Novel use of online optimization in a mathematical model of COVID-19 to guide the relaxation of pandemic mitigation measures.

Since early 2020, non-pharmaceutical interventions (NPIs)-implemented at varying levels of severity and based on widely-divergent perspectives of risk tolerance-have been the primary means to control SARS-CoV-2 transmission. This paper aims to identify how risk tolerance and vaccination rates impact the rate at which a population can return to pre-pandemic contact behavior. To this end, we developed a novel mathematical model and we used techniques from feedback control to inform data-driven decision-making. We use this model to identify optimal levels of NPIs across geographical regions in order to guarantee that hospitalizations will not exceed given risk tolerance thresholds. Results are shown for the state of Colorado, United States, and they suggest that: coordination in decision-making across regions is essential to maintain the daily number of hospitalizations below the desired limits; increasing risk tolerance can decrease the number of days required to discontinue NPIs, at the cost of an increased number of deaths; and if vaccination uptake is less than 70%, at most levels of risk tolerance, return to pre-pandemic contact behaviors before the early months of 2022 may newly jeopardize the healthcare system. The sooner we can acquire population-level vaccination of greater than 70%, the sooner we can safely return to pre-pandemic behaviors.

Environmental Factors Influencing COVID-19 Incidence and Severity.

Emerging evidence supports a link between environmental factors-including air pollution and chemical exposures, climate, and the built environment-and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission and coronavirus disease 2019 (COVID-19) susceptibility and severity. Climate, air pollution, and the built environment have long been recognized to influence viral respiratory infections, and studies have established similar associations with COVID-19 outcomes. More limited evidence links chemical exposures to COVID-19. Environmental factors were found to influence COVID-19 through four major interlinking mechanisms: increased risk of preexisting conditions associated with disease severity; immune system impairment; viral survival and transport; and behaviors that increase viral exposure. Both data and methodologic issues complicate the investigation of these relationships, including reliance on coarse COVID-19 surveillance data; gaps in mechanistic studies; and the predominance of ecological designs. We evaluate the strength of evidence for environment-COVID-19 relationships and discuss environmental actions that might simultaneously address the COVID-19 pandemic, environmental determinants of health, and health disparities.

Early Adoption of Longitudinal Surveillance for SARS-CoV-2 among Staff in Long-Term Care Facilities: Prevalence, Virologic and Sequence Analysis.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in 2019 and has become a major global pathogen in an astonishingly short period of time. The emergence of SARS-CoV-2 has been notable due to its impacts on residents in long-term care facilities (LTCFs). LTCF residents tend to possess several risk factors for severe outcomes of SARS-CoV-2 infection, including advanced age and the presence of comorbidities. Indeed, residents of LTCFs represent approximately 40% of SARS-CoV-2 deaths in the United States. Few studies have focused on the prevalence and transmission dynamics of SARS-CoV-2 among LTCF staff during the early months of the pandemic, prior to mandated surveillance testing. To assess the prevalence and incidence of SARS-CoV-2 among LTCF staff, characterize the extent of asymptomatic infections, and investigate the genomic epidemiology of the virus within these settings, we sampled staff for 8 to 11 weeks at six LTCFs with nasopharyngeal swabs from March through June of 2020. We determined the presence and levels of viral RNA and infectious virus and sequenced 54 nearly complete genomes. Our data revealed that over 50% of infections were asymptomatic/mildly symptomatic and that there was a strongly significant relationship between viral RNA (vRNA) and infectious virus, prolonged infections, and persistent vRNA (4+ weeks) in a subset of individuals, and declining incidence over time. Our data suggest that asymptomatic SARS-CoV-2-infected LTCF staff contributed to virus persistence and transmission within the workplace during the early pandemic period. Genetic epidemiology data generated from samples collected during this period support that SARS-CoV-2 was commonly spread between staff within an LTCF and that multiple-introduction events were less common. IMPORTANCE Our work comprises unique data on the characteristics of SARS-CoV-2 dynamics among staff working at LTCFs in the early months of the SARS-CoV-2 pandemic prior to mandated staff surveillance testing. During this time period, LTCF residents were largely sheltering-in-place. Given that staff were able to leave and return daily and could therefore be a continued source of imported or exported infection, we performed weekly SARS-CoV-2 PCR on nasal swab samples collected from this population. There are limited data from the early months of the pandemic comprising longitudinal surveillance of staff at LTCFs. Our data reveal the surprisingly high level of asymptomatic/presymptomatic infections within this cohort during the early months of the pandemic and show genetic epidemiological analyses that add novel insights into both the origin and transmission of SARS-CoV-2 within LTCFs.

Estimating the Impact of Statewide Policies to Reduce Spread of Severe Acute Respiratory Syndrome Coronavirus 2 in Real Time, Colorado, USA.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic necessitated rapid local public health response, but studies examining the impact of social distancing policies on SARS-CoV-2 transmission have struggled to capture regional-level dynamics. We developed a susceptible-exposed-infected-recovered transmission model, parameterized to Colorado, USA‒specific data, to estimate the impact of coronavirus disease‒related policy measures on mobility and SARS-CoV-2 transmission in real time. During March‒June 2020, we estimated unknown parameter values and generated scenario-based projections of future clinical care needs. Early coronavirus disease policy measures, including a stay-at-home order, were accompanied by substantial decreases in mobility and reduced the effective reproductive number well below 1. When some restrictions were eased in late April, mobility increased to near baseline levels, but transmission remained low (effective reproductive number <1) through early June. Over time, our model parameters were adjusted to more closely reflect reality in Colorado, leading to modest changes in estimates of intervention effects and more conservative long-term projections.

Guidelines for Modeling and Reporting Health Effects of Climate Change Mitigation Actions.

BACKGROUND: Modeling suggests that climate change mitigation actions can have substantial human health benefits that accrue quickly and locally. Documenting the benefits can help drive more ambitious and health-protective climate change mitigation actions; however, documenting the adverse health effects can help to avoid them. Estimating the health effects of mitigation (HEM) actions can help policy makers prioritize investments based not only on mitigation potential but also on expected health benefits. To date, however, the wide range of incompatible approaches taken to developing and reporting HEM estimates has limited their comparability and usefulness to policymakers. OBJECTIVE: The objective of this effort was to generate guidance for modeling studies on scoping, estimating, and reporting population health effects from climate change mitigation actions. METHODS: An expert panel of HEM researchers was recruited to participate in developing guidance for conducting HEM studies. The primary literature and a synthesis of HEM studies were provided to the panel. Panel members then participated in a modified Delphi exercise to identify areas of consensus regarding HEM estimation. Finally, the panel met to review and discuss consensus findings, resolve remaining differences, and generate guidance regarding conducting HEM studies. RESULTS: The panel generated a checklist of recommendations regarding stakeholder engagement: HEM modeling, including model structure, scope and scale, demographics, time horizons, counterfactuals, health response functions, and metrics; parameterization and reporting; approaches to uncertainty and sensitivity analysis; accounting for policy uptake; and discounting. DISCUSSION: This checklist provides guidance for conducting and reporting HEM estimates to make them more comparable and useful for policymakers. Harmonization of HEM estimates has the potential to lead to advances in and improved synthesis of policy-relevant research that can inform evidence-based decision making and practice. https://doi.org/10.1289/EHP6745.

Viral CpG Deficiency Provides No Evidence That Dogs Were Intermediate Hosts for SARS-CoV-2.

Due to the scope and impact of the COVID-19 pandemic there exists a strong desire to understand where the SARS-CoV-2 virus came from and how it jumped species boundaries to humans. Molecular evolutionary analyses can trace viral origins by establishing relatedness and divergence times of viruses and identifying past selective pressures. However, we must uphold rigorous standards of inference and interpretation on this topic because of the ramifications of being wrong. Here, we dispute the conclusions of Xia (2020. Extreme genomic CpG deficiency in SARS-CoV-2 and evasion of host antiviral defense. Mol Biol Evol. doi:10.1093/molbev/masa095) that dogs are a likely intermediate host of a SARS-CoV-2 ancestor. We highlight major flaws in Xia's inference process and his analysis of CpG deficiencies, and conclude that there is no direct evidence for the role of dogs as intermediate hosts. Bats and pangolins currently have the greatest support as ancestral hosts of SARS-CoV-2, with the strong caveat that sampling of wildlife species for coronaviruses has been limited.