Probabilistic Modelling of COVID-19 Dynamic in the Context of Madagascar

Raherinirina, Angelo; Fandresena, Tsilefa Stefana; Hajalalaina, Aimé Richard; Rabetafika, Haja; Rakotoarivelo, R.; Rafamatanantsoa, Fontaine

doi:10.4236/ojmsi.2021.93014

Cited by 7 publications

(3 citation statements)

References 31 publications

(17 reference statements)

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“…The study findings revealed that Markov Chains have a unique steady state distribution regardless of the initial state that is approached by successive iterations from any starting distributions. Other researchers (Din et al, 2020;Kharroubi, 2020;Raherinirina et al, 2021)…”

Section: Discussionmentioning

confidence: 99%

Probabilistic analysis of COVID-19 transmission in Kenya using Markov chain

Mugambi¹,

Atitwa²,

Morris³

et al. 2023

Int. j. adv. appl. sci.

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Since the outbreak of the COVID-19 pandemic, many countries have continued to suffer economically due to trade losses. COVID-19 has evolved into different forms and hence became a problem to analyze its transmission. As a result of increased COVID-19 infections, there has been a scarcity of resources like hospital facilities, quarantine centers, and personal protective equipment (PPEs) for the medics. Therefore, accurate planning has to be made by the government of Kenya to ensure that resources are made available to combat the rising COVID-19 cases. To ensure effective future planning for the COVID-19 pandemic, proper analysis of the COVID-19 pandemic among the population is key. Therefore, this study will go a long way in providing insights on how to plan for the Kenyan population through probabilistic analysis of the COVID-19 pandemic using the Markov chain. The study used Secondary Cumulative data from the Kenya ministry of health for a period between 1st June 2021 and 1st May 2022. The data was analyzed using a steady-state Markov chain in which the transition probability matrix for the COVID-19 pandemic was computed. The number of individuals infected by the COVID-19 virus and who recovered at the end of the study period was set at zero since COVID-19 disease is not curable. The results were presented in the table and reported at a 95% confidence level. Based on the findings, the study concluded that a steady-state Markov chain is beneficial in simulating the coronavirus infection in numerous stages. Also, it is noted that the use of the steady-state Markov chain model allows for capturing short and long-term memory effects that greatly improve the estimation of the number of new cases of COVID-19 and indicate whether the disease has an upward/downward trend.

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Section: Discussionmentioning

confidence: 99%

Probabilistic analysis of COVID-19 transmission in Kenya using Markov chain

Mugambi¹,

Atitwa²,

Morris³

et al. 2023

Int. j. adv. appl. sci.

View full text Add to dashboard Cite

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“…Although many studies have examined the different factors affecting the spread of covid'19 in a country as in the article by Raherinirina A. et all who modeled the spread of Covid'19 with control variantsRaherinirina et al (2021b) Raherinirina et al (2021a), Malheiro et all published a propagation article with contact restriction Malheiro et al (2020), there are gaps in research on the dynamics of the epidemic, prediction of its future trend taking into account appropriate policies, and the effects of neighbors on the dynamics of covid'19 intensity. Consequently, the aim of this article is to consider spatial neighborhoods in the dynamics of covid'19 in Madagascar's 22 administrative regions.…”

Section: Introductionmentioning

confidence: 99%

Spatialization of the Markov model to measure the spatial dependencies of an epidemiological spread : case covid19 Madagascar

Tsilefa,

Raherinirina

2024

Preprint

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This article presents a Markov chain variant that explicitly incorporates spatial effects. It is an extension of the Markovian class allowing a more complete analysis of the spatial dimensions of transitional dynamics. The aim is to have an explicit model that allows analysis of spatial dimensions. Here, the question is to study and quantify whether neighborhood context affects transitional dynamics. Rather than estimating a homogeneous law, the model requires the estimation of k transition laws, each dependent on spatial neighbor state. Consequently, this article used published data on confirmed cases of COVID19 in the 22 regions of Madagascar. These data were discretized to obtain a discrete state of propagation intensity. The analysis gave us the transition probabilities between Covid19 intensity states knowing the context of neighboring regions, and the propagation time laws knowing the spatial contexts. The results showed that neighboring regions had an effect on the propagation of Covid19 in Madagascar.

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“…Estimation of R t , or its related counterpart, r, the epidemic growth rate ( Wallinga and Lipsitch, 2007 , Park et al, 2020 ), from available case count data is challenged by limitations or variability in surveillance, uncertainty surrounding the shape of disease parameter distributions, and delays in reporting ( Gostic et al, 2020 ). Despite the enormity of these challenges in the limited surveillance settings common to many lower- and middle-income countries (LMICs), real-time estimation of R t from COVID-19 case-counts has been attempted for most regions of the globe ( $author1$ et al, 7]</id><AuthGrp><Author><in>S.</in><dl> </dl><au>Abbott</au></Author><dl>, </dl><Author><in>J.</in><dl> </dl><au>Hellewell</au></Author><dl>, </dl><Author><in>R.N.</in><dl> </dl><au>Thompson</au></Author><dl>, </dl><Author><in>K.</in><dl> </dl><au>Sherratt</au></Author><dl>, </dl><Author><in>H.P.</in><dl> </dl><au>Gibbs</au></Author><dl>, </dl><Author><in>N.I.</in><dl> </dl><au>Bosse</au></Author><dl>, </dl><Author><in>J.D.</in><dl> </dl><au>Munday</au></Author><dl>, </dl><Author><in>S.</in><dl> </dl><au>Meakin</au></Author><dl>, </dl><Author><in>E.L.</in><dl> </dl><au>Doughty</au></Author><dl>, </dl><Author><in>J.Y.</in><dl> </dl><au>Chun</au></Author><dl>, </dl><Author><in>Y.-W.D.</in><dl> </dl><au>Chan</au></Author><dl>, </dl><Author><in>F.</in><dl> </dl><au>Finger</au></Author><dl>, </dl><Author><in>P.</in><dl> </dl><au>Campbell</au></Author><dl>, </dl><Author><in>A.</in><dl> </dl><au>Endo</au></Author><dl>, </dl><Author><in>C.A.B.</in><dl> </dl><au>Pearson</au></Author><dl>, </dl><Author><in>A.</in><dl> </dl><au>Gimma</au></Author><dl>, </dl><Author><in>T.</in><dl> </dl><au>Russell</au></Author><dl>, </dl><Author><in>C.C.</in><dl> </dl><au>modelling Group</au></Author><dl>, </dl><Author><in>S.</in><dl> </dl><au>Flasche</au></Author><dl>, </dl><Author><in>A.J.</in><dl> </dl><au>Kucharski</au></Author><dl>, </dl><Author><in>R.M.</in><dl> </dl><au>Eggo</au></Author><dl>, </dl><Author><in>S.</in><dl> </dl><au>Funk</au></Author></AuthGrp> ) and has been implemented locally in Madagascar ( Rasambainarivo et al, 2020 , Raherinirina et al, 2021 , Raherinirina et al, 2021 , Narison and Maltezos, 2021 ).…”

Section: Introductionmentioning

confidence: 99%