Modeling Region Based Regimes for COVID-19 Mitigation: Inverse Gompertz function fitting to the Cumulative Confirmed Coronavirus infections in the U.S., New York and New Jersey

15 Pages Posted: 24 Jun 2020

See all articles by Kingsley E. Haynes

Kingsley E. Haynes

Schar School of Policy and Government, George Mason University

Rajendra Kulkarni

Schar School of Policy & Government, GMU

Date Written: June 17, 2020

Abstract

The world tried to control the spread of COVID-19 at national and regional levels through various mitigation strategies. The most extreme of which was large scale national and regional lockdowns. One major side-effect of large scale lockdowns was the shuttering of the economy, leading to massive layoffs, loss of income and livelihood for millions of people all over the world. Lockdowns were justified in part by scientific models (computer forecast and simulations) that assumed exponential growth in infections and predicted millions of fatalities without the so called “Non-pharmaceutical intervention” (NPI). Some scientists questioned these assumptions and showed that there was no exponential growth and thus the strict NPI regime for “flattening the curve” might have been unnecessary. Regions that followed other softer mitigation strategies such as work from home, crowd limits, use of masks, individual quarantining, basic social-distancing, testing and tracing saw similar health outcomes as the ones that had stay-at-home large scale state and community lockdowns. Ultimately what kind of mitigation strategy is enforced is a political decision that is partly informed by policy analysis based on scientific models.

We do not test for what levels of NPI are necessary, however we use the “inverse-fitting Gompertz function” methodology suggested by anti-lockdown advocate Dr. Levitts to estimate the rate of growth/decline in COVID-19 infections as well to determine when disease peaking occurred. Our estimates may help predict levels of future infections, which can help a region to monitor new outbreaks as the region starts the process of opening its economy. The inverse fitting function is applied to the U.S., New York and New Jersey for COVID-19 confirmed cases reported for the time period March 15, 2020 to June 15, 2020. The estimates for the rates of growth/decline are computed and used to predict underlying future infections, so that decision makers can monitor the disease threat as they open their economies. The preliminary and exploratory analysis and findings are discussed briefly and presented primarily in charts and tables.

Note: Funding: We are funded by a small education research grant from the Schar Foundation.

Declaration of Interest: We have no conflict of interest with respect to our research or findings.

Keywords: Gompertz curve, COVID-19, Outbreaks, Mitigation, Lockdowns, Economic impacts, Social Distancing, Flattening-the-Curve, Exponential Curve

JEL Classification: Z10, Z18, A, C, H, I

Suggested Citation

Haynes, Kingsley E. and Kulkarni, Rajendra, Modeling Region Based Regimes for COVID-19 Mitigation: Inverse Gompertz function fitting to the Cumulative Confirmed Coronavirus infections in the U.S., New York and New Jersey (June 17, 2020). Available at SSRN: https://ssrn.com/abstract=3629898 or http://dx.doi.org/10.2139/ssrn.3629898

Kingsley E. Haynes (Contact Author)

Schar School of Policy and Government, George Mason University ( email )

Founders Hall
3351 Fairfax Dr.
Arlington, VA 22201
United States

Rajendra Kulkarni

Schar School of Policy & Government, GMU ( email )

Founders Hall
3351 Fairfax Dr.
Arlington, VA 22201
United States

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