Matthew (Matt) Ferrari
Study systems include
The beetle-borne bacterial wilt Erwinia tracheiphila in the wild gourd Cucurbita pepo.
Takahashi, S., Metcalf, C.J.E., Ferrari, M.J., Moss, W.J., Truelove, S.A., Tatem, A.J., Grenfeel, B.T., Lessler, J. 2015. Reduced vaccination and the risk of measles and other childhood infectious post-Ebola. Science 347(6227):1240-1242.
McKee, A., Ferrari, M.J., Shea, K. 2015. The effects of maternal immunity and age distribution on population immunity to measles. Theoretical Ecology 10.1007/s12080-014-0250-9.
Drake, J.M., Kaul, R.B., Alexander, L.W., O'Reagan, S.M., Kramer, A.M., Pulliam, J.T., Ferrari, M.J., Park, A.W. 2015. Ebola cases and health system demand in Liberia. PLoS Biology DOI: 10.1371/journal.pbio.1002056.
Jongejans, E., Skarpaas, O., Ferrari, M.J., Long, E.S., Dauer, J.T. Schwarz, C.M., Rauschert, E.S.J., Jabbout, R., Mortensen, D.A., Isard, S.A., Lieb, D.A. ,Sezen, Z., Hultin, A.G., Shea, K. 2014. A Unifying gravity framework for dispersal. Theoretical Ecology. 8(2). DOI: 10.1007/s12080-014-0245-5.
Simmons, H.E., Prendeville, H.R., Dunham, J.P., Ferrari, M.J., Earnest, J.D., Munkvold, G.P., Holmes, E.C., Stephenson, A.G. 2014. Transgenic virus-resistance in crop-wild Cucurbita pepo does not prevent vertical transmission of zucchini yellow mosaic virus. Plant Disease, DOI
Lofgren, E., Halloran, M.E., Rivers, C.M., Drake, J.M., Porco, T.C., Lewis, B., Yang, W., Vespignani, A., Shaman, J., Eisenberg, J.N.S., Eisenberg, M.C., Marathe, M., Scarpino, S.V., Alexander, K.A., Meza, R., Ferrari, M.J., Human, J.M., Meyers, L.A. Eubank, S. 2014. Opinion: Mathematical models: A key tool for outbreak response. Proceedings of the National Academy of Sciences.
Halloran, M.E., Vespignani, A., Bharti, N., Feldstein, L.R., Alexander, K.A., Ferrari, M.J., Shaman, J., Drake, J.M., Porco, T., Eisenberg, J.N.S., Del Valle, S.Y., Lofgren, E., Scarpino, S.V., Eisenberg, M.C., Gao, D., Hyman, J.M., Eubank, S., Longini, R.M. Ebola: Mobility data. Science 346(6208):433.
Shea, K., Tildesley, M.J., Runge, M.C., Fonnesbeck, C.J., Ferrari, M.J. 2014. Adaptive management and the value of information: learning via intervention in epidemiology. PLOS Biology, DOI: 10.1371/journal.pbio.1001970.
Ferrari, M.J., Fermon, F., Nackers, F., Llosa, A., Magone, C., Grais, R.F. 2014. Time is (still) of the essence: quantifying the impact5 of emergency meningitis vaccination response in Katsina State, Nigeria. International Health. DOI:10.1093/inthealth/ihu062.
Minetti, A., Hurtado, N., Grais, R.F., Ferrari, M.J. 2013. Reaching the hard-to-reach: nonselective versus targeted outbreak response vaccination for measles. American Journal of Epidemiology. DOI: 10.1093/aje/kwt236.
Ferrari, MJ, Grenfell BT, Strebel PM. 2013. Think globally, act locally: the role of local demographics and vaccination coverage in the dynamic response of measles infection to control. Philosophical Transactions of the Royal Society of London
Simons, E., Ferrari M.J., Fricks J., Wannamuehler K, Anand A, Burton A, Strebel P. 2012. Assessment of the 2010 global measles mortality reduction goal: results from a model of surveillance data. The Lancet S0140-6736(12)60522-4
Bhart5i, N., Tatem, A.j., Ferrari, M.J., Grais, R.F., Djibo, A., Grenfell B.T. 2011. Explaining seasonal fluctuations of measles in Niger using nighttime lights imagery. Science 334:1424-1427.
Chen, S., Fricks, J., Ferrari, M.J. Tracking measles infection through non-linear state space models. Journal of the Royal Society of Statistics, C.
Bharti, N., Djibo, A., Ferrari, M.J., Grais, R.F., Tatem, A.J., McCabe, C.A., Bjornstad, O.N., Grenfell, B.T. 2010. Measles hotspots and epidemiological connectivity. Epidemiology and Infection 138(9): 1308-1316.
Sasu, M, Ferrari, M.J., Du, D., Winsor, J.A., Stephenson, A.G. 2009. Indirect costs of a non-target pathogen mitigate the direct benefits of a virus resistant transgene in wild Cucurbita. Proceedings of the National Academy of Science 106: 19067-19071
Ferrari, M.J., Grais, R.F., Bharti, N., Conlan, A.J.K., Bornstad, O.N., Wolfson, L.J., Guerin, P.J., Djibo, A., Grenfell, B.T. The dynamics of measles in sub-Saharan Africa. Nature 451:679-685.
For more information see the lab website at: theferrarilab.com
I use mathematical and statistical tools to understand patterns of disease incidence, and the effects of heterogeneity, in time and space.
Measles dynamics in developing countries
View a recent seminar on this topic.
Measles still kills hundreds of thousands of children each year in developing countries. Attempts to eradicate the disease through mass vaccination are hampered by both logistical and epidemiological challenges; for instance, high birth rates can make it difficult to maintain the necessary 95% vaccine coverage.
In collaboration with Medecins Sans Frontiers we are investigating local and regional dynamics of annual measles epidemics in West African countries (Niger, Tchad, Democratic Republic of Congo), in order to recommend vaccination strategies to minimize mortality and morbidity due to measles. We are using time series analysis and epidemic models to investigate:
- The nature of the strong annual seasonality in incidence at the regional scale
- Local variation in the scale of measles outbreaks
Vector behavior and spatial transmission
Disease vectors can transmit pathogens while foraging. Given a heterogeneous host population, choice of foraging locations by vectors will lead to differential host exposure to pathogens. Bacterial wilt — a pathogen of gourd species — is transmitted by a beetle, Acaymma vittata. Using field and lab experiments we are investigating how vectors respond to plant quality, and the implications for epidemic spread and pathogen mediated host selection.
Scaling within-host immune dynamics to populations
The rapid clearance or long-term persistence of parasites within hosts is determined by the interaction of both parasite life-history characteristics and the immune response of the host to infection. Variation along this axis has implications for the rate of parasite shedding, the accumulation of transmissible stages in the environment, and the encounter rate and transmission rate in naive hosts. Thus, the host immune system is a critical regulator of the cycle of infection and transmission that determines large-scale patterns of parasite distribution and burden at the population scale.
I work with Dr. Isabella Cattadori to study the impact of interactions between worm life-history characteristics and host immune response on population-level transmission processes. We combine lab-scale experiments in a rabbit/worm model with long-term temporal observations of worm burden and distribution in wild populations of rabbit to quantify the role of within host processes in determining population scale processes.
Dynamics of directly transmitted pathogens on host networks
I use simulation and analytical techniques to investigate how the spread of disease in social networks of hosts is affected by heterogeneities in contacts and local restrictions on transmission. These have important implications for the scaling of transmission across networks of different size and geometries — and can even lead to structural evolution of the network itself (as hosts are removed by mortality or acquired immunity).
Statistical methods for estimating transmission rates
Disease incidence data are often gathered at spatial and temporal scales that are coarse relative to scales considered by quantitative epidemiological models of host-pathogen systems (e.g. case counts are generally reported over discrete time intervals, while many classic epidemic models employ differential calculus, which makes predictions in continuous time). Furthermore, observed data often suffer from incomplete reporting, imperfect diagnosis, measurement error and other biases. One of the great challenges in quantitative epidemiology is to develop statistical models that provide a coherent link between theory and data. I am developing:
- Discrete time, stochastic models to develop statistical methods to estimate transmission rates for incidence data
- Computational methods (e.g. Markov chain Monte Carlo) to account for the uncertainty due to imperfect measurement