A catalytic model for SARS-CoV-2 reinfections : performing simulation-based validation and extending the model to include nth infections

Simple item page
dc.contributor.advisorVan Schalkwyk, Carien_ZA
dc.contributor.advisorPulliam, Julieten_ZA
dc.contributor.authorLombard, Belindaen_ZA
dc.contributor.otherStellenbosch University. Faculty of Science. Dept. of Applied Mathematics.en_ZA
dc.date.accessioned2023-11-13T09:42:53Z
dc.date.accessioned2024-01-08T17:05:29Z
dc.date.available2023-11-13T09:42:53Z
dc.date.available2024-01-08T17:05:29Z
dc.date.issued2023-12
dc.descriptionThesis (MSc)--Stellenbosch University, 2023.en_ZA
dc.description.abstractENGLISH SUMMARY: Background: A global pandemic of COVID-19, caused by SARS-CoV-2, was declared in March 2020. Subsequently, studies have revealed a high seroprevalence of SARS-CoV-2 in both South African and global populations, along with instances of multiple reinfections. Among various models, a catalytic model has been developed for detecting population-level increases in risk of reinfection, following primary infection. This thesis aims to assess how potential biases from imperfect data observation processes affect the catalytic model’s ability to detect increases in reinfection risk. Furthermore, the thesis extends the catalytic model to detect increases in the risk of multiple reinfections. Methods: Simulation-based validation involved creating different reinfection scenarios representing real life data, which were then used in the model’s fitting and projection procedure. Observed reinfections were simulated using a time-series of primary infections, representative of South African data. Scenarios included considering both imperfect observation (with constant observation probability or a probability dependent on primary infection count) and mortality. The method’s ability to detect increases in the reinfection risk was measured by determining both the clusters of reinfections and the proportion of points that fell above the projection interval. Following simulation-based validation, the method was extended to detect population-level increases in the risk of 𝑛𝑡ℎ infections. This extended method was applied to observed third infections in South Africa, with an additional model parameter representing increased reinfections during the Omicron wave. Simulation-based validation was conducted on the extended method to assess its ability to detect increases of varying magnitudes in the risk of third infection. Results: During the simulation-based validation of the original catalytic model, model parameters converged in most scenarios. Failure to converge was mostly related to insufficient cases to properly inform the model parameters during the fitting procedure. Scenarios where the model parameters did not converge, or where the simulated data did not accurately fit the model, were excluded from interpretation. Introducing an increase in the reinfection risk resulted in successful detection of an increase (even with small increments), although with delayed timing under lower observed infection numbers. Mortality from first infections, unaccounted for in the model, did not impact the method’s ability to detect increases in the reinfection risk. The method demonstrated high specificity, reliably distinguishing true increases in the reinfection risk from noise. The catalytic model was extended to detect increases in the risk of 𝑛𝑡ℎ infections, and the extended method’s ability to detect increases in the risk of third infections was validated. The additional third infection hazard representing increased reinfection risk observed during the Omicron wave was successfully fitted to the data, and the method effectively detected increases in the risk of third infections. Conclusion: The findings highlight the need for sufficient infection data and the importance of convergence as a prerequisite for result interpretation. The extended model reliably detected increases in the risk of two or more reinfections and demonstrated robustness under different observation processes and increases in reinfection risk scenarios.en_ZA
dc.description.abstractAFRIKAANSE OPSOMMING: Agtergrond: 'n Wereldwye pandemie van COVID-19, veroorsaak deur SARS-CoV-2, is in Maart 2020 verklaar. Studies het 'n hoe seroprevalensie van SARS-CoV-2 in beide Suid-Afrikaanse en wereldbevolkings getoon, tesame met gevalle van veelvuldige herinfeksies. Verskeie modelle, onder andere 'n katalitiese model, is ontwikkel om bevolkingsvlak verhogings in risiko van herinfeksie op te spoor. Hierdie tesis het ten doel om te bepaal hoe potensiele sydighede veroorsaak deur onvolmaakte data waarnemings prosesse die katalitiese model se vermoe om toenames in herinfeksierisiko op te spoor, beinvloed; asook om die katalitiese model uit te brei om toenames in die risiko van veelvuldige herinfeksies op te spoor. Metodes: Simulasie-gebaseerde validering behels die skep van verskillende herinfeksie-scenario's wat werklike data verteenwoordig, en die toepas daarvan in die model. Waargenome herinfeksies is gesimuleer deur gebruik te maak van 'n tydreeks van primere infeksies, verteenwoordigend van Suid-Afrikaanse data. Scenario's het beide onvolmaakte waarneming (met konstante waarnemingswaarskynlikheid of 'n waarskynlikheid wat afhanklik is van primere infeksietelling) en mortaliteit ingesluit. Die metode se vermoe om toenames in die herinfeksierisiko op te spoor is gemeet deur beide `n groep herinfeksies en die proporsie punte wat bo die projeksie-interval geval het. Die metode is daarna uitgebrei om bevolkingsvlak verhogings in die risiko van n-de infeksies op te spoor. Hierdie uitgebreide metode is toegepas op waargenome derde infeksies in Suid-Afrika, met 'n addisionele model parameter wat 'n verhoogde tweede infeksie risiko tydens die Omicron golf verteenwoordig. Simulasie-gebaseerde validering is uitgevoer op die uitgebreide metode om sy vermoe te bepaal om toenames van verskillende groottes in die risiko van derde infeksie op te spoor. Resultate: Tydens die simulasie-gebaseerde validering van die oorspronklike katalitiese model het modelparameters in die meeste scenario's konvergeer. Gebrek aan konvergering het meestal verband gehou met onvoldoende gevalle om die modelparameters behoorlik in te lig tydens die pas prosedure. Scenario's waar die modelparameters nie konvergeer nie, of waar die gesimuleerde data nie akkuraat by die model pas nie, is uitgesluit van interpretasie. Die bekendstelling van 'n toename in die herinfeksierisiko het gelei tot suksesvolle opsporing van 'n toename (selfs met klein inkremente), hoewel met vertraagde tydsberekening onder laer infeksiegetalle. Mortaliteit van eerste infeksies, wat nie in die model verantwoord is nie, het nie die metode se vermoe om toenames in die herinfeksierisiko op te spoor beinvloed nie. Die metode het hoe spesifisiteit gedemonstreer, wat ware toenames in die herinfeksierisiko van stogastiese gedrag onderskei. Die katalitiese model is uitgebrei om verhogings in die risiko van n-de infeksies op te spoor, en sy vermoe om verhogings in die risiko van derde infeksies op te spoor is bekragtig. Die bykomende parameter wat verhoogde herinfeksierisiko verteenwoordig wat tydens die Omicron-golf waargeneem is, is suksesvol by die data aangepas, en die metode het suksesvol toenames in die risiko van derde infeksies opgespoor. Afsluiting: Die bevindinge beklemtoon die behoefte aan voldoende infeksie data en die belangrikheid van konvergensie as 'n voorvereiste vir resultaat interpretasie. Die uitgebreide model het betroubaar verhogings in die risiko van twee of meer herinfeksies opgespoor en robuustheid getoon onder verskillende waarneming en verhogings in herinfeksierisiko-scenario's.af_ZA
dc.description.versionMasters
dc.format.extentxii, 66 pages : illustrations, includes annexures
dc.identifier.urihttps://scholar.sun.ac.za/handle/10019.1/128954
dc.language.isoen_ZAen_ZA
dc.publisherStellenbosch : Stellenbosch University
dc.rights.holderStellenbosch University
dc.subject.lcshCOVID-19 (Disease) -- Transmission -- South Africaen_ZA
dc.subject.lcshCoronavirus infections -- Risk Assessment -- South Africaen_ZA
dc.subject.lcshCoronavirus infections -- South Africaen_ZA
dc.subject.lcshRisk assessment -- Mathematical models -- South Africaen_ZA
dc.subject.nameUCTD
dc.titleA catalytic model for SARS-CoV-2 reinfections : performing simulation-based validation and extending the model to include nth infectionsen_ZA
dc.typeThesisen_ZA
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
lombard_catalytic_2023.pdf
Size:
3.37 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Masters Degrees (Applied Mathematics)