Mathematical Biology

SARS-CoV-2 is a novel pathogen causes the COVID-19 pandemic. Some of the basic epidemiological parameters, such as the exponential epidemic growth rate and R0 are debated. We collected and analyzed data from China, eight European countries and the US using a variety of inference approaches. In all countries, the early epidemic grew exponentially at rates between 0.19-0.29/day (epidemic doubling times between 2.4-3.7 days). I will discuss the appropriate serial intervals to estimate the basic reproductive number R0 and argue that existing evidence suggests a highly infectious virus with an R0 likely between 4.0 and 7.1. Further, we found that similar levels of intervention efforts are needed, no matter the goal is mitigation or containment. Early, strong and comprehensive intervention efforts to achieve greater than 74-86% reduction in transmission are necessary.

On March 23rd and March 30th, 2020, the Mexican Federal government implemented social distancing measures to mitigate the COVID-19 epidemic. In this work a mathematical model is used to explore atypical transmission events within the confinement period, triggered by the timing and strength of short time perturbations of social distancing. Is shown that social distancing measures were successful in achieving a significant reduction of the epidemic curve growth rate in the early weeks of the intervention. However, “flattening the curve” had an undesirable effect, since the epidemic peak was delayed too far, almost to the government preset day for lifting restrictions (June 1st, 2020). If the peak indeed occurs in late May or early June, then the events of children's day and Mother’s Day may either generate a later peak (worst case scenario), a long plateau with relatively constant but high incidence (middle case scenario) or the same peak date as in the original baseline epidemic curve, but with a post-peak interval of slower decay.

This is the prototype of an agent based model for a closed universe of a population experiencing a contagion-based epidemic, in which risk factors, movement, time of incubation and asymptomatic infection are all parameters. The model allows the operator to intervene at any step and change parameters, thus analytically visualizing the effect of policies like more testing, contract tracing, and shelter in place. Under current development, CovidSimMV is an ABM that supports a Multiverse of different environments, in which agents move from one to another according to ticket with stops. Each universe has its own characteristic mix of residents, transients and attached staff, and persons are able to adopt different roles and characteristics in different universes. The fundamental disease characteristics of incubation, asymptomatic infection, confirmed cases will be preserved. The Multiverse model will support a rich diversity of environments and interpersonal dynamics. These are JavaScript programs that can be run in a browser as HTML files. The code is open source, and available on github.com/ecsendmail.

Multiscale multicellular models combine representations of subcellular biological networks, cell behaviors, tissue level effects and whole body effects to describe tissue outcomes during development, homeostasis and disease. I will briefly introduce these simulation methodologies, the CompuCell3D simulation environment and their applications, then focus on a multiscale simulation of an acute primary infection of an epithelial tissue infected by a virus like SARS-CoV-2, a simplified cellular immune response and viral and immune-induced tissue damage. The model exhibits four basic parameter regimes: where the immune response fails to contain or significantly slow the spread of viral infection, where it significantly slows but fails to stop the spread of infection, where it eliminates all infected epithelial cells, but reinfection occurs from residual extracellular virus and where it clears the both infected cells and extracellular virus, leaving a substantial fraction of epithelial cells uninfected. Even this simplified model can illustrate the effects of a number of drug therapy concepts. Inhibition of viral internalization and faster immune-cell recruitment promote containment of infection. Fast viral internalization and slower immune response lead to uncontrolled spread of infection. Existing antivirals, despite blocking viral replication, show reduced clinical benefit when given later during the course of infection. Simulation of a drug which reduces the replication rate of viral RNA, shows that a low dosage that provides only a relatively limited reduction of viral RNA replication greatly decreases the total tissue damage and extracellular virus when given near the beginning of infection. However, even a high dosage that greatly reduces the rate of RNA replication rapidly loses efficacy when given later after infection. Many combinations of dosage and treatment time lead to distinct stochastic outcomes, with some replicas showing clearance or control of the virus (treatment success), while others show rapid infection of all epithelial cells (treatment failure). This switch between a regime of frequent treatment success and frequent failure occurs is quite abrupt as the time of treatment increases. The model is open-source and modular, allowing rapid development and extension of its components by groups working in parallel.

TBA