Global prediction model for COVID-19 pandemic with the characteristics of the multiple peaks and local fluctuations

Abstract

Background With the spread of COVID-19, the time-series prediction of COVID-19 has become a research hotspot. Unlike previous epidemics, COVID-19 has a new pattern of long-time series, large fluctuations, and multiple peaks. Traditional dynamical models are limited to curves with short-time series, single peak, smoothness, and symmetry. Secondly, most of these models have unknown parameters, which bring greater ambiguity and uncertainty. There are still major shortcomings in the integration of multiple factors, such as human interventions, environmental factors, and transmission mechanisms. Methods A dynamical model with only infected humans and removed humans was established. Then the process of COVID-19 spread was segmented using a local smoother. The change of infection rate at different stages was quantified using the continuous and periodic Logistic growth function to quantitatively describe the comprehensive effects of natural and human factors. Then, a non-linear variable and NO2 concentrations were introduced to qualify the number of people who have been prevented from infection through human interventions. Results The experiments and analysis showed the R2 of fitting for the US, UK, India, Brazil, Russia, and Germany was 0.841, 0.977, 0.974, 0.659, 0.992, and 0.753, respectively. The prediction accuracy of the US, UK, India, Brazil, Russia, and Germany in October was 0.331, 0.127, 0.112, 0.376, 0.043, and 0.445, respectively. Conclusion The model can not only better describe the effects of human interventions but also better simulate the temporal evolution of COVID-19 with local fluctuations and multiple peaks, which can provide valuable assistant decision-making information.

Planning for healthcare services during the COVID-19 pandemic in the Southeast of England: a system dynamics modelling approach

“…Additionally, work previously conducted for the Kent JSNA 8 was used to determine risk stratification of the population, which was included as an additional demographic factor. Dai et al 38 employed a variation of the SEIR model, including a loop to return recovered individuals to the susceptible population. Their model demonstrated ability to accurately predict historical trends.…”

Diffusion and Prediction of Covid-19 Pandemic: Limits of Models and Strategies to Improve Outlook and Preparedness to Face Next Pandemics

“…Hence, since the forecasting of pandemics in the short and long run using current epidemiologic models, as all human activity of prediction, has manifold shortcomings and can provide misleading results, the main goals of this study are twofold: first, the analysis of anthropogenic activities and factors that may trigger pandemic threats; the planning of optimal strategies to reduce the hazards and risks of emergence of pandemic threat and/or in the initial phase to reduce negative impact associated with the emergence and diffusion of new viruses that can generate problems on public health, environment and socioeconomic systems. In particular, the investigation and understanding of sources and driving factors concerning the emergence and diffusion of new pandemics have critical aspects for designing appropriate strategic actions of prevision, prevention and planning of effective policy responses to improve preparedness to cope with next pandemic crises and health emergencies (Coccia, 2022c;Dai et al, 2022;Krechetov et al, 2022;Kuvvetli et al, 2021;Liu et al, 2022;Šušteršič et al, 2021). Hence, this study endeavors, whenever possible, to clarify these problems to increase the knowledge of the sources and factor determining the emergence of new viral agents in order to design optimal response policies to face next pandemic diseases similar to COVID-19 (Farazmand, 2001(Farazmand, , 2014.…”