Modeling region based regimes for COVID‐19 mitigation: An inverse Gompertz approach to coronavirus infections in the USA, New York, and New Jersey

The world tried to control the spread of coronavirus disease 2019 (COVID‐19) at national and regional levels through various mitigation strategies. In the first wave of infections, the most extreme strategies included large‐scale national and regional lockdowns or stay‐at‐home orders. One major side effect of large‐scale lockdowns was the shuttering of the economy, leading to massive layoffs, loss of income, and livelihood. Lockdowns were justified in part by scientific models (computer forecast and simulations) that assumed exponential growth in infections and predicted millions of fatalities without these ‘non‐pharmaceutical interventions’ (NPI). Some scientists questioned these assumptions. 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 – at least in the first wave of infections – saw similar health outcomes. Clear results were confusing, complicated, and difficult to assess. Ultimately, in the USA, what kind of mitigation strategy was enforced became a political decision only partly based on scientific models. We do not test for what levels of NPI are necessary for appropriate management of the first wave of the pandemic. Rather we use the ‘inverse‐fitting Gompertz function’ methodology suggested by anti‐lockdown advocate and Nobel Laureate 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 first‐wave infections in the future and help a region to monitor new outbreaks prior to opening its economy. The inverse fitting function is applied to the first wave of infections in the USA and in the hard‐hit New York and New Jersey regions for the time period March to June 2020. This is the earliest days of pandemic in the USA. 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. This preliminary and exploratory analysis and findings are discussed briefly and presented primarily in charts and tables, but the following waves of disease diffusion are not included and certainly were not anticipated.