CityReads | Resilience Management During Epidemic Outbreaks

275

Resilience Management During Epidemic Outbreaks

Three scenarios of different levels of travel ban and system resilience.

Emanuele Massaro, Alexander Ganin, Nicola Perra, Igor Linkov & Alessandro Vespignani. Resilience management during large-scale epidemic outbreaks. Sci Rep 8, 1859 (2018). https://doi.org/10.1038/s41598-018-19706-2

Sources:https://www.nature.com/articles/s41598-018-19706-2#citeas

http://www.emanuelemassaro.com/

Cover picture(With modification) ：

https://www.who.int/emergencies/diseases/managing-epidemics-interactive.pdf

Emanuele Massaro is currently a Senior Scientist at the Ecole Polytechnique Fédérale de Lausanne (EFPL) and Data Scientist at the ISI Foundation in Turin (Italy). When he studied in the University of Florence (Italy), his main contribution was to understand the interplay between information, human risk perception and epidemic spreading in multiplex networks. In one of his papers published on Scientific Reports in 2018, Massaro and his coauthors use the concept of resilience and build models to assess the impacts of large-scale epidemic outbreak on society. They show the importance of achieving a balance between risk reduction and the disruption to critical functions of society. We can draw some useful implications for the efforts of containing the ongoing coronavirus outbreak in China and beyond. Here is an edited excerpt from the paper.

Assessing and managing the impact of large-scale epidemics considering only the individual risk and severity of the disease is exceedingly difficult and could be extremely expensive. Economic consequences, infrastructure and service disruption, as well as the recovery speed, are just a few of the many dimensions along which to quantify the effect of an epidemic on society’s fabric. Here, we extend the concept of resilience to characterize epidemics in structured populations, by defining the system-wide critical functionality that combines an individual’s risk of getting the disease (disease attack rate) and the disruption to the system’s functionality (human mobility deterioration). we consider the system-wide critical functionality as a function of the individual’s risk of getting the disease and the disruption to the system’s functionality generated by the human mobility deterioration.

By studying both conceptual and data-driven models, we show that the integrated consideration of individual risks and societal disruptions under resilience assessment framework provides an insightful picture of how an epidemic might impact society. In particular, containment interventions intended for a straightforward reduction of the risk may have net negative impact on the system by slowing down the recovery of basic societal functions. The presented study operationalizes the resilience framework, providing a more nuanced and comprehensive approach for optimizing containment schemes and mitigation policies in the case of epidemic outbreaks.

The evaluation of vulnerabilities and consequences of epidemics is a highly dimensional complex problem that should consider societal issues such as infrastructures and services disruption, forgone output, inflated prices, crisis-induced fiscal deficits and poverty. Therefore, it is important to broaden the model-based approach to epidemic analysis, expanding the purview by including measures able to assess the system resilience, i.e. response of the entire system to disturbances, their aftermath, the outcome of mitigation as well as the system’s recovery and retention of functionality.

Most important, operationalizing resilience must include the temporal dimension; i.e. a system’s recovery and retention of functionality in the face of adverse events. The assessment and management of system resilience to epidemics must, therefore, identify the critical functionalities of the system and evaluate the temporal profile of how they are maintained or recover in response to adverse events.

Here, we introduce a resilience framework to the analysis of the global spreading of an infectious disease in structured populations. We simulate the spread of infectious diseases across connected populations, and monitor the system–level response to the epidemic by introducing a definition of engineering resilience that compounds both the disruption caused by the restricted travel and social distancing, and the incidence of the disease.

We find that while intervention strategies, such as restricting travel and encouraging self-initiated social distancing, may reduce the risk to individuals of contracting the disease, they also progressively degrade population mobility and reduce the critical functionality thus making the system less resilient. Our numerical results show a transition point that signals an abrupt change of the overall resilience in response to these mitigation policies. Consequently, containment measures that reduce risk may drive the system into a region associated with long-lasting overall disruption and low resilience.

Interestingly, this region is in proximity of the global invasion threshold of the system, and it is related to the slowing down of the epidemic progression. Our study highlights that multiple dimensions of a socio-technical system must be considered in epidemic management and sets forward a new framework of potential interest in analyzing contingency plans at the national and international levels.

Model results

We provide a general framework for the analysis of the system-level resilience to epidemics by initially considering a metapopulation network. In this case we consider a system made of V distinct subpopulations. These form a network in which each subpopulation i is made of N_i individuals and is connected to a set k_i of other subpopulations.

A: Schematic representation of the metapopulation model. The system is composed of a network of subpopulations or patches, connected by diffusion processes. Each patch contains a population of individuals who are characterized with respect to their stage of the disease (e.g. susceptible, exposed, susceptible with fear, infected, removed), and identified with a different color in the picture. Individuals can move from a subpopulation to another on the network of connections among subpopulations.

B: Schematic illustration of the system’s critical functionality. The system if fully functional (CF(t)=1) during ordinary conditions when all the subpopulations are healthy and the number of real commuters is equal to the number of virtual commuters, i.e. D(t)=0 and C(t)=Z(t).

After the outbreak takes place (T₀) the system’s functionality decreases because of the disease propagation and the eventual travel reduction. Next the system starts to recover until the complete extinction of the epidemic (T_E ) which corresponds to the time when no more infected individuals are in the system. The curves (a) and (b) represent the critical functionality of scenarios corresponding to high and low values of resilience.

Epidemic containment measures, based on limiting or constraining human mobility, are often considered in the contingency planning and always re-emerge when there are new infectious disease threats. The target of these control measures are travels to/from the areas affected by an epidemic outbreak and the corresponding decrease of infected individuals reaching areas not yet affected by the epidemic.

At the same time, travel restrictions have a large impact on the economy and affect the delivery of services, including medical supplies and the deployment of specialized personnel to manage the epidemic. For this reason, travel restrictions must be carefully scrutinized to trade off the costs and benefits. We introduce the parameter p∈[10⁻⁵,1] that allows us to simulate policy-induced system-wide travel restrictions.

Resilience and epidemic size in the data-driven scenario. (A) The plot shows the difference between resilience (blue) and the final fraction of diseased populations (red) for different values of the diffusion rate p.

Here, we can identify three critical regions of the system. (i) diffusion rate p=0.1 above the critical invasion threshold. Even if the system is characterized by sub-optimal resilience, the disease spreads all over the system. (ii) the reduction of the diffusion parameter p results in a significant decrease of the number of diseased populations but also in a dramatic decrease of resilience; (iii) below the critical invasion threshold resilience goes back to high values as fraction of diseased populations approaches zero.

The figure clearly illustrates three regimes: i) for low travel reduction, a very severe epidemic hits all the subpopulations, but the short duration allows the system to go back to normal in a short time (high values of resilience); ii) for travel reduction close to the global invasion threshold, a small number of subpopulations are hit but the system critical functionality is compromised for a very long time, thus, resulting in a low values of resilience; iii) travel reduction above the critical threshold allows the system to contain the epidemic at the origin with low risk and high values of resilience. It is worth remarking that in the data-driven model, the minimum value of resilience is reached for travel restrictions that correspond to a reduction of mobility of three to four orders of magnitude. This is because in modern transportation networks the global invasion threshold is reached only for very severe travel restrictions that are virtually impossible to achieve. In other words, in realistic settings the feasible increase of travel restrictions appears always to decrease resilience. This calls for a careful scrutiny of the trade-off between individual’s risk and resilience, as the region where both are achieved is virtually not accessible.

Discussion

The realistic threat quantification is difficult and evaluation of vulnerabilities and consequences of new disease epidemics is certainly a challenge. We analyzed the impact of an infectious disease epidemic in structured populations by considering a definition of system resilience that takes into consideration not only the number of infected individuals but also society’s need for maintaining certain critical functions in space and time. In particular, we assume that the limitations and disruptions of human mobility deteriorate the system’s functionality. We observe that containment measures, that limit individuals’ mobility, are advantageous in reducing risk but may deteriorate the system’s functionality for a very long time and thus correspond to low resilience. Although we have considered only two of the many dimensions encompassing the functionality of socio-technical systems, we show that study of resilience allows stakeholders to measure the impact of epidemic threats and differentiate between different management alternatives.

It is straightforward to envision more realistic definition of the critical functionality. The components of critical functionality could be weighted according to objective/subjective evaluation of their relevance to stakeholders. Finally, cost-benefit analyses and ethical considerations should be included in the analysis of the societal impacts of disease that could lead to long lasting effects or even death of the affected individuals. This study highlights the importance of resilience-focused analysis for selecting intervention strategies. The natural tendency to be conservative in managing potentially inflated risks associated with new and emerging epidemics can result in unnecessary burdensome and possibly ineffective actions like quarantines as well as travel bans.

The emerging field of resilience assessment and management and its implementation could thus evaluate cross-domain alternatives to identify a policy design that enhances the system’s ability to (i) plan for such adverse events, (ii) absorb stress, (iii) recover, and (iv) predict and prepare for future stressors through necessary adaptation. To this end, the framework we presented can be of potential use for optimizing the policy response to a disease outbreak by balancing risk reduction with the disruption to critical functions that is associated with public health interventions.

Related CityReads

13. CityReads│Is the compact city more sustainable?（Click the title or enter our WeChat menu and reply number ）

CityReads ∣Notes On Cities

"CityReads", a subscription account on WeChat, 

posts our notes on city reads weekly. 

Please follow us by searching "CityReads"