The starting dates of COVID-19 multiple waves

Impact of vaccine measures on the transmission dynamics of COVID-19

In many nations, efforts to prevent and control COVID-19 have been significantly impeded by the SARS-CoV-2 virus ongoing mutation. The Omicron strain, a more recent and prevalent strain, has had more significant detrimental effects in countries worldwide. To investigate the impact of the Omicron BA.2 strain on vaccine efficacy, we proposed a model with vaccination and immunological decline in this research. Then, we fitted our model based on the number of daily new instances reported by the government in Jilin and Shanghai, China. We estimated the effective reproduction number Re = 4.71 for the Jilin and Re = 3.32 for Shanghai. Additionally, we do sensitivity analysis to identify the critical factors affecting the effective reproduction number Re. It was found that vaccination rate, effectiveness rate, and declining rate had a significant effect on Re. Further, we investigate the relevant parameter thresholds that make Re lower than unity. Finally, rich numerical experiments were then carried out. We observed that even when vaccine efficiency was not high, increasing vaccination rates had a significant effect on early disease transmission, that limiting social distance was the most economical and rational measure to control the spread of disease, and that for a short period, reducing immune decline was not significant in curbing disease transmission.

Uncertainty quantification in mechanistic epidemic models via cross-entropy approximate Bayesian computation

This paper proposes a data-driven approximate Bayesian computation framework for parameter estimation and uncertainty quantification of epidemic models, which incorporates two novelties: (i) the identification of the initial conditions by using plausible dynamic states that are compatible with observational data; (ii) learning of an informative prior distribution for the model parameters via the cross-entropy method. The new methodology's effectiveness is illustrated with the aid of actual data from the COVID-19 epidemic in Rio de Janeiro city in Brazil, employing an ordinary differential equation-based model with a generalized SEIR mechanistic structure that includes time-dependent transmission rate, asymptomatics, and hospitalizations. A minimization problem with two cost terms (number of hospitalizations and deaths) is formulated, and twelve parameters are identified. The calibrated model provides a consistent description of the available data, able to extrapolate forecasts over a few weeks, making the proposed methodology very appealing for real-time epidemic modeling.

Five waves of the COVID-19 pandemic and green-blue spaces in urban and rural areas in Poland

Several waves of COVID-19 caused by different SARS-CoV-2 variants have been recorded worldwide. During this period, many publications were released describing the influence of various factors, such as environmental, social and economic factors, on the spread of COVID-19. This paper presents the results of a detailed spatiotemporal analysis of the course of COVID-19 cases and deaths in five waves in Poland in relation to green‒blue spaces. The results, based on 380 counties, reveal that the negative correlation between the indicator of green‒blue space per inhabitant and the average daily number of COVID-19 cases and deaths was clearly visible during all waves. These relationships were described by a power equation (coefficient of determination ranging from 0.83 to 0.88) with a high level of significance. The second important discovery was the fact that the rates of COVID-19 cases and deaths were significantly higher in urban counties (low values of the green-blue space indicator in m²/people) than in rural areas. The developed models can be used in decision-making by local government authorities to organize anti-COVID-19 prevention measures, including local lockdowns, especially in urban areas.

Multiple waves of COVID-19: a pathway model approach

A generalized pathway model, with time-dependent parameters, is applied to describe the mortality curves of the COVID-19 disease for several countries that exhibit multiple waves of infections. The pathway approach adopted here is formulated explicitly in time, in the sense that the model's growth rate for the number of deaths or infections is written as an explicit function of time, rather than in terms of the cumulative quantity itself. This allows for a direct fit of the model to daily data (new deaths or new cases) without the need of any integration. The model is applied to COVID-19 mortality curves for ten selected countries and found to be in very good agreement with the data for all cases considered. From the fitted theoretical curves for a given location, relevant epidemiological information can be extracted, such as the starting and peak dates for each successive wave. It is argued that obtaining reliable estimates for such characteristic points is important for studying the effectiveness of interventions and the possible negative impact of their relaxation, as it allows for a direct comparison of the time of adoption/relaxation of control measures with the peaks and troughs of the epidemic curve.

Extreme COVID-19 waves reveal hyperexponential growth and finite-time singularity

Coronavirus disease 2019 (COVID-19) has rapidly spread throughout our planet, bringing human lives to a standstill. Understanding the early transmission dynamics of a wave helps plan intervention strategies such as lockdowns that mitigate further spread, minimizing the adverse impact on humanity and the economy. Exponential growth of infections was thought to be the defining feature of an epidemic in its initial growth phase. Here we show that, contrary to common belief, early stages of extreme COVID-19 waves have an unbounded growth and finite-time singularity accompanying a hyperexponential power-law. The faster than exponential growth phase is hazardous and would entail stricter regulations to minimize further spread. Such a power-law description allows us to characterize COVID-19 waves better using single power-law exponents, rather than using piecewise exponentials. Furthermore, we identify the presence of log-periodic patterns decorating the power-law growth. These log-periodic oscillations may enable better prediction of the finite-time singularity. We anticipate that our findings of hyperexponential growth and log-periodicity will enable accurate modeling of outbreaks of COVID-19 or similar future outbreaks of other emergent epidemics.