Bertalanffy-Pütter models for the first wave of the COVID-19 outbreak

Brunner, Norbert; Kühleitner, Manfred

doi:10.1016/j.idm.2021.03.003

Cited by 3 publications

(3 citation statements)

References 27 publications

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“…To develop proper responses, not only are more accurate disease spread models needed, but also those that are easy to use. 13 , 20 Few studies have been published in search for a straightforward mathematical model that can be easily used by clinicians to forecast the approximate rate of COVID-19 spread, and unfortunately, none of these few models published are as simple as expected. 18 , 20 , 21 Therefore, it may be useful for clinicians and healthcare managers working daily in hospitals to have access to fundamental formulas that can easily predict the course of the epidemic based on its current state of evolution.…”

Section: Introductionmentioning

confidence: 99%

A simple mathematical tool to forecast COVID-19 cumulative case numbers

Balak¹,

İnan²,

Ganau³

et al. 2021

Clinical Epidemiology and Global Health

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Objective Mathematical models are known to help determine potential intervention strategies by providing an approximate idea of the transmission dynamics of infectious diseases. To develop proper responses, not only are more accurate disease spread models needed, but also those that are easy to use. Materials and methods As of July 1, 2020, we selected the 20 countries with the highest numbers of COVID-19 cases in the world. Using the Verhulst–Pearl logistic function formula, we calculated estimates for the total number of cases for each country. We compared these estimates to the actual figures given by the WHO on the same dates. Finally, the formula was tested for longer-term reliability at t = 18 and t = 40 weeks. Results The Verhulst–Pearl logistic function formula estimated the actual numbers precisely, with only a 0.5% discrepancy on average for the first month. For all countries in the study and the world at large, the estimates for the 40th week were usually overestimated, although the estimates for some countries were still relatively close to the actual numbers in the forecasting long term. The estimated number for the world in general was about 8 times that actually observed for the long term. Conclusions The Verhulst–Pearl equation has the advantage of being very straightforward and applicable in clinical use for predicting the demand on hospitals in the short term of 4–6 weeks, which is usually enough time to reschedule elective procedures and free beds for new waves of the pandemic patients.

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Section: Introductionmentioning

confidence: 99%

A simple mathematical tool to forecast COVID-19 cumulative case numbers

Balak¹,

İnan²,

Ganau³

et al. 2021

Clinical Epidemiology and Global Health

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“…A lot of models have been created to understand the COVID-19 pandemic, including the Richards model, generalized growth model, classical logistic growth model, generalized logistic model, suspected-infected-recovered (SIR) model (Zreiq et al, 2020), Verhulst model (Abusam et al, 2020), Bertalanffy-Putter model (Brunner & Kühleitner, 2021), suspectedinfected-recovered-dead (SIRD) model (Lounis & Raeei, 2021), suspected-exposed-infectedrecovered (SEIR) model (Yang et al, 2021), susceptible-exposed-infected-quarantinedrecovered-dead (SEIQRD) model, susceptible-exposed-infected-asymptotic undetectedasymptotic detected-recovered (SEIAuAdR) model (Aldila et al, 2020), and susceptibleexposed-infected-hospitalized-critical-recovered-dead (SEIHCRD) model (Mbogo & Orwa, 2021) model. Later models tried to assess more states and details but we are not going to have similar features in our model due to lack of data and even though we have many selfisolation and hospitalizations, we chose not to model them because both health conditions of the cases and bed availability in hospitals play a significant part to determine the number of hospitalizations.…”

Section: Bayesian Pird Multi-wave Modelmentioning

confidence: 99%

Jakarta COVID-19 Forecast with Bayesian PIRD Multiwave Model

Chandra,

Abdullah

2022

ASEAN J. Sci. Eng.

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Governments have to consider both socioeconomic and health conditions in handling the COVID-19 pandemic. To help them in understanding possible scenarios behind the numbers and deciding optimum policy, this study proposed a Bayesian protected-infected-recovered-dead (PIRD) multi-wave model. Compounds of the Richards curve are used to understand how many pandemic waves possibly occur, how significant is the occurrence of every single wave, and dynamic in every single wave. The model also estimated the mortality rate due to the COVID-19 pandemic and the duration between infection to death, also infection to recovery. We fitted Jakarta’s COVID-19 data from 3 March 2020 to 25 November 2021 with help of OpenBUGS. We learned that their pandemic should consist of at least two waves, expected to have three waves already. By letting social restriction be looser together with decreasing number of new infection cases, Jakarta could have its fourth and even fifth pandemic wave that starts around mid-May to mid-July 2022 and reach its peak around January to February 2023. Vice versa, they could enter the endemic phase around the end of August 2022 until the beginning of February 2023 and finally have zero COVID-19 new infection around mid-January until mid-June 2023 by having stricter social restrictions.

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“…As the above literature reviews, the models for describing the COVID-19 outbreak are based on time independent variable. For example, the forecasting models are the models with time independent variables such as the logistic and Gompertz models [1][2][3], the Bertalanffy model [4], the Boltzmann growth curve [5,6], and Regression analysis [7,8]. The mathematical models, a system of differential equations, are the models with time independent variable such SEIQCRW model [19], SIR model [20], SEIR model [21], and SEIQRD [24].…”

mentioning

confidence: 99%

Probabilistic Analysis Depending on the Distance from A COVID-19 Outbreak

Areepong

Sunthornwat²

2023

Emerg Sci J

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COVID-19 has been affecting human beings since the end of 2019. Studying the characteristics of a COVID-19 outbreak is significant because it will add to the knowledge that is necessary for protecting the general public and controlling future viral outbreaks. The aims of the present research are to analyze COVID-19 outbreaks in Thailand depending on the distance from the outbreak center by using a differential equation, to construct a probability density function from the solution of the differential equation, and to prove the theorem for the probability density function depending on the distance from the outbreak. The least-squares-error method is adopted to estimate the parameters of the function describing the COVID-19 outbreak. Moreover, a cumulative distribution function, a quantile function, a sojourn function, a hazard function, the median, the expected value, variance, skewness, and kurtosis are derived, and their practicability is shown. Applying the exponentially weighted moving average control chart to monitor a COVID-19 outbreak based on distance is proposed and compared with monitoring the COVID-19 outbreak based on time. The results show that using the former more quickly detected the out-of-control first passage time of the COVID-19 outbreak than the latter. Doi: 10.28991/ESJ-2023-SPER-012 Full Text: PDF

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Bertalanffy-Pütter models for the first wave of the COVID-19 outbreak

Cited by 3 publications

References 27 publications

A simple mathematical tool to forecast COVID-19 cumulative case numbers

A simple mathematical tool to forecast COVID-19 cumulative case numbers

Jakarta COVID-19 Forecast with Bayesian PIRD Multiwave Model

Probabilistic Analysis Depending on the Distance from A COVID-19 Outbreak

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