Health Product Manufacturers and Innovators COVID-19 Impact Assessment: Lessons Learned and Compelling Needs

United States health care spending consumes nearly a fifth of the GDP [1]. While, in many respects, the U.S. health care system is enviable and highly innovative, it is also characterized by elements of ineffectiveness, inefficiency, and inequity. These aspects, resulting from pre-existing vulnerabilities within the system and interactions between the various stakeholders, were acutely highlighted by the COVID-19 pandemic. As health product manufacturers and innovators (HPMI) took steps to mitigate the immediate crisis and simultaneously begin to develop a longer-term sustainable solution, six common themes arose as areas for transformational change: support for science, data sharing, supply chain resiliency, stockpiling, and surge capacity, regulatory and reimbursement clarity and flexibility, public- and private-sector coordination and communication, and minimizing substandard care offerings. Within these categories, the authors of this paper suggest policy priorities to increase the effectiveness, efficiency, and equity of the HPMI sector and writ large across the U.S. health care system. These priorities call for increased scientific funding to diversify the pipeline for research and development, strengthening the nation’s public health infrastructure, building and maintaining “ever warm” manufacturing capacity and related stockpiles, instituting efficient and effective regulatory and reimbursement frameworks that promote innovation and creativity, devising structures and processes that enable more efficient collaboration and more effective communication to the public, and implementing rewards that incentivize desired behaviors among stakeholders. This assessment draws from the collective experience of the authors to provide a perspective for the diagnostics, hospital supplies and equipment, medical devices, therapeutics, and vaccines segments. While the authors of this paper agree on a common set of key policies, sub-sector-specific nuances are important to consider when putting any action priority into effect. With thoughtful implementation, these policies will enable a quicker, more robust response to future pandemics and enhance the overall performance of the U.S. health care system.

Uncomfortable Decisions

Some decision-making processes are uncomfortable. Many of us do not like to make significant decisions, such as whether to have a child, solely based on social science research. We do not like to choose randomly, even in cases where flipping a coin is plainly the wisest choice. We are often reluctant to defer to another person, even if we believe that the other person is wiser, and have similar reservations about appealing to powerful algorithms. And, while we are comfortable with considering and weighing different options, there is something strange about deciding solely on a purely algorithmic process, even one that takes place in our own heads.What is the source of our discomfort? We do not present a decisive theory here—and, indeed, the authors have clashing views over some of these issues—but we lay out the arguments for two (consistent) explanations. The first is that such impersonal decision-making processes are felt to be a threat to our autonomy. In all of the examples above, it is not you who is making the decision, it is someone or something else. This is to be contrasted with personal decision-making, where, to put it colloquially, you “own” your decision, though of course you may be informed by social science data, recommendations of others, and so on. A second possibility is that such impersonal decision-making processes are not seen as authentic, where authentic decision making is one in which you intentionally and knowledgably choose an option in a way that is “true to yourself.” Such decision making can be particularly important in contexts where one is making a life-changing decision of great import, such as the choice to emigrate, start a family, or embark on a major career change.

Utilizing citizen science to model the distribution of Aedes aegypti in West Africa

In a rapidly urbanizing region such as West Africa, Aedes mosquitoes pose an emerging threat of infectious disease that is compounded by limited vector surveillance. Citizen science has been proposed as a way to fill surveillance gaps by training local residents to collect and share information on disease vectors. Increasing citizen science efforts can begin to bridge the gaps in our current knowledge of Aedes distribution while engaging locals with mosquito control and public health efforts. Understanding the distribution of disease vectors in West Africa can inform researchers and public health officials on where to conduct disease surveillance and focus public health interventions. We aimed to compare citizen science data to published literature observations of Aedes mosquitoes and to quantify how incorporating citizen science changes our understanding of Aedes mosquito distribution in West Africa. We utilized citizen science data collected through NASAs GLOBE Observer mobile phone application and data from a previously published literature review on Aedes mosquito distribution to examine the contribution of citizen science to understanding the distribution of Ae. aegypti in West Africa using Maximum Entropy modeling. Combining citizen science and literature-derived observations improved the fit of the model compared to models created by each data source alone, but did not alleviate location bias within the models, likely due to lack of widespread observations. Understanding Ae. aegypti distribution will require greater investment in Aedes mosquito surveillance in the region, and citizen science should be utilized as a tool in this mission to increase the reach of surveillance.

Spatiotemporal Cluster Patterns of Hand, Foot, and Mouth Disease at the Province level in Mainland China, 2009-2018

BackgroundAlthough three monovalent EV-A71 vaccines have been launched in mainland China since 2016, hand, foot, and mouth disease (HFMD) still causes a considerable disease burden in China. Vaccines’ use may change the epidemiological characters of HFMD. This study aims to analyze the spatiotemporal cluster of HFMD at the province level in mainland China from 2009 to 2018 and compare the difference before and after the vaccines were launched. MethodsAll HFMD cases’ data from January 2009 to December 2018 were obtained from the public health science data center given by the Chinese Center for Diseases Control and Prevention. Spatial autocorrelation analysis and space-time scan statistics analysis were used to explore the spatiotemporal distribution pattern of this disease at the provincial level in mainland China. ResultsThe median annual incidence of HFMD was 143.22 per 100,000 (ranging from 87.01 to 205.06) in mainland China from 2009 to 2018. Two peaks of infections were observed per year. Children 5 years and under were the main morbid population. The global autocorrelation analysis showed that the spatial distribution of HFMD was presented a significant clustering pattern in each year (P<0.001), and the local autocorrelation analysis indicated that the high incidence areas were clustered in the southern and southeastern coastal provinces. The distribution of HFMD cases was clustered in time and space. The range of cluster time was between April and October. The most likely cluster appeared in the southern coastal provinces (Guangxi, Guangdong, Hainan) from 2010 to 2017 and in the southeastern coastal provinces (Shanghai, Jiangsu, Zhejiang) in 2018. ConclusionChanges in the spatiotemporal cluster of HFMD after the launch of EV-A71 vaccines were observed at the province level in mainland China in 2018. It is necessary to advance the EV-A71 vaccination plan, analyze the spatial-temporal distribution characteristics of different enterovirus pathogens of HFMD, and promote HFMD multivalent vaccines.

High-contrast Imaging with Fizeau Interferometry: the Case of Altair*

The Large Binocular Telescope (LBT) has two 8.4 m primary mirrors that produce beams that can be combined coherently in a “Fizeau” interferometric mode. In principle, the Fizeau point-spread function (PSF) enables the probing of structure at a resolution up to three times better than that of the adaptive-optics-corrected PSF of a single 8.4 m telescope. In this work, we examined the nearby star Altair (5.13 pc, type A7V, hundreds of Myr to ≈1.4 Gyr) in the Fizeau mode with the LBT at Brα (4.05 μm) and carried out angular differential imaging to search for companions. This work presents the first filled-aperture LBT Fizeau science data set to benefit from a correcting mirror that provides active phase control. In the analysis of the λ/D angular regime, the sensitivity of the data set is down to ≈0.5 M ⊙ at 1″ for a 1.0 Gyr system. This sensitivity remains limited by the small amount of integration time, which is in turn limited by the instability of the Fizeau PSF. However, in the Fizeau fringe regime we attain sensitivities of Δm ≈ 5 at 0.″2 and put constraints on companions of 1.3 M ⊙ down to an inner angle of ≈0.″15, closer than any previously published direct imaging of Altair. This analysis is a pathfinder for future data sets of this type, and represents some of the first steps to unlocking the potential of the first Extremely Large Telescope. Fizeau observations will be able to reach dimmer targets with upgrades to the instrument, in particular the phase detector.

CHILES. VII. Deep Imaging for the CHILES Project, an SKA Prototype

Radio astronomy is undergoing a renaissance, as the next generation of instruments provides a massive leap forward in collecting area and therefore raw sensitivity. However, to achieve this theoretical level of sensitivity in the science data products, we need to address the much more pernicious systematic effects, which are the true limitation. These become all the more significant when we consider that much of the time used by survey instruments, such as the Square Kilometre Array (SKA), will be dedicated to deep surveys. CHILES is a deep H i survey of the COSMOS field, with 1000 hr of Very Large Array time. We present our approach for creating the image cubes from the first epoch, with discussions of the methods and quantification of the data quality from 946 to 1420 MHz—a redshift range of 0.5−0. We lay out the problems we had to solve and describe how we tackled them. These are important because CHILES is the first deep wide-band multiepoch H i survey and has relevance for ongoing and future surveys. We focus on the accumulated systematic errors in the imaging, as the goal is to deliver a high-fidelity image that is only limited by the random thermal errors. To understand and correct these systematic effects, we ideally manage them in the domain in which they arise, and that is predominately the visibility domain. CHILES is a perfect test bed for many of the issues we can expect for deep imaging with the SKA or ngVLA, and we discuss the lessons we have learned.

An investigation into the spatiotemporal patterns of the Nymphalid butterfly Vagrans egista sinha (Kollar, [1844])

Vagrans egista sinha (Kollar, [1844]), the Himalayan Vagrant is a subspecies of Nymphalid (Brush-footed) butterflies spread across Asia, whose western limit is in the north-west India. Observations of this subspecies have considerably increased over the past half-a-decade, with a spike in new sightings to the west of their previously known range. This has been considered as a range extension. The current study reports new records of this species from Bilaspur District, Himachal Pradesh, India (which are the first records for the district), through systematic and opportunistic sampling. This raises the question of whether the purported range extension towards the west could instead be a range shift or vagrancy, and whether there is any shift in elevational ranges in the populations across their known range. Questions pertaining to spatial differences in elevational ranges and seasonal variation, across their range, also piqued our curiosity. Using data from academic sources (such as published literature and museum collections), supplemented by data from public participation in scientific research and personal observations, these research questions are addressed. The accuracy of results when using citizen science data is also explored using the same dataset, focused on the impact of method of extraction of coordinates, and elevation derived from it under different scenarios. It was discovered that there has not been a range shift (either longitudinal or latitudinal) and observations do not suggest vagrancy but a case of range extension. Other results indicated that there was no climb of population to higher elevations, no spatial differences in elevational ranges in the populations, or seasonal variation in activities across their range. It was also discovered that the method of data collection by, and extraction from, citizen science databases, can influence the accuracy of the results. Some problems involved in collecting data are discussed, and remedial solutions are suggested.