ABSTRACT Incidence estimation and calibration from cross-sectional data of acute infection HIV-1 seroconvertors. May 2007 Eustasius Musenge Masters in Medicine in the Field of Biostatistics and Epidemiology Supervised by: Mr E Marinda and Dr A Welte Background: The HIV-1 incidence (a very important measure used as a proxy for disease burden) can be estimated from a cross-sectional study. This incidence estimate has the advantage of reducing on costs and time, thus enabling more timely intervention; it is also ideal for developing nations. A common procedure used in making this estimate utilizes two antibody tests (Sensitive/Less sensitive tests). Due to the long window period of such tests (at least three months), persons classified as recently infected would have been infected more than three months prior to the test date. Detecting acute HIV-1 infection is very important since this is the most infectious stage of the disease. This research report explores a method of estimating incidence using an antibody test and a virological test, Polymerase Chain Reaction Ribonucleic Acid (PCR-RNA).The cross-sectional data used are from the Centre for the AIDS Programme of Research in South Africa (CAPRISA). Methods: Actual follow-up cohort data from CAPRISA acute infection cohort (AIC), comprised of 245 sex workers, were used to estimate the incidence of HIV-1 using a PCR-RNA ,virology test based, incidence formula. The result obtained was compared to the incidence estimate obtained by the classical method of estimating incidence the AIDS Programme of Research in South Africa (CAPRISA). Methods: Actual follow-up cohort data from CAPRISA acute infection cohort (AIC), comprised of 245 sex workers, were used to estimate the incidence of HIV-1 using a PCR-RNA ,virology test based, incidence formula. The result obtained was compared to the incidence estimate obtained by the classical method of estimating incidence (prospective cohort follow-up). As a measure to reduce costs inherent in virological tests (PCR-RNA), multistage pooling was discussed and several pooling strategies simulations were proposed with their uncertainties. Point estimates and interval estimates of the window period, window period prevalence and incidence from crosssectional study of the AIC cohort were computed. Findings: The mean window period was 6.6 days 95% CI: (2.7 – 13.0). The monthly window period prevalence was 0.09423 percent 95 % CI: (0.0193 – 0.1865)%. The incidence from the prospective cohort follow-up was 5.43 percent 95% CI: (3.9 – 9.2) %. The incidence estimate from cross-sectional formulae was 5.21 percent 95% CI: (4.1– 4.6). It was also shown by use of simulations that an optimum pool sample size is obtained when at least half the samples are removed on every run. Interpretation and recommendations: The PCR-RNA test is very sensitive at detecting acute HIV-1 infected persons. The incidence estimate from the crosssectional study formulae was very similar to that obtained from a follow-up study. The number of tests needed can be reduced and a good estimate of the incidence can still be obtained. The calibration was not accurate since the samples used were small and the window period duration was too short, hence, it was difficult to extrapolate to the whole population. Further work still needs to be done on the calibration of the proposed incidence formulae as it could be a very useful public health tool.