Selected article for: "additional variation and local state"

Author: Philip J. Turk; Shih-Hsiung Chou; Marc A. Kowalkowski; Pooja P. Palmer; Jennifer S. Priem; Melanie D. Spencer; Yhenneko J. Taylor; Andrew D. McWilliams
Title: Modeling COVID-19 latent prevalence to assess a public health intervention at a state and regional scale
  • Document date: 2020_4_18
  • ID: j5o8it22_46
    Snippet: If we compare the two locations, the estimated R0 of 2.36 for the CRI prior to March 26 is more typical of the range of R0 values in the literature for COVID-19, while the value of 1.79 for NC is substantially lower (Table 1 ). This could be attributed to the fact that the CRI contains the largest city in NC, and one of the US's busiest airports, setting the stage for this region to have become another COVID-19 hotspot. It is interesting to note .....
    Document: If we compare the two locations, the estimated R0 of 2.36 for the CRI prior to March 26 is more typical of the range of R0 values in the literature for COVID-19, while the value of 1.79 for NC is substantially lower (Table 1 ). This could be attributed to the fact that the CRI contains the largest city in NC, and one of the US's busiest airports, setting the stage for this region to have become another COVID-19 hotspot. It is interesting to note that the NC SIR-Int model showed a better fit when the changepoint was also set to March 26, rather than March 30 when the statewide stay-at-home order went into place. One possible explanation for this could be that as the pandemic began in earnest, the general population's fear of the virus also increased, perhaps causing most NC citizens to shelter-in-place prior to the order going into effect. Another explanation is that Mecklenburg County accounts for almost 11% of the NC population and so the effect of the county order directly impacted adjoining counties in the CRI, thus influencing the observed effect at the state level. Two additional interesting observations highlight the critical influence of spatial variation. First, the CRI infection curve evidences relatively more flattening and a later peak infection date ( Figure 7, Figure 8 , Table 3 ). Second, the intervention effect in the CRI also appears stronger ( Table 2 ). The likely explanations for these differences are the Mecklenburg County stay-at-home policy going into effect five days before the state order, the different reaction of the local population to the order and its related messaging, and innumerable other unknown covariates such as early canceling of religious services, public gathering policies, and canceling of elective medical visits and procedures.

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