The 'climate emergency' is a myth – natural variability, not CO2, drives climate change, says Nobel prize winner John Clauser. Dr Rudolph Kalveks explains why he's right.

by Rudolf Kalveks Since the onset of autumn it has become increasingly clear that SARS-CoV-2 (‘coronavirus’) epidemics cannot generally be analysed as waves with single peaks. The virus, which had largely disappeared from Europe by the middle of summer, made a comeback during the autumn. So, what conclusions should we draw about coronavirus dynamics from the historic time series data? And do these justify ongoing Government responses? Figure 1. Cumulative Death Statistics for Selected Countries. Cumulative coronavirus deaths, expressed as a percentage of country populations, are plotted on a logarithmic scale, as time series up to December 21st, 2020. Source: Worldometer. As previously, we focus on the ‘Canaries in the Mine’, or the coronavirus death statistics for a selection of European and other countries, published by Worldometer. It has been shown that these statistics exaggerate the impact of the coronavirus. Several countries classify deaths simply according to the presence of coronavirus, rather than apportioning causation to other contributing pathologies. This problem is compounded by the PCR test, which is notorious for false positives (as discussed elsewhere). Nonetheless, the death statistics are considerably more relevant than the ‘cases’ identified by PCR tests alone (which continue to provide the basis for much of the prevailing government and media narrative). Before proceeding, it is helpful to recap the coronavirus fatalities for our selection...

by Rudolph Kalveks We should always keep in mind the rationale for focusing on reported deaths, a.k.a. “the Canaries in the Mine”, in order to assess the state of the Coronavirus epidemic. The sensible reason for constructing models around death statistics rather than identified cases was given early on by the UK government’s advisers – “Reported deaths are likely to be far more reliable than case data…”1 The many factors that contribute to problems with case data, such as increased testing rates, false positives, the conflation of asymptomatic with serious presentations, and double counting, have all been echoed by numerous healthcare professionals in recent weeks and need not be elaborated here. Nonetheless, it is clear from the Worldometer death statistics that there has been some resurgence of the virus in European countries from late summer onwards. Should we be concerned that this is a “second wave”? Or are we merely seeing natural fluctuations in response to the many factors that influence the evolution of an epidemic, such as (a) inhomogeneous populations, (b) changes in virulence and (c) changes in social behaviour (whether in response to government restrictions, or otherwise)? Any such fluctuations must inevitably be compounded by the vagaries of national reporting systems. So, how can we objectively distinguish a “wave”, which may represent a cause for concern, from a...

Dr Rudolph Kalveks FT 29/7/2020: "Europe battles to contain surge in Covid-19 cases. Experts surprised at how fast the lifting of restrictions led to a rise in infections." Telegraph 1/8/2020: "The virus warning light is flashing." Let us see what the “Canaries in the Mine” (i.e., the coronavirus death statistics, courtesy of Worldometer) tell us about the actual development of the epidemic in Europe and other parts of the world. First of all, bearing in mind the usual caveats about reliability, we should recap the death statistics in our selection of countries. These are summarised as time series in Figure 1 below, where a logarithmic scale has been used. (An upward sloping straight line on such a graph would indicate an exponential growth rate). Figure 1. Cumulative Death Statistics for Selected Countries (July 31, 2020).Cumulative coronavirus deaths, expressed as a % of country populations, are plotted on a logarithmic scale, as time series up to July 31, 2020. Source: Worldometer. The curves show that when the penetration of coronavirus in a country reaches a ceiling, typically represented by a fatality rate below 0.1% of its population, its spread slows to a standstill, with few further fatalities arising. This certainly appears to have been the case in mainland Western Europe, where the average daily death rates from the coronavirus have now...

by Rudolph Kalveks For several weeks now, I have been tracking the fate of the “Canaries in the Mine” to see what the Coronavirus death statistics (courtesy of Worldometer) can tell us about the progress of the epidemic in the UK, along with a selection of other countries. The method used is to fit the evolving historic death statistics to a simple Susceptible Infected Recovered/Resolved (“SIR” ) epidemiological model, as explained in the first article in this series. This data fitting exercise identifies four essential parameters that govern an archetypal epidemic in a given country or region. These correspond to the early rate of spread of infections, the rate at which infected individuals recover (or expire), the size of the (fatally) susceptible sub-population, and the date at which the epidemic starts. Historic death statistics are augmented daily, and so we should not generally expect the parameters obtained from such a data fitting exercise to remain constant over time. The circumstances where the parameters do remain stable are those where the new death statistics match those extrapolated from the historic statistics under the simple model. This requires (i) that an epidemic conform to a simple SIR profile, (ii) that there is no material change in the combined effects of the many surrounding factors that influence the parameters of the SIR model...

by Rudolph Kalveks This was the snapshot of the parameters that describe the shape of the Coronavirus epidemic in terms of simple SIR models, based on the death statistics up to June 7th, shown in Table 1 of Canaries in the Mine: We can repeat the analysis making use of the additional death statistics over the two weeks up to June 21st. We observe that over this two week interval: There has been no substantial change in any of the parameters in Europe, USA, Australia.In particular, the parameter gamma for fatally susceptible sub-populations has only fluctuated by a couple of percent in these countries over the two weeks.Parameters for fatally susceptible sub-populations are looking better in South Africa, but worse in Brazil and India, and also worse globally. In conclusion, although the epidemics are obviously further progressed, over the last two weeks there has been no signal for any material change in the shape of the epidemic SIR model curves in Europe, the USA and Australia. Thus, the relaxation of lockdowns (well documented elsewhere) has so far had no discernible impact on the recovery from the epidemic in these countries. This undermines the analysis by Flaxman et al (published June 8th in Nature) that continues to predict a tenfold increase in the population at risk from the relaxation of lockdown...

by Rudolph Kalveks Scarcely a day goes by without a new and apparently contradictory announcement by some epidemiologist regarding the outlook for the COVID-19 coronavirus (“Coronavirus”). But what lies behind their models and to what extent should their pronouncements be taken at face value? It is well known to all experienced data analysts, whether in science or in economics, that given a sufficient number of model parameters to play with, one can achieve a spectacular fit to historic data, only to find that model projections bear no relation to subsequent events. The philosopher of science, Willard Van Orman Quine, coined the phrase “Underdetermination of Theory”, which applies in the situation where different theories are consistent with the same body of evidence. Models are inherently underdetermined and it is easy for their assumptions to take on a life of their own and to start predicting phenomena that are pure model artefacts rather than being representative of the physical system being studied. Given observational data in science, great care needs to be taken to distinguish the signal from the noise. This is easiest when there are prior grounds for expecting a certain type of signal and when the signal can be described with a few parameters. Gravitational physicists working with the LIGO interferometer apply such principles when detecting gravitational waves from merging...