Naive Forecast for COVID-19 in Utah Based on the South Korea and Italy Models-the Fluctuation between Two Extremes

Abstract

Differences in jurisdictional public health actions have played a significant role in the relative success of local communities in combating and containing the COVID-19 pandemic. We forecast the possible COVID-19 outbreak in one US state (Utah) by applying empirical data from South Korea and Italy, two countries that implemented disparate public health actions. Forecasts were created by aligning the start of the pandemic in Utah with that in South Korea and Italy, getting a short-run forecast based on actual daily rates of spread, and long-run forecast by employing a log-logistic model with four parameters. Applying the South Korea model, the epidemic peak in Utah is 169 cases/day, with epidemic resolution by the end of May. Applying the Italy model, new cases are forecast to exceed 200/day by mid-April, with the potential for 250 new cases a day at the epidemic peak, with the epidemic continuing through the end of August. We identify a 3-month variation in the likely length of the pandemic, a 1.5-fold difference in the number of daily infections at outbreak peak, and a 3-fold difference in the expected cumulative cases when applying the experience of two developed countries in handling this virus to the Utah context.

What Protective Health Measures Are Americans Taking in Response to COVID-19? Results from the COVID Impact Survey

“…Generally, these associations were robust over time with the exception of some regions and rural communities having positive rates in April. This could be correlated to an increase in coronavirus infections in the U.S. in April [ 43 ], which was the month of the first peak in some states [ 25 ]. As reported by the CDC, there was a 14 fold increase in the number of coronavirus infections, moving from 68,440 confirmed cases in March to 957,875 confirmed cases by the end of April (5 April 2020 case numbers 330,891; April 26 cases 957,875) [ 43 ].…”

“…In March, the president and all 50 governors declared a state of emergency [ 21 ], which is defined as any event for which the president deems it necessary to provide additional federal assistance to protect property and/or public health and safety in an effort to lessen or avert catastrophe [ 22 ]. Although the U.S. developed pandemic plans in response to the 2003 severe acute respiratory syndrome (SARS) and the growing avian influenza threat, preparedness for and response to viral disasters [ 23 ] varies greatly from country to country and even state to state [ 24 , 25 ] here in the U.S. As of 1 April 2020, 33 states had issued statewide stay-at-home orders and 13 states had issued partial stay-at-home orders [ 26 ]. The pandemic disaster response has taken the form of voluntary international, national, and local guidelines and some involuntary policies, regulations, and orders (e.g., wearing facemasks, working from home, and closing of non-essential businesses) [ 27 , 28 , 29 , 30 ].…”

A review on COVID-19 forecasting models

“…One of the famous S-shaped curves is logistic a function with application in biology, chemistry, linguistics, political science, and statistics. [24,37,38] provide examples of applications of logistic functions in COVID-19.…”

A Review on COVID-19 Forecasting Models

“…One of the famous S-shaped curve is logistic function with application in biology, chemistry, linguistics, political science, and statistics. (Chen, Chen et al 2020, Li, Feng et al 2020, Qeadan, Honda et al 2020 are instances of application of logistic functions in COVID-19.…”