Research

Understanding complex interactions between pathogens, hosts, and environment requires coordinated investigation across venues. We link pipettes to populations by integrating data collection in the field where diseases are endemic and in the laboratory, taking advantage of molecular technologies. Synthesis of these data via computational analysis produces insight into transmission dynamics and host-pathogen interactions, including acquired immunity.

Field epidemiology-  Our approach to uncovering complex interactions between pathogens, humans, and the environment starts with detailed field studies. Working with an incredible team of international collaborators, we leverage these studies to answer questions and identify new ones.
  • Malaria and SARS-CoV-2 sentinel site surveillance.Identifying cases that present to health facilities for treatment is a cornerstone of infectious disease epidemiology. With our UCSF and Ugandan collaborators, we study the epidemiology of malaria and other diseases in Uganda through a countrywide network of public health facilities. We use these data and targeted collection of biological samples to track the impact of real-world community interventions such as indoor residual spraying, evaluate novel diagnostics and molecular surveillance strategies, and measure SARS-CoV-2 and its impact on malaria.
  • Cohort studies of malaria. Our Program for Resistance, Immunology, Surveillance, and Modeling of Malaria (PRISM) cohorts in Uganda, part of the NIH International Centers of Excellence in Malaria Research (ICEMR) network, have set the standard for detailed investigation of the epidemiology and biology of malaria for the last decade. These cohorts include frequent active and continuous passive surveillance for malaria and other diseases, coupled with household-level entomologic studies.
  • Population-based studies of arboviruses. With collaborators from Universidad Industrial de Santander, we have conducted population-based (longitudinal and cross-sectional) studies of arboviral infections in Colombia since 2014.
Genomic epidemiology- Pathogens carry a permanent record of who they are and where they have been in their genomes, informing pathogen surveillance and response. We create the tools to harness this information and deploy them to study and respond to pathogens of global importance with a current focus on malaria.​​​​​
  • Assembling an end-to-end ecosystem to produce actionable information on malaria transmission. A coordinated approach is needed to translate genomic data into useful information for public health. Our team, working closely with a global network of collaborators, is working to create a seamless pipeline from study design to results, including field sampling, genomic technologies, analytical methods, integration with epidemiologic data, and communication with relevant stakeholders. Applications include measuring the intensity of malaria transmission, defining the movement of malaria parasites, classifying malaria cases as local vs. imported, and determining the reservoirs and drivers of local transmission.
  • Studying the longitudinal dynamics of malaria parasites within individuals. People can be infected repeatedly  with malaria and carry malaria parasites in their blood without being sick. We use genomic data from longitudinal studies in Uganda to understand how frequently people are being infected, how their immune system reacts to those infections, and how likely those parasites are to infect mosquitoes, leading to onward transmission.
  • Improving genomic epidemiology in malaria endemic areas. Malaria genomics has greater and more rapid impact when performed by scientists living in malaria endemic areas in close coordination with local public health officials. We are working to advance the uptake and performance of genomics by working with our collaborators to develop training materials, mentor scientists, and facilitate technology transfer of various aspects of the genomic work we are pursuing.
Immune dynamics- Immune protection from infection is critical in maintaining the health of individuals and determines the risk of future outbreaks in communities.   Using detailed data from longitudinal studies, we study how immunity develops in individuals and within populations.
  • Development of strain-specific and strain-transcendent immunity to malaria. Using rich data from longitudinal studies where participants have been followed intensively during their early childhood, we are sequencing malaria parasites and comprehensively profiling the host antibody response to identify key immunologic targets and understand how immunity to these targets is generated with repeated infection.
  • Computational models of malaria immunity. Immunity to malaria naturally develops in those repeatedly infected by malaria parasites, but is a complex process involving different parts of the immune system and therefore poorly understood. We are broadly profiling the immune systems of children and adults living in a malaria endemic area over multiple points in time, building state-of-the-art computational models to learn how infection affects the immune system and how this eventually leads to protection against malaria. 
  • Characterizing kinetics of antibody responses. Understanding how antibodies vary over time following an infection has important implications for understanding protection for the individual and for interpreting results from serological tests. Using data from longitudinal cohorts, we develop models to understand how antibody levels to multiple diseases change over time.
Infectious disease serosurveillance- We form antibodies to pathogens after most infections. Measuring these antibodies in surveys (serosurveillance) is often the most accurate way to quantify the risk of infection, as they provide a direct measure of the proportion of the population that has been infected and the proportion that remains susceptible. We collect data and develop tools and methods for serosurveillance to better understand the dynamics of several infectious diseases.
  • Identifying markers of recent malaria exposure. We are working to identify an informative set of novel serologic markers that allows accurate estimation of malaria exposure in countries with a range of malaria endemicity. The goal is to implement these measures in surveys to more accurately and efficiently measure malaria transmission and the effect of interventions than current approaches based on the prevalence of infection.
  • Developing methods to analyze data from novel serological assays. High-throughput multiplexed assays allow measurement of responses to thousands of antigens at the same time. We are developing methods to analyze data from these assays in order to study the transmission dynamics of multiple pathogens simultaneously, characterize immune profiles of populations, and test hypotheses regarding immunological interactions between pathogens.
  • Emergence of arboviruses.  Using data from longitudinal and cross-sectional studies in Colombia, we are studying the emergence of chikungunya and Zika in areas that were previously endemic for dengue. By modeling serologic data, we study competing hypotheses regarding the interactions of these viruses.
  • Mapping risk of infectious diseases. Understanding the distribution and dynamics of human pathogens is fundamental to control infectious diseases by guiding where and when to target resources. We map risk of infectious diseases at different scales by integrating serologic and epidemiologic data. Along with our broad network of collaborators, we have conducted serological surveys in multiple countries to quantify the risk of malaria, arboviruses and COVID-19 at local, regional, and national scales.
COVID outbreak response- Since early 2020, our team has contributed existing expertise and resources to help advance public health surveillance and scientific understanding of the SARS-CoV-2 pandemic.
  • Citywide serosurveillance of SARS-CoV-2 in San Francisco. Serosurveillance can be a particularly powerful tool to track infectious diseases if implemented efficiently. Filling a gap in local surveillance, our team piloted a scalable, continuous, and systematic pipeline called Serosurveillance for Continuous, ActionabLe Epidemiologic Intelligence of Transmission (SCALE-IT), through which we tested remnant samples from routine blood draws in hospital networks. This hybrid serosurveillance approach also has strong potential for future application beyond this local context and beyond SARS-CoV-2.
  • Seroprevalence studies. Early in the pandemic, our team helped lead community studies of SARS-CoV-2 prevalence and seroprevalence in the Bay Area, identifying important disparities in risk which led to improvements in public health interventions. We are also leading a serosurveillance study of malaria and SARS-CoV-2 in Uganda linked to our sentinel site surveillance network as well collaborating in the national serosurveys in Colombia.
  • Cohort studies of SARS-CoV-2 infection. We enrolled a diverse cohort of individuals with SARS-CoV-2 infection to understand the natural history and immunology of this infection. Key aspects of this study are frequent, detailed clinical follow-up coupled with biological specimen banking for our team, collaborators, and as a public resource.
  • Sero-epidemiological models for COVID control. Serological data, particularly when collected at high spatial and temporal resolutions, are a key resource to measure transmission rates of SARS-CoV-2. However, despite the publication of results from multiple serosurveys in recent months, there are still important gaps in how to interpret these data and integrate them into transmission models. Leveraging data from our longitudinal studies, we are developing methods to estimate SARS-CoV-2 attack rates from seroprevalence data while accounting for antibody kinetics.
  • Modeling of SARS-CoV-2 vaccines and testing strategies. We are studying applications of routine testing for SARS-CoV-2 in high-risk populations such as healthcare workers, airline travelers, and schools as a mechanism of public health control, through simulation modeling and empirical study of testing programs. We are also applying simulation analysis to estimate vaccine prioritization strategies for SARS-CoV-2. 

 

 

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