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Communication in Biomathematical Sciences
Published by Indonesian Biomathematical Society
ISSN : -     EISSN : 25492896     DOI : 10.5614/cbms
Core Subject : Social,
Social Sciences
Full research articles in the area of Applications of Mathematics in biological processes and phenomena
Arjuna Subject : Matematika - Matematika Terapan
Articles 6 Documents
 1 
Search results for , issue "Vol. 3 No. 1 (2020)" : 6 Documents clear
Modeling Simulation of COVID-19 in Indonesia based on Early Endemic Data Nuning Nuraini; Kamal Khairudin; Mochamad Apri
Communication in Biomathematical Sciences Vol. 3 No. 1 (2020)
Publisher : Indonesian Bio-Mathematical Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/cbms.2020.3.1.1

Abstract

The COVID-19 pandemic has recently caused so much anxiety and speculation around the world. This phenomenon was mainly driven by the drastic increase in the number of infected people with the COVID-19 virus worldwide. Here we propose a simple model to predict the endemic in Indonesia. The model is based on the Richard's Curve that represents a modified logistic equation. Based on the similar trends of initial data between Indonesia and South Korea, we use parameter values that are obtained through parameter estimation of the model to the data in South Korea. Further, we use a strict assumption that the implemented strategy in Indonesia is as effective as in South Korea. The results show that endemic will end in April 2020 with the total number of cases more than 8000.
On the Analysis of Covid-19 Transmission in Wuhan, Diamond Princess and Jakarta-cluster Edy Soewono
Communication in Biomathematical Sciences Vol. 3 No. 1 (2020)
Publisher : Indonesian Bio-Mathematical Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/cbms.2020.3.1.2

Abstract

The whole world has been recently shocked by the massive spread of Covid-19 without any sign of when it will end. This phenomenon of this scale is understood as a plague that has never been happening in a lifetime. Almost all countries do not have proper preparedness when positive cases are found in a region. In a relatively short time, cases then spread quickly, and panic broke out in the community. With the rapid human to human transmission, and there is no vaccine available, the only way to control the spread of the disease is by implementing a contact tracing and isolation policy. The fact indicated that health officials in many affecting countries have difficulty in detecting individuals who are potentially exposed to the virus. The success of controlling the disease is very much dependent on the ability of the health authority in tracking and isolating the infected and the suspected cases. A transmission model for Covid-19 transmission in the form of SEIR is chosen to fit with the cases in Wuhan, Diamond Princess, and Jakarta-cluster. These cases represent the transmission in a large city, a relatively restricted and dense area, and a small cluster, respectively. The basic reproductive ratio and the infection rate are obtained based on the cumulative data for each case. These indicators can be used for predicting the progress of transmission for similar cases. A simple model for estimating the completing time of contact tracing and isolation is constructed in the form of a differential operator on the cumulative case. This operator represents the number of daily new infected cases. It is shown that for the case of Wuhan, the completing time for contact tracing and isolation is 55 days. This result is important for analyzing the intervention strategy of Covid-19 in an affected region.
An Analysis of Covid-19 Transmission in Indonesia and Saudi Arabia Meksianis Z. Ndii; Panji Hadisoemarto; Dwi Agustian; Asep K. Supriatna
Communication in Biomathematical Sciences Vol. 3 No. 1 (2020)
Publisher : Indonesian Bio-Mathematical Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/cbms.2020.3.1.3

Abstract

An outbreak of novel coronavirus has been happening in more than 200 countries and has shocked society. Several measures have been implemented to slowing down the epidemics while waiting for vaccine and pharmaceutical intervention. Using a deterministic and stochastic model, we assess the effectiveness of current strategies: reducing the transmission rate and speeding up the time to detect infected individuals. The reproductive ratio and the probability of extinction are determined. We found that the combination of both strategies is effective to slow down the epidemics. We also find that speeding up the time to detect infected individuals without reducing the transmission rate is not sufficient to slow down the epidemics.
How Many Can You Infect? Simple (and Naive) Methods of Estimating the Reproduction Number H. Susanto; V.R. Tjahjono; A. Hasan; M.F. Kasim; N. Nuraini; E.R.M. Putri; R. Kusdiantara; H. Kurniawan
Communication in Biomathematical Sciences Vol. 3 No. 1 (2020)
Publisher : Indonesian Bio-Mathematical Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/cbms.2020.3.1.4

Abstract

This is a pedagogical paper on estimating the number of people that can be infected by one infectious person during an epidemic outbreak, known as the reproduction number. Knowing the number is crucial for developing policy responses. There are generally two types of such a number, i.e., basic and effective (or instantaneous). While basic reproduction number is the average expected number of cases directly generated by one case in a population where all individuals are susceptible, effective reproduction number is the number of cases generated in the current state of a population. In this paper, we exploit the deterministic susceptibleinfected-removed (SIR) model to estimate them through three different numerical approximations. We apply the methods to the pandemic COVID-19 in Italy to provide insights into the spread of the disease in the country. We see that the effect of the national lockdown in slowing down the disease exponential growth appearedabout two weeks after the implementation date. We also discuss available improvements to the simple (and naive) methods that have been made by researchers in the field. Authors of this paper are members of the SimcovID (Simulasi dan Pemodelan COVID-19 Indonesia) collaboration.
The COVID-19 outbreak in Germany – Models and Parameter Estimation Peter Heidrich; Moritz Schäfer; Mostafa Nikouei; Thomas Götz
Communication in Biomathematical Sciences Vol. 3 No. 1 (2020)
Publisher : Indonesian Bio-Mathematical Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/cbms.2020.3.1.5

Abstract

Since the end of 2019 an outbreak of a new strain of coronavirus, called SARS–CoV–2, is reported from China and later also from other parts of the world. Since 21 January 2020, World Health Organization (WHO) reports daily data on confirmed cases and deaths from both China and other countries [1]. The Johns Hopkins University [2] collects those data from various sources worldwide on a daily basis. For Germany, the Robert–Koch–Institute (RKI) also issues daily reports on the current number of infections and infection related fatal cases and also provides estimates of several disease-related parameters [3]. In this work we present an extended SEIRD–model to describe these disease dynamics in Germany. The model takes into account the susceptible, exposed, infected, recovered and deceased fractions of the population. Epidemiological parameters like the transmission rate, lethality or the detection rate of infected individuals are estimated by fitting the model output to available data. For the parameter estimation itself we compare two methods: an adjoint based approach and a Monte–Carlo based Metropolis algorithm.
SHAR and effective SIR models: from dengue fever toy models to a COVID-19 fully parametrized SHARUCD framework Maira Aguiar; Nico Stollenwerk
Communication in Biomathematical Sciences Vol. 3 No. 1 (2020)
Publisher : Indonesian Bio-Mathematical Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/cbms.2020.3.1.6

Abstract

We review basic models of severe/hospitalized and mild/asymptomatic infection spreading (with classes of susceptibles S, hopsitalized H, asymptomatic A and recovered R, hence SHAR-models) and develop the notion of comparing different models on the same data set as exemplified in the comparison of SHAR models with effective SIR models, where only the H-class of the SHAR model is taken into account in the SIR model. This is done via the so-called Bayes factor. A simpler pair of models with analytical expressions up to the Bayes factor will be briefly mentioned as well. The notions developed with respect to dengue fever epidemiology will then be used to analyze recently becoming available data on coronavirus disease 2019, COVID-19, where models can be fully parametrized including hospital admission and more extensions like intensive care unit (ICU) admission and deceased, always with a close look on as simple as possible models but not simpler, as exercised in Ocham's razor and analyzed by e.g. the Bayes factor. We present the resulting models of SHAR-type with additional classes of ICU admissions U, and deceased D, and for data analysis of cumulative disease data, also accounting the cumulative classes C, in the so-called SHARUCD framework. Besides a first basic version, SHARUCD model 1, we investigate also in detail a refined version, SHARUCD model 2, which could be achieved by a closer analysis of available data only obtained after the exponential growth phase of the epidemic, when lockdown control measures showed effects. Namely, the ICU admissions turned out to be more in synchrony with the hospitalized than with e.g. the deceased cases, such that we could adjust the transitions so that ICU admissions are modeled like hospitalizations in model 2, and not like recovery or disease induced death as assumed in model 1, explaining much better the empirical data, specially after the effects of the lockdown became visible. Special attention will be given here, for the first time, to the initial phase of the COVID-19 epidemics, before all variables entered into the exponential phase, and its interplay between asymptomatic and severe hospitalized cases, always in close check with the SIR-limiting case. Such improved understanding of the initial phase will help in the future analysis of re-emergent outbreaks of COVID-19, likely to happen in the next or a subsequent respiratory disease season in autumn or winter.

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