The SARS-CoV-2 pandemic initially made landfall in the United States in early 2020, and at that point in the pandemic, few developed treatments left the initial prevention of the disease largely up to preventative measures like mask mandates, quarantines for infected individuals, and social distancing policies. As a result, we must understand how preventative measures affect the transmission of infectious diseases to prepare us to fight the future spread of similar diseases. To accomplish this, we used a SEIR model with a variable transmission rate and fit SARS-CoV-2 case data to it. Principally, we used four models for the change in transmission rate: instant, linear, exponential, and logistic. Then using these models for the decay of transmission rate, we obtained SSR and parameter values that allowed us to compare models for each state. After comparing models between the four states we fit, there was no evident best-fit model for the decay in transmission. These results may suggest that regional differences like behavior, socioeconomic status, and exact preventative measures enforced could be responsible for the disparity in how the transmission rate decayed.

The most common immunological models for analyzing viral infections assume even spatial distribution between virus particles and healthy target cells. However, throughout an infection, the spatial distribution of virus and cells changes. Initially, virus and infected cells are localized so that a target cell in an area with lower virus presence will be less likely to be infected than a cell close to a location of viral production. A density-dependent rate has the potential to improve models that treat cellular infection probability as constant. A Beddington-DeAngelis model was used to understand how density dependent parameters could impact the severity of an influenza infection. Parameter values were varied to understand implications of density constraints. For low density dependence, a steeper increase in number of virus and greater viral peak was predicted. Higher density dependence predicted a longer time to viral load maximum and a greater infection duration. Initial localization of infected cells likely slows the progression of infection. The model demonstrates that accounting for density dependence when analyzing influenza infection severity can result in an altered expectation for viral progression. A density-dependent infection rate may provide a more complete view of the interaction between infected and healthy cells.

Mathematical models of cancer cells can be used by researchers to study the use of oncolytic viruses to treat tumors. With these models, we are able to help predict the viral characteristics needed in order for a virus to effectively kill a tumor. Our approach uses non-cancerous cells in addition to the tumor to determine when the virus will spread to non-cancerous cells. However, there are several models used to describe cancer growth, including the exponential, Mendelsohn, logistic, linear, surface, Gompertz, and Bertalanffy. We study how the choice of a particular model affects the predicted outcome of treatment.

Abstract: Researchers hypothesize that the initial amount of virus will affect the severity of the disease. They also believe that this will affect the amount of antivirals needed. We used mathematical modeling to study the effect of the initial viral dose on the effectiveness of antivirals. We simulated Sars-Cov-2 infections starting with different amounts of virus and treated with different amounts of antivirals, then measured the duration of the infection. This mathematical model predicts little to no effect on the amount of antivirals needed when the starting dose of virus is changed.

Title: Saving Important Material: An Examination of Offloading, Memory, and Metacognition.

Authors: Ashley J. Berdelis, Morgan D. Shumaker, Sarah K. Tauber

Cognitive offloading—externally storing information to reduce internal cognitive load (e.g., on a smartphone)—has become widespread with technological advances (Risko & Dunn, 2015). Often, offloading is used when we need to remember information in the future (e.g., setting calendar reminders). However, sometimes how much to-be-remembered material we can offload is constrained by time or by available storage space. The agenda-based regulation (ABR) framework posits that learners assess task constraints prior to study and construct agendas to achieve the task goal within these constraints (Ariel & Dunlosky, 2009; 2013). For instance, learners allocate more study time to and selectively study more important (high-value) over less important (low-value) material, allowing them to maximize test performance under such constraints (Soderstrom & McCabe, 2011; Middlebrooks & Castel, 2018). Thus, learners might adopt similar offloading strategies by offloading important material and using internal memory for unimportant material. Critically, people often engage in offloading with the expectation that their external store will be available to them at the time of need; however, this is not always the case (e.g., technology failing). When offloaded material is available at the time of need, memory for that material is enhanced (Park et al., 2022). When offloaded material is unavailable at the time of need, memory for offloaded material suffers compared to memory for internally stored (recalled) material (Park et al., 2022). To use external tools most effectively, it may be useful for learners to be aware of their ability to remember externally and internally stored material. Thus, the current study examined whether learners are aware of their ability to later remember offloaded and internally stored material. Participants completed a series of memory tasks with the option to offload only a portion of the to-be-remembered items. Before the study phase in each task, participants made judgments about how much of the offloaded and recalled items they could later identify as having been seen before. After the study phase, participants made similar post-task judgements and were given a surprise recognition test on the studied material, during which the external store was unavailable.
We also examined whether learners could transfer their metacognitive awareness from one task to another, as offloading is relevant to various life scenarios. Finally, we examined how the value of the to-be-remembered material influences offloading, and how offloading and recall influence later memory. Participants’ pre-task judgements on the first task indicated that they would recognize more offloaded items than recalled items. However, this difference was not present on tasks two and three, suggesting that participants used experience with the first task to update their judgments for offloaded items. Participants offloaded more high-value than low-value items and had better recognition memory for recalled items than offloaded items, in all three tasks. Overall, people strategically offload important over unimportant material, but memory for offloaded material suffers compared to memory for recalled material. Learning about the relationships between value, offloading, memory, and metacognition can allow us to use external storage devices more effectively.