What Can We Learn from the Time Evolution of COVID-19 Epidemic in Slovenia?

Abstract

A recent work (DOI 10.1101/2020.05.06.20093310) indicated that temporarily splitting larger populations into smaller groups can efficiently mitigate the spread of SARS-CoV-2 virus. The fact that, soon afterwards, on May 15, 2020, the two million people Slovenia was the first European country proclaiming the end of COVID-19 epidemic within national borders may be relevant from this perspective. Motivated by this evolution, in this paper we investigate the time dynamics of coronavirus cases in Slovenia with emphasis on how efficient various containment measures act to diminish the number of COVID-19 infections. Noteworthily, the present analysis does not rely on any speculative theoretical assumption; it is solely based on raw epidemiological data. Out of the results presented here, the most important one is perhaps the finding that, while imposing drastic curfews and travel restrictions reduce the infection rate kappa by a factor of four with respect to the unrestricted state, they only improve the kappa-value by ~15 % as compared to the much bearable state of social and economical life wherein (justifiable) wearing face masks and social distancing rules are enforced/followed. Significantly for behavioral and social science, our analysis of the time dependence kappa = kappa(t) may reveal an interesting self-protection instinct of the population, which became manifest even before the official lockdown enforcement.

Quantifying Inaccuracies in Modeling COVID-19 Pandemic within a Continuous Time Picture

“…Concerning the κ-independence of τ , one should note that that the above value κ = 0.42 day −1 corresponds to a regime wherein infections were found to growth exponentially in time while κ = 0.07 day −1 is almost half the κ-value at complete lockdown. 28 To conclude, the above finding contradicts the expectation based on naive intuition that faster population dynamics is poor(er) describable in continuous time, or, put differently, that slower infections are better described within a continuous time approach.…”

“…This backward shift in time τ ≡ T c 1/2 − T d 1/2 < 0 (which does not affect the height of the epi peak) amounts to τ ≈ −2.65 day, a robust estimate, which was found to be independent of the infection rate κ ranging from large values deduced for situations where 10 COVID-19 infections growth exponentially in time to small values obtained at complete lockdown. 28 This finding contradicts naive intuition expecting that faster infections (larger κ) are poorer described within continuous time approaches than slower infections (smaller κ).…”

“…[26][27][28] As compared to various SIR-based approaches, the logistic growth model possesses a great advantage: it can be directly validated against ongoing epidemiological reports. 28 In the continuous time description, logistic growth in time of an infected population obeys a first-order differential equation…”

“…[12][13][14][15] Along with numerous applications in various other fields, [16][17][18][19][20][21] this model turned out to be also useful in previous studies on epidemics, [22][23][24][25] also including COVID-19 pandemic. [26][27][28] As compared to various SIR-based approaches, the logistic growth model possesses a great advantage: it can be directly validated against ongoing epidemiological reports. 28 In the continuous time description, logistic growth in time of an infected population obeys a first-order differential equation…”

“…the value n 0 /N merely serves to appropriately set the time origin. To be specific, guided by our recent study, 28 in all the figures presented below we used n 0 /N ≈ 1/1500. However, as just said, this particular choice does by no means affect the generality of the results reported below.…”