Locking down everyone causes 2.5 times more covid-deaths.
The assumption made with lockdowns is that they automatically reduce total covid deaths. But do they?
The initial models that panicked the world into lockdown came from the likes of Prof Neil1 Ferguson at Imperial College, London and Tomas Pueyo. These models assumed a single number for the effect of locking down everyone, irrespective of their age. This is called the isolation value.
The SARS-CoV-2 virus infection mortality rate has such a steep difference between the healthy and vulnerable; essentially, those under 60 in contrast to those over 60 or those with known risk factors such as obesity, high blood pressure, cardiovascular disease. If we instead split the isolation value into two parts, one for the healthy group, and one for vulnerable cohort, then we can use this property of the virus to bring the pandemic under control.
Using the same SEIRS2 epidemiological modelling principles as Imperial College and NPHET, this more sophisticated model shows that if you apply the same isolation value (lockdown) to everyone, you get approximately 2.5 times more covid-deaths than the lowest death strategy.
In terms of deaths, mathemathically, the best strategy is achieved by immediately returning to normal for the healthy; while the vulnerable cocoon for just 90 days.
In just 3 months3, the entire Irish population would then have the same levels of naturally acquired immunity as a fully vaccinated population with an 80% effective vaccine. Importantly, the hospitals would not be overrun so we’re protecting our healthcare heroes at the same time as our vulnerable.
In 90 days, it’s all over. With summer, sunshine, viral seasonality and the vaccine roll out, there is no better time. Policy makers, let’s not waste this opportunity.
SARS-CoV-2 waves in Europe: A 2-stratum SEIRS model solution. Levan Djaparidze, Federico Lois. 2021.
In order to design actionable SARS-CoV-2 strategies, we extended the SEIRS model to support stratified isolation levels for healthy <60 and vulnerable individuals. At first, we forced isolation levels to be uniform, showing that daily deaths curves of all metropolitan areas in the analysis can be fitted using homogeneous Ro=3.3. In the process, we established the possibility that an extremely short infectiousness period of 2 days coupled with 5 days exposure may be responsible for the multiple deaths valleys observed during the weeks following lockdowns. Regardless of the infectiousness period, we realized that is possible to infer non-uniform isolation levels for healthy <60 and vulnerable by forcing the model to match the <60 to >60 age serology ratio reported in seroprevalence studies. Since the serology ratio is more robust than absolute values, we argue immunity level estimations made in this way (Madrid 41%; Catalonia 23%; Brussels 49%; and Stockholm 62%) are closer to reality. In locations where we didn’t find reliable serology, we performed immunity estimations assuming Spain’s serology ratio (Paris: 23%; London: 33%). We predict that no location can return to normal life without having a second wave (albeit in Stockholm a smaller one). We searched what isolation values allow to return to normal life in 90 days minimizing final deaths, shockingly all found isolations for healthy <60 were negative (i.e. coronavirus parties minimize final deaths). Then, assuming an ideal 1-day long vaccination campaign with a 77% efficacy vaccine, we compared predicted final deaths of those 90-day strategies for all possible vaccination dates with a 180-day long vaccine waiting strategy that imposes 0.40 mandatory isolation to healthy <60 and results in 0.65 isolation to vulnerable. We found that 180-day of mandatory isolations to healthy <60 (i.e. schools and workplaces closed) produces more final deaths if the vaccination date is later than (Madrid: Feb 23 2021; Catalonia: Dec 28 2020; Brussels Apr 25 2021; Paris: Jan 14 2021; London: Jan 22 2021). We also modeled how average isolation levels change the probability of getting infected for a single individual that isolates differently than average. That led us to realize disease damages to third parties due to virus spreading can be calculated and to postulate that an individual has the right to avoid mandatory isolation during epidemics (SARS-CoV-2 or any other) if these damages can be covered with a novel proposed isolation exemption insurance policy. As secondary findings in Appendix III we hypothesize that an early D614 like strain wave might be the cause of low mortality in Asia, and show the negligible reduction of HIT due to heterogeneity. Finally we conclude that our 2-stratum SEIRS model is suitable to predict SARS-CoV-2 epidemic behavior and can be used to minimize covid-19 disease and isolations related damages. To the sole effect of understanding and verifying its content the same model used through this paper has been made available online at www.sars2seir.com/paper-12-2020/
1 “The more successful a strategy is at temporary suppression, the larger the later epidemic is predicted to be in the absence of vaccination, due to lesser build-up of herd immunity.”. Neil M. Ferguson et al., “Impact of Non-Pharmaceutical Interventions (NPIs) to Reduce COVID-19 Mortality and Healthcare Demand,” Imperial College London, March 16, 2020. Tomas Pueyo, Head of Growth, Hero: https://www.prnewswire.com/news-releases/course-hero-welcomes-tomas-pueyo-as-vice-president-of-growth-300747666.htm
2 SEIRS epidemic model involving the relationships between the susceptible S, exposed E, infected I, and recovered R individuals for understanding the proliferation of infectious diseases.
3 For a virus with an R0 in the region of 3.3 to 4.5