Nebraska Case Study

[Updated 04/18/20. Added Case 2]

[05/12/20: Meat Packing Plant Outbreaks are Driving Increased Deaths]

Two case studies are presented here:

Case 1: Nebraska maintains current state and then moderately relaxes in May.  This results in a low level death rate and ultimately about 400 deaths by the end of the summer.

Case 2: Nebraska maintains their current state of social distancing through the summer, relaxing in July.  This results in a around 150 deaths.

In both cases we assume there is a fairly uniform infection distribution over Nebraska’s population of approximately 2 million. For this study we are using a simulation population of 2 million.  Graphic below is from the University of Nebraska Covid19 infection map.

Key simulation space parameters are presented below in table 1.  These virus infection parameters are largely derived from consideration of a variety of sources listed on worldometer.


Cumulative probability distribution bins and definitions are defined below in table 2 for the general population of of Nebraska are based on generally published data on Wikipedia and other demographic sources.  Some of the parameters relative to health and mobility are based on reasonable assumptions.


The infection outcome distribution table is shown in table 3.  This table is derived from the Imperial College Covid-19 response team Report 9.  Additional scaled outcomes for asymptomatic cases have been added as an input variable. A nominal value is used to represent the present consensus that a high number of cases are asymptomatic:

Case 1: Relax from 80% Reduction to 65% Reduction in Potentially Infectious Contacts

Figure 2 shows the dynamic input parameters for this baseline run.  Test access increases over time, test processing time decreases and transmission factor is scaled due to increased social distancing and stay at home orders.  Nebraska is not assumed to be in a hard lock-down and the transmission factor bottoms at 0.2%.  In May the transmission factor is scaled up to 0.35% to reflect a return to normal with reduced social contacts.  Test access increased throughout late March and April.  Test processing time is reduced as well over the same period. The number of seed infected people in the simulation population is 5800 at the beginning to accelerate the infection and cut the simulation time.

Figure 3 shows simulation results compared to actual data through April 13th, 2020.  Data has been tracked comprehensively on the University of Nebraska COVID-19 site.

Figure 4 shows the view of simulated infections and deaths per day.  Note the significant delay between the infections and death curves.  March 15th restrictions cut the back end off the infection curve. The loosening of restrictions to return 35% less contacts than baseline reduces ongoing death rate to something akin to the the flu.  This reduction is likely easy to achieve since people are likely to remain overly cautious at this point in time as restrictions are lifted.   Note that the IHME models for Nebraska as of 14 April 2020 predict 281 deaths from the first wave but have no consideration for managing a return to normal and a subsequent low level death rate akin to the flu.

Figure 5 shows the sequencing of the curves for infections, standard hospital care, critical hospital care, recovery and death.  Hospital demand peaks before deaths will peak and is very light.

Figure 6 shows active infections and cumulative recoveries, health people (no infection) and deaths.

Figure 7 shows the cumulative percentage of susceptible people who are not infected.  Susceptible people are those over age 70 and / or people with degraded health immunity (nominal or high risk).

Figure 8 shows the proportion of positive tests to the set of those infected or recovered.

Figure 9 shows the relative risk of infection and the death rate in time.  Risk at present is significantly less that what it was prior to the lock-down.

Figure 10 shows the derived R0, the reproduction rate, over time.  R0 is a dynamic indicator of whether the virus spread is growing or dying out.  R0 is a measure of the number of infections caused by infectious person.  Numbers greater than 1 show growth, numbers less than one show decline.  In this scenario, the lock-down effectively kills the growth of the model and a then a modest return to normalcy allows for a controlled uptick in the infection rate.

Figure 11 shows the ratio of active infections to healthy population.  This is the risk without factoring for reduced contact.


Figure 10 Dynamic R0 Versus Time



CASE 2: Hold to 80% Reduction in Infectious Contacts Through the Summer

Inputs for this case are shown below. There is loosening in late July to 65% of prior contacts.


Total deaths roll over and peak at just over 140.

R0, the multiplication factor, pops up at a low level out in July, but is very close to 1.  R0 below one is when the outbreak will die off .

A view of infections, recover, deaths and hospital demand:


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