A mechanistic population balance model to evaluate the impact of interventions on infectious disease outbreaks: Case for COVID19

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. 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 several possible interventions that have been or may be applied to slow the spread of the COVID-19 outbreak is evaluated. Key findings in our model simulation results indicate that (i) universal social isolation measures may be effective in reducing total fatalities only if they are strict and the average number of daily social interactions is reduced to very low numbers; (ii) selective isolation of only the age groups most vulnerable to the disease (i.e. older than 60) appears almost as effective in reducing total fatalities but at a much lower economic damage; (iii) the use of protective equipment (PPE) appears capable of very significantly reducing total fatalities if implemented extensively and to a high degree; (iv) extensive random testing of the population leading to infection recognition and subsequent immediate (self) isolation of the infected individuals, appears to be an ineffective intervention due to the required (unreachable with existing test sensitivities) high percentage of infection detections and the incapability to be sustained over time; (v) an increase in the number of critical care beds to directly save significant numbers of lives with a direct reduction in total final fatalities per each extra available critical care bed unit.