#FEMSmicroBlog: Simulating the COVID-19 epidemic with computing models


Microbial epidemics are extremely complex phenomena and no one epidemic is like another. Even though we have seen several epidemics in our history, understanding the current COVID-19 epidemic requires more than our cumulative experience. The dynamics of the COVID-19 epidemic is subject to the change of viral and human biology, as well as human sociology, individual behaviour, climate and economy. The article “Simulating the impact of non-pharmaceutical interventions limiting transmission in COVID-19 epidemics using a membrane computing model” published in microLife uses computer models to better understand the dynamics of COVID-19 transmission and how intervention strategies impact them. Fernando Baquero explains for the #FEMSmicroBlog what we can learn from these models. #FascinatingMicrobes


We cannot experiment with epidemics

The COVID-19 epidemic is a “viro-anthropogenic” pathogenic event. This means that without our human actions, the dynamics of this natural epidemic would be different. Human actions like reducing the transmission with lockdowns, but also the massive concentrations of people have influenced the viral spread.

Human actions like lockdowns and massive gatherings of people influence the viral spread as a viro-anthropogenic pathogenic event.

Modern science relies heavily on conducting experiments. However, in epidemics, no experiment can be conducted – both due to ethical reasons and the lack of real control groups. Because of that, we are limited to “observe and quantify” measurements mainly at a local scale. From these, we can obtain at least some – difficult to generalize – correlations.

One way out is the use of mathematical and computational models. These mimic, as realistically as possible, a “virtual human community” and observe how an epidemic might evolve. By changing the parameters of the model, we can even hypothesize different scenarios.

The article “Simulating the impact of non-pharmaceutical interventions limiting transmission in COVID-19 epidemics using a membrane computing model” published in microLife applies a membrane computing technology. Membrane computing allows reproducing in silico, with an unprecedented level of detail, a “society”. Into such an average virtual Western town, the causative agent COVID-19 is introduced for the first time.


But we can model epidemics

Generally, epidemics occur in nested spaces; the virus replicates at a specific rate inside of an individual. Depending on their age and time period during the day, the individual resides at living spaces at home, with their family, or at external places. Also, the locations of family members like schools, common spaces like streets, leisure areas, retirement homes, elderly nursing homes, or hospital and intensive care units can be considered.

Membrane computing allows to also include the different types of immunological responses in each individual – innate and acquired natural immunity. It can further predict the number of cases, the progress to the severity and the mortality in the natural immunity-response groups.

Interactions among individuals occur at random in the model in each one of these spaces. Like this, a “real epidemic” takes place in the model of the virtual town. While the basal model reproduced the epidemic “without interventions”, the next level was to analyze the effect of time, interventions and their intensity in reducing transmission.

membrane computing model for a viral transmissible disease
Computing simulation for Covid-19. From Campos et al. (2021).


Let’s learn from computing models

The main result of this study is how important time is to inhibit virus transmission. The earlier, the better! The model showed that by adopting strict interventions after 15 or 23 days, deaths could be prevented almost totally. However, the study did not show clear differences between adopting interventions after 15 or 21 days from the onset of the epidemic.

Interventions adopted after 37 days and 45 days have, however, a weak effect. The exception is when the more severe cases in the elderly groups were reduced by 80%. Yet, intervening at earlier stages of infection would still be more efficient.

Hence, early interventions reduce the overall number of cases, including those that could have evolved to high severity (mostly in elderly people). Also, the model showed that an early lockdown for elderly individuals (>60 years old) as a single intervention would have a positive effect on the evolution of epidemics.

Early interventions can reduce the overall number of cases and could almost totally prevent any deaths.

The main message this study highlighted is that “reality-mimicking” computer modelling might be useful to test possible intervention strategies. They also constitute an alternative to the lack of experimental methods in epidemic diseases.



About the author of this blog

Fernando Baquero, M.D., Ph.D. is a Research Professor at the Division of Microbial Biology and Evolution of the Ramón y Cajal Institute for Health Research (IRYCIS), Department of Microbiology of the Ramón y Cajal University Hospital, Evobiome Lab, in Madrid, and Center of Network Research in Epidemiology and Public Health (CIBER-ESP) from the Carlos III Institute of Health of Spain.

About this blog section

The section #FascinatingMicrobes for the #FEMSmicroBlog explains the science behind a paper and highlights the significance and broader context of a recent finding. One of the main goals is to share the fascinating spectrum of microbes across all fields of microbiology.

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