An age-structured SEIR model for COVID-19 incidence in Dublin, Ireland with framework for evaluating health intervention cost.

Strategies adopted globally to mitigate the threat of COVID-19 have primarily involved lockdown measures with substantial economic and social costs with varying degrees of success. Morbidity patterns of COVID-19 variants have a strong association with age, while restrictive lockdown measures have association with negative mental health outcomes in some age groups. Reduced economic prospects may also afflict some age cohorts more than others. Motivated by this, we propose a model to describe COVID-19 community spread incorporating the role of age-specific social interactions. Through a flexible parameterisation of an age-structured deterministic Susceptible Exposed Infectious Removed (SEIR) model, we provide a means for characterising different forms of lockdown which may impact specific age groups differently. Social interactions are represented through age group to age group contact matrices, which can be trained using available data and are thus locally adapted. This framework is easy to interpret and suitable for describing counterfactual scenarios, which could assist policy makers with regard to minimising morbidity balanced with the costs of prospective suppression strategies. Our work originates from an Irish context and we use disease monitoring data from February 29th 2020 to January 31st 2021 gathered by Irish governmental agencies. We demonstrate how Irish lockdown scenarios can be constructed using the proposed model formulation and show results of retrospective fitting to incidence rates and forward planning with relevant "what if / instead of" lockdown counterfactuals. Uncertainty quantification for the predictive approaches is described. Our formulation is agnostic to a specific locale, in that lockdown strategies in other regions can be straightforwardly encoded using this model.

Rethinking Macro Measurement

Economists have long recognized that widely reported and followed economic indicators such as GDP, productivity, and real wage growth often fail to do a good job of capturing the concepts they purport to measure. And as the chief economist of Credit Suisse begins by noting in this article, this mismeasurement problem has been viewed as significant in the cases of inflation and inflation‐adjusted (or real) measures, and even more troubling in the case of productivity.Even before the onset of the COVID‐19 pandemic, the U.S. economy, as viewed through the lens of conventional macro statistics, was almost universally agreed to be struggling with pronounced demographic and productivity slowdowns. But if the slowdowns in nominal GDP and wage growth are indisputable, the author suggests that systematic overstatement of U.S. inflation may well have not only exaggerated the extent of such slowdowns, but obscured the possibility of actual increases in productivity and real wages during the past three or four decades.Along with the possible mismeasurement of some macroeconomic data, the author also discusses the ways in which macro measures tend to be misused. Though the limitations of macro data have long been understood by experts, the widespread use and uncritical acceptance of such numbers as representing concepts such as living standards, the cost of living, worker efficiency, technology growth, and welfare have very significant implications for today's policy debates and public assessment of the health of the economy. After discussing some of these measurement challenges and calling for greater acknowledgment of the limitations of conventional macro statistics by economists when citing them, the author suggests that “the prestige of national accounts data has likely peaked,” and better approaches appear to be emerging. Notable among such approaches are alternative measures, some spawned by the pandemic, that are improving how economists track the economy in real time and, in so doing, making possible “a deeper, more accurate, and more realistic view of economic activity.”