Abstract
As the number of cases of COVID-19 continues to grow exponentially, local health services are likely to be overwhelmed with patients requiring intensive care. We develop and implement an algorithm to provide optimal re-routing strategies to either transfer patients requiring Intensive Care Units (ICU) or ventilators, constrained by feasibility of transfer. We validate our approach with realistic data extracted from UK and Spain. For the UK case, we coarse-grain the NHS system at the level of NHS trusts and, subsequently cover the whole set of geopositioned trusts to extract a 4-regular geometric graph which indicates, for a given trust, its four nearest neighbors. The Spanish case is analysed at the autonomous community level, and we extract a contact network where nodes correspond to autonomous communities and links indicate adjacent communities. Estimates of weekly ICU demand could be extrapolated from an age structured epidemiological model by considering contagion-to-ICU likelihood estimates or alternatively from available data. Through random search optimisation we identify the best load sharing strategy, where the cost function to minimise is based on the total number of ICU units above capacity and we implement and test two optimisation strategies. Our framework is flexible allowing for additional criteria, different cost functions, and this methodology is general enough that it can easily be extended to optimise other resources beyond ICU units or ventilators. Assuming a uniform ICU demand across trusts, we show that using our method it is possible to enable access to ICU treatment to up to 1000 cases in the UK in a single step of the algorithm, and with more realistic demand the algorithm is able to balance about 600 beds per step in the Spanish system – leading to potentially saving a large percentage of these lives that would otherwise not have access to ICU if no load sharing was implemented.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
LL gratefully acknowledges the financial support of the EPSRC via Early Career Fellowship EP/P01660X/1. RCh gratefully acknowledges the financial support of the EPSRC via grant EP/N014391/1 and NHS England, Global Digital Exemplar programme. LD gratefully acknowledges the financial support of The Alan Turing Institute under the EPSRC grant EP/N510129/1.
Author Declarations
All relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript.
Yes
All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.
Yes
I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).
Yes
I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.
Yes
Data Availability
Codes and data are available at https://github.com/lucaslacasa/loadsharing