The impact of policy timing on the spread of COVID-19

Abstract

We model COVID-19 data for 89 nations and US states with a recently developed formalism that describes mathematically any pattern of growth with the minimum number of parameters. The results show that the disease has a typical duration of 18 days, with a significant increase in fatality when it lasts longer than about 4 months. Searching for correlations between “flattening of the curve” and preventive public policies, we find strong statistical evidence for the impact of the first implemented policy on decreasing the pandemic growth rate; a delay of one week in implementation nearly triples the size of the infected population, on average. Without any government action, the initial outburst still slows down after 36 days, possibly thanks to changes in public behavior in response to the pandemic toll. Stay-at-home (lockdown) was not the first policy of any sample member, and we could not find statistically meaningful evidence for its added impact, similar to a recent study that employed an entirely different approach. However, lockdown was mostly imposed only shortly before the exponential rise was arrested by other measures, too late for a meaningful impact. A third of the sample members that did implement lockdown imposed it only after the outburst had already started to slow down. The possibility remains that lockdown might have significantly shortened the initial exponential rise had it been employed as first resort rather than last.

A General Description of Growth Trends

“…The hindering analysis of the COVID-19 pandemic first wave was reported for 89 nations and US states [6]. Here, we reproduce the results for the COVID-19 case counts in New York State, one of the hardest hit locations in the pandemic's early days.…”

“…A phenomenological description of data would not be particularly useful if it involved an unwieldy number of parameters. However, all cases studied to date required no more than two hindering terms Elitzur et al 2020 [6], indicating that the hindering approach did capture essential properties of the growth process in those cases. The role of successive hindering terms is clearly visible in the fits of COVID-19 cases in New York (Figure 4).…”

“…These examples showcase the power and versatility of the new hindering formalism. While earlier versions of these analyses have already been reported Elitzur et al 2020 [6], the formulation in Section 3.1 of a universal description for decelerated growth provides newly gained insight into the successes and difficulties of these modeling efforts.…”

“…The best-fitting two-term model, shown in the solid red line, has k = [1,8]. (Thanks to the improved handling of higher-order terms, this model is superior to the one presented in [6].) Its RSS error is an improvement by a factor 1.67 over the best-fitting single term; the F-test shows this improvement to be statistically highly significant, with a p-value of 1.11×10 −16 .…”

“…The results show that in spite of highly volatile growth rates, the long-term time variations of both GDP and population in both the US and UK are rather smooth and regular. The formalism has also been used to model the COVID-19 pandemic outburst in 89 nations and US states [6]. The sizeable sample enabled a meaningful search for correlations that yielded strong statistical evidence for the impact of preventive policies on slowing the pandemic initial growth; a delay of one week in the implementation of the first policy nearly tripled the size of the infected population, on average.…”