Unpredictable, Counter-Intuitive Geoclimatic and Demographic Correlations of COVID-19 Spread Rates

Abstract

We present spread parameters for first and second waves of the COVID-19 pandemic for USA states, and for consecutive nonoverlapping periods of 20 days for the USA and 51 countries across the globe. We studied spread rates in the USA states and 51 countries, and analyzed associations between spread rates at different periods, and with temperature, elevation, population density and age. USA first/second wave spread rates increase/decrease with population density, and are uncorrelated with temperature and median population age. Spread rates are systematically inversely proportional to those estimated 80–100 days later. Ascending/descending phases of the same wave only partially explain this. Directions of correlations with factors such as temperature and median age flip. Changes in environmental trends of the COVID-19 pandemic remain unpredictable; predictions based on classical epidemiological knowledge are highly uncertain. Negative associations between population density and spread rates, observed in independent samples and at different periods, are most surprising. We suggest that systematic negative associations between spread rates 80–100 days apart could result from confinements selecting for greater contagiousness, a potential double-edged sword effect of confinements.

Age Dependent Epidemic Modeling of COVID-19 Outbreak in Kuwait, France, and Cameroon

“…For example, on the site of Johns Hopkins University dedicated to COVID-19 [ 1 ], data clearly shows this influence on the case fatality rate corresponding to the cumulative deaths recorded 5 months after the beginning of the outbreak (12 May 2020) vs. the median age of many countries in 2017 ( Figure 1 ). This first observation has been confirmed by many studies in different countries [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ] such as in France ( Figure 2 ), and we will confirm in this paper that the age of the patients suffering from COVID-19 is a good predictor of severity.…”

Estimation of Daily Reproduction Numbers during the COVID-19 Outbreak

“…In the recipient of the virus, individual or public policies of prevention, protection, eviction or vaccination, which evolve according to the epidemic severity and the awareness of individuals and socio-political forces, can change the sensitivity of the susceptible individuals [32].…”

“…The dynamical stability for L 2 distance to the stationary infection age pyramid P = lim j X j /Σ i=j,j−r+1 X i is related to |λ − λ |, the modulus of the difference between the dominant and sub-dominant eigenvalues of L, namely λ = e R and λ , where R is the Malthusian growth rate and P is the left eigenvector of L corresponding to λ. The dynamical stability for the distance (or symmetrized divergence) of Kullback-Leibler to P considered as stationary distribution is related to the population entropy H [26][27][28][29][30][31][32], which is defined if l j = ∏ i=1,j−1 b i and p j = l j R j /λ j , as follows: H = −Σ j=1,r p j Log(p j )/Σ j=1,r j p j (18) The mathematical characterization by the population entropy defined in Equation ( 16) of the stochastic stability of the dynamics described by Equation ( 16) has its origin in the theory of large deviations [40][41][42]. This notion of stability pertains to the rate at which the system returns to its steady state after a random exogenous and/or endogenous perturbation and it could be useful to quantify further the variations of the distribution of the daily reproduction numbers observed for many countries [43][44][45][46][47][48][49][50][51][52][53].…”

Effects of antioxidant supplement on immune health and physical wellbeing: A randomized, controlled trial

“…This study was conducted during the COVID-19 pandemic which caused underlying stress due to its unpredictability in spread [13] . Global stress is known to be related to altered sleep patterns [9] .…”