COVID-19 Pandemic: How Effective Are Preventive Control Measures and Is a Complete Lockdown Justified? A Comparison of Countries and States

Abstract

For fighting the COVID-19 pandemic, countries used control measures of different severity, from “relaxed” to lockdown. Drastic lockdown measures are considered more effective but also have a negative impact on the economy. When comparing the financial value of lost lives to the losses of an economic disaster, the better option seems to be lockdown measures. We developed a new parameter, the effectiveness of control measures, calculated from the 2nd time derivative of daily case data, for 92 countries, states and provinces. We compared this parameter, and also the mortality during and after the effective phase, for countries with and without lockdowns measures by means of the Mann–Whitney test. We did not find any statistically significant difference in the effectiveness between countries with and without lockdowns (p > 0.76). There was also no significant difference in mortality during the effective phase (p > 0.1); however, a significant difference after the effective phase, with higher mortality for lockdown countries, was identified. The effectiveness correlated well with a parameter derived from the reproductive number (R2 = 0.9480). The average duration of the effective phase was 17.3 ± 10.5 days. The results indicated that lockdown measures are not necessarily superior to relaxed measures, which in turn are not necessarily a recipe for failure. Relaxed measures are, however, more economy-friendly.

Which preventive control measure initiated the “flattening of the curve”

“…1 , the effective reproductive number R eff could have been consulted, at the transition from epidemic to endemic, where R eff = 1; however, as Fuss et al. [ 3 ] pointed out, the start and the end of the “effective phase” of control measures cannot be determined from R eff , but rather conveniently and exactly from the acceleration of the spreading viral disease, i.e., the time derivative of the velocity data (daily case numbers); however, the acceleration and R eff are inherently related mathematically, as both are calculated from the gradient of the new daily case numbers, the acceleration from the original velocity data, and R eff from the natural logarithm of the velocity data. The gradient of the natural logarithm of the velocity data corresponds to the logarithmic growth rate K .…”

“…Fuss et al. [ 3 ] are referring to a “force” generated by control measures, that is required for interrupting the natural growth (daily case data; velocity) and for bending the slope of the velocity curve from concave-up to concave-down, such that the acceleration decreases. This scenario is shown in Fig.…”

“…The first time-derivative of the cumulative case numbers is worth exploring. The new daily case numbers, i.e., the velocity of the disease spreading [ 3 ] are, strictly speaking, not exactly the first derivative of the cumulative case numbers as the cumulative case numbers are the sum of the daily increase in cases but not the integral of the daily case numbers. Nevertheless, the daily case numbers show a distinct marker (in contrast to the cumulative case numbers) at their peak datum.…”

“…[ 3 ] used the gradient of the daily case data to define the start and end of the effective phases of control measures (irrespective of their nature) and calculated their effectiveness from the 2nd time derivative of the daily case data; however, Fuss et al. [ 3 ] evaluated the difference between severe and relaxed control measures and compared these measures across 92 countries, but not within the same country. The same principle applied to Haug et al.…”

Forecast of Omicron Wave Time Evolution

“…Not many details are known so far about its infection characteristics [3,4] apart from alarming hints (1) that it is spreading at least four times quicker than the β-mutant with a short doubling time of t 2 = 3 days [5], and (2) that the existing vaccines, tailored to prevent infections from the earlier alpha (α), beta (β), gamma (γ) and delta (δ) mutants, are less efficient against the action of the omicron mutant especially without the current booster campaigns [5][6][7][8][9]. The α, β, γ, and δ mutants have caused the first four COVID-19 waves, respectively [7,8,[10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25], with side effects on societies and markets [26][27][28][29]. Positively, the omicron mutant seems to lead to, on average, milder symptoms and, thus, to smaller hospitalization fractions compared to the earlier mutants [30,31].…”