situations approximated real-world reactions. For example, during the “corrupted-blood” epidemic, players with healing ability were the first to rush to the aid of infected players. This action probably affected the dynamics of the epidemic because infected players survived longer and were able to travel and spread the infection. A more reality-based smart phone app called Plague Inc ., downloaded more than 85 million times, asks: “Can you infect the world?” and gives players the opportunity to choose a pathogen and influence its evolution. Players compete against the clock, trying to destroy humanity before the world can develop a cure.

Scientists themselves recognize the educational value of such games. A professor at Drexel University developed CD4 Hunter , in which players enter the bloodstream as a human immunodeficiency virus type 1 particle. The goal is to find and infect CD4 +T cells, white blood cells of the adaptive immune system that are the main targets in this infection. The game mimics virus binding and entry, and was created as a supplementary teaching tool for graduate students and undergraduates in advanced-level courses ( http://bit.ly/Virology_Twiv489).

With all of these games, successful players learn to integrate multiple variables simultaneously, including environment, time, and population density. These applications also demonstrate how the reproductive cycle of a virus may change over the course of an epidemic. However, the parallels to real-world epidemiology end there; a defeated player can begin again with the click of a button or the flick of a finger. Alas, real life does not come with “do-overs.”

Lofgren ET, Fefferman NH. 2007. The untapped potential of virtual game worlds to shed light on real world epidemics. Lancet Infect Dis 7:625–629.

Although infections of natural populations differ from those under controlled conditions in the laboratory, it is possible to determine if one or more variables affect disease incidence and viral transmission in nature. Two general experimental approaches are used: prospective(also called cohort or longitudinal) and retrospective(or case-controlled) studies. In prospective studies, a population is randomly divided into two groups (cohorts). One group then gets the “treatment of interest,” such as a vaccine or a drug, and the other does not. The negative-control population often receives a placebo. Whether a person belongs to the treatment or placebo cohort is not known to either the recipient or the investigator until the data are collected and the code is broken (“ double blind”). This strategy removes potential investigator bias and patient expectations that may otherwise influence data collection. Prospective studies require a large number of subjects, who often are followed for months or years. The number of subjects and time required depend on the incidence of the disease or side effect under consideration and the statistical power, the probability of detecting a difference that is sufficiently significant to draw conclusions.

The terminology used to calculate the number of people who are infected and/or who become ill following a viral outbreak can be confusing. The following fictional example will be used to clarify these definitions.