Promoting healthy populations as a pandemic preparedness strategy: a simulation study from Mexico.

Background: The underlying health status of populations was a major determinant of the impact of the COVID-19 pandemic, particularly obesity prevalence. Mexico was one of the most severely affected countries during the COVID-19 pandemic and its obesity prevalence is among the highest in the world. It is unknown by how much the COVID-19 burden could have been reduced if systemic actions had been implemented to reduce excess weight in Mexico before the onset of the pandemic. Methods: Using a dynamic epidemic model based on nationwide data, we compare actual deaths with those under hypothetical scenarios assuming a lower body mass index in the Mexican population, as observed historically. We also model the number of deaths that would have been averted due to earlier implementation of front-of-pack warning labels or due to increases in taxes on sugar-sweetened beverages and non-essential high-energy foods in Mexico. Findings: We estimate that 52.5% (95% prediction interval (PI) 43.2, 61.6%) of COVID-19 deaths were attributable to obesity for adults aged 20-64 and 23.8% (95% PI 18.7, 29.1%) for those aged 65 and over. Had the population BMI distribution remained as it was in 2000, 2006, or 2012, COVID-19 deaths would have been reduced by an expected 20.6% (95% PI 16.9, 24.6%), 9.9% (95% PI 7.3, 12.9%), or 6.9% (95% PI 4.5, 9.5%), respectively. If the food-labelling intervention introduced in 2020 had been introduced in 2018, an expected 6.2% (95% PI 5.2, 7.3%) of COVID-19 deaths would have been averted. If taxes on sugar-sweetened beverages and high-energy foods had been doubled, trebled, or quadrupled in 2018, COVID-19 deaths would have been reduced by an expected 4.1% (95% PI 2.5, 5.7%), 7.9% (95% PI 4.9, 11.0%), or 11.6% (95% PI 7.3, 15.8%), respectively. Interpretation: Public health interventions targeting underlying population health, including non-communicable chronic diseases, is a promising line of action for pandemic preparedness that should be included in all pandemic plans. Funding: This study received funding from Bloomberg Philanthropies, awarded to Juan A. Rivera from the National Institute of Public Health; Community Jameel, the UK Medical Research Council (MRC), Kenneth C Griffin, and the World Health Organization.

The societal value of SARS-CoV-2 booster vaccination in Indonesia.

OBJECTIVES: To estimate the expected socio-economic value of booster vaccination in terms of averted deaths and averted closures of businesses and schools using simulation modelling. METHODS: The value of booster vaccination in Indonesia is estimated by comparing simulated societal costs under a twelve-month, 187-million-dose Moderna booster vaccination campaign to costs without boosters. The costs of an epidemic and its mitigation consist of lost lives, economic closures and lost education; cost-minimising non-pharmaceutical mitigation is chosen for each scenario. RESULTS: The cost-minimising non-pharmaceutical mitigation depends on the availability of vaccines: the differences between the two scenarios are 14 to 19 million years of in-person education and $153 to $204 billion in economic activity. The value of the booster campaign ranges from $2,500 ($1,400-$4,100) to $2,800 ($1,700-$4,600) per dose in the first year, depending on life-year valuations. CONCLUSIONS: The societal benefits of booster vaccination are substantial. Much of the value of vaccination resides in the reduced need for costly non-pharmaceutical mitigation. We propose cost minimisation as a tool for policy decision-making and valuation of vaccination, taking into account all socio-economic costs, and not averted deaths alone.

Data needs for integrated economic-epidemiological models of pandemic mitigation policies.

The COVID-19 pandemic and the mitigation policies implemented in response to it have resulted in economic losses worldwide. Attempts to understand the relationship between economics and epidemiology has led to a new generation of integrated mathematical models. The data needs for these models transcend those of the individual fields, especially where human interaction patterns are closely linked with economic activity. In this article, we reflect upon modelling efforts to date, discussing the data needs that they have identified, both for understanding the consequences of the pandemic and policy responses to it through analysis of historic data and for the further development of this new and exciting interdisciplinary field.

Optimizing social and economic activity while containing SARS-CoV-2 transmission using DAEDALUS.

To study the trade-off between economic, social and health outcomes in the management of a pandemic, DAEDALUS integrates a dynamic epidemiological model of SARS-CoV-2 transmission with a multi-sector economic model, reflecting sectoral heterogeneity in transmission and complex supply chains. The model identifies mitigation strategies that optimize economic production while constraining infections so that hospital capacity is not exceeded but allowing essential services, including much of the education sector, to remain active. The model differentiates closures by economic sector, keeping those sectors open that contribute little to transmission but much to economic output and those that produce essential services as intermediate or final consumption products. In an illustrative application to 63 sectors in the United Kingdom, the model achieves an economic gain of between £161 billion (24%) and £193 billion (29%) compared to a blanket lockdown of non-essential activities over six months. Although it has been designed for SARS-CoV-2, DAEDALUS is sufficiently flexible to be applicable to pandemics with different epidemiological characteristics.

The impact of a COVID-19 lockdown on work productivity under good and poor compliance.

BACKGROUND: In response to the COVID-19 pandemic, governments across the globe have imposed strict social distancing measures. Public compliance to such measures is essential for their success, yet the economic consequences of compliance are unknown. This is the first study to analyze the effects of good compliance compared with poor compliance to a COVID-19 suppression strategy (i.e. lockdown) on work productivity. METHODS: We estimate the differences in work productivity comparing a scenario of good compliance with one of poor compliance to the UK government COVID-19 suppression strategy. We use projections of the impact of the UK suppression strategy on mortality and morbidity from an individual-based epidemiological model combined with an economic model representative of the labour force in Wales and England. RESULTS: We find that productivity effects of good compliance significantly exceed those of poor compliance and increase with the duration of the lockdown. After 3 months of the lockdown, work productivity in good compliance is £398.58 million higher compared with that of poor compliance; 75% of the differences is explained by productivity effects due to morbidity and non-health reasons and 25% attributed to avoided losses due to pre-mature mortality. CONCLUSION: Good compliance to social distancing measures exceeds positive economic effects, in addition to health benefits. This is an important finding for current economic and health policy. It highlights the importance to set clear guidelines for the public, to build trust and support for the rules and if necessary, to enforce good compliance to social distancing measures.

Optimal national prioritization policies for hospital care during the SARS-CoV-2 pandemic.

In response to unprecedented surges in the demand for hospital care during the SARS-CoV-2 pandemic, health systems have prioritized patients with COVID-19 to life-saving hospital care to the detriment of other patients. In contrast to these ad hoc policies, we develop a linear programming framework to optimally schedule elective procedures and allocate hospital beds among all planned and emergency patients to minimize years of life lost. Leveraging a large dataset of administrative patient medical records, we apply our framework to the National Health Service in England and show that an extra 50,750-5,891,608 years of life can be gained compared with prioritization policies that reflect those implemented during the pandemic. Notable health gains are observed for neoplasms, diseases of the digestive system, and injuries and poisoning. Our open-source framework provides a computationally efficient approximation of a large-scale discrete optimization problem that can be applied globally to support national-level care prioritization policies.