The bioscience department focuses on the molecular biology, epidemiology and immunology of infectious diseases with particular emphasis on the development of vaccines and understanding the transmission of pathogens. We work on malaria, viral pathogens, HIV and bacterial disease, and provide strategic direction to the Laboratory Research Platform

Our work on malaria covers the three life-cycle stages; gametocytes (where we are developing a human challenge model to test transmission blocking immunity and profiling the transmitting population in the community and developing markers of infectiousness); blood stage (where we are identifying the merozoite and red cell surface targets of human immunity and examining the role of parasite exposure on host immunity using a systems immunology approach); and pre-erythrocytic stages where we are examining vaccine-induced immunity.

Field surveillance for acute HIV infection and monitoring of a cohort and collaboration with IAVI has facilitated interests in functional assays (specifically the viral inhibition assay) and on the impact of transmitted drug resistance in Kenya.

We are undertaking work on the genomics of malaria, viral pathogens and bacterial pathogens in order to determine what the source of infection is (i.e. “who infects whom”) in collaboration with the Epidemiology and Demography Department.

Featured Dataverses

In order to use this feature you must have at least one published or linked dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Advanced Search

401 to 410 of 446 Results
Stata Syntax - 10.1 KB - MD5: 45ec888c27b5ea46b920e4c1af352169
Code
STATA regression analysis code. Use with 4b_coxReg_JunjuChonyiNge_slide_(cut-off 2,500_parasites per ul).csv
Apr 1, 2019
Muthui, Michelle K.; Mogeni, Polycarp; Mwai, Kennedy; Nyundo, Christopher; Macharia, Alex; Williams, Thomas N.; Nyangweso, George; Wambua, Juliana; Mwanga, Daniel; Marsh, Kevin; Bejon, Philip; Kapulu, Melissa C., 2019, "Kilifi Malaria Longitudinal Cohort cross-sectional survey and weekly-follow-up surveillance data for the estimation of parasite prevalence and factors associated with gametocyte carriage.", https://doi.org/10.7910/DVN/18QB3V, Harvard Dataverse, V2, UNF:6:VSyf/XWsFzLJUkYIBzxl4A== [fileUNF]
Data from a longitudinally monitored cohort of children from Kilifi county located along the Kenyan coast collected between 1998-2016 were analysed to describe the distribution and prevalence of gametocytaemia in relation to transmission intensity, time and age. The datasets comprise data from cross-sectional surveys and weekly follow-up visits car...
R Syntax - 16.9 KB - MD5: 5477acd92bf3f3715b4bb9adf8fcf04b
Code
R code used to plot out the variation in parasite prevalence with age.
R Syntax - 2.3 KB - MD5: b661f5b2dc00ef15b6cef32afedcf269
Code
R code used to draw 2 X 2 tables based on asexual and sexual parasite positiviy and carry out chi-square analysis
R Syntax - 3.8 KB - MD5: ac5a9944bcd15de6156452d0159f29e2
Code
R code used to plot out the frequency of the different number of cross-sectional surveys attended by the study participants
Tabular Data - 1.5 KB - 3 Variables, 76 Observations - UNF:6:HCdQf837gliHQwM8Zlrayg==
Data
List of the number of cross-sectional surveys carried out each year for each of the KMLC cohorts
ZIP Archive - 5.9 MB - MD5: fc77ff74e7693274bf2189ad9a1b50b5
Data
Zipped package containing all data files: csbleed_summary.csv imm_csbleed_data.csv imm_weekly_fu_overall.csv sickle.thal data.xlsx
R Syntax - 6.5 KB - MD5: 00ad6f242b53f40bad66acd6c680f313
Code
R code used to assess malaria episodes occurring before a cross-sectional survey and generate a models to predict gametocyte positivity
R Syntax - 11.7 KB - MD5: dbad32fbda10913a3a065916abe5e9cf
Code
R code used to generate models to predict gametocyte positivity from select covariates
Add Data

Sign up or log in to create a dataverse or add a dataset.

Share Dataverse

Share this dataverse on your favorite social media networks.

Link Dataverse
Reset Modifications

Are you sure you want to reset the selected metadata fields? If you do this, any customizations (hidden, required, optional) you have done will no longer appear.