San Diego County Case Study

[Update 09/11/20: We assumed tighter social distancing / lower connectivity than actually happened.  See our analysis California: Roadmap for a Balanced Recovery]

[Update 05/03/20: Updated to add slow lockdown case]

[Update 05/13/20: Cross Border Surge Driving SD Covid Cases.  The effect of the outbreak in Tijuana and Baja is driving additional cases into SD County, not accounted for in the original analysis.]

The original model run in early April assumed a more effective lockdown in San Diego.  The model was rerun with relaxed transmission factor and a slower lockdown.  The cumulative death prediction is represented in the tracking plot as the up down and nominal case based on the updated transmission factor versus time.

[Original April 13, 2020]

A case study of San Diego County is presented with the assumption that the cases and death are spread over San Diego County, which has a population of approximately 3 million people.   Graphic below is from the KBPS 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 the San Diego based on generally published data on Wikipedia and other 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:

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.  In May the transmission factor is scaled up to 0.25% 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 300 (.01%) per day for 7 days to start the infection.  This model also includes a mortality adjustment with seasonal temperature that rolls off the death rate as temperatures peak through summer (test feature to be calibrated later, used for example purposes).

Figure 3 shows simulation results compared to actual data through April 10th, 2020.  Data has been tracked comprehensively on the San Diego County website.

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 25% 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.

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.

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 ratio of positive test results in time as compared to the simulated totals for infected and recovered.  This is a gauge of the unknown infected population.

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 over time.  This is a dynamic indicator of whether the virus spread is growing or dying out.  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 is another view of the relative risk of infection looking only at the ratio of active infections to the healthy uninfected population.  This takes out the effect of reduced contact which drives the steep reduction in risk shown in Figure 9.


Figure 10 Dynamic R0 Versus Time




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