Pub Date : 2021-05-07DOI: 10.1142/S179304802150003X
E. Ahmed, M. Sohaly
Viruses are obligatory minute intra-cellular infectious agents with very simple composition. They are nonliving (not active) macromolecules outside the host cell while turning into living active organisms inside host cells. The genetic material (DNA or RNA) carrying the information is crucial for virus replication and enforces the cell to approve virus replication. Consequently, it is cellular resistance against the virus that determines whether a cell at any site is infected or not. In this study, we are interested in the resistance of cells which may be infected by some disturbance such as a function of [Formula: see text] or as a random variable. Antimicrobial resistance (AMR) is the wider word for resistance in various kinds of microorganisms and includes resistance to antibacterial, antiviral, anti-parasitic, and anti-fungal medicines. Here we study the AMR problem and also, the waning vaccination in the Percolation area. Percolation is a purely geometric problem in which clusters of connected sites or bonds are clearly defined static objects. We are studying cellular automata from Domany–Kinzel on the population of AMRs as on the spreading network. Each connection is rewired on a one-dimensional chain and combined with any probability p node. Additionally, the Domany–Kinzel model will be applied for AMR and waning vaccination in two dimensions.
{"title":"Cell Resistance and Antimicrobial Resistance with Waning Vaccination","authors":"E. Ahmed, M. Sohaly","doi":"10.1142/S179304802150003X","DOIUrl":"https://doi.org/10.1142/S179304802150003X","url":null,"abstract":"Viruses are obligatory minute intra-cellular infectious agents with very simple composition. They are nonliving (not active) macromolecules outside the host cell while turning into living active organisms inside host cells. The genetic material (DNA or RNA) carrying the information is crucial for virus replication and enforces the cell to approve virus replication. Consequently, it is cellular resistance against the virus that determines whether a cell at any site is infected or not. In this study, we are interested in the resistance of cells which may be infected by some disturbance such as a function of [Formula: see text] or as a random variable. Antimicrobial resistance (AMR) is the wider word for resistance in various kinds of microorganisms and includes resistance to antibacterial, antiviral, anti-parasitic, and anti-fungal medicines. Here we study the AMR problem and also, the waning vaccination in the Percolation area. Percolation is a purely geometric problem in which clusters of connected sites or bonds are clearly defined static objects. We are studying cellular automata from Domany–Kinzel on the population of AMRs as on the spreading network. Each connection is rewired on a one-dimensional chain and combined with any probability p node. Additionally, the Domany–Kinzel model will be applied for AMR and waning vaccination in two dimensions.","PeriodicalId":88835,"journal":{"name":"Biophysical reviews and letters","volume":"1 1","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2021-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47921245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-09DOI: 10.1142/S1793048021500090
L. Mondaini, B. Meirose, F. Mondaini
In this article, a stochastic SIR-type model for COVID-19 epidemic is built using the standard field theoretical language based on creation and annihilation operators. From the model, we derive the time evolution of the mean number of infectious (active cases) and deceased individuals. In order to capture the effects of lockdown and social distancing, we use a time-dependent infection rate. The results are in good agreement with the data for three different waves of epidemic activity in South Korea.
{"title":"Second Quantization Approach to COVID-19 Epidemic","authors":"L. Mondaini, B. Meirose, F. Mondaini","doi":"10.1142/S1793048021500090","DOIUrl":"https://doi.org/10.1142/S1793048021500090","url":null,"abstract":"In this article, a stochastic SIR-type model for COVID-19 epidemic is built using the standard field theoretical language based on creation and annihilation operators. From the model, we derive the time evolution of the mean number of infectious (active cases) and deceased individuals. In order to capture the effects of lockdown and social distancing, we use a time-dependent infection rate. The results are in good agreement with the data for three different waves of epidemic activity in South Korea.","PeriodicalId":88835,"journal":{"name":"Biophysical reviews and letters","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42001454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-01DOI: 10.1142/S1793048021500028
S. Ribar, V. Mitić, G. Lazovic
Artificial neural networks (ANNs) are basically the structures that perform input–output mapping. This mapping mimics the signal processing in biological neural networks. The basic element of biological neural network is a neuron. Neurons receive input signals from other neurons or the environment, process them, and generate their output which represents the input to another neuron of the network. Neurons can change their sensitivity to input signals. Each neuron has a simple rule to process an input signal. Biological neural networks have the property that signals are processed through many parallel connections (massively parallel processing). The activity of all neurons in these parallel connections is summed and represents the output of the whole network. The main feature of biological neural networks is that changes in the sensitivity of the neurons lead to changes in the operation of the entire network. This is called adaptation and is correlated with the learning process of living organisms. In this paper, a set of artificial neural networks are used for classifying the human skin biophysical impedance data.
{"title":"Neural Networks Application on Human Skin Biophysical Impedance Characterizations","authors":"S. Ribar, V. Mitić, G. Lazovic","doi":"10.1142/S1793048021500028","DOIUrl":"https://doi.org/10.1142/S1793048021500028","url":null,"abstract":"Artificial neural networks (ANNs) are basically the structures that perform input–output mapping. This mapping mimics the signal processing in biological neural networks. The basic element of biological neural network is a neuron. Neurons receive input signals from other neurons or the environment, process them, and generate their output which represents the input to another neuron of the network. Neurons can change their sensitivity to input signals. Each neuron has a simple rule to process an input signal. Biological neural networks have the property that signals are processed through many parallel connections (massively parallel processing). The activity of all neurons in these parallel connections is summed and represents the output of the whole network. The main feature of biological neural networks is that changes in the sensitivity of the neurons lead to changes in the operation of the entire network. This is called adaptation and is correlated with the learning process of living organisms. In this paper, a set of artificial neural networks are used for classifying the human skin biophysical impedance data.","PeriodicalId":88835,"journal":{"name":"Biophysical reviews and letters","volume":"16 1","pages":"9-19"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47738836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-21DOI: 10.1142/S1793048021500016
A. Adamatzky, A. Chiolerio, G. Sirakoulis
We study long-term electrical resistance dynamics in mycelium and fruit bodies of oyster fungi P. ostreatus. A nearly homogeneous sheet of mycelium on the surface of a growth substrate exhibits trains of resistance spikes. The average width of spikes is c. 23[Formula: see text]min and the average amplitude is c. 1[Formula: see text]k[Formula: see text]. The distance between neighboring spikes in a train of spikes is c. 30[Formula: see text]min. Typically, there are 4–6 spikes in a train of spikes. Two types of electrical resistance spikes trains are found in fruit bodies: low frequency and high amplitude (28[Formula: see text]min spike width, 1.6[Formula: see text]k[Formula: see text] amplitude, 57[Formula: see text]min distance between spikes) and high frequency and low amplitude (10[Formula: see text]min width, 0.6[Formula: see text]k[Formula: see text] amplitude, 44[Formula: see text]min distance between spikes). The findings could be applied in monitoring of physiological states of fungi and future development of living electronic devices and sensors.
{"title":"Electrical Resistive Spiking of Fungi","authors":"A. Adamatzky, A. Chiolerio, G. Sirakoulis","doi":"10.1142/S1793048021500016","DOIUrl":"https://doi.org/10.1142/S1793048021500016","url":null,"abstract":"We study long-term electrical resistance dynamics in mycelium and fruit bodies of oyster fungi P. ostreatus. A nearly homogeneous sheet of mycelium on the surface of a growth substrate exhibits trains of resistance spikes. The average width of spikes is c. 23[Formula: see text]min and the average amplitude is c. 1[Formula: see text]k[Formula: see text]. The distance between neighboring spikes in a train of spikes is c. 30[Formula: see text]min. Typically, there are 4–6 spikes in a train of spikes. Two types of electrical resistance spikes trains are found in fruit bodies: low frequency and high amplitude (28[Formula: see text]min spike width, 1.6[Formula: see text]k[Formula: see text] amplitude, 57[Formula: see text]min distance between spikes) and high frequency and low amplitude (10[Formula: see text]min width, 0.6[Formula: see text]k[Formula: see text] amplitude, 44[Formula: see text]min distance between spikes). The findings could be applied in monitoring of physiological states of fungi and future development of living electronic devices and sensors.","PeriodicalId":88835,"journal":{"name":"Biophysical reviews and letters","volume":"1 1","pages":"169-175"},"PeriodicalIF":0.0,"publicationDate":"2021-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64047299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1142/s1793048020400019
D. H. Margarit, M. V. Reale, A. Scagliotti
Individual neuron models give a comprehensive explanation of the behavior of the electrical potential of cell membranes. These models were and are a source of constant analysis to understand the functioning of, mainly, the complexity of the brain. In this work, using the Izhikevich model, we propose, analyze and characterize the transmission of a signal between two neurons unidirectionally coupled. Two possible states were characterized (sub-threshold and over-threshold) depending on the values of the signal amplitude, as well also the relationship between the transmitted and received signal taking into account the coupling. Furthermore, the activation of the emitting neuron (its transition from a resting state to spiking state) and the transmission to the receptor neuron were analyzed by adding white noise to the system.
{"title":"Analysis of a Signal Transmission in a Pair of Izhikevich Coupled Neurons","authors":"D. H. Margarit, M. V. Reale, A. Scagliotti","doi":"10.1142/s1793048020400019","DOIUrl":"https://doi.org/10.1142/s1793048020400019","url":null,"abstract":"Individual neuron models give a comprehensive explanation of the behavior of the electrical potential of cell membranes. These models were and are a source of constant analysis to understand the functioning of, mainly, the complexity of the brain. In this work, using the Izhikevich model, we propose, analyze and characterize the transmission of a signal between two neurons unidirectionally coupled. Two possible states were characterized (sub-threshold and over-threshold) depending on the values of the signal amplitude, as well also the relationship between the transmitted and received signal taking into account the coupling. Furthermore, the activation of the emitting neuron (its transition from a resting state to spiking state) and the transmission to the receptor neuron were analyzed by adding white noise to the system.","PeriodicalId":88835,"journal":{"name":"Biophysical reviews and letters","volume":"15 1","pages":"195-206"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46298583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1142/s1793048020770019
H. Coster, Z. Jia, Xiangrong Liu, Z. Ouyang
{"title":"In Memoriam Professor P. C. Huang, Managing Editor","authors":"H. Coster, Z. Jia, Xiangrong Liu, Z. Ouyang","doi":"10.1142/s1793048020770019","DOIUrl":"https://doi.org/10.1142/s1793048020770019","url":null,"abstract":"","PeriodicalId":88835,"journal":{"name":"Biophysical reviews and letters","volume":"15 1","pages":"293-294"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48295619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1142/s1793048020990015
{"title":"Author Index Volume 15 (2020)","authors":"","doi":"10.1142/s1793048020990015","DOIUrl":"https://doi.org/10.1142/s1793048020990015","url":null,"abstract":"","PeriodicalId":88835,"journal":{"name":"Biophysical reviews and letters","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46195755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1142/s1793048020500083
K. Zioutas, M. Maroudas, S. Hofmann, A. Kryemadhi, E. Matteson
Streams from the dark universe may affect biologic processes on Earth including occurrence of cancer. Here, we have recovered complete daily melanoma incidence rates 1982–2014 in Australia. If there is no other external cause for melanoma, its seasonal rate should be steady with a broad maximum during local summertime due to increased solar UV exposure. The reported melanoma cases show instead: (A) a modulation repeating every [Formula: see text] weeks; (B) a time-dependence which relates also with Moon’s geocentric orbital position, while it does not fit concurrently measured solar activity; and (C) a modulation periodicity strikingly coinciding with the Moon’s sidereal periodicity of 27.3 days, which is fixed to remote stars and not to the Sun. These findings show that the associated cause must be of exo-solar origin, and not only due steady solar UV exposure. A possible interpretation of the finding is the underlying working hypothesis itself, namely the flux of slow speed streaming DM is temporally enhanced due to gravitational (self-)focusing by the solar system. Interestingly, the gravitational focusing effect by the Moon itself towards the Earth covers speeds up to [Formula: see text][Formula: see text]km/s, fitting within the widely assumed velocity distribution of DM constituents at [Formula: see text][Formula: see text]km/s. The derived observations for melanoma diagnosis are a novel model-independent finding with potential public health implications and possible relevance for other diseases. The results of this work also serve as accumulating and independent signatures strengthening previous physics claims being at work at the Sun and Earth’s upper atmosphere, inspiring a novel cross-disciplinary approach for the dark matter (DM) paradigm. In future, the 27.3 days periodicity does not necessarily require long-term physical and medical measurements to search for a direct cause-effect relationship. Potential candidates from the dark sector are the highly ionizing anti-quark nuggets, magnetic monopoles, but also particles like dark photons; other as yet unpredicted DM constituents with sufficient impact on the highly sensitive living matter could be at work.
{"title":"Observation of a 27 Days Periodicity in Melanoma Diagnosis","authors":"K. Zioutas, M. Maroudas, S. Hofmann, A. Kryemadhi, E. Matteson","doi":"10.1142/s1793048020500083","DOIUrl":"https://doi.org/10.1142/s1793048020500083","url":null,"abstract":"Streams from the dark universe may affect biologic processes on Earth including occurrence of cancer. Here, we have recovered complete daily melanoma incidence rates 1982–2014 in Australia. If there is no other external cause for melanoma, its seasonal rate should be steady with a broad maximum during local summertime due to increased solar UV exposure. The reported melanoma cases show instead: (A) a modulation repeating every [Formula: see text] weeks; (B) a time-dependence which relates also with Moon’s geocentric orbital position, while it does not fit concurrently measured solar activity; and (C) a modulation periodicity strikingly coinciding with the Moon’s sidereal periodicity of 27.3 days, which is fixed to remote stars and not to the Sun. These findings show that the associated cause must be of exo-solar origin, and not only due steady solar UV exposure. A possible interpretation of the finding is the underlying working hypothesis itself, namely the flux of slow speed streaming DM is temporally enhanced due to gravitational (self-)focusing by the solar system. Interestingly, the gravitational focusing effect by the Moon itself towards the Earth covers speeds up to [Formula: see text][Formula: see text]km/s, fitting within the widely assumed velocity distribution of DM constituents at [Formula: see text][Formula: see text]km/s. The derived observations for melanoma diagnosis are a novel model-independent finding with potential public health implications and possible relevance for other diseases. The results of this work also serve as accumulating and independent signatures strengthening previous physics claims being at work at the Sun and Earth’s upper atmosphere, inspiring a novel cross-disciplinary approach for the dark matter (DM) paradigm. In future, the 27.3 days periodicity does not necessarily require long-term physical and medical measurements to search for a direct cause-effect relationship. Potential candidates from the dark sector are the highly ionizing anti-quark nuggets, magnetic monopoles, but also particles like dark photons; other as yet unpredicted DM constituents with sufficient impact on the highly sensitive living matter could be at work.","PeriodicalId":88835,"journal":{"name":"Biophysical reviews and letters","volume":"15 1","pages":"275-291"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1142/s1793048020500083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47207671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1142/S179304802050006X
Meghadri Das, G. Samanta
In Japan, the first case of Coronavirus disease 2019 (COVID-19) was reported on 15th January 2020 In India, on 30th January 2020, the first case of COVID-19 in India was reported in Kerala and the number of reported cases has increased rapidly The main purpose of this work is to study numerically the epidemic peak for COVID-19 disease along with transmission dynamics of COVID-19 in Japan and India 2020 Taking into account the uncertainty due to the incomplete information about the coronavirus (COVID-19), we have taken the Susceptible-Asymptomatic-Infectious-Recovered (SAIR) compartmental model under fractional order framework for our study We have also studied the effects of fractional order along with other parameters in transfer dynamics and epidemic peak control for both the countries An optimal control problem has been studied by controlling social distancing parameter
{"title":"Optimal Control of Fractional Order COVID-19 Epidemic Spreading in Japan and India 2020","authors":"Meghadri Das, G. Samanta","doi":"10.1142/S179304802050006X","DOIUrl":"https://doi.org/10.1142/S179304802050006X","url":null,"abstract":"In Japan, the first case of Coronavirus disease 2019 (COVID-19) was reported on 15th January 2020 In India, on 30th January 2020, the first case of COVID-19 in India was reported in Kerala and the number of reported cases has increased rapidly The main purpose of this work is to study numerically the epidemic peak for COVID-19 disease along with transmission dynamics of COVID-19 in Japan and India 2020 Taking into account the uncertainty due to the incomplete information about the coronavirus (COVID-19), we have taken the Susceptible-Asymptomatic-Infectious-Recovered (SAIR) compartmental model under fractional order framework for our study We have also studied the effects of fractional order along with other parameters in transfer dynamics and epidemic peak control for both the countries An optimal control problem has been studied by controlling social distancing parameter","PeriodicalId":88835,"journal":{"name":"Biophysical reviews and letters","volume":"15 1","pages":"207-236"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1142/S179304802050006X","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44172353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-10DOI: 10.1142/s1793048020500071
Tchule Nguiwa, Mibaile Justin, Djaouda Moussa, G. Betchewe, A. Mohamadou
In this paper, we investigated the dynamical behavior of a fractional-order model of the cholera epidemic in Mayo-Tsanaga Department. We extended the model of Lemos-Paião et al. [A. P. Lemos-Paião, C. J. Silva and D. F. M. Torres, J. Comput. Appl. Math. 16, 427 (2016)] by incorporating the contact rate [Formula: see text] by handling cholera death and optimal control strategies such as vaccination [Formula: see text], water sanitation [Formula: see text]. We provide a theoretical study of the model. We derive the basic reproduction number [Formula: see text] which determines the extinction and the persistence of the infection. We show that the disease-free equilibrium is globally asymptotically stable whenever [Formula: see text], while when [Formula: see text], the disease-free equilibrium is unstable and there exists a unique endemic equilibrium point which is locally asymptotically stable on a positively invariant region of the positive orthant. Using the sensitivity analysis, we find that the parameter related to vaccination and therapeutic treatment is more influencing the model. Theoretical results are supported by numerical simulations, which further suggest use of vaccination in endemic area. In case of a lack of necessary funding to fight again cholera, Figure 6 revealed that efforts should focus to keep contamination rate [Formula: see text] (susceptible-to-cholera death) in other to die out the disease.
本文研究了美约-津永省霍乱流行的分数阶模型的动力学行为。我们扩展了lemos - pai等人的模型[A]。P. lemos - pai o, C. J. Silva, D. F. M. Torres, J. Comput。达成。通过处理霍乱死亡和疫苗接种[公式:见文本]、水卫生[公式:见文本]等最佳控制策略，结合接触率[公式:见文本]。我们对该模型进行了理论研究。我们推导出基本繁殖数[公式:见文本]，它决定了感染的灭绝和持续。我们证明了当[公式:见文]时，无病平衡点是全局渐近稳定的，而当[公式:见文]时，无病平衡点是不稳定的，并且存在一个唯一的地方性平衡点，该平衡点在正正交的正不变区域上是局部渐近稳定的。通过敏感性分析，我们发现与疫苗接种和治疗相关的参数对模型的影响更大。数值模拟结果支持了理论结果，进一步表明在流行地区应接种疫苗。图6显示，在缺乏必要资金再次抗击霍乱的情况下，应集中努力保持污染率[公式:见文](易受霍乱影响的死亡率)在其他方面，以消灭这种疾病。
{"title":"Dynamic Study of SIQR-B Fractional-Order Epidemic Model of Cholera with Optimal Control Strategies in Mayo-Tsanaga Department of Cameroon Far North Region","authors":"Tchule Nguiwa, Mibaile Justin, Djaouda Moussa, G. Betchewe, A. Mohamadou","doi":"10.1142/s1793048020500071","DOIUrl":"https://doi.org/10.1142/s1793048020500071","url":null,"abstract":"In this paper, we investigated the dynamical behavior of a fractional-order model of the cholera epidemic in Mayo-Tsanaga Department. We extended the model of Lemos-Paião et al. [A. P. Lemos-Paião, C. J. Silva and D. F. M. Torres, J. Comput. Appl. Math. 16, 427 (2016)] by incorporating the contact rate [Formula: see text] by handling cholera death and optimal control strategies such as vaccination [Formula: see text], water sanitation [Formula: see text]. We provide a theoretical study of the model. We derive the basic reproduction number [Formula: see text] which determines the extinction and the persistence of the infection. We show that the disease-free equilibrium is globally asymptotically stable whenever [Formula: see text], while when [Formula: see text], the disease-free equilibrium is unstable and there exists a unique endemic equilibrium point which is locally asymptotically stable on a positively invariant region of the positive orthant. Using the sensitivity analysis, we find that the parameter related to vaccination and therapeutic treatment is more influencing the model. Theoretical results are supported by numerical simulations, which further suggest use of vaccination in endemic area. In case of a lack of necessary funding to fight again cholera, Figure 6 revealed that efforts should focus to keep contamination rate [Formula: see text] (susceptible-to-cholera death) in other to die out the disease.","PeriodicalId":88835,"journal":{"name":"Biophysical reviews and letters","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1142/s1793048020500071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43105629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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