Uncertainty governs Covid-19 projections, so multidisciplinary research is vital
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Graham Barr reminded us in a recent article in this publication of some of the challenges we have encountered while estimating the possible number of SA Covid-19 cases and associated healthcare resource use as part of the SA Covid-19 Modelling Consortium (“Government shunned proper statistical tools to tackle pandemic (,” August 23). Among these are the choice of the modelling methods, understating the uncertainty surrounding one's estimates, and assessing the potential effect of regulations.
It is unclear who exactly, or what model, the article was aimed at. One focus was the question of whether the government decision regarding the alcohol ban was based on correct analysis. We are not in a position to comment on this as we had no part in this analysis. But there are a number of other contentions in the article that seem to take aim at what was in fact our work.
Barr states that “the government focused on models that gave uncertain and alarmist projections of loss of life”. Our projections of the potential number of cumulative deaths have always been accompanied by ranges representing the uncertainty associated with them; whether the reporting on them was alarmist or not is outside of our control. He also states that the models “attached too little weight to the certain loss of jobs, tax revenue and government services”.
Our model has never attempted to attach any weight to these aspects, though others’ have done so. Our model steered clear of incorporating these aspects not because we do not believe they are important — quite the opposite — but because they lie outside the scope of work the consortium was formed to address. However, from the beginning we worked with other groups that tackled the macroeconomic effects of both the pandemic and potential measures for its control, sharing model outputs and updates.
Barr also contends that “statisticians must always give model estimates that include the uncertainty of their estimates. If the uncertainty associated with model estimates is not made explicit, the government and the public may be given the impression that the model estimates have a weight of scientific knowledge behind them that they do not have.” We could not agree more.
In all our outputs, be they part of public reports, results shared with government or media engagements, we have made it clear that our projections are subject to great uncertainty, largely owing to, as Barr rightly ...
It is unclear who exactly, or what model, the article was aimed at. One focus was the question of whether the government decision regarding the alcohol ban was based on correct analysis. We are not in a position to comment on this as we had no part in this analysis. But there are a number of other contentions in the article that seem to take aim at what was in fact our work.
Barr states that “the government focused on models that gave uncertain and alarmist projections of loss of life”. Our projections of the potential number of cumulative deaths have always been accompanied by ranges representing the uncertainty associated with them; whether the reporting on them was alarmist or not is outside of our control. He also states that the models “attached too little weight to the certain loss of jobs, tax revenue and government services”.
Our model has never attempted to attach any weight to these aspects, though others’ have done so. Our model steered clear of incorporating these aspects not because we do not believe they are important — quite the opposite — but because they lie outside the scope of work the consortium was formed to address. However, from the beginning we worked with other groups that tackled the macroeconomic effects of both the pandemic and potential measures for its control, sharing model outputs and updates.
Barr also contends that “statisticians must always give model estimates that include the uncertainty of their estimates. If the uncertainty associated with model estimates is not made explicit, the government and the public may be given the impression that the model estimates have a weight of scientific knowledge behind them that they do not have.” We could not agree more.
In all our outputs, be they part of public reports, results shared with government or media engagements, we have made it clear that our projections are subject to great uncertainty, largely owing to, as Barr rightly ...