Nicole is a graduate student co-advised by Dr. Erin Mordecai and Dr. Dmitri Petrov in the Department of Biology at Stanford University. She received her undergraduate and graduate training in dental surgery at Karolinska Institutet in Sweden, and earned a M.S. in Statistics at Stanford University. Nicole has previously worked on (1) mathematical modeling of cancer evolution at Dana-Farber/Harvard Cancer Center, and on (2) eco-evolutionary dynamics of infectious diseases at Duke University. Nicole is generally interested in ecology, evolution, statistics, data science, mathematical biology, infectious disease, population genetics, comparative genomics, public health and conservation.

Honors & Awards

  • Excellence in Teaching Award, Stanford University, Department of Biology (June 2017)
  • P.E.O. Scholar Award, International Chapter of the P.E.O. Sisterhood (April 2020)

Education & Certifications

  • Master of Science, Stanford University, STATS-MS (2020)
  • M.S., Stanford University, Statistics (2020)
  • M.Sc., B.Sc., Karolinska Institutet, Dental Surgery (2012)

All Publications

  • Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing. Nature human behaviour Allen, W. E., Altae-Tran, H., Briggs, J., Jin, X., McGee, G., Shi, A., Raghavan, R., Kamariza, M., Nova, N., Pereta, A., Danford, C., Kamel, A., Gothe, P., Milam, E., Aurambault, J., Primke, T., Li, W., Inkenbrandt, J., Huynh, T., Chen, E., Lee, C., Croatto, M., Bentley, H., Lu, W., Murray, R., Travassos, M., Coull, B. A., Openshaw, J., Greene, C. S., Shalem, O., King, G., Probasco, R., Cheng, D. R., Silbermann, B., Zhang, F., Lin, X. 2020


    Despite the widespread implementation of public health measures, coronavirus disease 2019 (COVID-19) continues to spread in the United States. To facilitate an agile response to the pandemic, we developed How We Feel, a web and mobile application that collects longitudinal self-reported survey responses on health, behaviour and demographics. Here, we report results from over 500,000 users in the United States from 2 April 2020 to 12 May 2020. We show that self-reported surveys can be used to build predictive models to identify likely COVID-19-positive individuals. We find evidence among our users for asymptomatic or presymptomatic presentation; show a variety of exposure, occupational and demographic risk factors for COVID-19 beyond symptoms; reveal factors for which users have been SARS-CoV-2 PCR tested; and highlight the temporal dynamics of symptoms and self-isolation behaviour. These results highlight the utility of collecting a diverse set of symptomatic, demographic, exposure and behavioural self-reported data to fight the COVID-19 pandemic.

    View details for DOI 10.1038/s41562-020-00944-2

    View details for PubMedID 32848231

  • The biogeography of ecoregions: Descriptive power across regions and taxa JOURNAL OF BIOGEOGRAPHY Smith, J. R., Hendershot, J., Nova, N., Daily, G. C. 2020

    View details for DOI 10.1111/jbi.13871

    View details for Web of Science ID 000533465200001

  • Mosquito and primate ecology predict human risk of yellow fever virus spillover in Brazil. Philosophical transactions of the Royal Society of London. Series B, Biological sciences Childs, M. L., Nova, N., Colvin, J., Mordecai, E. A. 2019; 374 (1782): 20180335


    Many (re)emerging infectious diseases in humans arise from pathogen spillover from wildlife or livestock, and accurately predicting pathogen spillover is an important public health goal. In the Americas, yellow fever in humans primarily occurs following spillover from non-human primates via mosquitoes. Predicting yellow fever spillover can improve public health responses through vector control and mass vaccination. Here, we develop and test a mechanistic model of pathogen spillover to predict human risk for yellow fever in Brazil. This environmental risk model, based on the ecology of mosquito vectors and non-human primate hosts, distinguished municipality-months with yellow fever spillover from 2001 to 2016 with high accuracy (AUC = 0.72). Incorporating hypothesized cyclical dynamics of infected primates improved accuracy (AUC = 0.79). Using boosted regression trees to identify gaps in the mechanistic model, we found that important predictors include current and one-month lagged environmental risk, vaccine coverage, population density, temperature and precipitation. More broadly, we show that for a widespread human viral pathogen, the ecological interactions between environment, vectors, reservoir hosts and humans can predict spillover with surprising accuracy, suggesting the potential to improve preventive action to reduce yellow fever spillover and avert onward epidemics in humans. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.

    View details for DOI 10.1098/rstb.2018.0335

    View details for PubMedID 31401964

  • Ecological interventions to prevent and manage zoonotic pathogen spillover. Philosophical transactions of the Royal Society of London. Series B, Biological sciences Sokolow, S. H., Nova, N., Pepin, K. M., Peel, A. J., Pulliam, J. R., Manlove, K., Cross, P. C., Becker, D. J., Plowright, R. K., McCallum, H., De Leo, G. A. 2019; 374 (1782): 20180342


    Spillover of a pathogen from a wildlife reservoir into a human or livestock host requires the pathogen to overcome a hierarchical series of barriers. Interventions aimed at one or more of these barriers may be able to prevent the occurrence of spillover. Here, we demonstrate how interventions that target the ecological context in which spillover occurs (i.e. ecological interventions) can complement conventional approaches like vaccination, treatment, disinfection and chemical control. Accelerating spillover owing to environmental change requires effective, affordable, durable and scalable solutions that fully harness the complex processes involved in cross-species pathogen spillover. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.

    View details for DOI 10.1098/rstb.2018.0342

    View details for PubMedID 31401951