Dynamics Of COVID-19 Outbreak Research Articles

Effect of Long-Distance Domestic Travel Ban Policies in Japan on COVID-19 Outbreak Dynamics During Dominance of the Ancestral Strain: Ex Post Facto Retrospective Observation Study

Background In Japan, long-distance domestic travel was banned while the ancestral SARS-CoV-2 strain was dominant under the first declared state of emergency from March 2020 until the end of May 2020. Subsequently, the “Go To Travel” campaign travel subsidy policy was activated, allowing long-distance domestic travel, until the second state of emergency as of January 7, 2021. The effects of this long-distance domestic travel ban on SARS-CoV-2 infectivity have not been adequately evaluated. Objective We evaluated the effects of the long-distance domestic travel ban in Japan on SARS-CoV-2 infectivity, considering climate conditions, mobility, and countermeasures such as the “Go To Travel” campaign and emergency status. Methods We calculated the effective reproduction number R(t), representing infectivity, using the epidemic curve in Kagoshima prefecture based on the empirical distribution of the incubation period and procedurally delayed reporting from an earlier study. Kagoshima prefecture, in southern Japan, has several resorts, with an airport commonly used for transportation to Tokyo or Osaka. We regressed R(t) on the number of long-distance domestic travelers (based on the number of airport limousine bus users provided by the operating company), temperature, humidity, mobility, and countermeasures such as state of emergency declarations and the “Go To Travel” campaign in Kagoshima. The study period was June 20, 2020, through February 2021, before variant strains became dominant. A second state of emergency was not declared in Kagoshima prefecture but was declared in major cities such as Tokyo and Osaka. Results Estimation results indicated a pattern of declining infectivity with reduced long-distance domestic travel volumes as measured by the number of airport limousine bus users. Moreover, infectivity was lower during the “Go To Travel” campaign and the second state of emergency. Regarding mobility, going to restaurants, shopping malls, and amusement venues was associated with increased infectivity. However, going to grocery stores and pharmacies was associated with decreased infectivity. Climate conditions showed no significant association with infectivity patterns. Conclusions The results of this retrospective analysis suggest that the volume of long-distance domestic travel might reduce SARS-CoV-2 infectivity. Infectivity was lower during the “Go To Travel” campaign period, during which long-distance domestic travel was promoted, compared to that outside this campaign period. These findings suggest that policies banning long-distance domestic travel had little legitimacy or rationale. Long-distance domestic travel with appropriate infection control measures might not increase SARS-CoV-2 infectivity in tourist areas. Even though this analysis was performed much later than the study period, if we had performed this study focusing on the period of April or May 2021, it would likely yield the same results. These findings might be helpful for government decision-making in considering restarting a “Go To Travel” campaign in light of evidence-based policy.

Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems

Understanding real-world dynamical phenomena remains a challenging task. Across various scientific disciplines, machine learning has advanced as the go-to technology to analyze nonlinear dynamical systems, identify patterns in big data, and make decision around them. Neural networks are now consistently used as universal function approximators for data with underlying mechanisms that are incompletely understood or exceedingly complex. However, neural networks alone ignore the fundamental laws of physics and often fail to make plausible predictions. Here we integrate data, physics, and uncertainties by combining neural networks, physics informed modeling, and Bayesian inference to improve the predictive potential of traditional neural network models. We embed the physical model of a damped harmonic oscillator into a fully-connected feed-forward neural network to explore a simple and illustrative model system, the outbreak dynamics of COVID-19. Our Physics Informed Neural Networks seamlessly integrate data and physics, robustly solve forward and inverse problems, and perform well for both interpolation and extrapolation, even for a small amount of noisy and incomplete data. At only minor additional cost, they self-adaptively learn the weighting between data and physics. They can serve as priors in a Bayesian Inference, and provide credible intervals for uncertainty quantification. Our study reveals the inherent advantages and disadvantages of Neural Networks, Bayesian Inference, and a combination of both and provides valuable guidelines for model selection. While we have only demonstrated these different approaches for the simple model problem of a seasonal endemic infectious disease, we anticipate that the underlying concepts and trends generalize to more complex disease conditions and, more broadly, to a wide variety of nonlinear dynamical systems. Our source code and examples are available at https://github.com/LivingMatterLab/xPINNs.

Spatiotemporal modeling of first and second wave outbreak dynamics of COVID-19 in Germany

The COVID-19 pandemic has kept the world in suspense for the past year. In most federal countries such as Germany, locally varying conditions demand for state- or county-level decisions to adapt to the disease dynamics. However, this requires a deep understanding of the mesoscale outbreak dynamics between microscale agent models and macroscale global models. Here, we use a reparameterized SIQRD network model that accounts for local political decisions to predict the spatiotemporal evolution of the pandemic in Germany at county resolution. Our optimized model reproduces state-wise cumulative infections and deaths as reported by the Robert Koch Institute and predicts the development for individual counties at convincing accuracy during both waves in spring and fall of 2020. We demonstrate the dominating effect of local infection seeds and identify effective measures to attenuate the rapid spread. Our model has great potential to support decision makers on a state and community politics level to individually strategize their best way forward during the months to come.

The dynamics of COVID-19 outbreak in Nigeria: A sub-national analysis

The African health crisis feared at the beginning of the COVID-19 pandemic has not materialized, and there is interest globally in understanding possible peculiarities in COVID-19 outbreak dynamics in the tropics and sub-tropics that have led to a much milder African outbreak than initial projections. Towards this, Susceptible-Infected-Recovered-Dead compartmental models were fitted to COVID-19 data from all Nigerian states in this study, from which four parameters were estimated per state. A density-based clustering method was used to identify states with similar outbreak dynamics, and the stage of the outbreak determined per state. Subsequently, outbreak dynamics were correlated with absolute humidity, temperature, population density and distance to the international passenger travel gateways in the country. The models revealed that while the outbreak is still increasing nationally, outbreaks in at least 12 states have peaked. A total of at least 519,672 confirmed cases were predicted by January 2021, with a worst case scenario of at least 14,785,457. Weak positive correlations were found between COVID-19 spread and absolute humidity (Pearson’s Coefficient = 0.136, p< 0.05) and temperature (Pearson’s Coefficient = 0.021, p< 0.05). While many studies have established links between temperature and humidity and COVID-19 spread, the correlation has most usually been negative where it exists. The findings in this study of possible positive correlation is in line with a number of previous studies showing such unexpected correlations in the tropics or subtropics. This highlights even more the importance of additional studies on COVID-19 dynamics in Africa.

Understanding the Dynamics of COVID-19 Outbreak: Structure, Diagnosis, Prevention and Treatment

Currently, mankind is fighting against an invisible enemy. The novel Coronavirus disease (COVID-19) has been spreading at a rapid rate across the world, which made the World Health Organization (WHO) to declare it as pandemic disease. COVID-19 is an acute respiratory tract infection that was first reported in December 2019, initially presented as pneumonia of unknown etiology in a group of patients in Wuhan, a city in the Hubei Province of China. Sometimes, animals infected with coronavirus infect humans and spread further via human-to-human transmission similar to the case of Middle East respiratory syndrome coronavirus (MERS-CoV), severe acute respiratory syndrome-related coronavirus (SARS-CoV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), previously referred to as 2019-nCoV (COVID-19). SARS-- CoV-2 is an extremely pathogenic virus and is crushing the well-equipped health systems of developed countries. According to the WHO situation report updated on October 25, 2020 over 42 million cases and 1.1 million deaths have been reported globally. Since the figure of cases continues to increase, these viruses create a threat to global public health. This review summarizes an overview of the study of the novel coronavirus, including origin, epidemiology, etiology, targets for viral entry, and describes the clinical manifestation, diagnosis, and therapeutics used in clinical settings. Furthermore, our review focuses on the most up-to-date clinical information for the effective management, prevention, and counseling to control COVID-19 worldwide.

A six compartments with time-delay model SHIQRD for the COVID-19 pandemic in India: During lockdown and beyond

Dynamics of COVID-19 outbreak in India are different from other countries for her huge population and different administrative approaches. This paper presents a six compartments (SHIQRD) model with different time delays between them, to analyse and forecast the COVID-19 pandemic in India. The model introduces separate compartments for ‘home-quarantined’, ‘quarantined infectious’, and ‘undetected infectious’ pertaining to the Indian scenario. It also incorporates incubation time, sample testing time, and recovery window as time delays between blocks. With the proposed model, reproduction number of different phases during lock-down are evaluated. Besides, dynamics of pandemic for the next one year are also explored.

Transmission dynamics of COVID-19 outbreak in India and effectiveness of self-quarantine: a phase-wise data driven analysis

Transmission dynamics of COVID-19 outbreak in India and effectiveness of self-quarantine: a phase-wise data driven analysis

A Human-Pathogen Model for COVID-19 Outbreak: Flattening Epidemic Curve in Kenya

This work introduces a deterministic model of COVID-19 spread aimed at analyzing non-pharmaceutical interventions in Kenya. The model accounts for symptomatic, asymptomatic and environmental transmission. Using the SEIR (Susceptible-Exposed- Infected-Recovered) compartmental model with additional component of the pathogen, the dynamics of COVID-19 outbreak is simulated while focussing on the impact of different control measures in the reduction of the basic reproduction number.

New statistical RI index allow to better track the dynamics of COVID-19 outbreak in Italy

COVID-19 pandemic in Italy displayed a spatial distribution that made the tracking of its time course quite difficult. The most relevant anomaly was the marked spatial heterogeneity of COVID-19 diffusion. Lombardia region accounted for around 60% of fatal cases (while hosting 15% of Italian population). Moreover, 86% of fatalities concentrated in four Northern Italy regions. The ‘explosive’ outbreak of COVID-19 in Lombardia at the very beginning of pandemic fatally biased the R-like statistics routinely used to control the disease dynamics. To (at least partially) overcome this bias, we propose a new index RI = dH/dI (daily derivative ratio of H and I, given H = Healed and I = Infected), corresponding to the ratio between healed and infected patients relative daily changes. The proposed index is less flawed than R by the uncertainty related to the estimated number of infected persons and allows to follow (and possibly forecast) epidemic dynamics in a largely model-independent way. To analyze the dynamics of the epidemic, starting from the beginning of the virus spreading—when data are insufficient to make an estimate by adopting SIR model—a "sigmoidal family with delay" logistic model was introduced. That approach allowed in estimating the epidemic peak using the few data gathered even before mid-March. Based on this analysis, the peak was correctly predicted to occur by end of April. Analytical methodology of the dynamics of the epidemic we are proposing herein aims to forecast the time and intensity of the epidemic peak (forward prediction), while allowing identifying the (more likely) beginning of the epidemic (backward prediction). In addition, we established a relationship between hospitalization in intensive care units (ICU) versus deaths daily rates by avoiding the necessity to rely on precise estimates of the infected fraction of the population The joint evolution of the above parameters over time allows for a trustworthy and unbiased estimation of the dynamics of the epidemic, allowing us to clearly detect the qualitatively different character of the ‘so-called’ second wave with respect to the previous epidemic peak.

A mathematical epidemic model using genetic fitting algorithm with cross-validation and application to early dynamics of COVID-19 in Algeria

A compartmental epidemic model based on genetic fitting algorithm and a cross-validation method to overcome the overfitting problem are proposed. This generic enhanced SEIR model allows to estimate approximate nowcast and forecast of epidemic evolution including key epidemic parameters and non-measurable asymptomatic infected portion of the susceptible population. The model is used to study COVID-19 outbreak dynamics in Algeria between February 25th and May 24th. The Basic reproduction number is estimated to 3.78 (95% CI 3.033-4.53) and effective reproduction number on May 24th after three months of the outbreak is estimated to 0.651 (95% CI 0.539-0.761). The Infections peak time is predicted to the end of April while active cases peak time is predicted to the end of May 2020. The disease incidence, CFR and IFR are calculated. Information provided by this study could help establish a realistic assessment of the situation in Algeria for the time being, inform predictions about potential future evolution, and guide the design of appropriate public health measures.