Simulating the progression of the COVID-19 disease in Cameroon using SIR models

Nguemdjo, Ulrich; Meno, Freeman; Dongfack, Audric; Ventelou, Bruno

doi:10.1371/journal.pone.0237832

Cited by 39 publications

(24 citation statements)

References 13 publications

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“…This can be eventually extended to predicting the effects of different containment measures or the lack thereof [ 8 ]. Presented analysis corroborates the finding of other authors about the utility of the SIR model in analysing the COVID-19 pandemics [ 4 , 6 , 7 , 8 ]. Presented results indicate that there is a universality in the time evolution of COVID-19 and the same epidemic model, notably SIR, can be applied to countries having large differences in populations sizes and densities.…”

Section: Discussionsupporting

confidence: 90%

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Analytical Parameter Estimation of the SIR Epidemic Model. Applications to the COVID-19 Pandemic

Prodanov

2020

Entropy

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The SIR (Susceptible-Infected-Removed) model is a simple mathematical model of epidemic outbreaks, yet for decades it evaded the efforts of the mathematical community to derive an explicit solution. The present paper reports novel analytical results and numerical algorithms suitable for parametric estimation of the SIR model. Notably, a series solution of the incidence variable of the model is derived. It is proven that the explicit solution of the model requires the introduction of a new transcendental special function, describing the incidence, which is a solution of a non-elementary integral equation. The paper introduces iterative algorithms approximating the incidence variable, which allows for estimation of the model parameters from the numbers of observed cases. The approach is applied to the case study of the ongoing coronavirus disease 2019 (COVID-19) pandemic in five European countries: Belgium, Bulgaria, Germany, Italy and the Netherlands. Incidence and case fatality data obtained from the European Centre for Disease Prevention and Control (ECDC) are analysed and the model parameters are estimated and compared for the period Jan-Dec 2020.

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Section: Discussionsupporting

confidence: 90%

“…Therefore, outcome variables, such as mortality, can be foretasted more easily using a simpler model, e.g., using the SIR model. Recently, several authors demonstrated that the first wave of COVID-19 followed SIR dynamic, which by itself, is an interesting finding [ 4 , 5 , 6 , 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Analytical Parameter Estimation of the SIR Epidemic Model. Applications to the COVID-19 Pandemic

Prodanov

2020

Entropy

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“…There has been significant research on the acquisition of user behavior using Wi-Fi logs in various fields [10][11][12][13][14][15][16][17][18][19][20]. For tourism behavior analysis, in particular, various studies using Wi-Fi logs have been conducted to understand the state of tourism behavior through facility and visitor stay times and OD (Origin-Destination) Tables [21,22]. For example, the Internet-of-Things (IoT) data sent over Bluetooth and Wi-Fi have been used to monitor and estimate the number of passengers and the waiting time for buses and subway trains [23,24].…”

Section: Related Workmentioning

confidence: 99%

Optimizing intra-facility crowding in Wi-Fi environments using continuous-time Markov chains

Mizuno

Ohba

2022

Preprint

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Recently, increased measures are being devised to reduce crowdedness as a countermeasure for the spread of COVID-19. In this study, we propose a solution to reduce intra-facility crowdedness based on the usage of Wifi networks. This study maximizes the Wi-Fi logs that are continually generated in vast quantities in the ever-expanding Wi-Fi network environment to calculate the transition probabilities between nodes and the mean stay time at each node. Then, we model this data as a continuous-time Markov chain to obtain the variance of the stationary distribution, which we use as a metric of intra-facility crowdedness. Therefore, to minimize intra-facility crowdedness, we solved the optimization problem using stay rate as a parameter and demonstrated a numerical solution. In the optimization results, we succeeded in reducing intra-facility crowding by approximately 30%. This solution is a realistic approach for reducing intra-facility crowdedness as it makes adjustments to people’s stay times without any changes in their movements. We used the k-means method to categorize Wi-Fi users into a set of classes and documented the behavioral characteristics of each class, which helped implement class-specific measures to reduce intra-facility crowdedness. This enables facility managers to implement fine-grained countermeasures against crowdedness according to circumstances. Herein, we describe our computing environment and workflow for the basic analysis of vast quantities of Wi-Fi logs. Because the data we used are general-purpose, we believe that this research will be useful for both analysts and facility operators.

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“…Since the beginning of the COVID-19 epidemic, there has been various mathematical and statistical modelling that have predicted the global and national epidemic with varying degrees of accuracy and reliability (see [1], [2], [4], [10], [21], [25], [27], [28], [29], [32], [35], [40]). The accuracy of prediction and its uncertainty depend on the assumptions, availability and quality of data (see [31]).…”

Section: Introductionmentioning

confidence: 99%