Assessment of a measles outbreak response vaccination campaign, and two measles parameter estimation methods
| dc.contributor.advisor | Pulliam, Juliet R. C. | en_ZA |
| dc.contributor.author | Azam, James Mba | en_ZA |
| dc.contributor.other | Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. | en_ZA |
| dc.date.accessioned | 2018-02-26T10:55:21Z | |
| dc.date.accessioned | 2018-04-09T07:00:22Z | |
| dc.date.available | 2018-02-26T10:55:21Z | |
| dc.date.available | 2018-04-09T07:00:22Z | |
| dc.date.issued | 2018-03 | |
| dc.description | Thesis (MSc)--Stellenbosch University, 2018. | en_ZA |
| dc.description.abstract | ENGLISH ABSTRACT : Measles is highly transmissible, and is a leading cause of vaccine-preventable death among children. Consequently, it is regarded as a public health issue worldwide and has been targeted for elimination by 5 out of the 6 WHO regions by 2020, the exception being the WHO Africa region. The hope of achieving this target, however, seems bleak as regular outbreaks continue to occur. Data from these outbreaks are useful for pursu- ing important questions about measles dynamics and control. This thesis is structured to investigate two questions: the first is on how well the time series susceptible-infected- recovered (TSIR) model and removal method perform when they are used to estimate parameters from poor quality data on measles epidemics. We simulate stochastic epidemics for four spatial patches, resembling data that are collected in low-income coun- tries where resources are limited for properly collecting and reporting data on measles epidemics. We then obtain from the simulated data sets, the size of the initial susceptible population S0, and the basic reproductive ratio R0 - for the TSIR; and S0, and either the effective reproduction number Re f f , or the basic reproductive ratio R0 - for the re- moval method, depending on the simulation assumptions. To assess performance, we quantify the biases that result when we tweak some of the simulation assumptions and modify the data to ensure it is in a form usable for each of the two methods. We find that the performance of the methods depends on the assumptions underlying the data gen- eration process, the degree of spatial aggregation, the chosen method of modifying the data to put it in a form usable for the estimation method, and the parameter being fitted. The removal method S0 estimates at the patch level are almost unbiased when the pop- ulation is naive, but are biased when aggregated to the population level, whether the population is initially naive or not. Furthermore, the removal R0 and Re f f estimates are generally biased. The TSIR model, on the other hand, seems more robust in estimating both S0 and R0 for non-naive populations. These findings are useful because they give us an idea of the biases in the fits of these methods to actual data of the same nature as the simulated epidemics. For the second question, we assess the impact of an outbreak response vaccination campaign which was organised in reaction to a measles outbreak in an all-boys high school in Stellenbosch, South Africa. We achieve this by formulating a discrete stochastic susceptible-exposed-infected-recovered (SEIR) model with daily time-steps, ignoring births and deaths. Using the model, we analyse multiple scenarios that allow us to estimate the cases averted, and to predict the cases remaining until the epidemic ended, and the time frame within which those cases would occur. Summarizing across scenarios, we estimate that a median of 255 cases (range 60 − 493) were averted. Also, a median of 15 remaining cases (range 1 − 33), and a median of 4 remaining weeks (range 1 − 16) were expected until the epidemic ended. We conclude that the campaign was successful in averting many potential cases. | en_ZA |
| dc.description.abstract | AFRIKAANSE OPSOMMING : Masels is hoogs oordraagbaar, en is ‘n leidende oorsaak van entstof-voorkombare sterftes onder kinders. Gevolglik word dit beskou as ‘n wêreldwy openbare gesondheidskwessie en word daar beoog om teen 2020 die virus in 5 uit die 6 WHO-streke te elimineer, met uitsondering die WGO-Afrika-streek. Die kans om hierdie teiken te bereik, lyk egter skraal aangesien gereelde uitbrake steeds voorkom. Data van hierdie uitbrake is nuttig om belangrike vrae oor masels-dinamika en beheer te ondersoek. Hierdie proefskrif ondersoek twee vrae. Die eerste is hoe goed die tydreeks vatbaaraansteeklik-herstel (TSIR) model en die verwyderings metode presteer wanneer dit gebruik word om parameters met behulp van data van swak gehalte oor maselsepidemies te skat. Ons simuleer stogastiese epidemies vir vier areas, wat ooreenstem met data wat in lae-inkomste lande versamel word, waar hulpbronne vir die behoorlike versameling en rapportering van data oor maselsepidemies beperk is. Ons kry dan uit die gesimuleerde datastelle, die grootte van die aanvanklike vatbare populasie S0, en die basiese reproduktiewe verhouding R0 - vir die TSIR; en S0, en óf die effektiewe reproduktiewe getal Re f f , of die basiese reproduktiewe verhouding R0 - vir die verwydering metode, afhangende van die simulasie aannames. Om prestasie te evalueer, bereken ons die sydigheid wat ontstaan as ons sommige van die simulasie aannames verander of die data verander word om te verseker dat dit in ‘n vorm is wat bruikbaar is vir elk van die twee metodes. Ons vind dat die prestasie van die metodes afhang van die aannames wat die aan data-genereringsproses onderliggend is, die mate van ruimtelike samestelling, die gekose metode om die data te verander om dit in ‘n bruikbare vorm te kry vir die skattingsmetode en die parameter wat gepas word. Die S0 beraam deur die verwydering metode op area vlak is byna onsydig wanneer die bevolking naïef is, maar sydig wanneer dit op die bevolkingsvlak geag word, of die bevolking aanvanklik naïef is of nie. Verder is die verwyderings metode R0 en Re f f ramings oor die algemeen sydig. Die TSIR-model, aan die ander kant, lyk beter om beide S0 en R0 vir nie-naïewe bevolkings te beraam. Hierdie bevindings is nuttig omdat hulle ons ’n idee gee van die sydighede in die pas van hierdie metodes tot werklike data van dieselfde aard as die gesimuleerde epidemies. Vir die tweede vraag, beraam ons die impak van ‘n uitbraakrespons-inentingsveldtog wat georganiseer is in reaksie op ‘n maselsuitbraak in ‘n hoörskool in Stellenbosch, Suid-Afrika. Ons bereik dit deur ‘n diskrete stogastiese vatbaar-blootgestel-aansteeklik-herstel (SEIR) model met daaglikse tydstappe te formuleer, wat geboortes en sterftes ignoreer. Deur die model te gebruik, analiseer ons verskeie scenario’s wat ons toelaat om die aantal afgeweerde gevalle te skat, en om die oorblywende aantal gevalle te tot die epidemie geëindig het te skat en die tydsraamwerk waarbinne sulke gevalle sou plaasvind. Opsommend oor scenario’s, skat ons dat ‘n mediaan van 255 gevalle (omvang 60 - 493) afgeweer is. Daar is ook ’n mediaan van 15 oorblywende gevalle (omvang 1 − 33) en ‘n mediaan van 4 oorblywende weke (omvang 1 − 16) verwag totdat die epidemie geëindig het. Ons kom tot die gevolgtrekking dat die veldtog suksesvol was om baie potensiële gevalle te voorkom. | af_ZA |
| dc.format.extent | xiv, 99 pages : illustrations (chiefly colour) | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/10019.1/103553 | |
| dc.language.iso | en_ZA | en_ZA |
| dc.publisher | Stellenbosch : Stellenbosch University | en_ZA |
| dc.rights.holder | Stellenbosch University | en_ZA |
| dc.subject | UCTD | en_ZA |
| dc.subject | Measles -- Vaccination -- South Africa | en_ZA |
| dc.subject | Measles -- South Africa -- Western Cape | en_ZA |
| dc.subject | Epidemiology -- Mathematical models | en_ZA |
| dc.subject | Communicable diseases -- Epidemiology -- Research | en_ZA |
| dc.subject | Disease management -- South Africa -- Mathematical models | en_ZA |
| dc.title | Assessment of a measles outbreak response vaccination campaign, and two measles parameter estimation methods | en_ZA |
| dc.type | Thesis | en_ZA |