Chaos, solitons, and fractals最新文献

{"title":"Assessment of lockdown effect in some states and overall India: A predictive mathematical study on COVID-19 outbreak.","authors":"Tridip Sardar,&nbsp;Sk Shahid Nadim,&nbsp;Sourav Rana,&nbsp;Joydev Chattopadhyay","doi":"10.1016/j.chaos.2020.110078","DOIUrl":"https://doi.org/10.1016/j.chaos.2020.110078","url":null,"abstract":"<p><p>In the absence of neither an effective treatment or vaccine and with an incomplete understanding of the epidemiological cycle, Govt. has implemented a nationwide lockdown to reduce COVID-19 transmission in India. To study the effect of social distancing measure, we considered a new mathematical model on COVID-19 that incorporates lockdown effect. By validating our model to the data on notified cases from five different states and overall India, we estimated several epidemiologically important parameters as well as the basic reproduction number (<i>R</i> ₀). Combining the mechanistic mathematical model with different statistical forecast models, we projected notified cases in the six locations for the period May 17, 2020, till May 31, 2020. A global sensitivity analysis is carried out to determine the correlation of two epidemiologically measurable parameters on the lockdown effect and also on <i>R</i> ₀. Our result suggests that lockdown will be effective in those locations where a higher percentage of symptomatic infection exists in the population. Furthermore, a large scale COVID-19 mass testing is required to reduce community infection. Ensemble model forecast suggested a high rise in the COVID-19 notified cases in most of the locations in the coming days. Furthermore, the trend of the effective reproduction number (<i>R_t</i> ) during the projection period indicates if the lockdown measures are completely removed after May 17, 2020, a high spike in notified cases may be seen in those locations. Finally, combining our results, we provided an effective lockdown policy to reduce future COVID-19 transmission in India.</p>","PeriodicalId":520585,"journal":{"name":"Chaos, solitons, and fractals","volume":" ","pages":"110078"},"PeriodicalIF":7.8,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.chaos.2020.110078","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38295678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A mathematical study on the spread of COVID-19 considering social distancing and rapid assessment: The case of Jakarta, Indonesia.","authors":"Dipo Aldila,&nbsp;Sarbaz H A Khoshnaw,&nbsp;Egi Safitri,&nbsp;Yusril Rais Anwar,&nbsp;Aanisah R Q Bakry,&nbsp;Brenda M Samiadji,&nbsp;Demas A Anugerah,&nbsp;M Farhan Alfarizi Gh,&nbsp;Indri D Ayulani,&nbsp;Sheryl N Salim","doi":"10.1016/j.chaos.2020.110042","DOIUrl":"https://doi.org/10.1016/j.chaos.2020.110042","url":null,"abstract":"<p><p>The aim of this study is to investigate the effects of rapid testing and social distancing in controlling the spread of COVID-19, particularly in the city of Jakarta, Indonesia. We formulate a modified susceptible exposed infectious recovered compartmental model considering asymptomatic individuals. Rapid testing is intended to trace the existence of asymptomatic infected individuals among the population. This asymptomatic class is categorized into two subclasses: detected and undetected asymptomatic individuals. Furthermore, the model considers the limitations of medical resources to treat an infected individual in a hospital. The model shows two types of equilibrium point: COVID-19 free and COVID-19 endemic. The COVID-19-free equilibrium point is locally and asymptotically stable if the basic reproduction number <math><mrow><mo>(</mo> <msub><mi>R</mi> <mn>0</mn></msub> <mo>)</mo></mrow> </math> is less than unity. In contrast, COVID-19-endemic equilibrium always exists when <math> <mrow><msub><mi>R</mi> <mn>0</mn></msub> <mo>></mo> <mn>1</mn></mrow> </math> . The model can also show a backward bifurcation at <math> <mrow><msub><mi>R</mi> <mn>0</mn></msub> <mo>=</mo> <mn>1</mn></mrow> </math> whenever the treatment saturation parameter, which describes the hospital capacity, is larger than a specific threshold. To justify the model parameters, we use the incidence data from the city of Jakarta, Indonesia. The data pertain to infected individuals who self-isolate in their homes and visit the hospital for further treatment. Our numerical experiments indicate that strict social distancing has the potential to succeed in reducing and delaying the time of an outbreak. However, if the strict social distancing policy is relaxed, a massive rapid-test intervention should be conducted to avoid a large-scale outbreak in the future.</p>","PeriodicalId":520585,"journal":{"name":"Chaos, solitons, and fractals","volume":" ","pages":"110042"},"PeriodicalIF":7.8,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.chaos.2020.110042","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38301423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review.","authors":"Samuel Lalmuanawma,&nbsp;Jamal Hussain,&nbsp;Lalrinfela Chhakchhuak","doi":"10.1016/j.chaos.2020.110059","DOIUrl":"https://doi.org/10.1016/j.chaos.2020.110059","url":null,"abstract":"<p><strong>Background and objective: </strong>During the recent global urgency, scientists, clinicians, and healthcare experts around the globe keep on searching for a new technology to support in tackling the Covid-19 pandemic. The evidence of Machine Learning (ML) and Artificial Intelligence (AI) application on the previous epidemic encourage researchers by giving a new angle to fight against the novel Coronavirus outbreak. This paper aims to comprehensively review the role of AI and ML as one significant method in the arena of screening, predicting, forecasting, contact tracing, and drug development for SARS-CoV-2 and its related epidemic.</p><p><strong>Method: </strong>A selective assessment of information on the research article was executed on the databases related to the application of ML and AI technology on Covid-19. Rapid and critical analysis of the three crucial parameters, i.e., abstract, methodology, and the conclusion was done to relate to the model's possibilities for tackling the SARS-CoV-2 epidemic.</p><p><strong>Result: </strong>This paper addresses on recent studies that apply ML and AI technology towards augmenting the researchers on multiple angles. It also addresses a few errors and challenges while using such algorithms in real-world problems. The paper also discusses suggestions conveying researchers on model design, medical experts, and policymakers in the current situation while tackling the Covid-19 pandemic and ahead.</p><p><strong>Conclusion: </strong>The ongoing development in AI and ML has significantly improved treatment, medication, screening, prediction, forecasting, contact tracing, and drug/vaccine development process for the Covid-19 pandemic and reduce the human intervention in medical practice. However, most of the models are not deployed enough to show their real-world operation, but they are still up to the mark to tackle the SARS-CoV-2 epidemic.</p>","PeriodicalId":520585,"journal":{"name":"Chaos, solitons, and fractals","volume":" ","pages":"110059"},"PeriodicalIF":7.8,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.chaos.2020.110059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38297696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis on novel coronavirus (COVID-19) using machine learning methods.","authors":"Milind Yadav,&nbsp;Murukessan Perumal,&nbsp;M Srinivas","doi":"10.1016/j.chaos.2020.110050","DOIUrl":"https://doi.org/10.1016/j.chaos.2020.110050","url":null,"abstract":"<p><p>In this paper, we are working on a pandemic of novel coronavirus (COVID-19). COVID-19 is an infectious disease, it creates severe damage in the lungs. COVID-19 causes illness in humans and has killed many people in the entire world. However, this virus is reported as a pandemic by the World Health Organization (WHO) and all countries are trying to control and lockdown all places. The main objective of this work is to solve the five different tasks such as I) Predicting the spread of coronavirus across regions. II) Analyzing the growth rates and the types of mitigation across countries. III) Predicting how the epidemic will end. IV) Analyzing the transmission rate of the virus. V) Correlating the coronavirus and weather conditions. The advantage of doing these tasks to minimize the virus spread by various mitigation, how well the mitigations are working, how many cases have been prevented by this mitigations, an idea about the number of patients that will recover from the infection with old medication, understand how much time will it take to for this pandemic to end, we will be able to understand and analyze how fast or slow the virus is spreading among regions and the infected patient to reduce the spread based clear understanding of the correlation between the spread and weather conditions. In this paper, we propose a novel Support Vector Regression method to analysis five different tasks related to novel coronavirus. In this work, instead of simple regression line we use the supported vectors also to get better classification accuracy. Our approach is evaluated and compared with other well-known regression models on standard available datasets. The promising results demonstrate its superiority in both efficiency and accuracy.</p>","PeriodicalId":520585,"journal":{"name":"Chaos, solitons, and fractals","volume":" ","pages":"110050"},"PeriodicalIF":7.8,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.chaos.2020.110050","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38301396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Not all interventions are equal for the height of the second peak.","authors":"Joost Jorritsma,&nbsp;Tim Hulshof,&nbsp;Júlia Komjáthy","doi":"10.1016/j.chaos.2020.109965","DOIUrl":"https://doi.org/10.1016/j.chaos.2020.109965","url":null,"abstract":"<p><p>In this paper we conduct a simulation study of the spread of an epidemic like COVID-19 with temporary immunity on finite spatial and non-spatial network models. In particular, we assume that an epidemic spreads stochastically on a scale-free network and that each infected individual in the network gains a <i>temporary immunity</i> after its infectious period is over. After the temporary immunity period is over, the individual becomes susceptible to the virus again. When the underlying contact network is embedded in Euclidean geometry, we model three different intervention strategies that aim to control the spread of the epidemic: social distancing, restrictions on travel, and restrictions on maximal number of social contacts per node. Our first finding is that on a finite network, a long enough average immunity period leads to extinction of the pandemic after the first peak, analogous to the concept of \"herd immunity\". For each model, there is a critical average immunity duration <i>L_c</i> above which this happens. Our second finding is that all three interventions manage to flatten the first peak (the travel restrictions most efficiently), as well as decrease the critical immunity duration <i>L_c</i> , but elongate the epidemic. However, when the average immunity duration <i>L</i> is shorter than <i>L_c</i> , the price for the flattened first peak is often a high second peak: for limiting the maximal number of contacts, the second peak can be as high as 1/3 of the first peak, and twice as high as it would be without intervention. Thirdly, interventions introduce oscillations into the system and the time to reach equilibrium is, for almost all scenarios, much longer. We conclude that network-based epidemic models can show a variety of behaviors that are not captured by the continuous compartmental models.</p>","PeriodicalId":520585,"journal":{"name":"Chaos, solitons, and fractals","volume":" ","pages":"109965"},"PeriodicalIF":7.8,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.chaos.2020.109965","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38421422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HIV and shifting epicenters for COVID-19, an alert for some countries.","authors":"Emile F Doungmo Goufo,&nbsp;Yasir Khan,&nbsp;Qasim Ali Chaudhry","doi":"10.1016/j.chaos.2020.110030","DOIUrl":"https://doi.org/10.1016/j.chaos.2020.110030","url":null,"abstract":"<p><p>Were southern hemisphere countries right to undertake national lockdown during their summer time? Were they right to blindly follow the self-isolation wave that hit European countries in full winter? As a southern hemisphere country like South Africa stands now as the most COVID-19 and HIV affected country in Africa, we use in this paper, recent COVID-19 data to provide a statistical and comparative analysis that may alert southern hemisphere countries entering the winter season. After that, we use a generalized simple mathematical model of HIV-COVID-19 together with graphs, curves and tables to compare the pandemic situation in countries that were once the epicenter of the disease, such as China, Italy, Spain, United Kingdom (UK) and United States of America (USA). We perform stability and bifurcation analysis and show that the model contains a forward and a backward bifurcation under certain conditions. We also study different scenarios of stability/unstability equilibria for the model. The fractional (generalized) COVID-19 model is solved numerically and a predicted prevalence for the COVID-19 is provided. Recall that Brazil and South Africa share number of similar social features like Favellas (Brazil) and Townships (South Africa) with issues like promiscuity, poverty, and where social distanciation is almost impossible to observe. We can now ask the following question: Knowing its HIV situation, is South Africa the next epicenter in weeks to come when winter conditions, proven to be favorable to the spread of the new coronavirus are comfily installed?</p>","PeriodicalId":520585,"journal":{"name":"Chaos, solitons, and fractals","volume":" ","pages":"110030"},"PeriodicalIF":7.8,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.chaos.2020.110030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38295674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determinants of the infection rate of the COVID-19 in the U.S. using ANFIS and virus optimization algorithm (VOA).","authors":"Ali Behnood,&nbsp;Emadaldin Mohammadi Golafshani,&nbsp;Seyedeh Mohaddeseh Hosseini","doi":"10.1016/j.chaos.2020.110051","DOIUrl":"https://doi.org/10.1016/j.chaos.2020.110051","url":null,"abstract":"<p><p>Recently, anovel coronavirus disease (COVID-19) has become a serious concern for global public health. Infectious disease outbreaks such as COVID-19 can also significantly affect the sustainable development of urban areas. Several factors such as population density and climatology parameters could potentially affect the spread of the COVID-19. In this study, a combination of the virus optimization algorithm (VOA) and adaptive network-based fuzzy inference system (ANFIS) was used to investigate the effects of various climate-related factors and population density on the spread of the COVID-19. For this purpose, data on the climate-related factors and the confirmed infected cases by the COVID-19 across the U.S counties was used. The results show that the variable defined for the population density had the most significant impact on the performance of the developed models, which is an indication of the importance of social distancing in reducing the infection rate and spread rate of the COVID-19. Among the climatology parameters, an increase in the maximum temperature was found to slightly reduce the infection rate. Average temperature, minimum temperature, precipitation, and average wind speed were not found to significantly affect the spread of the COVID-19 while an increase in the relative humidity was found to slightly increase the infection rate. The findings of this research show that it could be expected to have slightly reduced infection rate over the summer season. However, it should be noted that the models developed in this study were based on limited one-month data. Future investigation can benefit from using more comprehensive data covering a wider range for the input variables.</p>","PeriodicalId":520585,"journal":{"name":"Chaos, solitons, and fractals","volume":" ","pages":"110051"},"PeriodicalIF":7.8,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.chaos.2020.110051","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38301397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel covid-19 mathematical model with fractional derivatives: Singular and nonsingular kernels.","authors":"Zizhen Zhang","doi":"10.1016/j.chaos.2020.110060","DOIUrl":"10.1016/j.chaos.2020.110060","url":null,"abstract":"<p><p>In this paper, we considered a new mathematical model depicting the possibility of spread within a given general population. The model is constructed with five classes including susceptible, exposed, infected, recovered and deaths. We presented a detailed analysis of the suggested model including, the derivation of equilibrium points endemic and disease-free, reproductive number using the next generation matrix, the stability analysis of the equilibrium points and finally the positiveness of the model solutions. The model was extended to the concept of fractional differentiation to capture different memories including power law, decay and crossover behaviors. A numerical method based on the Newton was used to provide numerical solutions for different memories.</p>","PeriodicalId":520585,"journal":{"name":"Chaos, solitons, and fractals","volume":" ","pages":"110060"},"PeriodicalIF":5.6,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7321058/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38297697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of isolation measures for patients with mild symptoms on the spread of COVID-19.","authors":"Qiuhui Pan,&nbsp;Ting Gao,&nbsp;Mingfeng He","doi":"10.1016/j.chaos.2020.110022","DOIUrl":"https://doi.org/10.1016/j.chaos.2020.110022","url":null,"abstract":"<p><p>During the transmission of COVID-19, the hospital isolation of patients with mild symptoms has been a concern. In this paper, we use a differential equation model to describe the propagation of COVID-19, and discuss the effects of intensity of hospital isolation and moment of taking measures on development of the epidemic. The results show that isolation measures can significantly reduce the epidemic final size and the number of dead, and the greater the intensity of measures, the better, but duration of the epidemic will be prolonged. Whenever isolation measures are taken, the epidemic final size and the number of dead can be reduced. In early stage of the epidemic, taking measures one day later has little impact, but after a certain period, if taking measures one day later, the epidemic final size and the number of dead increase sharply. Taking measures as early as possible makes the maximum number of patients appear later, which is conducive to expanding medical bed resources and reducing the pressure on medical resource demand. As long as possible, high-intensity isolation measures should be taken in time for patients with mild symptoms.</p>","PeriodicalId":520585,"journal":{"name":"Chaos, solitons, and fractals","volume":" ","pages":"110022"},"PeriodicalIF":7.8,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.chaos.2020.110022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38295671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables.","authors":"Ramon Gomes da Silva,&nbsp;Matheus Henrique Dal Molin Ribeiro,&nbsp;Viviana Cocco Mariani,&nbsp;Leandro Dos Santos Coelho","doi":"10.1016/j.chaos.2020.110027","DOIUrl":"https://doi.org/10.1016/j.chaos.2020.110027","url":null,"abstract":"<p><p>The novel coronavirus disease (COVID-19) is a public health problem once according to the World Health Organization up to June 24th, 2020, more than 9.1 million people were infected, and more than 470 thousand have died worldwide. In the current scenario, the Brazil and the United States of America present a high daily incidence of new cases and deaths. Therefore, it is important to forecast the number of new cases in a time window of one week, once this can help the public health system developing strategic planning to deals with the COVID-19. The application of the forecasting artificial intelligence (AI) models has the potential of deal with dynamical behavior of time-series like of COVID-19. In this paper, Bayesian regression neural network, cubist regression, <i>k</i>-nearest neighbors, quantile random forest, and support vector regression, are used stand-alone, and coupled with the recent pre-processing variational mode decomposition (VMD) employed to decompose the time series into several intrinsic mode functions. All AI techniques are evaluated in the task of time-series forecasting with one, three, and six-days-ahead the cumulative COVID-19 cases in five Brazilian and American states, with a high number of cases up to April 28th, 2020. Previous cumulative COVID-19 cases and exogenous variables as daily temperature and precipitation were employed as inputs for all forecasting models. The models' effectiveness are evaluated based on the performance criteria. In general, the hybridization of VMD outperformed single forecasting models regarding the accuracy, specifically when the horizon is six-days-ahead, the hybrid VMD-single models achieved better accuracy in 70% of the cases. Regarding the exogenous variables, the importance ranking as predictor variables is, from the upper to the lower, past cases, temperature, and precipitation. Therefore, due to the efficiency of evaluated models to forecasting cumulative COVID-19 cases up to six-days-ahead, the adopted models can be recommended as a promising models for forecasting and be used to assist in the development of public policies to mitigate the effects of COVID-19 outbreak.</p>","PeriodicalId":520585,"journal":{"name":"Chaos, solitons, and fractals","volume":" ","pages":"110027"},"PeriodicalIF":7.8,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.chaos.2020.110027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38295673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}