A cautious approach to subsidies for environmental sustainability.

Transformational change is possible, but design and implementation must seek to avoid lock-in.

Mainstreaming nature in US federal policy.

Integrated policy changes must be cross-sectoral, appropriate, strategic, and evidence-based.

The path to scientifically sound biodiversity valuation in the context of the Global Biodiversity Framework.

Successful implementation of the Kunming-Montreal Global Biodiversity Framework requires identifying a process for measuring and valuing changes in biodiversity that build on the recognition that economics and valuation must play a key role in "halting and reversing" biodiversity loss. Here, we discuss considerations for a practical path to valuing changes in biodiversity. Framing changes in the value of biodiversity as a summary of changes in certain natural assets enables leveraging existing approaches and international standards associated with environmental-economic accounting. We discuss why an approach that builds from individual species, evolutionary groups, or functional groups into a practical, hierarchical statistical classification system is better than the development of any one biodiversity index. We merge techniques from ecology and other natural sciences, national and environmental-economic accounting, and economics, which are all on the cusp of making measurement of the change in the value of biodiversity possible. The focus should be on scaling and integrating these approaches. The path forward appears to begin with imperfect but useful measures, grounded in robust concepts, while establishing ambition to further scale-up measurements-just like the past evolution of many other official statistical series.

Challenges of integrating economics into epidemiological analysis of and policy responses to emerging infectious diseases.

COVID-19 has shown that the consequences of a pandemic are wider-reaching than cases and deaths. Morbidity and mortality are important direct costs, but infectious diseases generate other direct and indirect benefits and costs as the economy responds to these shocks: some people lose, others gain and people modify their behaviours in ways that redistribute these benefits and costs. These additional effects feedback on health outcomes to create a complicated interdependent system of health and non-health outcomes. As a result, interventions primarily intended to reduce the burden of disease can have wider societal and economic effects and more complicated and unintended, but possibly not anticipable, system-level influences on the epidemiological dynamics themselves. Capturing these effects requires a systems approach that encompasses more direct health outcomes. Towards this end, in this article we discuss the importance of integrating epidemiology and economic models, setting out the key challenges which such a merging of epidemiology and economics presents. We conclude that understanding people's behaviour in the context of interventions is key to developing a more complete and integrated economic-epidemiological approach; and a wider perspective on the benefits and costs of interventions (and who these fall upon) will help society better understand how to respond to future pandemics.

Understanding the Barriers to Pooled SARS-CoV-2 Testing in the United States.

Pooled testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection is instrumental for increasing test capacity while decreasing test cost. Pooled testing programs permit sustainable, long-term surveillance measures, which are essential for the early detection of virus resurgence in communities or the emergence of variants of concern. While numerous pooled approaches have been proposed to increase test capacity, uptake by laboratories has been limited. On 9 December 2020, we invited 362 U.S. laboratories that inquired about the Yale School of Public Health SalivaDirect test to participate in a survey to evaluate testing constraints and pooling strategies for SARS-CoV-2 testing. The survey was distributed using Qualtrics, and three reminders were sent. The survey closed on 21 January 2021. Of 93 responses received (25.7% response rate), 90 were from Clinical Laboratory Improvement Amendments (CLIA)-certified laboratories conducting SARS-CoV-2 testing. The remaining three were excluded from the analyses. Responses indicated that the major barriers to the uptake of pooled testing in the United States may not simply be the number of tests a laboratory can process per day, but rather the lack of clear protocols and adequate resources; laboratories are working with fixed physical and human capital constraints. Importantly, laboratories across the country are heterogeneous in infrastructure and workflow. The need for SARS-CoV-2 testing will remain for years to come. Testing programs can be maintained through pooled PCR testing strategies, and while statisticians, operations researchers, and others with expertise in sampling design have important value to add, laboratories require support on how to transition from traditional diagnostic testing to pooled surveillance. IMPORTANCE While numerous pooled SARS-CoV-2 testing approaches have been described in an effort to increase testing capacity and decrease test prices, uptake by laboratories has been limited. Responses to our survey of United States-based laboratories highlight the importance of consulting end-users-those that solutions are being designed for-so challenges can be addressed in a manner tailored to meet the specific needs out in the field. It may be surprising to those designing pooled testing strategies to learn that laboratories view pooling as more time-consuming than testing samples individually, and therefore that it is thought to create delays in test reporting.

Maximizing the Efficiency of Active Case Finding for SARS-CoV-2 Using Bandit Algorithms.

Even as vaccination for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) expands in the United States, cases will linger among unvaccinated individuals for at least the next year, allowing the spread of the coronavirus to continue in communities across the country. Detecting these infections, particularly asymptomatic ones, is critical to stemming further transmission of the virus in the months ahead. This will require active surveillance efforts in which these undetected cases are proactively sought out rather than waiting for individuals to present to testing sites for diagnosis. However, finding these pockets of asymptomatic cases (i.e., hotspots) is akin to searching for needles in a haystack as choosing where and when to test within communities is hampered by a lack of epidemiological information to guide decision makers' allocation of these resources. Making sequential decisions with partial information is a classic problem in decision science, the explore v. exploit dilemma. Using methods-bandit algorithms-similar to those used to search for other kinds of lost or hidden objects, from downed aircraft or underground oil deposits, we can address the explore v. exploit tradeoff facing active surveillance efforts and optimize the deployment of mobile testing resources to maximize the yield of new SARS-CoV-2 diagnoses. These bandit algorithms can be implemented easily as a guide to active case finding for SARS-CoV-2. A simple Thompson sampling algorithm and an extension of it to integrate spatial correlation in the data are now embedded in a fully functional prototype of a web app to allow policymakers to use either of these algorithms to target SARS-CoV-2 testing. In this instance, potential testing locations were identified by using mobility data from UberMedia to target high-frequency venues in Columbus, Ohio, as part of a planned feasibility study of the algorithms in the field. However, it is easily adaptable to other jurisdictions, requiring only a set of candidate test locations with point-to-point distances between all locations, whether or not mobility data are integrated into decision making in choosing places to test.

Measuring voluntary and policy-induced social distancing behavior during the COVID-19 pandemic.

Staying home and avoiding unnecessary contact is an important part of the effort to contain COVID-19 and limit deaths. Every state in the United States enacted policies to encourage distancing and some mandated staying home. Understanding how these policies interact with individuals' voluntary responses to the COVID-19 epidemic is a critical initial step in understanding the role of these nonpharmaceutical interventions in transmission dynamics and assessing policy impacts. We use variation in policy responses along with smart device data that measures the amount of time Americans stayed home to disentangle the extent that observed shifts in staying home behavior are induced by policy. We find evidence that stay-at-home orders and voluntary response to locally reported COVID-19 cases and deaths led to behavioral change. For the median county, which implemented a stay-at-home order with about two cases, we find that the response to stay-at-home orders increased time at home as if the county had experienced 29 additional local cases. However, the relative effect of stay-at-home orders was much greater in select counties. On the one hand, the mandate can be viewed as displacing a voluntary response to this rise in cases. On the other hand, policy accelerated the response, which likely helped reduce spread in the early phase of the pandemic. It is important to be able to attribute the relative role of self-interested behavior or policy mandates to understand the limits and opportunities for relying on voluntary behavior as opposed to imposing stay-at-home orders.

Increased SARS-CoV-2 Testing Capacity with Pooled Saliva Samples.

We analyzed feasibility of pooling saliva samples for severe acute respiratory syndrome coronavirus 2 testing and found that sensitivity decreased according to pool size: 5 samples/pool, 7.4% reduction; 10 samples/pool, 11.1%; and 20 samples/pool, 14.8%. When virus prevalence is >2.6%, pools of 5 require fewer tests; when <0.6%, pools of 20 support screening strategies.

Risk compensation and face mask mandates during the COVID-19 pandemic.

Face masks are an important component in controlling COVID-19, and policy orders to wear masks are common. However, behavioral responses are seldom additive, and exchanging one protective behavior for another could undermine the COVID-19 policy response. We use SafeGraph smart device location data and variation in the date that US states and counties issued face mask mandates as a set of natural experiments to investigate risk compensation behavior. We compare time at home and the number of visits to public locations before and after face mask orders conditional on multiple statistical controls. We find that face mask orders lead to risk compensation behavior. Americans subject to the mask orders spend 11-24 fewer minutes at home on average and increase visits to some commercial locations-most notably restaurants, which are a high-risk location. It is unclear if this would lead to a net increase or decrease in transmission. However, it is clear that mask orders would be an important part of an economic recovery if people otherwise overestimate the risk of visiting public places.

Pooling saliva to increase SARS-CoV-2 testing capacity.

Expanding testing capabilities is integral to managing the further spread of SARS-CoV-2 and developing reopening strategies, particularly in regards to identifying and isolating asymptomatic and pre-symptomatic individuals. Central to meeting testing demands are specimens that can be easily and reliably collected and laboratory capacity to rapidly ramp up to scale. We and others have demonstrated that high and consistent levels of SARS-CoV-2 RNA can be detected in saliva from COVID-19 inpatients, outpatients, and asymptomatic individuals. As saliva collection is non-invasive, extending this strategy to test pooled saliva samples from multiple individuals could thus provide a simple method to expand testing capacity. However, hesitation towards pooled sample testing arises due to the dilution of positive samples, potentially shifting weakly positive samples below the detection limit for SARS-CoV-2 and thereby decreasing the sensitivity. Here, we investigated the potential of pooling saliva samples by 5, 10, and 20 samples prior to RNA extraction and RT-qPCR detection of SARS-CoV-2. Based on samples tested, we conservatively estimated a reduction of 7.41%, 11.11%, and 14.81% sensitivity, for each of the pool sizes, respectively. Using these estimates we modeled anticipated changes in RT-qPCR cycle threshold to show the practical impact of pooling on results of SARS-CoV-2 testing. In tested populations with greater than 3% prevalence, testing samples in pools of 5 requires the least overall number of tests. Below 1% however, pools of 10 or 20 are more beneficial and likely more supportive of ongoing surveillance strategies.