# Epidemiology and Population Genetics of Malaria Parasites in Kenya > **NIH NIH F31** · UNIVERSITY OF CALIFORNIA-IRVINE · 2022 · $42,223 ## Abstract Project Summary Scale-up of vector interventions, improved diagnosis, and effective treatment have collectively contributed to significant global reductions in malaria mortality and morbidity over the last 15 years. Nevertheless, malaria burden remains high in sub-Saharan Africa, which reported 215 million cases and 380,000 deaths in 2019. Malaria burden reduction is not uniform, with some countries or regions continuously exhibiting high incidence whereas others show drastic reductions. Consequently, many African countries have proposed sub-national stratification of malaria risk and have developed corresponding surveillance and intervention plans to target heterogeneous malaria burden. As the prevalence of clinical malaria cases decreases, transmission becomes more focal and the proportion of malaria infections that are asymptomatic increases. Asymptomatic cases typically go undetected and untreated. They constitute an obscure parasite reservoir, silently sustaining transmission and threatening malaria control and elimination efforts. Functional and responsive malaria surveillance systems are necessary for quantifying the true burden of disease, identifying residual transmission foci for targeted control, and evaluating the effectiveness of interventions. Enhanced surveillance methods and more sensitive malaria diagnostic methods are needed to detect symptomatic and asymptomatic infections in low-transmission settings. The central objective of this F31 application is to examine the spatial and genetic epidemiology of malaria parasites in low-transmission regions in Kenya in the context of rapidly declining transmission following the implementation of intensive control programs. I will examine the effectiveness of hospital-based clinical malaria cases and reactive case detection methods in transmission hotspot detection (Aim 1), and investigate the genetic connectivity and diversity of malaria infections within and among clusters of malaria cases using amplicon deep sequencing and microsatellite typing (Aim 2). Identification of transmission hotspots will inform malaria intervention strategies best suited to local epidemiological contexts. Patterns of genetic connectivity among parasite isolates will reveal the geographical spread and transmission network of malaria infections. These data will improve our understanding of malaria epidemiology in settings where transmission is rapidly declining, thus guiding decision-makers in tailoring optimal strategies to achieve local malaria control and elimination goals. This proposed research will have broad implications for malaria surveillance in other areas of Africa that are experiencing significant reductions in malaria transmission. Under the mentorship of a multidisciplinary team, I will gain skills in infectious disease epidemiology, spatial epidemiology, molecular population genetics, and bioinformatics while developing leadership and professional skills that are necessary to prepare me for a care... ## Key facts - **NIH application ID:** 10465993 - **Project number:** 1F31AI164846-01A1 - **Recipient organization:** UNIVERSITY OF CALIFORNIA-IRVINE - **Principal Investigator:** Brook Jeang - **Activity code:** F31 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2022 - **Award amount:** $42,223 - **Award type:** 1 - **Project period:** 2022-05-11 → 2025-04-10 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10465993 ## Citation > US National Institutes of Health, RePORTER application 10465993, Epidemiology and Population Genetics of Malaria Parasites in Kenya (1F31AI164846-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10465993. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*