Browsing by keyword "modeling"
Now showing items 1-5 of 5
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Analyzing the impact of a real-life outbreak simulator on pandemic mitigation: An epidemiological modeling studyAn app-based educational outbreak simulator, Operation Outbreak (OO), seeks to engage and educate participants to better respond to outbreaks. Here, we examine the utility of OO for understanding epidemiological dynamics. The OO app enables experience-based learning about outbreaks, spreading a virtual pathogen via Bluetooth among participating smartphones. Deployed at many colleges and in other settings, OO collects anonymized spatiotemporal data, including the time and duration of the contacts among participants of the simulation. We report the distribution, timing, duration, and connectedness of student social contacts at two university deployments and uncover cryptic transmission pathways through individuals' second-degree contacts. We then construct epidemiological models based on the OO-generated contact networks to predict the transmission pathways of hypothetical pathogens with varying reproductive numbers. Finally, we demonstrate that the granularity of OO data enables institutions to mitigate outbreaks by proactively and strategically testing and/or vaccinating individuals based on individual social interaction levels.
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From Tank to Treatment: Modeling Melanoma in ZebrafishMelanoma is the deadliest form of skin cancer and one of few cancers with a growing incidence. A thorough understanding of its pathogenesis is fundamental to developing new strategies to combat mortality and morbidity. Zebrafish-due in large part to their tractable genetics, conserved pathways, and optical properties-have emerged as an excellent system to model melanoma. Zebrafish have been used to study melanoma from a single tumor initiating cell, through metastasis, remission, and finally into relapse. In this review, we examine seminal zebrafish studies that have advanced our understanding of melanoma.
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Mathematic Modeling of COVID-19 in the United StatesSince the early reports of COVID-19 cases in China in late January 2020 (1-2), the worst pandemic in 100 years has spread to the entire globe with approximately 2.4 million diagnosed cases and over 165,000 deaths up to April 20, 2020. While scientists from various public and private groups use math and computer to simulate the course of this pandemic to try to predict how this outbreak might evolve (3), most of such analyses are either quite complicated or not publicly available. Here a simple mathematic modeling approach is taken to track the outbreaks of COVID-19 in the US and its selected states to identify the peak point of such outbreak within a given geographic population, the trend of decreasing numbers of new cases after the peak and the rough calculation of accumulated total cases in this population from the beginning to the end of June 2020. The sources of COVID-19 case data are taken from various public websites since not all the data are readily available.
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Modeling of Cisplatin-Induced Signaling Dynamics in Triple-Negative Breast Cancer Cells Reveals Mediators of SensitivityTriple-negative breast cancers (TNBCs) display great diversity in cisplatin sensitivity that cannot be explained solely by cancer-associated DNA repair defects. Differential activation of the DNA damage response (DDR) to cisplatin has been proposed to underlie the observed differential sensitivity, but it has not been investigated systematically. Systems-level analysis-using quantitative time-resolved signaling data and phenotypic responses, in combination with mathematical modeling-identifies that the activation status of cell-cycle checkpoints determines cisplatin sensitivity in TNBC cell lines. Specifically, inactivation of the cell-cycle checkpoint regulator MK2 or G3BP2 sensitizes cisplatin-resistant TNBC cell lines to cisplatin. Dynamic signaling data of five cell cycle-related signals predicts cisplatin sensitivity of TNBC cell lines. We provide a time-resolved map of cisplatin-induced signaling that uncovers determinants of chemo-sensitivity, underscores the impact of cell-cycle checkpoints on cisplatin sensitivity, and offers starting points to optimize treatment efficacy.
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Reconstruction of 60 Years of Chikungunya Epidemiology in the Philippines Demonstrates Episodic and Focal TransmissionProper understanding of the long-term epidemiology of chikungunya has been hampered by poor surveillance. Outbreak years are unpredictable and cases often misdiagnosed. Here we analyzed age-specific data from 2 serological studies (from 1973 and 2012) in Cebu, Philippines, to reconstruct both the annual probability of infection and population-level immunity over a 60-year period (1952-2012). We also explored whether seroconversions during 2012-2013 were spatially clustered. Our models identified 4 discrete outbreaks separated by an average delay of 17 years. On average, 23% (95% confidence interval [CI], 16%-37%) of the susceptible population was infected per outbreak, with > 50% of the entire population remaining susceptible at any point. Participants who seroconverted during 2012-2013 were clustered at distances of < 230 m, suggesting focal transmission. Large-scale outbreaks of chikungunya did not result in sustained multiyear transmission. Nevertheless, we estimate that > 350,000 infections were missed by surveillance systems. Serological studies could supplement surveillance to provide important insights on pathogen circulation.
