Age separation dramatically reduces COVID-19 mortality rate in a computational model of a large population

Abstract

COVID-19 pandemic has caused a global lockdown in many countries throughout the world. Faced with a new reality, and until a vaccine or efficient treatment is found, humanity must figure out ways to keep the economy going, on one hand, while keeping the population safe, on the other hand, especially those that are susceptible to this virus. Here, we use a Watts–Strogatz network simulation, with parameters that were drawn from what is already known about the virus, to explore five different scenarios of partial lockdown release in two geographical locations with different age distributions. We find that separating age groups by reducing interactions between them protects the general population and reduces mortality rates. Furthermore, the addition of new connections within the same age group to compensate for the lost connections outside the age group still has a strong beneficial influence and reduces the total death toll by about 62%. While complete isolation from society may be the most protective scenario for the elderly population, it would have an emotional and possibly cognitive impact that might outweigh its benefit. Therefore, we propose creating age-related social recommendations or even restrictions, thereby allowing social connections while still offering strong protection for the older population.

Is increased mortality by multiple exposures to COVID-19 an overseen factor when aiming for herd immunity?

“…In populations, in which age structure can be neglected this is sufficiently accurate. For models with an explicit age structure we refer to [49,50]. In general, age structure can be modeled explicitly in a rather simple way.…”

Do Old Age and Comorbidity via Non-Communicable Diseases Matter for COVID-19 Mortality? A Path Analysis

“…The existing studies indicate the various range of factors that contribute to the COVID-19 mortality which includes gender (23,24), hesitancy of being vaccinated (15,25), age (6,10,21,22,(26)(27)(28)(29)(30)(31)(32)(33), environmental, demographic factors, population density, biological and healthcare related factors (2,3,16), race (12,(18)(19)(20)34), international travel (17), and pre-existing morbidity (7)(8)(9)(35)(36)(37)(38).…”

Two-Age-Structured COVID-19 Epidemic Model: Estimation of Virulence Parameters to Interpret Effects of National and Regional Feedback Interventions and Vaccination

“…Since it was reported in December 2019 in China, the virus quickly took pandemic proportions throughout six continents and over 210 countries. Over 100 countries declared lockdowns and curfews, with an estimated global economic loss of one trillion US dollars in 2020 (see [1] and references therein). By October 2020, over 36 million people were definitely reported to be infected with COVID-19 and more than one million people had died from virus-related complications.…”

“…In this respect, despite containing simplifying assumptions, common variants of SIR-type models, including age-dependent substructures, are of great help in characterizing epidemics (see [14] and references therein). The reader is referred to the very recent [1,12,[14][15][16][17][18] for age-structured modeling of the COVID-19 epidemic and related age-dependent analyses. The reader is also referred to [19] for the latest study adopting a model with an age-dependent pre-pandemic contact matrix, reflecting the goal of a return to pre-pandemic routines once a vaccine is available, to compare five age-stratified vaccine prioritization strategies.…”