Pandemia de la COVID-19 y las Políticas de Salud Pública en el Perú: marzo-mayo 2020/COVID-19 pandemic and Public Health Policies in Peru: March-May 2020

Objetivo El trabajo tiene como objetivo analizar la dinámica del comportamiento de la COVID-19 en el Perú, estimar y evaluar el impacto de la política pública de supresión (cuarentena). Métodos El modelo epidemiológico SIR y la estimación con el método de Mínimos Cuadrados Ordinarios (MCO). Resultados Se encontró que el número básico de propagación (Ro) cayó de 6,0 a 3,2 habiéndose reducido en 54% por efecto de la estrategia de supresión, y dos meses después cayó a 1,7. Sin embargo, sigue siendo alto y evidencia que aún continúa en expansión el nivel de infectados, con los efectos sociales y económicos adversos que esta medida implica. Conclusión La COVID-19 es una enfermedad que crece exponencialmente, por lo cual, la política de salud basada en la estrategia de supresión ha permitido aplanar la curva de contagio, evitando el colapso del Sistema de Salud. Objective The objective of the study is to analyze the behavior dynamics of COVID-19 in Peru, estimate and evaluate the impact of the suppression public policy (quarantine). Methods The SIR epidemiological model and the estimation with the ordinary Least Squares (OLS) method. Results It was found that the basic number of propagation (Ro) fell from 6,0 to 3,2 having been reduced by 54% due to the suppression strategy;and two months later it falls to 1,7. However, it remains high and evidence that the level of those infected continues to expand with its adverse social and economic effects. Conclusion COVID-19 is a disease that grows exponentially, and that the health policy based on the suppression strategy has allowed to flatten the contagion curve, thus avoiding the collapse of the Health System.

Vaccination strategies in structured populations under partial immunity and reinfection

Optimal protocols of vaccine administration to minimize the effects of infectious diseases depend on a number of variables that admit different degrees of control. Examples include the characteristics of the disease and how it impacts on different groups of individuals as a function of sex, age or socioeconomic status, its transmission mode, or the demographic structure of the affected population. Here we introduce a compartmental model of infection propagation with vaccination and reinfection and analyze the effect that variations on the rates of these two processes have on the progression of the disease and on the number of fatalities. The population is split into two groups to highlight the overall effects on disease caused by different relationships between vaccine administration and various demographic structures. As a practical example, we study COVID-19 dynamics in various countries using real demographic data. The model can be easily applied to any other disease transmitted through direct interaction between infected and susceptible individuals, and any demographic structure, through a suitable estimation of parameter values. Two main conclusions stand out. First, the higher the fraction of reinfected individuals, the higher the likelihood that the disease becomes quasi-endemic. Second, optimal vaccine roll-out depends on demographic structure and disease fatality, so there is no unique vaccination protocol, valid for all countries, that minimizes the effects of a specific disease. Simulations of the general model can be carried out at this interactive webpage Atienza (2021 S2iyrd model simulator). © 2023 The Author(s). Published by IOP Publishing Ltd.

Nonlinear dynamic epidemiological analysis of effects of vaccination and dynamic transmission on COVID-19.

This paper is concerned with nonlinear modeling and analysis of the COVID-19 pandemic. We are especially interested in two current topics: effect of vaccination and the universally observed oscillations in infections. We use a nonlinear Susceptible, Infected, & Immune model incorporating a dynamic transmission rate and vaccination policy. The US data provides a starting point for analyzing stability, bifurcations and dynamics in general. Further parametric analysis reveals a saddle-node bifurcation under imperfect vaccination leading to the occurrence of sustained epidemic equilibria. This work points to the tremendous value of systematic nonlinear dynamic analysis in pandemic modeling and demonstrates the dramatic influence of vaccination, and frequency, phase, and amplitude of transmission rate on the persistent dynamic behavior of the disease.

Analyzing Commodity Prices in the Context of COVID-19, High Inflation, and the Ukrainian War: An Interview with James Hamilton

The following interview with Prof. James Hamilton was conducted in September 2022 by Dr. Fredj Jawadi with the assistance of Professor Adonis Yatchew in association with the 6th International Workshop on Financial Markets and Nonlinear Dynamics (FMND) held in Paris, France. The interview includes 20 questions related to commodity price dynamics. The aim of the discussion was, first, to help readers gain a better understanding of the factors driving commodity price volatility during the COVID-19 pandemic. Second, we analyzed commodity reactions to the ongoing Ukrainian war. Third, we examined the impact of changes in commodity prices on the economy as a whole and on inflation in particular. Finally, we discussed projections related to the dynamics of commodity prices in the future and the impact on the energy transition process. We hope that this interview will give readers clearer insights into the causes and consequences of commodity price changes and their evolution over time.

Inertia mass bio-sensors based on snap-through phenomena in electrostatic MEMS shallow arch resonators

This research examines on a new class of MEMS inertia mass sensors that are simple, sensitive, and selective in possibly detecting tiny objects. The sensor consists of two beams attached by an end-plate and is mounted on an electrostatically actuated shallow micro-arch. The presence of the end-plate overcomes the shortcoming of building inertia mass sensors using in-plane beam resonators. It gives more room to deposit detector material and, therefore, controls and mobilizes the quantity of the detector toward sensing a target. The design exploits the bistable behavior, resulting from the combination of the snap-through instability and the nonlinear force to move from one stable equilibrium to another. The transition can be controlled statically or dynamically depending on an operational modes. The eigenvalue problem assessment shows a considerable reduction in the first and third symmetrical resonant frequencies under DC voltage when a few picograms of the object substance are introduced. It is also corroborating that placing the added mass at the center of the end-plate and operating the sensor at vibration mode shape that dominated by the platform are more effective for mass detection through measuring the change in its frequency and bifurcation points. We found that superimposing the excitation signal to a small AC harmonic load, linear dynamic responses show a shift in the neighborhood of the first resonant frequency. On the other hand, increasing the actuation signal, dynamic responses show nonlinear trends offering possibilities to use the proposed design as a bifurcation-based type inertia sensor. This added mass leads to significant shifts at the locations of the bifurcation points compared to those in the absence of the object.

Nonlinear dynamic epidemiological analysis of effects of vaccination and dynamic transmission on COVID-19

This paper is concerned with nonlinear modeling and analysis of the COVID-19 pandemic. We are especially interested in two current topics: effect of vaccination and the universally observed oscillations in infections. We use a nonlinear Susceptible, Infected, & Immune model incorporating a dynamic transmission rate and vaccination policy. The US data provides a starting point for analyzing stability, bifurcations and dynamics in general. Further parametric analysis reveals a saddle-node bifurcation under imperfect vaccination leading to the occurrence of sustained epidemic equilibria. This work points to the tremendous value of systematic nonlinear dynamic analysis in pandemic modeling and demonstrates the dramatic influence of vaccination, and frequency, phase, and amplitude of transmission rate on the persistent dynamic behavior of the disease.

Limits of epidemic prediction using SIR models.

The Susceptible-Infectious-Recovered (SIR) equations and their extensions comprise a commonly utilized set of models for understanding and predicting the course of an epidemic. In practice, it is of substantial interest to estimate the model parameters based on noisy observations early in the outbreak, well before the epidemic reaches its peak. This allows prediction of the subsequent course of the epidemic and design of appropriate interventions. However, accurately inferring SIR model parameters in such scenarios is problematic. This article provides novel, theoretical insight on this issue of practical identifiability of the SIR model. Our theory provides new understanding of the inferential limits of routinely used epidemic models and provides a valuable addition to current simulate-and-check methods. We illustrate some practical implications through application to a real-world epidemic data set.

Piezoelectric Energy Harvesting from Low-Frequency Vibrations Based on Magnetic Plucking and Indirect Impacts.

This work proposes a mono-axial piezoelectric energy harvester based on the innovative combination of magnetic plucking and indirect impacts, e.g., impacts happening on the package of the harvester. The harvester exploits a permanent magnet placed on a non-magnetic mass, free to move within a predefined bounded region located in front of a piezoelectric bimorph cantilever equipped with a magnet as the tip mass. When the harvester is subjected to a low-frequency external acceleration, the moving mass induces an abrupt deflection and release of the cantilever by means of magnetic coupling, followed by impacts of the same mass against the harvester package. The combined effect of magnetic plucking and indirect impacts induces a frequency up-conversion. A prototype has been designed, fabricated, fastened to the wrist of a person by means of a wristband, and experimentally tested for different motion levels. By setting the magnets in a repulsive configuration, after 50 s of consecutive impacts induced by shaking, an energy of 253.41 µJ has been stored: this value is seven times higher compared to the case of harvester subjected to indirect impacts only, i.e., without magnetic coupling. This confirms that the combination of magnetic plucking and indirect impacts triggers the effective scavenging of electrical energy even from low-frequency non-periodical mechanical movements, such as human motion, while preserving the reliability of piezoelectric components.

Modeling dynamics of chemical reaction networks using electrical analogs: Application to autocatalytic reactions

Modeling complex chemical reaction networks has inspired a considerable body of research, and a variety of approaches to modeling nonlinear pathways are being developed. Here, a general methodology is formulated to convert an arbitrary reaction network into its equivalent electrical analog. The topological equivalence of the electrical analog is mathematically established for unimolecular reactions using Kirchoff's laws. The modular approach is generalized to bimolecular and nonlinear autocatalytic reactions. It is then applied to simulate the dynamics of nonlinear autocatalytic networks without making simplifying assumptions, such as use of the quasi-steady state/Bodenstein approximation and the assumption of an absence of nonlinear steps in the intermediates. This is among the few papers that quantify the dynamics of a nonlinear chemical reaction network by generating and simulating an electrical network analog. As a realistic biological application, the early phase of the spread of COVID-19 is modeled as an autocatalytic process, and the predicted dynamics are in good agreement with experimental data. The rate-limiting step of viral transmission is identified, leading to novel mechanistic insights.

Information Dynamics of Electric Field Intensity before and during the COVID-19 Pandemic.

This work investigates the temporal statistical structure of time series of electric field (EF) intensity recorded with the aim of exploring the dynamical patterns associated with periods with different human activity in urban areas. The analyzed time series were obtained from a sensor of the EMF RATEL monitoring system installed in the campus area of the University of Novi Sad, Serbia. The sensor performs wideband cumulative EF intensity monitoring of all active commercial EF sources, thus including those linked to human utilization of wireless communication systems. Monitoring was performed continuously during the years 2019 and 2020, allowing us to investigate the effects on the patterns of EF intensity of varying conditions of human mobility, including regular teaching and exam activity within the campus, as well as limitations to mobility related to the COVID-19 pandemic. Time series analysis was performed using both simple statistics (mean and variance) and combining the information-theoretic measure of information storage (IS) with the method of surrogate data to quantify the regularity of EF dynamic patterns and detect the presence of nonlinear dynamics. Moreover, to assess the possible coexistence of dynamic behaviors across multiple temporal scales, IS analysis was performed over consecutive observation windows lasting one day, week, month, and year, respectively coarse grained at time scales of 6 min, 30 min, 2 h, and 1 day. Our results document that the EF intensity patterns of variability are modulated by the movement of people at daily, weekly, and monthly scales, and are blunted during periods of restricted mobility related to the COVID-19 pandemic. Mobility restrictions also affected significantly the regularity of the EF intensity time series, resulting in lower values of IS observed simultaneously with a loss of nonlinear dynamics. Thus, our analysis can be useful to investigate changes in the global patterns of human mobility both during pandemics or other types of events, and from this perspective may serve to implement strategies for safety assessment and for optimizing the design of networks of EF sensors.

Analysis of non-pharmaceutical interventions impacts on COVID-19 pandemic in Iran.

The COVID-19 pandemic shows to have a huge impact on people's health and countries' infrastructures around the globe. Iran was one of the first countries that experienced the vast prevalence of the coronavirus outbreak. The Iranian authorities applied various non-pharmaceutical interventions to eradicate the epidemic in different periods. This study aims to investigate the effectiveness of non-pharmaceutical interventions in managing the current Coronavirus pandemic and to predict the next wave of infection in Iran. To achieve the research objective, the number of cases and deaths before and after the interventions was studied and the effective reproduction number of the infection was analyzed under various scenarios. The SEIR generic model was applied to capture the dynamic of the pandemic in Iran. To capture the effects of different interventions, the corresponding reproduction number was considered. Depending on how people are responsive to interventions, the effectiveness of each intervention has been investigated. Results show that the maximum number of the total of infected individuals will occur around the end of May and the start of June 2021. It is concluded that the outbreak could be smoothed if full lockdown and strict quarantine continue. The proposed modeling could be used as an assessment tool to evaluate the effects of different interventions in new outbreaks.

A Modified Iterative Algorithm for Numerical Investigation of HIV Infection Dynamics

The human immunodeficiency virus (HIV) mainly attacks CD4+ T cells in the host. Chronic HIV infection gradually depletes the CD4+ T cell pool, compromising the host’s immunological reaction to invasive infections and ultimately leading to acquired immunodeficiency syndrome (AIDS). The goal of this study is not to provide a qualitative description of the rich dynamic characteristics of the HIV infection model of CD4+ T cells, but to produce accurate analytical solutions to the model using the modified iterative approach. In this research, a new efficient method using the new iterative method (NIM), the coupling of the standard NIM and Laplace transform, called the modified new iterative method (MNIM), has been introduced to resolve the HIV infection model as a class of system of ordinary differential equations (ODEs). A nonlinear HIV infection dynamics model is adopted as an instance to elucidate the identification process and the solution process of MNIM, only two iterations lead to ideal results. In addition, the model has also been solved using NIM and the fourth order Runge–Kutta (RK4) method. The results indicate that the solutions by MNIM match with those of RK4 method to a minimum of eight decimal places, whereas NIM solutions are not accurate enough. Numerical comparisons between the MNIM, NIM, the classical RK4 and other methods reveal that the modified technique has potential as a tool for the nonlinear systems of ODEs.

Nel 2050. Passaggio al nuovo mondo

According to the International Risk Governance Council, interconnectivity within and between complex adaptive systems is one of the characteristics that define and determine today's world. The systemic nature of the Coronavirus spread is widely considered a paradigmatic example of this vulnerability: the pervasiveness and modalities of manifestation of the health crisis, and the consequent cascade developments, have produced multiple impacts which, going beyond the strictly epidemiological aspect, they poured on anthropic and ecological systems, according to non-linear dynamics, growing exponentially to involve every aspect of our interdependent world (UN ODRR, 2021). Factors such as climate change, loss of biodiversity, intensively exploitation of agrifood systems, together with the resources depletion and the reduction of natural habitats, which favor zoonoses responsible for pandemic infections, are associated with widespread social, health and environmental inequalities, creating contexts of marginalization, in which the effects of crises are less controllable and therefore more devastating (Cogliati Denza, 2020). The discussion is articulated around three crucial dates: 1989 which marks the start of the single market with the definitive collapse of Communism;2020 which, with the Pandemic, brought out the limits and contradictions of the globalized society;2050, which is considered the deadline for the survival of the Planet, the horizon within which, according to the scientific community, greenhouse gas emissions must be eliminated to prevent climate change from causing the collapse of the Earth.

COVID-19 Lockdown Unravels the Complex Interplay between Environmental Conditions and Human Activity

During the COVID-19 epidemic, draconian countermeasures forbidding nonessential human activities have been adopted in several countries worldwide, providing an unprecedented setup for testing and quantifying the current impact of humankind on climate and for driving potential sustainability policies in the postpandemic era from a perspective of complex systems. In this study, we consider heterogeneous sources of environmental and human activity observables, considered as components of a complex socioenvironmental system, and apply information theory, network science, and Bayesian inference to analyze their structural relations and nonlinear dynamics between January 2019 and August 2020 in northern Italy, i.e., before, during, and after the national lockdown. The topological structure of a complex system strongly impacts its collective behavior;therefore, mapping this structure is essential to fully understand the functions of the system as a whole and its fragility to unexpected disruptions or shocks. To this aim, we unravel the causal relationships between the 16 environmental conditions and human activity variables, mapping the backbone of the complex interplay between intervening physical observables—such as NO2 emissions, energy consumption, intervening climate variables, and different flavors of human mobility flows—to a causal network model. To identify a tipping point during the period of observation, denoting the presence of a regime shift between distinct network states (i.e., before and during the shock), we introduce a novel information-theoretic method based on statistical divergence widely used in statistical physics. We find that despite a measurable decrease in NO2 concentration, due to an overall decrease in human activities, locking down a region as a climate change mitigation is an insufficient remedy to reduce emissions. Our results provide a functional characterization of socioenvironmental interdependent systems, and our analytical framework can be used, more generally, to characterize environmental changes and their interdependencies using statistical physics.

The art of fitting ordinary differential equations models to experimental results

Advanced fitting of ordinary differential equations models to experimental results is presented within the context of different academic levels of students and diverse research fields. In many areas, the analysis of experimental results cannot be restricted to cases where particular solutions of the models' differential equations, valid only for specific limit conditions, apply. In those cases, analytical mathematical equations are not available and a complete description of the systems extends beyond the numerical minimization of statistical estimators, like the chi-square, because it requires solving numerically the models' differential equations. Dedicated fitting procedures that involve the interdependent processes of solving the ordinary differential equations and fitting the numerical solutions to the experimental results are required to obtain the best fitting sets of parameters with consistent physical meaning. A simple, but powerful, web-based ordinary differential equations solver and fitter is presented, and used to analyse both the complete motion of a rigid pendulum and the dynamics of a viral infection.

Estimation of the Basic Reproduction Number for the COVID-19 Pandemic in Minnesota

This paper focuses on the dynamics of the COVID-19 pandemic and estimation of associated real-time variables characterizing disease spread. A nonlinear dynamic model is developed which enhances the traditional SEIR epidemic model to include additional variables of hospitalizations, ICU admissions, and deaths. A 6-month data set containing Minnesota data on infections, hospital-ICU admissions and deaths is used to find least-squares solutions to the parameters of the model. The model is found to fit the measured data accurately. Subsequently, a cascaded observer is developed to find real-time values of the infected population, the infection rate, and the basic reproduction number. The observer is found to yield good real-time estimates that match the least-squares parameters obtained from the complete data set. The importance of the work is that it enables real-time estimation of the basic reproduction number which is a key variable for controlling disease spread. Copyright © 2021 The Authors. This is an open access article under the CC BY-NC-ND license

Epidemic oscillations induced by social network control

Epidemic spreading can be suppressed by the introduction of containment measures such as social distancing and lockdowns. Yet, when such measures are relaxed, new epidemic waves and infection cycles may occur. Here we explore this issue in compartmentalized epidemic models on graphs in presence of a feedback between the infection state of the population and the structure of its social network for the case of discontinuous control. We show that in random graphs the effect of containment measures is simply captured by a renormalization of the effective infection rate that accounts for the change in the branching ratio of the network. In our simple setting, a piece-wise mean-field approximation can be used to derive analytical formulae for the number of epidemic waves and their length. A variant of the model with imperfect information is used to model data of the recent COVID-19 epidemics in the Basque Country and Lombardy, where we estimate the extent of social network disruption during lockdowns and characterize the dynamical trajectories in the phase space.

The correlation analysis of the daily Covid-19 new cases data series in Albania

We analyzed herein the new covid-19 daily positive cases recorded in Albania. We observed that the distribution of the daily new cases is non-stationary and usually has a power law behavior in the low incidence zone, and a bell curve for the remaining part of the incidence interval. We qualified this finding as the indicator intensive dynamics and as proof that up now, the heard immunity has not been reached. By parallelizing the preferential attachment mechanisms responsible for a power law distribution in the social graphs elsewhere, we explain the low daily incidence distribution as result of the imprudent gatherings of peoples. Additionally, the bell-shaped distribution observed for the high daily new cases is agued as outcome of the competition between illness advances and restriction measures. The distribution is acceptably smooth, meaning that the management has been accommodated appropriately. This behavior is observed also for two neighbor countries Greece and Italy respectively, but was not observed for Turkey, Serbia, and North Macedonia. Next, we used the multifractal analysis to conclude about the features related with heterogeneity of the data. We have identified the local presence self-organization behavior in some separate time intervals. Formally and empirically we have identified that the full set of the data contain two regimes finalized already, followed by a third one which started in July 2021. © 2021 Institute of Physics Publishing. All rights reserved.

The impact of COVID-19 on the worldwide air transportation network.

Air travel has been one of the hardest hit industries of COVID-19, with many flight cancellations and airport closures as a consequence. By analysing structural characteristics of the Official Aviation Guide flight data, we show that this resulted in an increased average distance between airports, and in an increased number of long-range routes. Based on our study of network robustness, we uncover that this disruption is consistent with the impact of a mixture of targeted and random global attack on the worldwide air transportation network. By considering the individual functional evolution of airports, we identify anomalous airports with high centrality but low degree, which further enables us to reveal the underlying transitions among airport-specific representations in terms of both geographical and geopolitical factors. During the evolution of the air transportation network, we also observe how the network attempted to cope by shifting centralities between different airports around the world. Since these shifts are not aligned with optimal strategies for minimizing delays and disconnects, we conclude that they are consistent with politics trumping science from the viewpoint of epidemic containment and transport.

Modeling viral dynamics in SARS-CoV-2 infection based on differential equations and numerical analysis.

Computational modeling and simulation of viral dynamics would explain the pathogenesis for any virus. Such computational attempts have been successfully made to predict and control HIV-1 or hepatitis B virus. However, the dynamics for SARS-CoV-2 has not been adequately investigated. The purpose of this research is to propose different SARS-CoV-2 dynamics models based on differential equations and numerical analysis towards distilling the models to explain the mechanism of SARS-CoV-2 pathogenesis. The proposed four models formalize the dynamical system of SARS-CoV-2 infection, which consists of host cells and viral particles. These models undergo numerical analysis, including sensitivity analysis and stability analysis. Based on the sensitivity indices of the four models' parameters, the four models are simplified into two models. In advance of the following calibration experiments, the eigenvalues of the Jacobian matrices of these two models are calculated, thereby guaranteeing that any solutions are stable. Then, the calibration experiments fit the simulated data sequences of the two models to two observed data sequences, SARS-CoV-2 viral load in mild cases and that in severe cases. Comparing the estimated parameters in mild cases and severe cases indicates that cell-to-cell transmission would significantly correlate to the COVID-19 severity. These experiments for modeling and simulation provide plausible computational models for the SARS-CoV-2 dynamics, leading to further investigation for identifying the essential factors in severe cases.