Abstract
Infectious diseases can be devastating, especially when new and highly contagious, producing epidemic outbreaks that can become pandemics. Such is the case of COVID-19, the worst pandemic the world has seen in more than 100 years. Predicting the course and outcomes of such a pandemic in relation to possible interventions is crucial for societal and healthcare planning and forecasting of resource needs. Deterministic and mechanistic models can capture the most important phenomena for epidemics propagation while also allowing for a meaningful interpretation of results. In this work a deterministic model was developed, using elements from the SIR-type models, that describes individuals in a population in compartments by infection stage and age group. The model assumes a close well-mixed community with no migrations. Infection rates and clinical and epidemiological information govern the transitions between stages of the disease. The present model provides a platform to build upon and its current low complexity retains accessibility to both experts and non-experts as well as policy makers to comprehend the variables and phenomena at play.
The impact of specific interventions, including that of available critical care, on the outbreak time course, number of cases and outcome of fatalities were evaluated. Data available from the COVID-19 outbreak as of mid-May 2020 was used. Key findings in our model simulation results indicate that (i) universal social isolation measures appear effective in reducing total fatalities only if they are strict and the number of daily interpersonal contacts is reduced to very low numbers; (ii) selective isolation of only the elderly (at higher fatality risk) appears almost as effective in reducing total fatalities but at a possible lower economic and social impact; (iii) an increase in the number of critical care capacity directly avoids fatalities; (iv) the use of personal protective equipment (PPE) appears to be effective to dramatically reduce total fatalities when implemented extensively and in a high degree; (v) extensive random testing of the population allowing for more complete infection recognition, accompanied by subsequent (self) isolation of infected aware individuals, can dramatically reduce the total fatalities. This appears effective only if conducted extensively to almost the entire population and sustained over time; (vi) ending isolation measures while Rt values remain above 1.0 (with a safety factor) renders the isolation measures useless and total fatality numbers return to values as if nothing was ever done; (vii) ending the isolation measures for only the population under 60 y/o at Rt values still above 1.0 increases total fatalities but only around half as much as if isolation ends for everyone.
The interpretation of these results for the COVID-19 outbreak predictions and interventions should be considered with awareness of the assumptions of SIR-type models and the low confidence (lack of complete and valid data) on several of the parameter values available at the time of writing. Quantitative predictions of the results must be accompanied with a critical discussion in terms of model structure and parameters limitations.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
All authors wish to thank Khalifa University (Grant 8474000317 CRPA-2020-SEHA) and the Government of Abu Dhabi for the funding and support.
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
The for Matlab source code and Excel file containing all parameter values used as well as a non-age segregated version of the model are available at https://github.com/EnvBioProM/COVID_Model