Dr. Aristides Moustakas

Welcome to my site!

This is my personal research webpage

I have interdisciplinary interests in Data Analytics, Population Biology, Applied StatisticsEnvironmental Modelling, and Mathematical Ecology

The biodiversity-wind energy-land use nexus in a global biodiversity hotspot

Wind energy is the leading renewable technology towards achieving climate goals, yet biodiversity trade-offs via land take are emerging. Thus, we are facing the paradox of impacting on biodiversity to combat climate change. Greece, a global biodiversity hotspot, has licensed wind farms producing 502% more energy than the national target by 2030! Can we really mitigate against climate change by impacting biodiversity? Land use loss is a main driver of climate change.
We are suggesting spatial planning that minimizes protected areas, high mountains, and high diversity areas, while maximizing fragmented areas. This comes only at a 4% lower energy efficiency.

Abrupt events and population synchrony in epidemiology

Can rare, extreme (abrupt) events synchronize geographically distinct populations?
Abrupt or extreme events are generally hard to study as by definition they do not occur frequently. Synchrony after abrupt events has been reported also in climate sciences. In the Arctic, extreme weather events synchronized population fluctuations across animals. Do abrupt events synchronize the spread of diseases?
Routine testing for the harmful pathogen Bovine Tuberculosis (bTB) was suspended briefly during the foot and mouth disease epidemic of 2001 in Great Britain. We utilize bTB incidence data to demonstrate how the short-lasting abrupt lapse in management can alter epidemiological parameters, including the rate of new infections and duration of infection cycles. 
We show that the changes in epidemiological parameters during the short-lasting unmanaged time while testing was suspended, can increase new infections markedly, can have long-lasting effects, and generate longer-term temporal infection cycles. Infection cycles shifted from annual to 4-year after testing interruption. Spatial synchrony of new infections between different GB regions after the interruption of cattle testing increased. These effects persisted for over 15 years after the abrupt testing interruption. After annual testing was introduced in some GB regions, new infections have become more de-synchronised, a result also confirmed by a stochastic model. This study shows that amendments in the epidemiological parameters lead to chaotic patterns and that abrupt events synchronise disease dynamics.
Moustakas, A., Evans, M.R., Daliakopoulos, I.N. and Markonis, Y. (2018) Abrupt events and population synchrony in the dynamics of Bovine Tuberculosis. Nature Communications, 9: 2821
See also an animation of the paper:


This Research Topic aims to extend the angles and collect articles which propose data driven mathematical or statistical models of the spread of the COVID-19, and/or of its foreseen consequences on public health, society, industry, economics and technology. It also focuses on collecting the real-time big data of COVID-19 spreading, and further helps the scientists to establish the efficient databases for the risk management. 


The topics include but are not limited to:

• nonlinear dynamics and non-equilibrium processes of COVID-19;
• complex system and complex networks modeling of COVID-19;
• computational epidemiology, biophysics, systems biology and computational biology aspects of COVID-19;
• artificial intelligence, machine learning and big data analytics of COVID-19;
• self-organization and emergent phenomena of social organization with COVID-19 pandemics;
• applications to social science, Public health, economics, engineering and other aspects related to COVID-19 pandemics.

All articles submitted to this Research Topic before August 2020 were free of charge

 Minimal effect of prescribed burning on fire spread rate and intensity in savanna ecosystems

Prescribed or controlled burning treatments are debated as a potential measure for ameliorating the spread and intensity of wildfires.


Machine learning analysis using random forests was performed in a spatio-temporal data set comprising a large number of savanna fires across 22 years.


Results indicate that fire return interval was not an important predictor of fire spread rate or fire intensity, having a feature importance of 3.5%, among eight other predictor variables. Manipulating burn seasonality showed a feature importance of 6% or less regarding fire spread rate or fire intensity. Overall prescribed burning effects were low in comparison with meteorological (hydrological and climatic) variables.

Predicting fire spread rate and intensity has been a poor endeavour thus far and we show that more data of the variables already monitored would not result in higher predictive accuracy.

    Our recent calibrated individual based modelling paper showed that regular and frequent testing of cattle could eventually lead to the eradication of the Bovine Tuberculosis (bTB) disease, whether or not badgers were culled.

     The analysis found that in a region containing about 1.5m cows of which 3000 to 15,000 might have TB, badger culling could account for a reduction of 12 in the number of infected cattle. While reducing the testing interval by one month could reduce the number of those infected by 193.

     Regular and frequent testing of cattle could eventually lead to the eradication of the disease, whether or not badgers were culled and despite the current test being at most 80% accurate. Badger culling alone, however did not lead to TB eradication in the study and is therefore unlikely to be a successful control strategy.

     Housing cattle in large sheds over winter could potentially double the number of infected animals in a herd, as under such conditions there is a much greater chance of TB being passed between cows.

    The new research analysed publicly available UK Governmental official data on Bovine Tuberculosis (TB) in cattle from 2008-2014 and used time series statistical methods to smooth out seasonal variations.

    Testing is less frequent in England: every four years in some areas, every two years in others and annually in areas of high TB. The analysis showed TB in cattle is rising in England. 

    The Scottish programme of risk-based testing had led to the reduction in the number of tests there meaning that testing not only works but is cost-effective.However, the most rapid decline in infections was recorded in Wales since annual or even more frequent testing was applied.

    Not only did more testing lead to effective control of the disease but further proof of its importance was shown by spikes in infections and infected herds when there were interruptions to testing after the 2001 foot and mouth outbreak in the UK.

    This study based on publicly available data fully confirms the theoretical findings of our previous modelling paper regarding the efficacy of cattle testing in TB control as well as the impact of cattle winter housing on the dynamics of disease spread.

    “This new research provides extremely strong evidence of what many experts in veterinary disease control have known for many years - that it is crucial to test cattle as frequently as possible in order to control bovine TB,” said Professor Alastair Macmillan, veterinary adviser to the Humane Society International/UK and a former government scientist.

The largest simulation to date of the numbers of cattle and badgers infected with Bovine Tuberculosis (bTB) casts serious doubts about the extent to which badgers cause TB in cattle.

Using calibrated individual based models that combine a huge number of cattle and badgers that have bTB, we were able to quantify the relationship between the two animals and use a big data approach to show that the route of infection for cattle is from other cattle rather than from other species. Reciprocally, badgers are mainly infected by other infected badgers.

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