COVID Chronicles: Part 1
Today marks the 1st anniversary of the very first COVID-19 patient being admitted to our hospital. It is hard to believe that it has been only a year since that date. So much has happened since then. But I certainly recall the deep foreboding that many of us in the medical field felt. It was […]
Droplet Physics: Role of Relative Humidity in COVID
Relative humidity (RH) mediates a number of mechanisms through which viral infections such as COVID-19 can increase fatality rates during the fall/weather season. The night before last here in Boston (posted November 27, 2020), the temp dropped to 28 deg F (-2.2 C). The outside RH was ~ 40%. Meanwhile, the indoor temp is ~ […]
COVID & Superspreading Part 6: Causes of Overdispersion
In Part 5, we talked about epidemiological implications of overdispersion. In this section, we explore potential CAUSES of COVID-19 overdispersion. 1: VIRAL LOAD Based on nice system rev by Chen et al, https://doi.org/10.1101/2020.10.13.20212233…, the variability in Respiratory Viral Load for SARS-CoV-2 >> Influenza A across individuals. Interestingly, the mean viral load (VL) is actually […]
COVID & Superspreading Part 5: Implication of Overdispersion
In Part 4, we talked about Extreme Value Theory and its relevance to overdispersion. In this section, we talk about the implications of COVID-19 overdispersion to epidemiology. A good place to start is Althouse (@BMAlthouse) et al’s (https://doi.org/10.1371/journal.pbio.3000897…) 10,000 simulations of NB Dist spread (R0=2.6, k=0.16). Red dotted line = spread as if 2.6x per […]
COVID & Superspreading Part 4: Extreme Value Theory
In Part 3, we discussed amount of dispersion seen with COVID-19 and focused on NB Distribution. For this section, we talk about another model: Extreme Value Theory. TBH, I never heard of Extreme Value Theory (EVT) until reading about COVID-19 dispersion. It is used to model extreme rare events and has been applied in finance […]
COVID & Superspreading Part 3: Dispersion Factor for COVID
In Part 2, we talked about NB distribution and factor k – the parameter quantifying dispersion. In this section, we ask: What is the dispersion factor for SARS-CoV-2 and how does this compare to other infections? To my surprise, I didn’t find too many studies dedicated to this question. In Indonesia, the k for COVID-19 […]
COVID & Superspreading Part 2: Neg Binomial
As noted in Part 1, COVID-19 transmission is over-dispersed. Here we discuss about one method to quantify/characterize the dispersion: Negative Binomial Distribution (NBD). Before we go into NBD, it’s important to know another distribution first: Poisson Distribution. To illustrate relevance of Poisson, take our R0 = 3 example. R0 is the avg reproductive # that […]
COVID & Superspreading Part 1: Introduction
What makes COVID-19 unique from the perspective of epidemiological dynamics? In this multi-part series, we talk about over-dispersion: notion that a small fraction of infectious individuals/events account for large portion of transmissions. Be ready for some math. By now, we all have heard of R0 (or Rt). Overall, it’s a really useful parameter particularly for […]