We employ statistical modeling to analyze health data, enabling us to identify patterns and predict outcomes. By leveraging advanced statistical techniques, we develop predictive models that inform health decision-making, optimize resource allocation, and improve patient care.
Relationship between primary healthcare outcomes (maternal morality, neonatal mortality) and structural readiness of healthcare services
Excess mortality during a pandemic: A time series analysis
Impact of disruptions on systematic treatment programs for Neglected Tropical Diseases: A Systematic Review
Assessing factors associated with child mortality
Validation of the diagnostic potential of using rabies rapid diagnostic test kits as part of routine animal surveillance to support rabies elimination : a systematic review and meta-analysis
Comparison of Health Indicator Reporting from Routine Surveillance and De- mographic Health Survey in Kenya.
Modelling Approaches to inform disease burden and control strategies for Visceral Leishmaniasis: A Systematic Review
Mortality and Predictors of Mortality Among Covid-19 Patients in Kiambu County, Kenya
Time series analysis on the impact of seasonal malaria chemptherapy
Viral sequencing to inform the global elimination of dog-mediated rabies-a systematic review. Click here
Elimination of transmission of onchocerciasis (river blindness) with long-term ivermectin mass drug administration with or without vector control in sub-Saharan Africa: a systematic review and meta-analysis Click here
The impact of livestock interventions on nutritional outcomes of children younger than 5 years old and women in Africa: a systematic review and meta-analysis
Infectious disease modelling for SARS-CoV-2 in Africa to guide policy: A systematic review Click here
The COVID-19 pandemic and disruptions to essential health services in Kenya: a retrospective time-series analysis Click here
