Abstract We report the results of a study we conducted using a simple multiplayer online game that simulates the spread of an infectious disease through a population composed of the players. We use our virtual epidemics game to examine how people respond to epidemics. The analysis shows that people's behavior is responsive to the cost of self-protection, the reported prevalence of disease, and their experiences earlier in the epidemic. Specifically, decreasing the cost of self-protection increases the rate of safe behavior. Higher reported prevalence also raises the likelihood that individuals would engage in self-protection, where the magnitude of this effect depends on how much time has elapsed in the epidemic. Individuals' experiences in terms of how often an infection was acquired when they did not engage in self-protection are another factor that determines whether they will invest in preventive measures later on. All else being equal, individuals who were infected at a higher rate are more likely to engage in self-protective behavior compared to those with a lower rate of infection. Lastly, fixing everything else, people's willingness to engage in safe behavior waxes or wanes over time, depending on the severity of an epidemic: when prevalence is high, people are more likely to adopt self-protective measures as time goes by; when prevalence is low, a ‘self-protection fatigue’ effect sets in whereby individuals are less willing to engage in safe behavior over time.

Citation: Chen F, Griffith A, Cottrell A, Wong Y-L (2013) Behavioral Responses to Epidemics in an Online Experiment: Using Virtual Diseases to Study Human Behavior. PLoS ONE 8(1): e52814. https://doi.org/10.1371/journal.pone.0052814 Editor: Michelle Louise Gatton, The Queensland Institute of Medical Research, Australia Received: August 16, 2012; Accepted: November 21, 2012; Published: January 9, 2013 Copyright: © 2013 Chen et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was funded by Wake Forest University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.

Introduction Thanks to globalization, urbanization, and air travel, infectious diseases can spread faster, wider, and to more people today than ever before [1]. Moreover, diseases such as HIV/AIDS and TB continue to exert a significant burden on the world population, especially the people living in developing countries [2], [3]. Understanding how—and to what extent—people would change their behavior in response to an epidemic is critical for formulating public health policies to control the spread of infectious diseases. This is so since behavioral changes, even when there are no treatments or vaccines available, can be highly effective at slowing or even stopping disease transmission. For instance, a shift in behavior towards safer sexual practices lowered the prevalence of HIV/AIDS considerably in some developing countries [4]–[7]. It has also been argued that behavioral responses, such as wearing of mask, more frequent washing of hands, and avoiding crowded places, of people to the 2003 SARS outbreak contributed significantly to the containment of that epidemic [8]. There are several factors that appear to affect individuals' incentives to adopt precautionary measures in response to an epidemic. Surveys conducted during the 2009 H1N1 pandemic suggest that people's willingness to engage in self-protective behavior depends positively on, among other variables: the level of anxiety regarding the disease, the perceived risk of the disease, the perceived efficacy of the self-protective measures, and household size [9]–[13]. From the perspective of formulating public health policy, it is important to know how changes in individuals' incentive structures affect their level of self-protective behavior. Public policies, such as subsidizing vaccines, recommending social distancing, or providing updates on the severity of an epidemic, in general alter people's decision environments, which in turn can lead to behavior modifications. It is thus crucial for determining the optimal way to contain the spread of infectious diseases that we have a full grasp of how people make decisions regarding self-protective behavior during epidemics. However, data on how people respond to changes in their incentive structure, such as those resulting from government policies, are not always readily available in the context of infectious disease epidemiology. When an epidemic strikes, it is often simply not feasible to perform controlled experiments that allow us to compare the effects of different policies on a population. This is why epidemiologists rely heavily on the use of mathematical models to predict the effects of various policies and control measures [14]. Of course, the predictions of these models depend critically on the assumptions that the modeler makes about the behavior of people. When forecasting, for instance, the severity of a flu epidemic, the modeler may need to make some assumptions about the fraction of the population that would choose to get a flu shot. When looking at how many people would become infected with a sexually transmitted disease (STD) if certain policies were implemented, the epidemiologist may have to specify, among other things, how many sexual partners people typically have. Thus, unless the models employ the right behavioral specification, their predictions regarding the course of an epidemic or the impact of public policies can be off target. Some recent advances in the theoretical modeling of infectious disease transmission have incorporated the analytical tools from the fields of economics and game theory to capture more realistically the complex interplay between human behavior and the spread of infectious diseases [15]–[21]. Many of the results and implications derived from these models, however, are ultimately dependent on the assumptions that are made about how people arrive at their decisions when faced with choice problems. In order to better understand people's willingness to engage in self-protective behavior during an epidemic, we created a simple dynamic multiplayer online game that simulates the spread of an infectious disease through a population composed of the players. In every round of the game, healthy players have the option to choose—at a cost—a protective action that reduces the likelihood of getting infected. Players receive points based on their simulated health status and how often they choose the self-protective action. The use of our multiplayer virtual epidemics game to study how people respond to an infectious disease offers many advantages, including the ability to alter the players' decision environments and the epidemiological properties of the disease such as its transmission probability and the duration of infection. It also allows us to obtain a wealth of data on the players' behavior by tracking their decisions and the outcomes of their decisions throughout the course of the game. Our virtual epidemics game is similar in spirit to the ‘havatar’—human-avatar pairing—framework that others have proposed recently to study the spread of HIV/AIDS [22]. Methodologically, our study follows a growing trend in economics of utilizing controlled experiments to study economic decision-making (see [23] and [24] for an overview of some major results from the field of experimental economics). To the best of our knowledge, our work is the first virtual experimental study in economic epidemiology. Here, we report the results of a study we conducted using our epidemics game where the disease has the property that infected individuals recover after a certain amount of time and are once again fully susceptible. This general assumption is appropriate for some real world diseases, in particular STDs such as chlamydia and gonorrhea. We look at the effects of various factors such as prevalence, cost of the protective action, and individuals' infection history to determine what drives people's decision to adopt self-protective measures during an epidemic.

Materials and Methods The version of the virtual epidemics game that we used in our study is based on a susceptible-infected-susceptible (SIS) model of infectious disease transmission. We give a general overview of the game in this section; a more detailed description of the game setup is provided in Supporting Information S1. The game has T time periods or rounds, and every player is either ‘healthy’ or ‘infected’ in any round. Infection lasts for τ<T rounds, after which a player becomes healthy. In any round, a healthy player can choose a safe action (self-protect) or a risky action (not self-protect) by clicking on the appropriate button on the computer screen. The safe action guarantees that the player will remain healthy in the following (one) round. As an example, in the context of STDs, the safe action can be thought of as using condoms, which can be highly effective in preventing the spread of disease [25]. If the player chooses the risky action, then the player becomes infected in the next round with a probability that is proportional to the fraction of players that are infected; specifically, the probability that a healthy player in round t will get infected in round t+1 without taking the safe action is (1)where β is the transmission probability, I t is the number of infected players in round t, and N t is the total number of players in round t. Infected players have no decisions to make. We note that, since players in our study have the option to discontinue participation at any time, the total number of players may not be constant throughout the game. In every round (except for the first one) before the healthy players make their choices, all the players are told the disease prevalence—the fraction of players that are infected—in the previous round, i.e., at the beginning of round t>1, the players are informed of I t −1 /N t −1 . Throughout the play of the game, all the players are told the values of τ and T, as well as what the current round is; additionally, in every round, healthy players are informed that the probability of getting infected in the following round without taking the safe action is determined according to rule (1). Players earn points in every round based on their health status and the action that is chosen. Players receive more points for being healthy than for being infected. We assume that self-protection is a costly activity, so that healthy subjects who choose the safe action receive fewer points than healthy subjects who choose not to self-protect. Specifically, in any round, a healthy player who chooses the risky action receives P H points; a healthy player who chooses to self-protect receives P H −C points; and an infected player earns P I points, where P H >P H −C≥P I >0. We chose the following parameter values for the present study: T = 45, τ = 4, β = 0.8, P H = 0.6, P I = 0.1. For the cost parameter C, we chose two different values: 0.35 and 0.45. These choices were made partly for practical considerations (see the Discussion and Conclusion section for an explanation). Participants for our study were recruited using online classified advertisements. The first page of the website set up for our study is an informed consent page. After clicking on a button on the computer screen consenting to participate in the study and after signing up, the subjects were randomly assigned to the low cost condition (C = 0.35) and the high cost condition (C = 0.45). Three players in each condition were randomly selected to be infected in round 1 (with duration of infection equal to τ = 4), while the rest started the game in the healthy state. We note that, while all the players were told when they signed up for the study that three of them would be randomly picked to be infected to start the game, they were not reminded of this fact at the start of round 1. For participating in the study, players received an electronic Amazon.com gift card with a value equal to the total number of points that the players earned in the game. At the end of the game, all players were invited via e-mail to complete a brief questionnaire asking for basic demographic information. The players were given an additional $3 in gift card value for completing the questionnaire. The protocol for the study was approved by Wake Forest University's Institutional Review Board. For each condition, we had 53 players at the start of the game. Because the length of time for each round is fixed, if in any round a healthy player did not submit a choice by the time the round ended, a default choice of the risky action was assigned to the player. All players were informed at the beginning of every round the duration of each round and that the computer program would automatically select the default action—the risky action—for healthy players if they do not enter a choice by the end of any round. (See Figures S1 and S2 for sample screenshots of the game webpage.)

Discussion and Conclusion Here, we have reported on the results of an online experiment that we conducted to examine the incentives that people have for investing in self-protective action during epidemics. The analysis shows that people's behavior is responsive to the cost of self-protection, the reported prevalence of disease, and their experiences earlier in the epidemic. Specifically, decreasing the cost of the self-protective action increases the rate of safe behavior. Higher reported prevalence also raises the likelihood that individuals would engage in self-protection, where the magnitude of this effect depends on how much time has elapsed in the epidemic. Our results show that the effect of a change in reported prevalence on people's behavior is stronger later on in an epidemic compared to the earlier stages. Individuals' experiences in terms of how often an infection was acquired when they did not engage in self-protection are another factor that determines whether they will invest in preventive measures later on. All else being equal, individuals who were infected at a higher rate are more likely to engage in self-protective behavior compared to those with a lower rate of infection. Lastly, fixing everything else, people's willingness to engage in safe behavior waxes or wanes over time, depending on the severity of an epidemic: when prevalence is high, people are more likely to adopt self-protective measures as time goes by; when prevalence is low, a ‘self-protection fatigue’ effect sets in whereby individuals are less willing to engage in safe behavior over time. Some significant policy implications for controlling the spread of infectious diseases follow directly from our results. Firstly, making preventive measures available during an epidemic—where the cost of these measures are not prohibitive—can be highly effective in reducing the prevalence of disease. Secondly, people respond to incentives: making these preventive measures less costly—through subsidies, for example—encourages more people to engage in safer behavior that reduces the transmission of disease. It is important to note that the costs of self-protective behavior need not be entirely pecuniary. For instance, the cost of using condoms in the prevention of STDs reflects not only the purchase price of these products, but also the ease with which people can find them, the loss of pleasure one may experience from using them, as well as other factors such as cultural beliefs that impact people's willingness to wear them. In the case of influenza, the cost to an individual of getting a flu shot can include—besides the price of a vaccine—all the adverse side effects that the individual believes it to have. Therefore, decreasing the cost of engaging in self-protection does not necessarily entail lowering the amount of money individuals have to give up in exchange for the protective measures. Individual attributes such as preferences for risk-taking also determine the likelihood that one will choose to adopt preventive measures in response to an epidemic. Given that people can vary widely in terms of their preferences, it follows that any policy that is highly efficacious in inducing safer behavior in one group of people—say, those that are extremely risk-averse—may have smaller effects on other groups. Similarly, individuals can differ significantly in their infection history. Some people, for instance, may rarely get sick during flu season even without getting vaccinated, while others may be more prone to infections without getting a flu shot. If people react differentially to epidemics based on their infection history, then control policies that do not account for this heterogeneity in behavior may end up being less successful in inducing behavior change than anticipated. Thus, with respect to policy effectiveness, it may be preferable to implement a “menu” of policies and interventions that are tailored for different segments of the population than to attempt to formulate “one-size-fits-all” policies that are aimed at all members of a population. Lastly, because people's behavior in our study is time- and history-dependent, the results suggest that policy interventions should be dynamic, flexible, and adaptable. Given that individuals are not as responsive in their behavior to increases in reported prevalence early on, policies to actively encourage individuals to adopt protective measures in the initial phases of an epidemic could be particularly important in halting or slowing disease spread later on. The finding that, all else being fixed, individuals' propensity to self-protect can change over time depending on how widespread a disease is suggests that intervention efforts may need to be continually revised throughout the course of an epidemic in order to complement or offset changes in people's willingness to engage in self-protective behavior. Note that since the safe action in our epidemics game is perfectly effective in blocking transmission, eradication of the virtual disease in either condition can be brought about in any round beyond round 4. If all healthy players choose the safe action for at most 4 rounds (which is the length of infection in the game), then eradication is guaranteed to occur, after which all players can receive the maximum payoff (the payoff from being healthy) without having to incur the cost of being infected or the cost of taking the self-protective action. Theoretical models that utilize analytical tools from the fields of economics and game theory predict that, under certain conditions, if a disease will be endemic when no one has access to protective measures, then the disease will also be endemic when protective measures are available, no matter the cost or the efficacy of these protective measures [27]. The fact that the virtual disease in our experiment appeared to settle into an endemic phase in both conditions is consistent with this prediction and underscores how difficult it is for disease eradication to occur under decentralized independent decision-making in the population. Some aspects of our study design warrant further elaboration. We utilized an SIS model of transmission in our game mainly for two reasons. Firstly, we wanted to examine how an individual's infection history impacts that individual's future decisions to engage in self-protective activities. The second reason is a pragmatic one: to maximize the number of data points we can gather given a fixed number of players, we sought a game structure that would allow players to be actively involved for the entire duration of the game. For example, without some modifications to the game rules or added features, the susceptible-infected (SI) model—in which a player has no possibility of recovery—or the susceptible-infected-removed (SIR) model—in which an infected player can recover after a certain amount of time and subsequently become immune to infection—does not fulfill this criterion as well as an SIS model. With the SI model, no data points can be collected from a player once the player becomes infected. The same is essentially true of the SIR model since recovered players have no incentive to adopt precautionary measures. This is also one of the reasons why in our study we chose for our ‘safe’ action one that is effective for only one round. If, for instance, we had instead chosen a ‘vaccine’ for our safe action that is (perfectly) effective for multiple rounds (e.g., the duration of the game), then we would not have been able to collect any data points from a player once that player chose the safe action. Relatedly, our choice of parameter values for the study was—as mentioned earlier—motivated partly by practical considerations. We wanted the game to be long so that we can obtain a large number of observations, but we also wanted the length of time needed for study completion to be reasonable (from the participants' perspective). Since only players in the healthy state had to make decisions in our study, we purposely kept the duration of infection relatively short so that infected players would not be “disengaged” from the game for long. The transmission probability β was set high so that the likelihood that the virtual disease would die out before study completion is small, since no useful data can be collected in the disease-free outcome. This is also the reason why we set the number of players infected in round 1 to be three. The choice of how many points to assign to the different health states and of the cost of the safe action was driven by our research budget constraints, the amount of compensation that is typically given to participants of experimental economics studies, and a simple cost-benefit analysis of how the players in our study might behave. We chose the risky action to be the default action since we believe this more accurately reflects what happens in reality. To engage in self-protective behavior such as more frequent washing of hands in the case of flu or using condoms in the context of STDs requires actively and consciously changing one's routine; this is why we set up the game so that one has to manually select the safe action. Of course, whether one cares about maximizing the number of observations that can be collected per participant or not, our online experimental framework can accommodate different disease transmission processes as well as various types of self-protective actions (including ones that are not perfectly efficacious). We purposely employed a simple random mixing process for our study to better isolate and understand the effects of a few determinants of individual behavior such as reported prevalence, cost of self-protection, and infection history. An important extension of the present study is to consider network effects and how individuals' location in their social network, which determines how likely people will acquire infection from—or transmit infection to—their social contacts, impacts their decision to engage in self-protective behavior during an epidemic. Another line of research that can be pursued in the future is the formulation of mathematical models of behavior-disease interactions that can account for the results from our study. Such empirically grounded models would provide an analytical framework that can be used to make predictions regarding how, for instance, people would respond to an epidemic under various policies or interventions. While most modeling work in economic epidemiology, following the convention in economics, assumes that agents are rational, forward-looking, payoff- or utility-maximizers, the experimental results presented here suggest that some of these standard assumptions on behavior do not hold in our study. One of the major findings from our study is that one's decision to engage in self-protective behavior is strongly correlated with one's infection history, i.e., people are ‘backward-looking’ in their decision-making process. However, in economic models with forward-looking agents, where all agents are perfectly informed of the ‘rules of the game’, one's past experiences should have no impact on one's current or future behavior. In addition, results obtained from analyses of standard economic models looking at similar decision environments suggest that the players in our virtual epidemics game would utilize a threshold rule in deciding whether to engage in the self-protective behavior or not: choose the safe action if the reported prevalence is above a threshold level; otherwise, choose the risky action. Most of the players in our study, however, did not exhibit such threshold behavior (results not shown). Future modeling work should therefore relax some of the restrictions imposed by the standard economic approach and consider extensions that, for instance, incorporate elements of backward-looking decision-making. Disease transmission models with backward-looking agents have been explored recently (see, for example, [28] and the references therein); these could serve as useful building blocks for formulating models that better account for the experimental results presented here. Our study design possesses many of the advantages of experiments carried out in controlled laboratory settings, including having a framework to generate data that are otherwise not available and being able to easily manipulate and alter experimental conditions. In addition, since our study takes place entirely online in cyberspace, it has the added benefit of extreme flexibility—as long as one has access to a computer and the internet, experiments can be run anywhere and at any time. Our setup is also subject to many of the same caveats that apply to laboratory experiments. The game environment abstracts away many aspects of real world infectious diseases. Moreover, it may be difficult to capture all the motivational forces driving people's behavior during real epidemics in an online or virtual setting such as ours. However, as noted by the Nobel Prize-winning economist James Heckman and his colleague, criticizing laboratory experiments in the social sciences as lacking realism misses the point of what “the nature of evidence in science” is; in their words, “(t)he real issue is determining the best way to isolate the causal effect of interest,” and laboratory experiments are no less valid than other methods, such as field experiments and surveys, in accomplishing this [29]. Given that human beings respond to incentives and that understanding incentives is an important component of containing epidemics, furthering our knowledge of what affects people's decision to engage in self-protection—even if obtained from virtual world experiments—can only help us in controlling the spread of infectious diseases. We note that many of the results from our virtual epidemics study are consistent with those obtained by other researchers using survey-based methods to examine the determinants of self-protective behavior. For instance, a review of previous research on flu shot acceptance finds that demographic variables such as gender, level of education, and ethnicity generally have no impact on vaccination decisions, while perceived likelihood of getting the flu and having had a flu shot previously are significantly correlated with the decision to have a flu shot [30]. Given that the likelihood of infection in our virtual epidemics study is proportional to reported prevalence, our results regarding self-protective behavior with respect to the effects of demographic variables, reported prevalence, and past decision to engage in self-protection precisely mirror those obtained by others looking at flu vaccination behavior. This suggests that an experimental paradigm utilizing virtual diseases can be valuable for studying how people respond to the spread of infectious diseases. Moreover, while surveys can only provide one-time snapshots of people's behavior, our virtual epidemics framework allows researchers to examine behavioral responses in a dynamic context and to analyze how people's incentive to engage in self-protection changes over time. Thus, our experimental framework can serve as a complement to traditional survey methodology to provide a more complete understanding of the determinants of self-protective behavior.

Acknowledgments For programming assistance and technical support, we thank Rick Freedman.

Author Contributions Conceived and designed the experiments: FC. Performed the experiments: FC YW. Analyzed the data: FC AG AC. Wrote the paper: FC AG.