To study epidemiological dynamics, classical mathematical modeling considered only limited sets of dual factors. However, membrane computational modeling is increasingly used since it includes multi-level biological scenarios. The study “Simulating the efficacy of vaccines on the epidemiological dynamics of SARS-CoV-2 in a membrane computing model” in microLife applies this computing procedure to model different vaccination strategies during the SARS-CoV-2 pandemic. Fernando Baquero explains for the #FEMSmicroBlog what we can learn from such models about the epidemic spread and the efficacy of vaccine interventions. #FascinatingMicrobes
Why we need membrane computational modeling
As an observational science, epidemiology analyzes the factors that influence the emergence of epidemic events. Based on these findings, epidemiologists can then propose corrective interventions to limit the spread of infectious diseases.
Epidemiology — as far as human health is concerned — is not an experimental science. Consequently, predicting novel emergences, epidemic dynamics or the efficacy of interventions in particular demographic landscapes remains limited.
Only in the last few years, membrane computational modeling has been applied to study epidemiological dynamics. In contrast to classical mathematic modeling, membrane computing is based on a simulation of all objects that influence epidemic dynamics.
In contrast to classical mathematic modeling, membrane computing is based on simulating all objects that influence epidemic dynamics.
In this regard, an object is an individual entity and can be represented by a “membrane”. This could be for example an individual virus particle or a host. One membrane might also contain other membranes. The relations between nested membranes are further defined by a set of rules, for example by the probabilities of contact.
The publication “Simulating the efficacy of vaccines on the epidemiological dynamics of SARS-CoV-2 in a membrane computing model” in microLife applies these novel computational approaches to improve the predictability of the epidemic spread and the value of applying interventions.
Modeling SARS-CoV-2 epidemics
The publication simulates a town according to particular demographic data. These encompass an age pyramid, family structure(s), activities in different spaces – such as working from or outside of home – attendance at school, leisure time and time spent in public spaces. The data also account for social factors, for example, if an individual is in a nursing home, hospital or intensive care unit.
This simulation model suggests that individuals respond to the SARS-CoV-2 contagion in relation to their level of innate or acquired immunity. This results in different (positive or negative) viral loads and hence possibilities of transmission. At the same time, different degrees of morbidity and mortality rates can be evaluated in the model.
The publication first simulates such a basic scenario without any intervention. When certain parameters are changed, for instance, the duration of acquired immunity, a shorter and more endemic epidemic takes place.
Protecting the elderly as a key intervention
Applying additional interventions to the model changes the effect on the epidemic dynamics. A previous publication analyzed non-pharmacological interventions such as lockdowns to decrease transmission. Yet, the here highlighted publication focuses on how vaccination and double vaccination (booster vaccination) impact the endemic outcome. The study also models the effects of mixed interventions, for example, lockdown plus vaccination.
The results indicate how important it is to completely vaccinate elder individuals over 60 years as a key intervention in the SARS-CoV-2 epidemic. Breaking the transmission chain at this point would indeed be sufficient to sharply decrease the mortality of the affected population.
Breaking the transmission chain by completely vaccinating elder individuals would be sufficient to decrease mortality by SARS-CoV-2.
This study shows that membrane computing models might be relevant for testing assumptions in simulated epidemics and not only SARS-CoV-2. The membrane computing approach has been applied previously to predict the spread of antibiotic-resistance genes, mobile genetic elements and resistant bacterial clones in hospitals and the community. As such, membrane computing simulations can help clarify uncertainties in adopting public health decisions and implementing focused interventions.
- Read the article “Simulating the efficacy of vaccines on the epidemiological dynamics of SARS-CoV-2 in a membrane computing model” by Campos et al. (2022).
- Read the previous article “Simulating the impact of non-pharmaceutical interventions limiting transmission in COVID-19 epidemics using a membrane computing model” by Campos et al. (2021).
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.
| Do you want to be a guest contributor? |
| The #FEMSmicroBlog welcomes external bloggers, writers and SciComm enthusiasts. Get in touch if you want to share your idea for a blog entry with us! |