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 (2) eco-evolutionary dynamics of infectious diseases at Duke University. Nicole is generally interested in ecology, evolution, data science, statistics, complexity science, mathematical biology, disease ecology, molecular evolution, animal behavior, population dynamics, population genetics, eco-evolutionary dynamics, evolutionary genomics, planetary health, and wildlife conservation.

Honors & Awards

  • Outstanding Paper Award (honorable mention), Disease Ecology Section, Ecological Society of America (ESA) (August 2021)
  • Murray F. Buell Award for Excellence in Ecology runner-up (honorable mention), Ecological Society of America (ESA) (April 2021)
  • Annabelle B. Bush Memorial Endowed Scholar Award, Philanthropic Educational Organization (P.E.O.) (June 2020)
  • P.E.O. Scholar Award, International Chapter of the P.E.O. Sisterhood (April 2020)
  • Excellence in Teaching Award, Stanford University, Department of Biology (June 2017)

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

  • How will mosquitoes adapt to climate warming? eLife Couper, L. I., Farner, J. E., Caldwell, J. M., Childs, M. L., Harris, M. J., Kirk, D. G., Nova, N., Shocket, M., Skinner, E. B., Uricchio, L. H., Exposito-Alonso, M., Mordecai, E. A. 2021; 10


    The potential for adaptive evolution to enable species persistence under a changing climate is one of the most important questions for understanding impacts of future climate change. Climate adaptation may be particularly likely for short-lived ectotherms, including many pest, pathogen, and vector species. For these taxa, estimating climate adaptive potential is critical for accurate predictive modeling and public health preparedness. Here, we demonstrate how a simple theoretical framework used in conservation biology-evolutionary rescue models-can be used to investigate the potential for climate adaptation in these taxa, using mosquito thermal adaptation as a focal case. Synthesizing current evidence, we find that short mosquito generation times, high population growth rates, and strong temperature-imposed selection favor thermal adaptation. However, knowledge gaps about the extent of phenotypic and genotypic variation in thermal tolerance within mosquito populations, the environmental sensitivity of selection, and the role of phenotypic plasticity constrain our ability to make more precise estimates. We describe how common garden and selection experiments can be used to fill these data gaps. Lastly, we investigate the consequences of mosquito climate adaptation on disease transmission using Aedes aegypti-transmitted dengue virus in Northern Brazil as a case study. The approach outlined here can be applied to any disease vector or pest species and type of environmental change.

    View details for DOI 10.7554/eLife.69630

    View details for PubMedID 34402424

  • The influence of vector-borne disease on human history: socio-ecological mechanisms. Ecology letters Athni, T. S., Shocket, M. S., Couper, L. I., Nova, N., Caldwell, I. R., Caldwell, J. M., Childress, J. N., Childs, M. L., De Leo, G. A., Kirk, D. G., MacDonald, A. J., Olivarius, K., Pickel, D. G., Roberts, S. O., Winokur, O. C., Young, H. S., Cheng, J., Grant, E. A., Kurzner, P. M., Kyaw, S., Lin, B. J., Lopez, R. C., Massihpour, D. S., Olsen, E. C., Roache, M., Ruiz, A., Schultz, E. A., Shafat, M., Spencer, R. L., Bharti, N., Mordecai, E. A. 2021


    Vector-borne diseases (VBDs) are embedded within complex socio-ecological systems. While research has traditionally focused on the direct effects of VBDs on human morbidity and mortality, it is increasingly clear that their impacts are much more pervasive. VBDs are dynamically linked to feedbacks between environmental conditions, vector ecology, disease burden, and societal responses that drive transmission. As a result, VBDs have had profound influence on human history. Mechanisms include: (1) killing or debilitating large numbers of people, with demographic and population-level impacts; (2) differentially affecting populations based on prior history of disease exposure, immunity, and resistance; (3) being weaponised to promote or justify hierarchies of power, colonialism, racism, classism and sexism; (4) catalysing changes in ideas, institutions, infrastructure, technologies and social practices in efforts to control disease outbreaks; and (5) changing human relationships with the land and environment. We use historical and archaeological evidence interpreted through an ecological lens to illustrate how VBDs have shaped society and culture, focusing on case studies from four pertinent VBDs: plague, malaria, yellow fever and trypanosomiasis. By comparing across diseases, time periods and geographies, we highlight the enormous scope and variety of mechanisms by which VBDs have influenced human history.

    View details for DOI 10.1111/ele.13675

    View details for PubMedID 33501751

  • The impact of long-term non-pharmaceutical interventions on COVID-19 epidemic dynamics and control: the value and limitations of early models. Proceedings. Biological sciences Childs, M. L., Kain, M. P., Harris, M. J., Kirk, D., Couper, L., Nova, N., Delwel, I., Ritchie, J., Becker, A. D., Mordecai, E. A. 2021; 288 (1957): 20210811


    Mathematical models of epidemics are important tools for predicting epidemic dynamics and evaluating interventions. Yet, because early models are built on limited information, it is unclear how long they will accurately capture epidemic dynamics. Using a stochastic SEIR model of COVID-19 fitted to reported deaths, we estimated transmission parameters at different time points during the first wave of the epidemic (March-June, 2020) in Santa Clara County, California. Although our estimated basic reproduction number ([Formula: see text]) remained stable from early April to late June (with an overall median of 3.76), our estimated effective reproduction number ([Formula: see text]) varied from 0.18 to 1.02 in April before stabilizing at 0.64 on 27 May. Between 22 April and 27 May, our model accurately predicted dynamics through June; however, the model did not predict rising summer cases after shelter-in-place orders were relaxed in June, which, in early July, was reflected in cases but not yet in deaths. While models are critical for informing intervention policy early in an epidemic, their performance will be limited as epidemic dynamics evolve. This paper is one of the first to evaluate the accuracy of an early epidemiological compartment model over time to understand the value and limitations of models during unfolding epidemics.

    View details for DOI 10.1098/rspb.2021.0811

    View details for PubMedID 34428971

  • Environmental Drivers of Vector-Borne Diseases POPULATION BIOLOGY OF VECTOR-BORNE DISEASES Shocket, M. S., Anderson, C. B., Caldwell, J. M., Childs, M. L., Couper, L. I., Han, S., Harris, M. J., Howard, M. E., Kain, M. P., MacDonald, A. J., Nova, N., Mordecai, E. A., Drake, J. M., Bonsall, M. B., Strand, M. R. 2021: 85-118
  • Susceptible host availability modulates climate effects on dengue dynamics. Ecology letters Nova, N., Deyle, E. R., Shocket, M. S., MacDonald, A. J., Childs, M. L., Rypdal, M., Sugihara, G., Mordecai, E. A. 2020


    Experiments and models suggest that climate affects mosquito-borne disease transmission. However, disease transmission involves complex nonlinear interactions between climate and population dynamics, which makes detecting climate drivers at the population level challenging. By analysing incidence data, estimated susceptible population size, and climate data with methods based on nonlinear time series analysis (collectively referred to as empirical dynamic modelling), we identified drivers and their interactive effects on dengue dynamics in San Juan, Puerto Rico. Climatic forcing arose only when susceptible availability was high: temperature and rainfall had net positive and negative effects respectively. By capturing mechanistic, nonlinear and context-dependent effects of population susceptibility, temperature and rainfall on dengue transmission empirically, our model improves forecast skill over recent, state-of-the-art models for dengue incidence. Together, these results provide empirical evidence that the interdependence of host population susceptibility and climate drives dengue dynamics in a nonlinear and complex, yet predictable way.

    View details for DOI 10.1111/ele.13652

    View details for PubMedID 33300663

  • How to identify win-win interventions that benefit human health and conservation NATURE SUSTAINABILITY Hopkins, S. R., Sokolow, S. H., Buck, J. C., De Leo, G. A., Jones, I. J., Kwong, L. H., LeBoa, C., Lund, A. J., MacDonald, A. J., Nova, N., Olson, S. H., Peel, A. J., Wood, C. L., Lafferty, K. D. 2020
  • 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