Alex Perkins Associate Professor

Infectious disease epidemiology and population biology
Alex Perkins

Research Interests:

Research in the Perkins Lab applies mathematical, computational, and statistical approaches to answer basic and applied research questions about the ecology and epidemiology of infectious diseases. This work focuses primarily on mosquito-borne diseases of humans. These diseases pose a risk to billions of people, are the subject of intense development of new interventions, and are highly sensitive to numerous forms of global change. Accordingly, the goal of our research is to attain a predictive understanding of where and when these diseases occur, what the impact of interventions will be in curtailing their burden, and how the distribution of these diseases and their response to interventions will change over time as a result of changes in their underlying drivers. Through this research, we hope to elevate the rigor of the information on which decision makers act, both in terms of accuracy and quantification of uncertainty. We accomplish this through three interconnected themes of our research.

  • Incorporating transmission dynamics into infectious disease mapping: With relatively few exceptions, geographic mapping of infectious diseases relies on phenomenological descriptions of relationships between disease and its drivers and disregards a rich body of theory on transmission dynamics. The first theme of our research seeks to devise new approaches that leverage transmission dynamics theory to enhance the interpretability and utility of infectious disease mapping.
  • Model-guided assessment of interventions for infectious disease prevention: Mathematical modeling has a key role to play in designing and interpreting studies to assess intervention efficacy and to make projections of the impact of interventions when deployed at population scales. The second theme of our research seeks to advance these capabilities for prevention of mosquito-borne diseases.
  • Infectious disease dynamics in the context of global change: The dependence of vector-borne diseases on environmental conditions makes them highly sensitive to multiple forms of global change. The third theme of our research seeks to understand the interconnected nature of these dependencies.



  • Associate Professor, University of Notre Dame - Present
  • Eck Family Assistant Professor, University of Notre Dame 2014-2020
  • Concurrent Assistant Professor of Applied and Computational Mathematics and Statistics, University of Notre Dame 2015-Present
  • RAPIDD Postdoctoral Fellow, NIH Fogarty International Center and University of California, Davis 2011-2014
  • Ph.D. Population Biology, University of California, Davis 2011
  • B.A. Computational Ecology, University of Tennessee, Knoxville 2006


Selected Recent Papers:

  • TA Perkins*, I Rodriguez-Barraquer*, C Manore*, AS Siraj, G España, CM Barker, MA Johansson, RC Reiner*. Heterogeneous local dynamics revealed by classification analysis of spatially disaggregated time series data. Epidemics. In press.
  • G España, Y Yao, KB Anderson, MC Fitzpatrick, DL Smith, AC Morrison, A Wilder-Smith, TW Scott, TA Perkins. 2019. Model-based assessment of public health impact and cost-effectiveness of dengue vaccination following screening for prior exposure. PLOS Neglected Tropical Diseases 13:e0007482.
  • TA Perkins*, RC Reiner*, G España*, QA ten Bosch, A Verma, K Liebman, VA Paz-Soldan, JP Elder, AC Morrison, ST Stoddard, U Kitron, GM Vazquez-Prokopec, TW Scott, DL Smith. 2019. An agent-based model of dengue virus transmission shows how uncertainty about breakthrough infections influences vaccination impact projections. PLOS Computational Biology 15:e1006710.
  • RJ Oidtman RJ, S Lai, Z Huang, J Yang, AS Siraj, RC Reiner, AJ Tatem,
    TA Perkins, H Yu. 2019. Inter-annual variation in seasonal dengue epidemics driven by multiple interacting factors in Guangzhou, China. Nature Communications 10:1148.
  • G España, C Hogea, A Guignard, QA ten Bosch, AC Morrison, DL Smith, TW Scott, A Schmidt, TA Perkins. 2019. Biased efficacy estimates in phase-III dengue vaccine trials due to heterogeneous exposure and differential detectability of primary infections across trial arms. PLOS ONE 14:e0210041.
  • CBF Vogels*, C Ruckert*, SM Cavany*, TA Perkins, GD Ebel, ND Grubaugh. 2019. Arbovirus coinfection and co-transmission: a neglected public health concern? PLOS Biology 17:e3000130.
  • SM Moore, QA ten Bosch, AS Siraj, KJ Soda, G España, A Campo, S Gomez, D Salas, B Raybaud, E Wenger, P Welkhoff, TA Perkins. (2018) Local and regional dynamics of chikungunya virus transmission in Colombia: the role of mismatched spatial heterogeneity. BMC Medicine 16:152.
  • QA ten Bosch, F Castro-Llanos, H Manda, AC Morrison, JP Grieco, NL Achee, TA Perkins. (2018) Model-based analysis of experimental data from interconnected, row-configured huts elucidates multifaceted effects of a volatile chemical on Aedes aegypti mosquitoes. Parasites and Vectors 11:365.
  • QA ten Bosch, HE Clapham, L Lambrechts, V Duoung, P Buchy, BM Althouse, AL Lloyd, LA Waller, AC Morrison, U Kitron, GM Vazquez-Prokopec, TW Scott, TA Perkins. (2018) Contributions from the silent majority dominate dengue virus transmission. PLOS Pathogens 14:e1006965.
  • AS Siraj, RJ Oidtman, JH Huber, MUG Kraemer, OJ Brady, MA Johansson, TA Perkins. (2017) Temperature modulates dengue virus epidemic growth rates through its effects on reproduction numbers and generation intervals. PLOS Neglected Tropical Diseases 11:e0005797.
  • TA Perkins. (2017) Retracing Zika’s footsteps across the Americas with computational modeling. Proceedings of the National Academy of Sciences 114:5558-5560.
  • S Flasche*, M Jit*, I Rodriguez-Barraquer*, L Coudeville*, M Recker*, K Koelle*, G Milne*, T Hladish*, TA Perkins*, I Dorigatti, DAT Cummings, G Espana, J Kelso, I Longini, J Lourenco, C Pearson, RC Reiner, Jr., NM Ferguson. (2016) The long-term safety, public health impact, and cost-effectiveness of a routine vaccination with a recombinant, live-attenuated dengue vaccine (Dengvaxia): a model comparison study. PLOS Medicine 13:e1002181.
  • JH Huber, G Johnston, B Greenhouse, DL Smith, TA Perkins. (2016) Quantitative, model-based estimates of variability in the generation and serial intervals of Plasmodium falciparum malaria. Malaria Journal 15:490.
  • TA Perkins, AS Siraj, C Warren Ruktonanchai, MUG Kraemer, AJ Tatem. (2016) Model-based projections of Zika virus infections in childbearing women in the Americas. Nature Microbiology 1:16216.