COVID-19 epidemic peaks distribution in the United-States of America, from epidemiological modeling to public health policies.

COVID-19 prediction models are characterized by uncertainties due to fluctuating parameters, such as changes in infection or recovery rates. While deterministic models often predict epidemic peaks too early, incorporating these fluctuations into the SIR model can provide a more accurate representation of peak timing. Predicting R0, the basic reproduction number, remains a major challenge with significant implications for government policy and strategy. In this study, we propose a tool for policy makers to show the effects of possible fluctuations in policy strategies on different R0 levels. Results show that epidemic peaks in the United States occur at varying dates, up to 50, 87, and 82 days from the beginning of the second, third, and fourth waves. Our findings suggest that inaccurate predictions and public health policies may result from underestimating fluctuations in infection or recovery rates. Therefore, incorporating fluctuations into SIR models should be considered when predicting epidemic peak times to inform appropriate public health responses.

Delayed epidemic peak caused by infection and recovery rate fluctuations.

Forecasting epidemic scenarios has been critical to many decision-makers in imposing various public health interventions. Despite progresses in determining the magnitude and timing of epidemics, epidemic peak time predictions for H1N1 and COVID-19 were inaccurate, with the peaks delayed with respect to predictions. Here, we show that infection and recovery rate fluctuations play a critical role in peak timing. Using a susceptible-infected-recovered model with daily fluctuations on control parameters, we show that infection counts follow a lognormal distribution at the beginning of an epidemic wave, similar to price distributions for financial assets. The epidemic peak time of the stochastic solution exhibits an inverse Gaussian probability distribution, fitting the spread of the epidemic peak times observed across Italian regions. We also show that, for a given basic reproduction number R0, the deterministic model anticipates the peak with respect to the most probable and average peak time of the stochastic model. The epidemic peak time distribution allows one for a robust estimation of the epidemic evolution. Considering these results, we believe that the parameters' dynamical fluctuations are paramount to accurately predict the epidemic peak time and should be introduced in epidemiological models.

Interrupting vaccination policies can greatly spread SARS-CoV-2 and enhance mortality from COVID-19 disease: The AstraZeneca case for France and Italy.

Several European countries have suspended the inoculation of the AstraZeneca vaccine out of suspicion that it causes deep vein thrombosis. In this letter, we report some Fermi estimates performed using a stochastic model aimed at making a risk-benefit analysis of the interruption of the delivery of the AstraZeneca vaccine in France and Italy. Our results clearly show that excess deaths due to the interruption of the vaccination campaign injections largely overrun those due to thrombosis even in worst case scenarios of frequency and gravity of the vaccine side effects.