I-Corps: Mobidemics: Using Mobile Gaming for Healthcare

传统的传染病研究是基于纯粹的模拟、病人访谈或数学模型。由于这些技术不是建立在真实的社会交互数据上的，因此它们通常容易出错。为了缓解上述挑战，PI提出了Mobidetrix，这是一种基于移动的手机游戏系统，旨在改变流行病学家研究传染病传播的方式。Mobidetron提供了一个基于物理位置的实时互动社交游戏平台，并针对传染病的传播进行了调整。该团队与计算流行病学家合作，确定了一组可用于描述传染病的变量。例如，患者具有传染性的距离取决于疾病类别，并且可以在虚拟（游戏）世界中建模为玩家可以相互感染或射击的距离约束。类似地，可以使用Mobidetron后端动态地对游戏变量集进行其他更改，以提供对疾病传播研究的前所未有的控制，并对真实的生活事件和可能性进行建模。游戏前端为用户提供必要的激励，以分享他们的位置和社交互动数据。Mobidetron是一个收集位置数据、社交互动数据和感染传播数据的独特平台。它提供了根据正在研究的疾病类别调整策略的灵活性。感染传播的关键组成部分，例如对疾病的免疫力（这是年龄，体型和性别的函数）被建模为简单的语义。因此，可以使用Mobidetron进行传染病传播的真实实时仿真。H1N1流感等传染病每年造成数千人伤亡。随着对感染如何在社交网络中传播的更好理解，可以采取补救措施来遏制它们。Mobidetron提供了一个平台，通过一个基于移动的手机的真实的游戏来了解疾病传播以及收集和分析疾病传播数据。因此，Mobidetrial可以产生巨大的社会影响。收集的数据和提供的服务也将有助于更广泛的流行病学家，公司和对社会互动数据感兴趣的市政当局。

Traditional study of infectious diseases is based on pure simulation, patient interviews, or mathematical models. Since these techniques are not founded on real social interaction data, they are usually error-prone. To mitigate the above challenge, the PIs propose Mobidemics, a mobile phone-based gaming system that aims to change the way epidemiologists study the transmission of communicable diseases. Mobidemics provides a live interactive social gaming platform based on physical location and is tuned to the spread of infectious diseases. Working with computational epidemiologists, the team has identified a set of variables that can be used to describe infectious/contagious diseases. For instance, the distance at which a patient is infectious depends on the disease class and can be modeled in the virtual (gaming) world as a distance constraint at which a player can infect or shoot each other. Similarly, other changes to the game variable set can be made on the fly using the Mobidemics backend to provide an unprecedented control over a disease spread study and to model real life events and possibilities. The gaming frontend provides the necessary incentive to users to share their location and social interaction data. Mobidemics is a unique platform for collecting location data, social interaction data, and infection spread data. It provides the flexibility to tune the strategy based on the disease class being studied. The key components of infection spread, such as immunity to a disease (which is a function of age, build, and gender) is modeled as simple semantics. Hence, realistic real-time emulations of spread of infectious diseases can be performed using Mobidemics. Infectious diseases such as the H1N1 flu accounts for thousands of human casualties every year. With better understanding of how infections spread in a social network, remedial action can be taken to contain them. Mobidemics provides a platform for understanding disease spread as well as collecting and analyzing disease spread data through a real time mobile phone based game. Hence, Mobidemics can have a large societal impact. The data collected and the services provided would also be useful to broader group of epidemiologists, companies, and municipalities interested in social interaction data.