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1. Pandemics & people: Designing a virtual epidemic event for immersive, connected, and playful participation in an infectious disease outbreak.

   Fields, D. A. (January 2022, Educational designer)

   Researchers and educators have identified an urgent need for more rigorous teaching and learning about epidemiology topics and practices, such as engaging in behaviors that prevent the spread of viral disease such as COVID-19. Responding to this need, we designed a virtual epidemic as a special event hosted in a virtual world. In this paper we share the strategic, tactical, and detailed design of the SPIKEY-20 virtual epidemic and data that reflects back on the design in terms of player participation. Reflecting on the design, we ask: What kinds of players participated in the SPIKEY-20 virtual epidemic? How did players engage in designed activities (i.e., preventive measures, information seeking)? In what ways were players influenced by the concurrent real world pandemic of COVID-19? In the discussion we consider the potential connection points between real-life and virtual public health behaviors, new possibilities of classroom participation and teacher support for such a virtual event, and future design considerations for virtual epidemics. 

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2. Use Internet search data to accurately track state level influenza epidemics

   https://doi.org/10.1038/s41598-021-83084-5  

   Yang, Shihao; Ning, Shaoyang; Kou, S. C. (December 2021, Scientific Reports)

   null (Ed.)

   Abstract For epidemics control and prevention, timely insights of potential hot spots are invaluable. Alternative to traditional epidemic surveillance, which often lags behind real time by weeks, big data from the Internet provide important information of the current epidemic trends. Here we present a methodology, ARGOX (Augmented Regression with GOogle data CROSS space), for accurate real-time tracking of state-level influenza epidemics in the United States. ARGOX combines Internet search data at the national, regional and state levels with traditional influenza surveillance data from the Centers for Disease Control and Prevention, and accounts for both the spatial correlation structure of state-level influenza activities and the evolution of people’s Internet search pattern. ARGOX achieves on average 28% error reduction over the best alternative for real-time state-level influenza estimation for 2014 to 2020. ARGOX is robust and reliable and can be potentially applied to track county- and city-level influenza activity and other infectious diseases. 

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3. Multilayer network analysis of FMD transmission and containment among beef cattle farms

   https://doi.org/10.1038/s41598-022-19981-0  

   Yi, Chunlin; Yang, Qihui; Scoglio, Caterina M. (December 2022, Scientific Reports)

   Abstract As a highly contagious livestock viral disease, foot-and-mouth disease poses a great threat to the beef-cattle industry. Direct animal movement is always considered as a major route for between-farm transmission of FMD virus. Sharing contaminated equipment and vehicles have also attracted increasing interests as an indirect but considerable route for FMD virus transmission. With the rapid development of communication technologies, information-sharing techniques have been used to control epidemics. In this paper, we built farm-level time-series three-layer networks to simulate the between-farm FMD virus transmission in southwest Kansas by cattle movements (direct-contact layer) and truck visits (indirect-contact layer) and evaluate the impact of information-sharing techniques (information-sharing layer) on mitigating the epidemic. Here, the information-sharing network is defined as the structure that enables the quarantine of farms that are connected with infected farms. When a farm is infected, its infection status is shared with the neighboring farms in the information-sharing network, which in turn become quarantined. The results show that truck visits can enlarge the epidemic size and prolong the epidemic duration of the FMD outbreak by cattle movements, and that the information-sharing technique is able to mitigate the epidemic. The mitigation effect of the information-sharing network varies with the information-sharing network topology and different participation levels. In general, an increased participation leads to a decreased epidemic size and an increased quarantine size. We compared the mitigation performance of three different information-sharing networks (random network, contact-based network, and distance-based network) and found the outbreak on the network with contact-based information-sharing layer has the smallest epidemic size under almost any participation level and smallest quarantine size with high participation. Furthermore, we explored the potential economic loss from the infection and the quarantine. By varying the ratio of the average loss of quarantine to the loss of infection, we found high participation results in reduced economic losses under the realistic assumption that culling costs are much greater than quarantine costs. 

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4. Mathematical and Statistical Analysis of Doubling Times to Investigate the Early Spread of Epidemics: Application to the COVID-19 Pandemic

   https://doi.org/10.3390/math9060625  

   Smirnova, Alexandra; DeCamp, Linda; Chowell, Gerardo (March 2021, Mathematics)

   Simple mathematical tools are needed to quantify the threat posed by emerging and re-emerging infectious disease outbreaks using minimal data capturing the outbreak trajectory. Here we use mathematical analysis, simulation and COVID-19 epidemic data to demonstrate a novel approach to numerically and mathematically characterize the rate at which the doubling time of an epidemic is changing over time. For this purpose, we analyze the dynamics of epidemic doubling times during the initial epidemic stage, defined as the sequence of times at which the cumulative incidence doubles. We introduce new methodology to characterize epidemic threats by analyzing the evolution of epidemics as a function of (1) the number of times the epidemic doubles until the epidemic peak is reached and (2) the rate at which the doubling times increase. In our doubling-time approach, the most dangerous epidemic threats double in size many times and the doubling times change at a relatively low rate (e.g., doubling times remain nearly invariant) whereas the least transmissible threats double in size only a few times and the doubling times rapidly increases in the period of emergence. We derive analytical formulas and test and illustrate our methodology using synthetic and COVID-19 epidemic data. Our mathematical analysis demonstrates that the series of epidemic doubling times increase approximately according to an exponential function with a rate that quantifies the rate of change of the doubling times. Our analytic results are in excellent agreement with numerical results. Our methodology offers a simple and intuitive approach that relies on minimal outbreak trajectory data to characterize the threat posed by emerging and re-emerging infectious diseases. 

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5. The SIR dynamic model of infectious disease transmission and its analogy with chemical kinetics

   https://doi.org/10.7717/peerj-pchem.14  

   Simon, Cory M. (January 2020, PeerJ Physical Chemistry)

   null (Ed.)

   Mathematical models of the dynamics of infectious disease transmission are used to forecast epidemics and assess mitigation strategies. In this article, we highlight the analogy between the dynamics of disease transmission and chemical reaction kinetics while providing an exposition on the classic Susceptible–Infectious–Removed (SIR) epidemic model. Particularly, the SIR model resembles a dynamic model of a batch reactor carrying out an autocatalytic reaction with catalyst deactivation. This analogy between disease transmission and chemical reaction enables the exchange of ideas between epidemic and chemical kinetic modeling communities. 

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