The question “who infected whom” is a perennial one in the study of infectious disease dynamics. To understand characteristics of infectious diseases such as how many people will one case produce over the course of infection (the reproductive number), how much time between the infection of two connected cases (the generation interval), and what factors are associated with transmission, one must ascertain who infected whom. The current best practices for linking cases are contact investigations and pathogen whole genome sequencing (WGS). However, these data sources cannot perfectly link cases, are expensive to obtain, and are often not available for all cases in a study. This lack of discriminatory data limits the use of established methods in many existing infectious disease datasets. We developed a method to estimate the relative probability of direct transmission between any two infectious disease cases. We used a subset of cases that have pathogen WGS or contact investigation data to train a model and then used demographic, spatial, clinical, and temporal data to predict the relative transmission probabilities for all case-pairs using a simple machine learning algorithm called naive Bayes. We adapted existing methods to estimate the reproductive number and generation interval to use these probabilities. Finally, we explored the associations between various covariates and transmission and how they related to the associations between covariates and pathogen genetic relatedness. We applied these methods to a tuberculosis outbreak in Hamburg, Germany and to surveillance data in Massachusetts, USA. Through simulations we found that our estimated transmission probabilities accurately classified pairs as links and nonlinks and were able to accurately estimate the reproductive number and the generation interval. We also found that the association between covariates and genetic relatedness captures the direction but not absolute magnitude of the association between covariates and transmission, but the bias was improved by using effect estimates from the naive Bayes algorithm. The methods developed in this dissertation can be used to explore transmission dynamics and estimate infectious disease parameters in established datasets where this was not previously feasible because of a lack of highly discriminatory information, and therefore expand our understanding of many infectious diseases.

Master of Public Health - MPH / Two quantitative studies were carried out in randomly-selected facilities within all nine provinces of South Africa. First, a situational assessment of these randomly selected facilities was undertaken using key informant (health care personnel) interviews and record reviews to ascertain guidelines and procedures for early identification of HIV-exposed infants (HEI), the coverage of early infant diagnosis services, the human resource capacity of the health system, and existing linkage and referral system for antenatal and postnatal PMTCT services. This was followed by the South African national PMTCT survey (SAPMTCTE) which involved a collection of infant blood samples and maternal interview data from mother-infant pairs (infants age 4-8weeks) attending six weeks immunisation service points in the selected facilities. Interviews were conducted with mothers to assess antenatal and peripartum PMTCT services received and maternal intention to request for infant HIV testing at six weeks immunisation visits. Data on gestational age at birth, infant birth weight and HIV status was extracted from the road-to-health-card (RtHC). The HIV status of mothers was determined from maternal report or enzyme immunoassay (EIA) test conducted on infants dried blood spots (DBS). A weighted analysis (weighted for sample size realisation and population live births) was performed to assess uptake of services along the PMTCT cascade. Mothers who either self-reported an HIV-positive status or had an EIA positive infant were classified as HIV-positive mothers. Perinatal ARV regimen coverage was calculated from the total number of HIV-positive mothers who received maternal azidothymidine (AZT) or HAART for any duration during pregnancy plus infant nevirapine (NVP)/AZT received at birth. Descriptive methods were used to analyse national availability of EID services and approaches for identifying HEI at the six weeks immunisation visit. Logistic regression assessed key factors influencing maternal intention to receive EID. Logistic regression was also used to explore individual, health facility and provincial level factors that explain variability in mother-to-child-transmission rates.