Dr. Matthew Ferrari

Assistant Professor of Biology


<b>Matthew Ferrari,</b> PhD an assistant professor of biology and statistics at the Center for Infectious Disease Dynamics where he studies the transmission and distribution of infectious diseases and develops mathematical models to explain the impact of vaccination and global change on the incidence of infectious disease. The use of these mathematical and statistical tools assists in his understanding of patterns of disease incidence, and the effects of heterogeneity, in time and space. <br> <span><br>Matthew's specific areas of research include:<br><br></span> <b><a href="">Measles dynamics in developing countries</a></b> <a href="">&nbsp;</a> <br>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. <br> <br>In collaboration with&nbsp; <i><a href="">Medecins Sans&nbsp;</a></i> <b><i><a href="">Frontières</a></i></b>&nbsp;Matthew and his team 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. They are using time series analysis and epidemic models to investigate: <br> <ul> <li>The nature of the strong annual seasonality in incidence at the regional scale</li> <li>Local variation in the scale of measles outbreaks</li> </ul> <br> <b>Vector behavior and spatial transmission</b> <br>Scaling within-host immune dynamics to populations <br>Dynamics of directly transmitted pathogens on host networks <br>Statistical methods for estimating transmission rates <br> <ul> <li><span>Discrete time, stochastic models to develop statistical methods to estimate transmission rates for incidence data</span></li> <li><span>Computational methods (e.g. Markov chain Monte Carlo) to account for the uncertainty due to imperfect measurement</span></li> </ul> <br> <b>Scaling within-host immune dynamics to populations</b> <br>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. <br> <br>Matthew works with Dr. Isabella Cattadori to study the impact of interactions between worm life-history characteristics and host immune response on population-level transmission processes. &nbsp;They 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. <br> <br> <b>Dynamics of directly transmitted pathogens on host networks</b> <br>Matthew uses 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). <br> <br> <b>Statistical methods for estimating transmission rates</b> <br>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. He is developing: <br> <ul> <li>Discrete time, stochastic models to develop statistical methods to estimate transmission rates for incidence data</li> <li>Computational methods (e.g. Markov chain Monte Carlo) to account for the uncertainty due to imperfect measurement</li> </ul>


Epidemics - the Dynamics of Infectious Diseases

Epidemics - the Dynamics of Infectious Diseases