Research Project 1: Mapping and modelling of snakebite victims in Kenya
- Thematic field of study: Mapping and modelling of snakebite victims in Kenya
- Supervisor(s):Dr George Omondi, Dr Lucy Ochola, Dr Mary Ochieng
- Contact details: laochola@gmail.com
Summary of hosting Lab
| Research in the parasite immunology lab focuses on development of multiplex immunodiagnostic kits (antigen and antibody-based kits) for the detection of coronaviruses; malaria and schistosomiasis, and pre-clinical evaluation of efficacy, immunogenicity and safety profiles of vaccine candidates (SU). |
Project Description
| Snake bite is a neglected public health issue in many tropical countries including Kenya that is home to over 34 genera of venomous and medically important snakes. The true burden of this disease remains unclear.A Kenya snake bite and intervention established in 2017 under the Kenya Institute of Primate Research, to assist with better diagnosis, management, develop R&D for the next snakebite therapies and collect field data on cases. The centre since then has collected data from over 15M Kenyans in 20 counties and blood samples from snake bites victims and identified Samburu, Kitui, Baringo, Turkana as hotspots. The primary objectives are: (i) Establish the health economic data of costs to patients, facilities, burden to the country, (ii) Identify snakebite hotspots and map regions (iii) Predict the future burden of snake bite. (iv) Assessing immune responses in snakebite victims.. |
Publications
| Preclinical antivenom-efficacy testing reveals potentially disturbing deficiencies of snakebite treatment capability in East Africa.Harrison RA, Oluoch GO, Ainsworth S, Alsolaiss J, Bolton F, Arias AS, Gutiérrez JM, Rowley P, Kalya S, Ozwara H, Casewell NR.PLoS Negl Trop Dis. 2017 Oct 18;11(10):e0005969.Conducting epidemiological studies on snakebite in nomadic populations: A methodological paper.Oluoch GO, Otundo D, Nyawacha S, Ongeri D, Smith M, Meta V, Trelfa A, Ahmed S, Harrison RA, Lalloo DG, Stienstra Y, Tianyi FL. PLoS Negl Trop Dis. 2023 Dec 28;17(12):e0011792. |
Major Lab techniques
| ELISA, ELISAs, western blots, cell cultures, harvesting and immunoprofiling of venom and real-time PCR., collaborative research, data analysis, modelling of lab data |
Research Project 2: Assessing HIV service interruption in Kenya using Interrupted Time Series (ITS)
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- Thematic field of study: Assessing HIV service interruption in Kenya using Interrupted Time Series (ITS)
- Supervisor(s):Prof. SM Mwalili, Dr. DK Gathungu, Prof. Rachel Mbogo
- Contact details: smwalili@sthrathmore.edu
Summary of Hosting Lab
Strathmore University Institute of Mathematical Sciences (SIMS) was established in 2010 and launched on August 18, 2011, during the “First International Strathmore Mathematics Conference August 18 – 21, 2011” at Strathmore University. The main mandate of SIMS is to conduct and coordinate mathematical-based research in the University and to initiate, promote, and undertake collaborative research with industry and other institutions of higher learning. These collaborations between the Institute and other faculties and organizations are intended to serve as a driver for research enhancement and innovation across the University.
Project Description
Interrupted Time Series (ITS) analysis is instrumental in assessing the impact of interruptions on HIV services in Kenya, aiding stakeholders in making informed decisions and planning effectively. This statistical method involves modeling trends over time and comparing predicted and observed values during the intervention phase to gauge the effects of disruptions accurately. Typically, the outcome variable is related to HIV service utilization or health outcomes, and data are derived from routine program data from the Kenya Health Data and Information System (KDHIS).
The process of ITS begins with defining the outcome variable and selecting a suitable period, which encompasses pre-interruption, intervention, and post-intervention phases. Identifying the interruption point, marking when the disruption occurred, is a crucial step. Statistical techniques like segmented regression analysis are then applied to model the data, estimating the baseline trend before the interruption and any subsequent changes. By comparing predicted values from the model to observed values during the intervention phase, the magnitude and significance of the interruption's impact can be assessed accurately.
Moreover, potential confounders such as seasonal trends or concurrent events are considered to ensure the analysis reflects the interruption's effects comprehensively. This holistic approach furnishes stakeholders with actionable insights into the consequences
of service interruptions, aiding evidence-based decision-making for managing HIV services in Kenya. Understanding how interruptions influence service utilization and health outcomes enables stakeholders to devise strategies to mitigate disruptions and enhance overall service delivery. ITS contributes to more effective planning, and resource allocation, and ultimately benefits individuals living with HIV and the broader
community.Publications
Uetela, D. A. M., Augusto, O., Hughes, J. P., Uetela, O. A., Gudo, E. S., Chicumbe, S. A., … & Sherr, K. (2023). Impact of differentiated service delivery models on 12-month retention in HIV treatment in Mozambique: an interrupted time-series analysis. The Lancet HIV, 10(10), e674-e683.
Afirima, B., Iyamu, I. O., Yesufu, Z. A., Iwara, E., Chilongozi, D., Banda, L., … & Akolo, C. (2023). Assessing the impact of the COVID-19 restrictions on testing services in Malawi: an interrupted time series analysis. African Journal of AIDS Research, 22(2), 92-101.Pinto, R., Valentim, R., da Silva, L. F., de Souza, G. F., de Moura Santos, T. G. F., de Oliveira, C. A. P., … & Atun, R. (2022). Use of interrupted time series analysis in understanding the course of the congenital syphilis epidemic in Brazil. The Lancet Regional Health–Americas, 7.Major Lab techniques
Data collection and collation from MOH, in particular from Kenya Health Data and Information System (KDHIS. Call for close collaboration or working closely with the National AIDS and STI Control Programme (NASCCOP) and National Syndemic Disease Control Council (NSDCC).
Research Project 3: Immunity to SARS‐CoV‐2 induced by infection or vaccination
- Thematic field of study: Immunity to SARS‐CoV‐2 induced by infection or vaccination
- Supervisor(s):Dr Lucy Ochola, Dr Ruth Nyakundi, Vincent Were, Rose Bosire, Bernhards Ogutu, Dr Mary Ochieng
- Contact details: laochola@gmail.com
Summary of hosting Lab
| Research in the parasite immunology lab focuses on development of multiplex immunodiagnostic kits (antigen and antibody-based kits) for the detection of coronaviruses; malaria and schistosomiasis, and pre-clinical evaluation of efficacy, immunogenicity and safety profiles of vaccine candidates (SU). |
Project Description
| Kenya achieved only 19% vaccine coverage during the COVID-19 pandemic. Despite this low coverage the seropositivity rates based on hospital data rose from 9% (2020) to 50% and by October 2022. A recent study placed the seropositive rate at 70% for residents in a rural town in coast (Kagucia et al, 2023). However, when we surveyed 11 geographical zones in Kenya, we found low seroprevalence rates of 43% (Were et al, paper in preparation). A better understanding of immunity and its regulation in response to SARS-CoV-2, in children, adults and vaccinated persons in Kenya can assist in ensuring future vaccination programs are better managed and deployed.
Under a joint research project between CREATES, KEMRI and Kenya Institute of Primate Research (KIPRE), a cross sectional, population based, age stratified (with a subset of the population vaccinated), serosurvey of household members aged 2years and above was conducted. This study utilised commercially available rapid kits to determine IgM and IgG and PCR to determine past and active infections. The studied archived over 4000 plasma samples. We propose to determine, IgG antibody levels against spike protein in children aged (2-9years), adults and vaccinated group to assess the effect of natural exposure versus vaccination on immunity. |
Publications
| Cavin Mgawe, Clement Likhovole, Steven Ger, Eddy Odari, Jacqueline C Linnes, Bernard N. Kanoi, Jesse Gitaka, Lucy Ochola. Application of multiple binding sites for LAMP primers across P. falciparum genome improves detection of the parasite from whole blood samples. Frontiers in Malaria. Volume 1 – 2023 | https://doi.org/10.3389/fmala.2023.1303980 R. Lucinde, D. Mugo, C. Bottomley, Karani A, —-L. Ochola, Namdala E,Gaunya O et al. Sero-surveillance for IgG to SARS-CoV-2 at antenatal care clinics in three Kenyan referral hospitals: Repeated cross-sectional surveys 2020-21 Plos One October 14, 2022. https://doi.org/10.1371/journal.pone.0265478 Etyang AO, Lucinde R, Karanja H, Kalu C, Mugo D, Nyagwange J, Gitonga J, Tuju J, Wanjiku P, Karani A, Mutua S, Maroko H, Nzomo E, Maitha E, Kamuri E, Kaugiria T, Weru J, Ochola LB, Kilimo N, Charo S, Emukule N, Moracha W, Mukabi et al. Seroprevalence of Antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 Among Healthcare Workers in Kenya. Clin Infect Dis. 2022 Jan 29;74(2):288-293 Rebeccah. M. Ayako, Joshua. M. Mutiso, John. C. Macharia, David Langoi and Lucy Ochola. Concomitant Infection with Leishmania donovani and Plasmodium berghei Causes Pro-inflammatory Polarization Resulting in Malaria Exacerbation in BALB/c Mice. American Journal of Infectious Diseases and Microbiology. 2021, 9(3), 71- 82. DOI: 10.12691/ajidm-9-3-1 |
Major Lab techniques
| ELISA, flow cytometry, collaborative research, data analysis, modelling of lab data |
Research Project 4: Futurist SEIR Simulation Model in Preparedness for COVID-like Pandemics
- Thematic field of study: Futurist SEIR Simulation Model in Preparedness for COVID-like Pandemics
- Supervisor(s):Prof. SM Mwalili, Dr. DK Gathungu, Prof. Rachel Mbogo
- Contact details: smwalili@sthrathmore.edu
Summary of hosting Lab
Strathmore University Institute of Mathematical Sciences (SIMS) was established in 2010 and launched on August 18, 2011, during the “First International Strathmore Mathematics Conference August 18 – 21, 2011” at Strathmore University.
The main mandate of SIMS is to conduct and coordinate mathematical-based research in the University and to initiate, promote, and undertake collaborative research with industry and other institutions of higher learning. These collaborations between the Institute and other faculties and organizations is intended to serve as a driver for research enhancement and innovation across the University.
Project Description
The COVID-19 pandemic emerged as a global crisis, impacting nations worldwide. With its rapid spread and diverse clinical manifestations, effective management and decision-making have become imperative. To address this need, a futurist SEIR Simulation model will be developed with a simulation tool. This tool aims to assist public health officials, policymakers, and researchers in understanding and preparing for the
dynamics of future pandemics similar to the COVID-19 pandemic. The Future Simulation Model will be developed based on an extended SEIR (Susceptible-Exposed-Infectious-Recovered) model, incorporating epidemiological and economic parameters relevant to the pandemic. The model will simulate the spread of the virus deterministically, considering factors such as latency, prodromal, and infectious periods,
as well as interventions like isolation and social distancing measures. Mathematical formulations and algorithms will be implemented to ensure realistic dynamics and accurate representation of pandemic future scenarios. The expected outcomes of the project include the development of a comprehensive simulation tool that can significantly mirror the experience of the COVID-19 pandemic in preparedness and response efforts to future pandemics. By enabling users to explore different scenarios and evaluate intervention strategies, the Future Simulation Model has the potential to inform evidence-based decision-making, enhance resource allocation, and mitigate the impact of the pandemic on public health and the economy.
Publications
| Schneider, K. A., Ngwa, G. A., Schwehm, M., Eichner, L., & Eichner, M.(2020). The COVID-19 pandemic preparedness simulation tool: CovidSIM. BMC infectious diseases, 20, 1-11.López, L., & Rodo, X. (2021). A modified SEIR model to predict the COVID-19 outbreak in Spain and Italy: simulating control scenarios and multi-scale epidemics. Results in Physics, 21, 103746.Mwalili, S., Kimathi, M., Ojiambo, V., Gathungu, D., & Mbogo, R. (2020). SEIR model for COVID-19 dynamics incorporating the environment and social distancing. BMC Research Notes, 13(1), 352. |
Major Lab techniques
Working closing with National COVID-19 Modelling Committee and also National HIV modelling TWG under National Syndemic Disease Control Council (NSDCC), and more the
Research Project 5: A Comparative Evaluation of Feature Selection Methods in Cancer Genomics
- Thematic field of study: A Comparative Evaluation of Feature Selection Methods in Cancer Genomics
- Supervisor(s):Prof. Bernard Omolo
- Contact details: bernardo@strathmore.edu
Summary of hosting Lab
My research lab focuses on collaborative and team science research, as well as in statistical methodology research. Since 2009, we have been engaged in collaborative cancer research projects with biomedical and public health professionals seeking for biomarkers for the development of therapies, particularly for colorectal cancer (CRC) and melanoma. Cancer is the second leading cause of death in the USA and the third
leading cause of death in Kenya. While substantial advances have been made in the development of therapeutic targets, still much needs to be done in this area, as most cancers are detected late when it is nearly impossible to treat them effectively, especially in resource-constrained settings like Kenya. Statistical methods have been employed in the classification of cancer types and the identification of biomarkers for drug
development in the last two decades. However, most of these methods are limited by distributional assumptions and are suited to low-dimensional data. Yet, cancer data from microarray and DNA sequencing experiments are high-dimensional in nature. One of the most important contributions in my research has been the application of machine learning techniques and non-parametric statistical methods in identifying useful genes for cancer therapy development and prognosis. Sharing this knowledge and experience with researchers participating in the BRAINS network would enhance capacity-building in cancer research in Sub-Saharan Africa.
Project Description
Microarray and RNA-Seq technologies have enabled the study of the expression of thousands of genes simultaneously, with the aim of identifying significantly differentially expressed genes (DEGs) between two disease conditions. Several parametric and nonparametric statistical methods have been proposed in this regard, but none is regarded as the standard, due to their individual limitations. In this project, we will compare the performance of two methods in the analysis of cancer genomic data, namely, the linear models for microarrays (LIMMA) and the significant analysis of microarrays (SAM). We employ these methods of class comparison on microarray and RNA-Seq colorectal cancer (CRC) datasets, with binary groups, from the gene expression omnibus (GEO) and the sequence read archive (SRA), respectively, to obtain molecular signatures, and assess the performance of the molecular signatures in CRC classification via cross-validation. We will assess the impact of data pre-processing (log2-transformed vs untransformed raw data) and dimension reduction via principal component analysis (PCA) on the performance. Finally, we will recommend the best method for feature selection after external validation with independent cancer datasets
from the cancer genome atlas (TCGA).
Publications
| Lipesa, B.A., Okango, E., Omolo, B.O., Omondi, E.O. (2023). An application of a supervised machine learning model for predicting life expectancy. SN Appl. Sci., 5: 189. https://doi.org /10.1007/s42452-023-05404-w.
Akoth, M., Odhiambo, J.,Omolo, B. (2023). Genome-wide association testing in malaria studies in the presence of overdominance. Malaria J., 22(1): 119. [PMID:37038187]. Elbashir, M.K., Mohammed, M., Mwambi, H., Omolo, B. (2023). Identification of hub genes associated with breast cancer using integrated gene expression data with protein-protein interaction network. Appl. Sci., 13(4): 2403. https://doi.org/10.3390/app13042403. Omolo, B.O. and Manda, S.O. (2022). Editorial: Application of biostatistics and epidemiological methods for cancer research in Sub-Saharan Africa. Front. Public Health, 10: 1069098. [PMID: 36457323]. Mohammed, M., Mwambi, H., Mboya, I.B., Elbashir, M.K., Omolo, B. (2021). Predictors of colorectal cancer survival using Cox regression and random survival forests models based on gene expression data. PLoS One., 16(12): e0261625. [PMID:34965262]. Mohammed, M., Mwambi, H., Mboya, I.B., Elbashir, M.K., Omolo, B. (2021). A stacking ensemble deep learning approach to cancer type classification based on TCGA data. Sci Rep., 11(1): 15626. [PMID: 34341396], [PMCID: 8329290]. Mohammed, M., Mwambi, H., Omolo, B. (2020). Colorectal cancer classification and survival analysis based on an integrated RNA and DNA molecular signature. Current Bioinformatics, 15, 1–18. Omolo, B., Njuho, P. (2020). Adverse Event Risk Assessment on Patients Receiv- ing Combination Antiretroviral Therapy in South Africa. Int. J. Stats. Med. Res., 9(1), 10–19. Mohammed, M., Mwambi, H., Omolo, B., Elbashir, M. K. (2018). Using stack- ing ensemble for microarray-based cancer classification. In 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE), (pp. 1–8). IEEE. Okuto, E., Ongati, O., Omolo, B. (2018). Reconstructing earth observation vegetation index records with a Bayesian spatiotemporal dynamic model. International Journal of Statistics & Applied Mathematics, 3(4), 74–84. Odhiambo, C., Davis, J., Omolo, B. (2017). Risk for Cardiovascular Disease in Blacks with HIV/AIDS in America: A Systematic Review and Meta-analysis. Journal of Health Disparities Research and Practice, 10(2), 121–141. Chaba, L., Odhiambo, J., Omolo, B. (2017). Evaluation of Methods for Gene Selection in Melanoma Studies. Int. J. Stats. Med. Res., 6(1), 1–9 Odhiambo, C., Odhiambo, J., Omolo, B. (2017). Validation of the Smooth Test of Goodness-of-fit for Proportional Hazards in Cancer Survival Studies. Int. J. Stats. Med. Res., 6(2), 49–67. Odhiambo, C., Odhiambo, J., Omolo, B. (2017). A Smooth Test of Goodness-of-fit for the Weibull Distribution: An Application to an HIV Retention Data. Int. J. Stats. Med. Res., 6(2), 68–78. Odhiambo, C., Odhiambo, J., Omolo, B. (2017). A Smooth Test of Goodness- of-fit for the Baseline Hazard Function for Time-to-First Occurrence in Recurrent Events: An Application to an HIV Retention Data. Int. J. Stats. Med. Res., 6(3), 104–113. Chaba, L., Odhiambo, J., Omolo, B. (2017). Using Copulas to Select Prognostic Genes in Melanoma Patients. Int. J. Stats. Med. Res., 6(3), 114–122. Chaba, L., Odhiambo, J., Omolo, B. (2017). A Comparison of Parametric and Semi- Parametric Models for Microarray Data AnIanlty.isJ.. Stats. Med. Res., 6(4), 134–143. Cooley, D., Cisewski, J., Erhardt, R. J., Jeon, S., Mannshardt, E., Omolo, B. O., Sun, Y. (2012). A survey of spatial extremes: Measuring spatial dependence and modeling spatial effects. Revstat, 10, 135–165 |
Major Lab techniques
Our approach leverages well-known statistical models for gene expression data but seeks to address some underlying features of microarray and RNA-Seq data that may have been ignored up till now and may help in understanding the molecular basis of CRC and improving therapy. LIMMA was developed by Smyth in 2005 (Smyth, 2005) for microarrays but has recently been adapted to the RNA-Seq technology as well (Ritchie et al, 2015). On the other hand, SAM, originally developed for microarrays (Tusher et al., 2001), has now been modified to handle RNA-Seq data as well. These two methods may as well be regarded as the “gold standard” in transcriptomics with which new methods should be compared. This project seeks to explore their performance further in cancer genomic analysis.
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