https://journals.itb.ac.id/index.php/cbms/issue/feed Communication in Biomathematical Sciences 2021-12-31T06:49:17+07:00 Prof.Dr. Edy Soewono esoewono@math.itb.ac.id Open Journal Systems <p><a href="https://journals.itb.ac.id/index.php/cbms"><img class="imgdesc" src="https://journals.itb.ac.id/public/site/images/budini/cbms-small.png" alt="" width="189" height="265" /></a></p> <p style="text-align: justify;"><strong>Communication in Biomathematical Sciences</strong> welcomes full research articles in the area of <em>Applications of Mathematics in biological processes and phenomena</em>. Review papers with insightful, integrative and up-to-date progress of major topics are also welcome. Authors are invited to submit articles that have not been published previously and are not under consideration elsewhere.</p> <p style="text-align: justify;">The editorial board of CBMS is strongly committed to promoting recent progress and interdisciplinary research in Biomatematical Sciences.</p> <p>e-ISSN: 2549-2896</p> <p>Accreditation <a href="https://lppm.itb.ac.id/wp-content/uploads/sites/55/2021/12/Hasil_Akreditasi_Jurnal_Nasional_Periode_1_Tahun_2020.pdf">No. 85/M/KPT/2020</a></p> https://journals.itb.ac.id/index.php/cbms/article/view/17130 Defining Causality in Covid-19 and Google Search Trends in Java, Indonesia Cases: A Retrospective Analysis 2021-09-22T17:47:31+07:00 Afrina Andriani br Sebayang afrina.andriani@s.itb.ac.id Enrico Antonius afrina.andriani@s.itb.ac.id Elisabeth Victoria Pravitama afrina.andriani@s.itb.ac.id Jonathan Irianto afrina.andriani@s.itb.ac.id Shannen Widijanto afrina.andriani@s.itb.ac.id Muhammad Syamsuddin afrina.andriani@s.itb.ac.id <p>The Coronavirus disease 2019 (Covid-19) has led all countries around the world to the unpredicted situation. It is such a crucial to investigate novel approaches in predicting the future behaviour of the outbreak. In this paper, Google trend analysis will be employed to analyse the seek pattern of Covid-19 cases. The first method to investigate the seek information behaviour related to Covid-19 outbreak is using lag-correlation between two time series data per regional data. The second method is used to encounter the cause-effect relation between time series data. We apply statistical methods for causal inference in epidemics. Our focus is on predicting the causal-effect relationship between information-seeking patterns and Google search in the Covid-19 pandemic. We propose the using of Granger Causality method to analyse the causal relation between incidence data and Google Trend Data.</p> 2021-12-31T00:00:00+07:00 Copyright (c) 2021 Communication in Biomathematical Sciences https://journals.itb.ac.id/index.php/cbms/article/view/17008 Mathematical Modelling and Control of COVID-19 Transmission in the Presence of Exposed Immigrants 2021-09-29T12:27:03+07:00 Reuben Iortyer Gweryina gweryina.reuben@uam.edu.ng Chinwendu Emilian Madubueze gweryina.reuben@uam.edu.ng Martins Afam Nwaokolo gweryina.reuben@uam.edu.ng <p>In this paper, a mathematical model for COVID-19 pandemic that spreads through horizontal transmission in the presence of exposed immigrants is studied. The model has equilibrium points, notably, COVID-19-free equilibrium and COVID-19-endemic equilibrium points. The model exhibits a basic reproduction number, R0 which determines the elimination and persistence of the disease. It was found that when R0 &lt; 1, then the equilibrium becomes locally asymptotically stable and endemic equilibrium does not exists. However, when R0 &gt; 1, the equilibrium is found to be stable globally. This implies that continuous mixing of exposed immigrants with the susceptible population will make the eradication of COVID-19 difficult and endemic in the community. The system is also proved qualitatively to experience transcritical bifurcation close to the COVID-19-free equilibrium at the point R0 = 1. Numerically, the model is used to investigate the impact of certain other relevant parameters on the spread of COVID-19 and how to curtail their effect.</p> 2021-12-31T00:00:00+07:00 Copyright (c) 2021 Communication in Biomathematical Sciences https://journals.itb.ac.id/index.php/cbms/article/view/17370 Dynamics of COVID-19 Epidemic Model with Asymptomatic Infection, Quarantine, Protection and Vaccination 2021-11-10T20:50:23+07:00 Raqqasyi Rahmatullah Musafir raqqasyi@student.ub.ac.id Agus Suryanto suryanto@ub.ac.id Isnani Darti isnanidarti@ub.ac.id <p>We discuss the dynamics of new COVID-19 epidemic model by considering asymptomatic infections and the policies such as quarantine, protection (adherence to health protocols), and vaccination. The proposed model contains nine subpopulations: susceptible (S), exposed (E), symptomatic infected (I), asymptomatic infected (A), recovered (R), death (D), protected (P), quarantined (Q), and vaccinated (V ). We first show the non-negativity and boundedness of solutions. The equilibrium points, basic reproduction number, and stability of equilibrium points, both locally and globally, are also investigated analytically. The proposed model has disease-free equilibrium point and endemic equilibrium point. The disease-free equilibrium point always exists and is globally asymptotically stable if basic reproduction number is less than one. The endemic equilibrium point exists uniquely and is globally asymptotically stable if the basic reproduction number is greater than one. These properties have been confirmed by numerical simulations using the fourth order Runge-Kutta method. Numerical simulations show that the disease transmission rate of asymptomatic infection, quarantine rates, protection rate, and vaccination rates affect the basic reproduction number and hence also influence the stability of equilibrium points.</p> 2021-12-31T00:00:00+07:00 Copyright (c) 2021 Communication in Biomathematical Sciences https://journals.itb.ac.id/index.php/cbms/article/view/17426 Forward Bifurcation with Hysteresis Phenomena from Atherosclerosis Mathematical Model 2021-12-07T17:32:17+07:00 Dipo Aldila aldiladipo@sci.ui.ac.id Arthana Islamilova arthana.islamilova@sci.ui.ac.id Sarbaz H.A. Khosnaw sarbaz.hamza@uor.edu.krd Bevina D. Handari bevina@sci.ui.ac.id Hengki Tasman htasman@sci.ui.ac.id <p>Atherosclerosis is a non-communicable disease (NCDs) which appears when the blood vessels in the human body become thick and stiff. The symptoms range from chest pain, sudden numbness in the arms or legs, temporary loss of vision in one eye, or even kidney failure, which may lead to death. Treatment in cases with severe symptoms requires surgery, in which the number of doctors or hospitals is limited in some countries, especially countries with low health levels. This article aims to propose a mathematical model to understand the impact of limited hospital resources on the success of the control program of atherosclerosis spreads. The model was constructed based on a deterministic model, where the hospitalization rate is defined as a time-dependent saturated function concerning the number of infected individuals. The existence and stability of all possible equilibrium points were shown analytically and numerically, along with the basic reproduction number. Our analysis indicates that our model may exhibit various types of bifurcation phenomena, such as forward bifurcation, backward bifurcation, or a forward bifurcation with hysteresis depending on the value of hospitalization saturation parameter and the infection rate for treated infected individuals. These phenomenon triggers a complex and tricky control program of atherosclerosis. A forward bifurcation with hysteresis auses a possible condition of having more than one stable endemic equilibrium when the basic reproduction number is larger than one, but close to one. The more significant value of hospitalization saturation rate or the infection rate for treated infected individuals increases the possibility of the stable endemic equilibrium point even though the disease-free equilibrium is stable. Furthermore, the Pontryagin Maximum Principle was used to characterize the optimal control problem for our model. Based on the results of our analysis, we conclude that atherosclerosis control interventions should prioritize prevention efforts over endemic reduction scenarios to avoid high intervention costs. In addition, the government also needs to pay great attention to the availability of hospital services for this disease to avoid the dynamic complexity of the spread of atherosclerosis in the field.</p> 2021-12-31T00:00:00+07:00 Copyright (c) 2021 Communication in Biomathematical Sciences https://journals.itb.ac.id/index.php/cbms/article/view/17429 Analysis of A Coendemic Model of COVID-19 and Dengue Disease 2021-11-30T15:22:59+07:00 Hilda Fahlena hildafahlena@s.itb.ac.id Widya Oktaviana widyaoktaviana@students.itb.ac.id Farida farida19021997@yahoo.co.id Sudirman sudirmanlsibi13@gmail.com Nuning Nuraini nuning@math.itb.ac.id Edy Soewono esoewono@math.itb.ac.id <p>The coronavirus disease 2019 (COVID-19) pandemic continues to spread aggressively worldwide, infecting more than 170 million people with confirmed cases, including more than 3 million deaths. This pandemic is increasingly exacerbating the burden on tropical and subtropical regions of the world due to the pre-existing dengue fever, which has become endemic for a longer period in the same region. Co-circulation dengue and COVID-19 cases have been found and confirmed in several countries. In this paper, a deterministic model for the coendemic of COVID-19 and dengue is proposed. The basic reproduction ratio is obtained, which is related to the four equilibria, disease-free, endemic-COVID-19, endemic-dengue, and coendemic equilibria. Stability analysis is done for the first three equilibria. Furthermore, a condition for coexistence equilibrium is obtained, which gives a condition for bifurcation analysis. Numerical simulations were carried out to obtain a stable limit-cycle resulting from two Hopf bifurcation points with dengue transmission rate and COVID-19 transmission rate as the bifurcation parameter, representing a stable periodic coexistence of dengue and COVID-19 transmission. We identify the period of limit cycle decreases after reaching the maximum value.</p> 2021-12-31T00:00:00+07:00 Copyright (c) 2022 Communication in Biomathematical Sciences