Analysis of the outbreak of COVID-19 in Japan by SIQR model

An artificial neural network approach for the language learning model

The current study provides the numerical solutions of the language-based model through the artificial intelligence (AI) procedure based on the scale conjugate gradient neural network (SCJGNN). The mathematical learning language differential model is characterized into three classes, named as unknown, familiar, and mastered. A dataset is generalized by using the performance of the Adam scheme, which is used to reduce to mean square error. The AI based SCJGNN procedure works by taking the data with the ratio of testing (12%), validation (13%), and training (75%). An activation log-sigmoid function, twelve numbers of neurons, SCJG optimization, hidden and output layers are presented in this stochastic computing work for solving the learning language model. The correctness of AI based SCJGNN is noted through the overlapping of the results along with the small calculated absolute error that are around 10-06 to 10-08 for each class of the model. Moreover, the regression performances for each case of the model is performed as one that shows the perfect model. Additionally, the dependability of AI based SCJGNN is approved using the histogram, and function fitness.

New compartment model for COVID-19

The SIR or susceptible-infected-recovered model is the standard compartment model for understanding epidemics and has been used all over the world for COVID-19. While the SIR model assumes that infected patients are identical to symptomatic and infectious patients, it is now known that in COVID-19 pre-symptomatic patients are infectious and there are significant number of asymptomatic patients who are infectious. In this paper, population is separated into five compartments for COVID-19; susceptible individuals (S), pre-symptomatic patients (P), asymptomatic patients (A), quarantined patients (Q) and recovered and/or dead patients (R). The time evolution of population in each compartment is described by a set of ordinary differential equations. Numerical solution to the set of differential equations shows that quarantining pre-symptomatic and asymptomatic patients is effective in controlling the pandemic.

COVID-19 dynamics in Madrid (Spain): A new convolutional model to find out the missing information during the first three waves

This article presents a novel mathematical model to describe the spread of an infectious disease in the presence of social and health events: it uses 15 compartments, 7 convolution integrals and 4 types of infected individuals, asymptomatic, mild, moderate and severe. A unique feature of this work is that the convolutions and the compartments have been selected to maximize the number of independent input parameters, leading to a 56-parameter model where only one had to evolve over time. The results show that 1) the proposed mathematical model is flexible and robust enough to describe the complex dynamic of the pandemic during the first three waves of the COVID-19 spread in the region of Madrid (Spain) and 2) the proposed model allows us to calculate the number of asymptomatic individuals and the number of persons who presented antibodies during the first waves. The study shows that the following results are compatible with the reported data: close to 28% of the infected individuals were asymptomatic during the three waves, close to 29% of asymptomatic individuals were detected during the subsequent waves and close to 26% of the Madrid population had antibodies at the end of the third wave. This calculated number of persons with antibodies is in great agreement with four direct measurements obtained from an independent sero-epidemiological research. In addition, six calculated curves (total number of confirmed cases, asymptomatic who are confirmed as positive, hospital admissions and discharges and intensive care units admissions) show good agreement with data from an epidemiological surveillance database.

On the evolution of the COVID-19 epidemiological parameters using only the series of deceased. A study of the Spanish outbreak using Genetic Algorithms

We propose a methodology for estimating the evolution of the epidemiological parameters of a SIRD model (acronym of Susceptible, Infected, Recovered and Deceased individuals) which allows to evaluate the sanitary measures taken by the government, for the COVID-19 in the Spanish outbreak. In our methodology the only information required for estimating these parameters is the time series of deceased people; due to the number of asymptomatic people produced by the COVID-19, it is not possible to know the actual number of infected people at any given time. Therefore, among the different time series that quantify the pandemic we consider just the number of deceased people to minimize the square sum of errors. The time series of deaths considered runs from March to the end of September and is divided into four sub-periods reflecting the different isolation measures taken by the Spanish government. The parameters that we can estimate are the time from the beginning of the disease, the transmission rate, and the recovery rate; these last two ratios are estimated in each of the different sub-periods. In this way the model considered has 2x4+1=9 parameters that are estimated jointly over the whole period from the data of deceased. Given the complexity of the model, to estimate the parameters that minimize the square sum of errors, a Genetic Algorithm is used. Our methodology confirms the effectiveness of the sanitary measures taken by the Spanish government showing a dramatic reduction in the basic reproductive number R 0 during confinement; also, a further increase in R 0 after the end of the alarm state decreed by the government on June 21 was detected. Our results also point out that the Patient Zero in the COVID-19 Spanish outbreak emerged between the end of December and early January, at least four weeks before January 31st, that was the moment when the Spanish authorities reported the first positive case.

An epidemiology-based model for the operational allocation of COVID-19 vaccines: A case study of Thailand

This paper addresses a framework for the operational allocation and administration of COVID-19 vaccines in Thailand, based on both COVID-19 transmission dynamics and other vital operational restrictions that might affect the effectiveness of vaccination strategies in the early stage of vaccine rollout. In this framework, the SIQRV model is first developed and later combined with the COVID-19 Vaccine Allocation Problem (CVAP) to determine the optimal allocation/administration strategies that minimize total weighted strain on the whole healthcare system. According to Thailand's second pandemic wave data (17^th January 2021, to 15^th February 2021), we find that the epicenter-based strategy is surprisingly the worst allocation strategy, due largely to the negligence of provincial demographics, vaccine efficacy, and overall transmission dynamics that lead to higher number of infectious individuals. We also find that early vaccination seems to significantly contribute to the reduction in the number of infectious individuals, whose effects tend to increase with more vaccine supply. With these insights, healthcare policy-makers should therefore focus not only on the procurement of COVID-19 vaccines at strategic levels but also on the allocation and administration of such vaccines at operational levels for the best of their limited vaccine supply.

Dynamical analysis of the infection status in diverse communities due to COVID-19 using a modified SIR model

In this article, we model and study the spread of COVID-19 in Germany, Japan, India and highly impacted states in India, i.e., in Delhi, Maharashtra, West Bengal, Kerala and Karnataka. We consider recorded data published in Worldometers and COVID-19 India websites from April 2020 to July 2021, including periods of interest where these countries and states were hit severely by the pandemic. Our methodology is based on the classic susceptible-infected-removed (SIR) model and can track the evolution of infections in communities, i.e., in countries, states or groups of individuals, where we (a) allow for the susceptible and infected populations to be reset at times where surges, outbreaks or secondary waves appear in the recorded data sets, (b) consider the parameters in the SIR model that represent the effective transmission and recovery rates to be functions of time and (c) estimate the number of deaths by combining the model solutions with the recorded data sets to approximate them between consecutive surges, outbreaks or secondary waves, providing a more accurate estimate. We report on the status of the current infections in these countries and states, and the infections and deaths in India and Japan. Our model can adapt to the recorded data and can be used to explain them and importantly, to forecast the number of infected, recovered, removed and dead individuals, as well as it can estimate the effective infection and recovery rates as functions of time, assuming an outbreak occurs at a given time. The latter information can be used to forecast the future basic reproduction number and together with the forecast on the number of infected and dead individuals, our approach can further be used to suggest the implementation of intervention strategies and mitigation policies to keep at bay the number of infected and dead individuals. This, in conjunction with the implementation of vaccination programs worldwide, can help reduce significantly the impact of the spread around the world and improve the wellbeing of people.

Modified SIQR model for the COVID-19 outbreak in several countries

In this paper, we propose a modified Susceptible-Infected-Quarantine-Recovered (mSIQR) model, for the COVID-19 pandemic. We start by proving the well-posedness of the model and then compute its reproduction number and the corresponding sensitivity indices. We discuss the values of these indices for epidemiological relevant parameters, namely, the contact rate, the proportion of unknown infectious, and the recovering rate. The mSIQR model is simulated, and the outputs are fit to COVID-19 pandemic data from several countries, including France, US, UK, and Portugal. We discuss the epidemiological relevance of the results and provide insights on future patterns, subjected to health policies.

COVID-19 Spread Forecasting, Mathematical Methods vs. Machine Learning, Moscow Case

To predict the spread of the new coronavirus infection COVID-19, the critical values of spread indicators have been determined for deciding on the introduction of restrictive measures using the city of Moscow as an example. A model was developed using classical methods of mathematical modeling based on exponential regression, the accuracy of the forecast was estimated, and the shortcomings of mathematical methods for predicting the spread of infection for more than two weeks. As a solution to the problem of the accuracy of long-term forecasts for more than two weeks, two models based on machine learning methods are proposed: a recurrent neural network with two layers of long short-term memory (LSTM) blocks and a 1-D convolutional neural network with a description of the choice of an optimization algorithm. The forecast accuracy of ML models was evaluated in comparison with the exponential regression model and one another using the example of data on the number of COVID-19 cases in the city of Moscow.

The effect of weekend curfews on epidemics: a Monte Carlo simulation

The ongoing COVID-19 pandemic is being responded with various methods, applying vaccines, experimental treatment options, total lockdowns or partial curfews. Weekend curfews are among the methods for reducing the number of infected persons, and this method is practically applied in some countries such as Turkey. In this study, the effect of weekend curfews on reducing the spread of a contagious disease, such as COVID-19, is modeled using a Monte Carlo algorithm with a hybrid lattice model. In the simulation setup, a fictional country with three towns and 26,610 citizens were used as a model. Results indicate that applying a weekend curfew reduces the ratio of ill cases from 0.23 to 0.15. The results also show that applying personal precautions such as social distancing is important for reducing the number of cases and deaths. If the probability of disease spread can be reduced to 0.1, in that case, the death ratio can be minimized down to 0.

Self-organization of oscillation in an epidemic model for COVID-19

On the basis of a compartment model, the epidemic curve is investigated when the net rate λ of change of the number of infected individuals I is given by an ellipse in the λ - I plane which is supported in [ I ℓ , I h ] . With a ≡ ( I h - I ℓ ) ∕ ( I h + I ℓ ) , it is shown that (1) when a < 1 , oscillation of the infection curve is self-organized and the period of the oscillation is in proportion to the ratio of the difference ( I h - I ℓ ) and the geometric mean I h I ℓ of I h and I ℓ , (2) when a = 1 , the infection curve shows a critical behavior where it decays obeying a power law function with exponent - 2 in the long time limit after a peak, and (3) when a > 1 , the infection curve decays exponentially in the long time limit after a peak. The present result indicates that the pandemic can be controlled by a measure which makes I ℓ < 0 .

Mathematical Modeling of the Propagation of Covid-19 Pandemic Waves in the World

We develop a mathematical model of the coronavirus propagation in different countries (Brazil, India, US, Japan, Israel, Spain, Sweden), in the city of Moscow, and across the world. The pandemic spreads by a highly complex dynamics because it occurs in open nonhomogeneous systems where new infection foci erupt from time to time, triggering new transmission chains from infected to susceptible people. In general, statistical data collected as cumulative and epidemic curves are a superposition of many distinct local pandemic waves. In our modeling, we use the system of Feigenbaum’s discrete logistic equations (a logistic map) that describes the variation of the total number of infected over time. We show that this is the optimal model for the description of pandemic propagation in open nonhomogeneous systems with large errors in statistical data. We develop a procedure for isolating local waves, determining their model parameters, and predicting further evolution of each wave. We show that this model provides a good description of the statistical data and makes realistic forecasts. The forecast horizon depends on the degree of system closure and homogeneity. We calculate the start and end times of each wave, the peak, and the total number of infected in the current wave.

Modelling virus spreading in ride-pooling networks

Urban mobility needs alternative sustainable travel modes to keep our pandemic cities in motion. Ride-pooling, where a single vehicle is shared by more than one traveller, is not only appealing for mobility platforms and their travellers, but also for promoting the sustainability of urban mobility systems. Yet, the potential of ride-pooling rides to serve as a safe and effective alternative given the personal and public health risks considerations associated with the COVID-19 pandemic is hitherto unknown. To answer this, we combine epidemiological and behavioural shareability models to examine spreading among ride-pooling travellers, with an application for Amsterdam. Findings are at first sight devastating, with only few initially infected travellers needed to spread the virus to hundreds of ride-pooling users. Without intervention, ride-pooling system may substantially contribute to virus spreading. Notwithstanding, we identify an effective control measure allowing to halt the spreading before the outbreaks (at 50 instead of 800 infections) without sacrificing the efficiency achieved by pooling. Fixed matches among co-travellers disconnect the otherwise dense contact network, encapsulating the virus in small communities and preventing the outbreaks.

Exact properties of SIQR model for COVID-19

The SIQR model is reformulated where compartments for infected and quarantined are redefined so as to be appropriate to COVID-19, and exact properties of the model are presented. It is shown that the maximum number of infected at large depends strongly on the quarantine rate and that the quarantine measure is more effective than the lockdown measure in controlling the pandemic. The peak of the number of quarantined patients is shown to appear some time later than the time that the number of infected becomes maximum. On the basis of the expected utility theory, a theoretical framework to find out an optimum strategy in the space of lockdown measure and quarantine measure is proposed for minimizing the maximum number of infected and for controlling the outbreak of pandemic at its early stage.

Self-organized wavy infection curve of COVID-19

Exploiting the SIQR model for COVID-19, I show that the wavy infection curve in Japan is the result of fluctuation of policy on isolation measure imposed by the government and obeyed by citizens. Assuming the infection coefficient be a two-valued function of the number of daily confirmed new cases, I show that when the removal rate of infected individuals is between these two values, the wavy infection curve is self-organized. On the basis of the infection curve, I classify the outbreak of COVID-19 into five types and show that these differences can be related to the relative magnitude of the transmission coefficient and the quarantine rate of infected individuals.

On the effectiveness of the search and find method to suppress spread of SARS-CoV-2

Search and find methods*) such as cluster tracing1)-6) or large-scale PCR testing**) of those who exhibit no symptoms or only mild symptoms of COVID-19 is shown by data analysis to be a powerful means to suppress the spread of COVID-19 instead of, or in addition to, lockdown of the entire population. Here we investigate this issue by analyzing the data from some cities and countries and we establish that search and find method is as powerful as lockdown of a city or a country. Moreover, in contrast to lockdown, it neither causes inconvenience to citizens nor does it disrupt the economy. Generally speaking, it is advisable that both social distancing and increased test numbers be employed to suppress spread of the virus. The product of the total test number with the rate of positive cases is the crucial index.