Search

Despite the availability of effective vaccines against pneumococcal disease, pneumococcus is a common bacterial cause of pneumonia, causing approximately 100,000 hospitalizations among U.S. adults per year. In addition, approximately 30,000 invasive pneumococcal disease (IPD) cases and 3,000 IPD deaths occur among U.S. adults each year. Previous health care provider surveys identified gaps in provider knowledge about and understanding of the adult pneumococcal vaccine recommendations, and pneumococcal vaccine coverage remains suboptimal. To assess the feasibility and acceptability domains of the Advisory Committee on Immunization Practices (ACIP) Evidence to Recommendations (EtR) framework, a health care provider knowledge and attitudes survey was conducted during September 28--October 10, 2022, by the Healthcare and Public Perceptions of Immunizations Survey Collaborative before the October 2022 ACIP meeting. Among 751 provider respondents, two thirds agreed or strongly agreed with the policy option under consideration to expand the recommendations for the new 20-valent pneumococcal conjugate vaccine (PCV20) to adults who had only received the previously recommended 13-valent pneumococcal conjugate vaccine (PCV13). Gaps in providers' knowledge and perceived challenges to implementing recommendations were identified and were included in ACIP's EtR framework discussions in late October 2022 when ACIP updated the recommendations for PCV20 use in adults. Currently, use of PCV20 is recommended for certain adults who have previously received PCV13, in addition to those who have never received a pneumococcal conjugate vaccine. The survey findings indicate a need to increase provider awareness and implementation of pneumococcal vaccination recommendations and to provide tools to assist with patient-specific vaccination guidance. Resources available to address the challenges to implementing pneumococcal vaccination recommendations include the PneumoRecs VaxAdvisor mobile app and other CDC-developed tools, including summary documents and overviews of vaccination schedules and CDC's strategic framework to increase confidence in vaccines and reduce vaccine-preventable diseases, Vaccinate with Confidence. [ABSTRACT FROM AUTHOR]

The article presents a study which examined the bias from differential depletion of susceptibles in vaccine effectiveness estimates in settings of waning. Topics discussed include simulation of an outbreak of a pathogen with SARS-CoV-2 characteristics, the occurrence of spurious waning under settings of true waning, and a potential area for future research.

Background Many severe acute respiratory syndrome coronavirus 2 infections have not been detected, reported, or isolated. For community testing programs to locate the most cases under limited testing resources, we developed and evaluated quantitative approaches for geographic targeting of increased coronavirus disease 2019 testing efforts. Methods For every week from December 5, 2021, to July 23, 2022, testing and vaccination data were obtained in ∼340 cities/communities in Los Angeles County, and models were developed to predict which cities/communities would have the highest test positivity 2 weeks ahead. A series of counterfactual scenarios were constructed to explore the additional number of cases that could be detected under targeted testing. Results The simplest model based on most recent test positivity performed nearly as well as the best model based on most recent test positivity and weekly tests per 100 persons in identifying communities that would maximize the average yield of cases per test in the following 2 weeks and almost as well as the perfect knowledge of the actual positivity 2 weeks ahead. In the counterfactual scenario, increasing testing by 1% 2 weeks ahead and allocating all tests to communities with the top 10% of predicted positivity would yield a 2% increase in detected cases. Conclusions Simple models based on current test positivity can predict which communities may have the highest positivity 2 weeks ahead and hence could be allocated with more testing resources. [ABSTRACT FROM AUTHOR]

WHO recommends mass drug administration (MDA) of the antibiotic azithromycin for children aged 1–11 months in areas with high rates of infant and child mortality. Notwithstanding the substantial potential benefits of lowering childhood mortality, MDA raises understandable concerns about exacerbating antibiotic resistance. In this study, we aimed to evaluate the use of MDA using both quantitative and ethical considerations. We performed a series of literature searches between July 2019 and June 2022. We first compared MDA with other uses of antibiotics using the standard metric of 'number needed to treat', and five additional criteria: (1) other widely accepted uses of anti-infectives (2) absolute use (i.e. total number), of antibiotics, (3) risk-benefit trade-off, (4) availability of short-term alternatives, and (5) the precedent for implementing similar interventions. We found that MDA falls well within a justifiable range when compared with widely accepted uses of antibiotics in terms of the number needed to treat. The other five criteria we considered provided further support for the use of MDA to prevent childhood mortality. Although better data on antibiotic use and resistance are needed, efforts to reduce antibiotic use and resistance should not start with halting MDA of azithromycin in the areas with the highest rates of childhood mortality. Improving data to inform this decision is critical. However, on the basis of the best evidence available, we believe that concerns regarding resistance should not thwart MDA; instead, MDA should be accompanied by robust plans to monitor its efficacy and changes in resistance levels. Similar considerations could be included in a framework for evaluating the benefits of antibiotics against the risk of resistance in other contexts. [ABSTRACT FROM AUTHOR]

The article focuses on the design of clinical encounters in mental health care, discussing strategies to balance patient safety with creating therapeutic spaces in psychiatric care settings. Topics include the shortened length of psychiatric inpatient stays, safety measures in psychiatric unit design, and the ethical considerations of how design and policies may impact patient recovery and willingness to seek care.

Using an agent-based model, we examined the impact of community prevalence, the Delta variant, staff vaccination coverage, and booster vaccines for residents on outbreak dynamics in nursing homes. Increased staff coverage and high booster vaccine effectiveness leads to fewer infections, but cumulative incidence is highly dependent on community transmission. [ABSTRACT FROM AUTHOR]

Recent studies have provided key information about SARS-CoV-2 vaccines' efficacy and effectiveness (VE). One important question that remains is whether the protection conferred by vaccines wanes over time. However, estimates over time are subject to bias from differential depletion of susceptible individuals between vaccinated and unvaccinated groups. We examined the extent to which biases occur under different scenarios and assessed whether serological testing has the potential to correct this bias. By identifying nonvaccine antibodies, these tests could identify individuals with prior infection. We found that in scenarios with high baseline VE, differential depletion of susceptible individuals created minimal bias in VE estimates, suggesting that any observed declines are likely not due to spurious waning alone. However, if baseline VE was lower, the bias for leaky vaccines (which reduce individual probability of infection given contact) was larger and should be corrected for by excluding individuals with past infection if the mechanism is known to be leaky. Conducting analyses both unadjusted and adjusted for past infection could give lower and upper bounds for the true VE. Studies of VE should therefore enroll individuals regardless of prior infection history but also collect information, ideally through serological testing, on this critical variable. [ABSTRACT FROM AUTHOR]

Background Nursing home residents and staff were included in the first phase of coronavirus disease 2019 vaccination in the United States. Because the primary trial endpoint was vaccine efficacy (VE) against symptomatic disease, there are limited data on the extent to which vaccines protect against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and the ability to infect others (infectiousness). Assumptions about VE against infection and infectiousness have implications for changes to infection prevention guidance for vaccinated populations, including testing strategies. Methods We use a stochastic agent-based Susceptible-Exposed-Infectious (Asymptomatic/Symptomatic)-Recovered model of a nursing home to simulate SARS-CoV-2 transmission. We model 3 scenarios, varying VE against infection, infectiousness, and symptoms, to understand the expected impact of vaccination in nursing homes, increasing staff vaccination coverage, and different screening testing strategies under each scenario. Results Increasing vaccination coverage in staff decreases total symptomatic cases in the nursing home (among staff and residents combined) in each VE scenario. In scenarios with 50% and 90% VE against infection and infectiousness, increasing staff coverage reduces symptomatic cases among residents. If vaccination only protects against symptoms, and asymptomatic cases remain infectious, increased staff coverage increases symptomatic cases among residents. However, this is outweighed by the reduction in symptomatic cases among staff. Higher frequency testing—more than once weekly—is needed to reduce total symptomatic cases if the vaccine has lower efficacy against infection and infectiousness, or only protects against symptoms. Conclusions Encouraging staff vaccination is not only important for protecting staff, but might also reduce symptomatic cases in residents if a vaccine confers at least some protection against infection or infectiousness. [ABSTRACT FROM AUTHOR]

• Use an empirical contact network to model mitigation of SARS-CoV-2 on campuses • Account for range of implementation challenges universities will face • Provide a flexible framework that can be used to assess intervention strategies Objectives Universities have turned to SARS-CoV-2 models to examine campus reopening strategies. While these studies have explored a variety of modeling techniques, none have used empirical data. Methods In this study, we use an empirical proximity network of college freshmen obtained using smartphone Bluetooth to simulate the spread of the virus. We investigate the role of immunization, testing, isolation, mask wearing, and social distancing in the presence of implementation challenges and imperfect compliance. Results We show that frequent testing could drastically reduce the spread of the virus if levels of immunity are low, but its effects are limited if immunity is more ubiquitous. Furthermore, moderate levels of mask wearing and social distancing could lead to additional reductions in cumulative incidence, but their benefit decreases rapidly as immunity and testing frequency increase. However, if immunity from vaccination is imperfect or declines over time, scenarios not studied here, frequent testing and other interventions may play more central roles. Conclusions Our findings suggest that although regular testing and isolation are powerful tools, they have limited benefit if immunity is high or other interventions are widely adopted. If universities can attain even moderate levels of vaccination, masking, and social distancing, they may be able to relax the frequency of testing to once every four weeks. [ABSTRACT FROM AUTHOR]

This article, which forms the second part of a two-part article in this volume, reflects on what we argue to be the most crucial element of any Linked Data enterprise: its community. The first section discusses a variety of funded and unfunded activities conducted under the aegis of Pelagios, and specifically two 'small grant' programmes that dramatically expanded its scope and reach. The second section describes the evolution of Pelagios as a 'community of practice', in which the work of the initiative, conceived of in terms of both method and tool development, has been facilitated by, and coordinated with, individuals and groups external to its investigative teams. This growing and maturing community has ultimately been realized in the foundation of a formal voluntary organization: the Pelagios Network Association. The paper concludes with brief reflections on some interlinked sister initiatives that have emerged from the Pelagios community and continue to support scholars and practitioners working in related fields. [ABSTRACT FROM AUTHOR]

In this article, we examine the making of research infrastructures for digital research. In line with many scholars in this field, we understand research infrastructures as deeply relational and adaptive systems that are embedded in research practice. Our aim was to identify the relevant context factors, actor constellations, organizational settings, and strategies which contribute to the evolution of a basic service into an actual infrastructure. To this end, we conducted thirty-three case studies of non-commercial and commercial research services along the research life cycle. By examining how these services emerge, we hope to gain a better understanding of the conditions and strategies to transform a service into an infrastructure. We are able to identify competitive disadvantages for publicly financed infrastructure projects with regard to the mode of implementation and the resources invested in development and marketing. We suggest that the results of this study are of practical relevance, especially for individuals, communities, and organizations wanting to create research infrastructures, as well as for funders and policy makers wanting to support innovative and sustainable infrastructures. [ABSTRACT FROM AUTHOR]

• SARS-CoV-2 vaccine trials did not directly estimate vaccine efficacy against transmission. • We describe an approach to estimate a lower bound of vaccine efficacy against transmission. • We estimate one dose of the Moderna vaccine reduces the potential for transmission by at least 61%. • We recommend separate analysis of tests triggered by symptoms vs. cross-sectional tests. Randomized controlled trials (RCTs) have shown high efficacy of multiple vaccines against SARS-CoV-2 disease (COVID-19), and recent studies have shown the vaccines are also effective against infection. Evidence for the effect of each of these vaccines on ability to transmit the virus is also beginning to emerge. We describe an approach to estimate these vaccines' effects on viral positivity, a prevalence measure which under the reasonable assumption that vaccinated individuals who become infected are no more infectious than unvaccinated individuals forms a lower bound on efficacy against transmission. Specifically, we recommend separate analysis of positive tests triggered by symptoms (usually the primary RCT outcome) and cross-sectional prevalence of positive tests obtained regardless of symptoms. The odds ratio of carriage for vaccine vs. placebo provides an unbiased estimate of vaccine effectiveness against viral positivity, under certain assumptions, and we show through simulations that likely departures from these assumptions will only modestly bias this estimate. Applying this approach to published data from the RCT of the Moderna vaccine, we estimate that one dose of vaccine reduces the potential for transmission by at least 61%, possibly considerably more. We describe how these approaches can be translated into observational studies of vaccine effectiveness. [ABSTRACT FROM AUTHOR]

In response to the SARS-CoV-2 pandemic, unprecedented travel restrictions and stay-at-home orders were enacted around the world. Ultimately, the public's response to announcements of lockdowns—defined as restrictions on both local movement or long distance travel—will determine how effective these kinds of interventions are. Here, we evaluate the effects of lockdowns on human mobility and simulate how these changes may affect epidemic spread by analyzing aggregated mobility data from mobile phones. We show that in 2020 following lockdown announcements but prior to their implementation, both local and long distance movement increased in multiple locations, and urban-to-rural migration was observed around the world. To examine how these behavioral responses to lockdown policies may contribute to epidemic spread, we developed a simple agent-based spatial model. Our model shows that this increased movement has the potential to increase seeding of the epidemic in less urban areas, which could undermine the goal of the lockdown in preventing disease spread. Lockdowns play a key role in reducing contacts and controlling outbreaks, but appropriate messaging surrounding their announcement and careful evaluation of changes in mobility are needed to mitigate the possible unintended consequences. [ABSTRACT FROM AUTHOR]

The extent and duration of immunity following infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are critical outstanding questions about the epidemiology of this novel virus, and studies are needed to evaluate the effects of serostatus on reinfection. Understanding the potential sources of bias and methods for alleviating biases in these studies is important for informing their design and analysis. Confounding by individual-level risk factors in observational studies like these is relatively well appreciated. Here, we show how geographic structure and the underlying, natural dynamics of epidemics can also induce noncausal associations. We take the approach of simulating serological studies in the context of an uncontrolled or controlled epidemic, under different assumptions about whether prior infection does or does not protect an individual against subsequent infection, and using various designs and analytical approaches to analyze the simulated data. We find that in studies assessing whether seropositivity confers protection against future infection, comparing seropositive persons with seronegative persons with similar time-dependent patterns of exposure to infection by stratifying or matching on geographic location and time of enrollment is essential in order to prevent bias. [ABSTRACT FROM AUTHOR]

Background: As COVID-19 continues to spread around the world, understanding how patterns of human mobility and connectivity affect outbreak dynamics, especially before outbreaks establish locally, is critical for informing response efforts. In Taiwan, most cases to date were imported or linked to imported cases.Methods: In collaboration with Facebook Data for Good, we characterized changes in movement patterns in Taiwan since February 2020, and built metapopulation models that incorporate human movement data to identify the high risk areas of disease spread and assess the potential effects of local travel restrictions in Taiwan.Results: We found that mobility changed with the number of local cases in Taiwan in the past few months. For each city, we identified the most highly connected areas that may serve as sources of importation during an outbreak. We showed that the risk of an outbreak in Taiwan is enhanced if initial infections occur around holidays. Intracity travel reductions have a higher impact on the risk of an outbreak than intercity travel reductions, while intercity travel reductions can narrow the scope of the outbreak and help target resources. The timing, duration, and level of travel reduction together determine the impact of travel reductions on the number of infections, and multiple combinations of these can result in similar impact.Conclusions: To prepare for the potential spread within Taiwan, we utilized Facebook's aggregated and anonymized movement and colocation data to identify cities with higher risk of infection and regional importation. We developed an interactive application that allows users to vary inputs and assumptions and shows the spatial spread of the disease and the impact of intercity and intracity travel reduction under different initial conditions. Our results can be used readily if local transmission occurs in Taiwan after relaxation of border control, providing important insights into future disease surveillance and policies for travel restrictions. [ABSTRACT FROM AUTHOR]

Background: Voluntary individual quarantine and voluntary active monitoring of contacts are core disease control strategies for emerging infectious diseases such as COVID-19. Given the impact of quarantine on resources and individual liberty, it is vital to assess under what conditions individual quarantine can more effectively control COVID-19 than active monitoring. As an epidemic grows, it is also important to consider when these interventions are no longer feasible and broader mitigation measures must be implemented.Methods: To estimate the comparative efficacy of individual quarantine and active monitoring of contacts to control severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we fit a stochastic branching model to reported parameters for the dynamics of the disease. Specifically, we fit a model to the incubation period distribution (mean 5·2 days) and to two estimates of the serial interval distribution: a shorter one with a mean serial interval of 4·8 days and a longer one with a mean of 7·5 days. To assess variable resource settings, we considered two feasibility settings: a high-feasibility setting with 90% of contacts traced, a half-day average delay in tracing and symptom recognition, and 90% effective isolation; and a low-feasibility setting with 50% of contacts traced, a 2-day average delay, and 50% effective isolation.Findings: Model fitting by sequential Monte Carlo resulted in a mean time of infectiousness onset before symptom onset of 0·77 days (95% CI -1·98 to 0·29) for the shorter serial interval, and for the longer serial interval it resulted in a mean time of infectiousness onset after symptom onset of 0·51 days (95% CI -0·77 to 1·50). Individual quarantine in high-feasibility settings, where at least 75% of infected contacts are individually quarantined, contains an outbreak of SARS-CoV-2 with a short serial interval (4·8 days) 84% of the time. However, in settings where the outbreak continues to grow (eg, low-feasibility settings), so too will the burden of the number of contacts traced for active monitoring or quarantine, particularly uninfected contacts (who never develop symptoms). When resources are prioritised for scalable interventions such as physical distancing, we show active monitoring or individual quarantine of high-risk contacts can contribute synergistically to mitigation efforts. Even under the shorter serial interval, if physical distancing reduces the reproductive number to 1·25, active monitoring of 50% of contacts can result in overall outbreak control (ie, effective reproductive number <1).Interpretation: Our model highlights the urgent need for more data on the serial interval and the extent of presymptomatic transmission to make data-driven policy decisions regarding the cost-benefit comparisons of individual quarantine versus active monitoring of contacts. To the extent that these interventions can be implemented, they can help mitigate the spread of SARS-CoV-2.Funding: National Institute of General Medical Sciences, National Institutes of Health. [ABSTRACT FROM AUTHOR]

This paper is concerned with the ethical aspects of museum metadata. These are not always immediately evident when working with the metadata related to museum objects, although, I will argue, they are embedded in the object, accumulated at each phase of its journey into the institution; and continue to accumulate while it is part of a collection. This takes place against a backdrop of new development and possibilities afforded by digital technologies for building connections between and across heritage collections online, which can result in these complicated metadata potentially entering the data ecosystem. This eventuality, I will argue, has ethical and technical implications which need to be considered and understood through the theoretical lenses of critical data studies, museum informatics and the growing calls from museum scholars and others to decolonisation of museum collections. Using a small collection of drawings from the Pitt Rivers Museum of Anthropology and World Archaeology at the University of Oxford, I will demonstrate how difficult museum metadata can be buried deep in museum documentation, and how this data, once brought to the surface by digitisation, can expose the trauma of a collection's origins. I will go on to ask whether the current models used to share heritage data online are appropriate mechanisms for materials with such sensitive histories, and ask how best to handle them in the increasingly digital future. [ABSTRACT FROM AUTHOR]

Forecasting the spatiotemporal spread of infectious diseases during an outbreak is an important component of epidemic response. However, it remains challenging both methodologically and with respect to data requirements, as disease spread is influenced by numerous factors, including the pathogen's underlying transmission parameters and epidemiological dynamics, social networks and population connectivity, and environmental conditions. Here, using data from Sierra Leone, we analyze the spatiotemporal dynamics of recent cholera and Ebola outbreaks and compare and contrast the spread of these two pathogens in the same population. We develop a simulation model of the spatial spread of an epidemic in order to examine the impact of a pathogen's incubation period on the dynamics of spread and the predictability of outbreaks. We find that differences in the incubation period alone can determine the limits of predictability for diseases with different natural history, both empirically and in our simulations. Our results show that diseases with longer incubation periods, such as Ebola, where infected individuals can travel farther before becoming infectious, result in more longdistance sparking events and less predictable disease trajectories, as compared to the more predictable wave-like spread of diseases with shorter incubation periods, such as cholera. [ABSTRACT FROM AUTHOR]

Patients often report experiencing boredom during inpatient psychiatry stays. Because patients' vulnerabilities and conditions can be exacerbated when they feel bored, this article considers ethical dimensions of inpatient units' designs that limit patients' autonomy or access to activities or interactions with others. This commentary on a case also considers whether and how boredom should be considered an iatrogenic harm and influence discharge planning. [ABSTRACT FROM AUTHOR]

Vaccine efficacy against susceptibility to infection (VE S ), regardless of symptoms, is an important endpoint of vaccine trials for pathogens with a high proportion of asymptomatic infection, because such infections may contribute to onward transmission and long-term sequelae, such as congenital Zika syndrome. However, estimating VE S is resource-intensive. We aimed to identify approaches for accurately estimating VE S when limited information is available and resources are constrained. We modeled an individually randomized vaccine trial by generating a network of individuals and simulating an epidemic. The disease natural history followed a "susceptible-exposed-infectious/symptomatic (or infectious/asymptomatic)-recovered" model. We then used 7 approaches to estimate VE S , and we also estimated vaccine efficacy against progression to symptoms (VE P ). A corrected relative risk and an interval-censored Cox model accurately estimate VE S and only require serological testing of participants once, while a Cox model using only symptomatic infections returns biased estimates. Only acquiring serological endpoints in a 10% sample and imputing the remaining infection statuses yields unbiased VE S estimates across values of the basic reproduction number (R 0) and accurate estimates of VE P for higher R 0 values. Identifying resource-preserving methods for accurately estimating VE S and VE P is important in designing trials for diseases with a high proportion of asymptomatic infection. [ABSTRACT FROM AUTHOR]

In a Policy Forum, Marc Lipsitch and colleagues discuss trial design issues in infectious disease outbreaks. [ABSTRACT FROM AUTHOR]

Although CoronaVac was the only Covid-19 vaccine adopted in the first months of the Brazilian vaccination campaign, randomized clinical trials to evaluate its efficacy in elderly adults were limited. In this study, we use routinely collected surveillance and SARS-CoV-2 vaccination and testing data comprising the population of the fifth largest city of Brazil to evaluate the effectiveness of CoronaVac in adults 60+ years old against severe outcomes. Using large observational databases on vaccination and surveillance data from the city of Fortaleza, Brazil, we defined a retrospective cohort including 324,302 eligible adults aged ≥60 years to evaluate the effectiveness of the CoronaVac vaccine. The cohort included individuals vaccinated between January 21, 2021, and August 31, 2021, who were matched with unvaccinated persons at the time of rollout following a 1:1 ratio according to baseline covariates of age, sex, and Human Development Index of the neighborhood of residence. Only Covid-19-related severe outcomes were included in the analysis: hospitalization, ICU admission, and death. Vaccine effectiveness for each outcome was calculated by using the risk ratio between the two groups, with the risk obtained by the Kaplan-Meier estimator. We obtained 62,643 matched pairs for assessing the effectiveness of the two-dose regimen of CoronaVac. The demographic profile of the matched population was statistically representative of the population of Fortaleza. Using the cumulative incidence as the risk associated with each group, starting at day 14 since the receipt of the second dose, we found an 82.3 % (95 % CI 66.3–93.9) effectiveness against Covid-19-related death, 68.4 % (95 % CI 42.3–86.4) against ICU admission, and 55.8 % (95 % CI 42.7–68.3) against hospital admission. Our results show that, despite critical delays in vaccine delivery and limited evidence in efficacy trial estimates, CoronaVac contributed to preventing deaths and severe morbidity due to Covid-19 in elderly adults. [ABSTRACT FROM AUTHOR]

A personal narrative is presented which explores the author's experience of protests center on Lake Street a commercial thoroughfare of South Minneapolis.

SARS-CoV-2 variants of concern exhibit varying degrees of transmissibility and, in some cases, escape from acquired immunity. Much effort has been devoted to measuring these phenotypes, but understanding their impact on the course of the pandemic—especially that of immune escape—has remained a challenge. Here, we use a mathematical model to simulate the dynamics of wild-type and variant strains of SARS-CoV-2 in the context of vaccine rollout and nonpharmaceutical interventions. We show that variants with enhanced transmissibility frequently increase epidemic severity, whereas those with partial immune escape either fail to spread widely or primarily cause reinfections and breakthrough infections. However, when these phenotypes are combined, a variant can continue spreading even as immunity builds up in the population, limiting the impact of vaccination and exacerbating the epidemic. These findings help explain the trajectories of past and present SARS-CoV-2 variants and may inform variant assessment and response in the future. [Display omitted] • Modeled impacts of different SARS-CoV-2 variants across range of scenarios • Enhanced transmissibility leads to more infections and narrows margin for control • Partial immune escape often remains rare and may not limit vaccination impact • Impact of immune escape is much greater when paired with enhanced transmissibility A modeling approach looking at the impact of SARS-CoV-2 variants with partial immune escape and/or increased transmissibility suggests that enhanced transmissibility is more strongly linked to epidemic severity and narrows the margin for control. [ABSTRACT FROM AUTHOR]

Legionnaires disease is a serious infection acquired by inhalation of water droplets from human-made building water systems that contain Legionella bacteria. On July 11 and 12, 2022, Napa County Public Health (NCPH) in California received reports of three positive urinary antigen tests for Legionella pneumophila serogroup 1 in the town of Napa. By July 21, six Legionnaires disease cases had been confirmed among Napa County residents, compared with a baseline of one or two cases per year. NCPH requested assistance from the California Department of Public Health (CDPH) and CDC to aid in the investigations. Close temporal and geospatial clustering permitted a focused environmental sampling strategy of high-risk facilities which, coupled with whole genome sequencing results from samples and investigation of water system maintenance, facilitated potential linking of the outbreak with an environmental source. NCPH, with technical support from CDC and CDPH, instructed and monitored remediation practices for all environmental locations that tested positive for Legionella. The investigation response to this community outbreak illustrates the importance of interdisciplinary collaboration by public health agencies, laboratory support, timely communication with the public, and cooperation of managers of potentially implicated water systems. Timely identification of possible sources, sampling, and remediation of any facility testing positive for Legionella is crucial to interrupting further transmission. [ABSTRACT FROM AUTHOR]

Background: Mobile health (m-health) tools are a promising strategy to facilitate the work of community health workers (CHWs) in low- and middle-income countries (LMICs). Despite their potential value, little is known about CHWs' experiences working with m-health tools in their outreach activities with community members.Methods: To understand the benefits of and barriers to using m-health tools for CHWs, we conducted semi-structured interviews with 57 CHWs employed in six primary care centers in São Paulo, Brazil. All CHWs had experience using a cell phone application called Geohealth for collecting health and demographic data of community members. We assessed their experiences using Geohealth and recommendations for improvements.Results: CHWs described key benefits of using Geohealth as helping them save time with bureaucratic paperwork, organizing the data that they needed to collect, and by replacing sheaves of paper, reducing the weight that they carried in the field. However, there were many technical and social barriers to the successful adoption of the m-health tool. Key among these were poor quality hardware, faulty software programs, and negative community member perceptions of the m-health program. The CHWs provided valuable input as to how Geohealth could be improved to fit their needs.Conclusion: m-health tools have the potential to facilitate the work of CHWs in LMICs. However, such tools must be designed and implemented thoughtfully. Technical barriers related to both hardware and software must be anticipated and addressed to maximize their efficiency and successful adoption. CHW input on the design of the tool should be sought to maximize its utility and minimize barriers to use. [ABSTRACT FROM AUTHOR]

Background: Coronavirus disease 2019 (COVID-19) has caused a heavy disease burden globally. The impact of process and timing of data collection on the accuracy of estimation of key epidemiological distributions are unclear. Because infection times are typically unobserved, there are relatively few estimates of generation time distribution.Methods: We developed a statistical framework to jointly estimate generation time and incubation period from human-to-human transmission pairs, accounting for sampling biases. We applied the framework on 80 laboratory-confirmed human-to-human transmission pairs in China. We further inferred the infectiousness profile, serial interval distribution, proportions of presymptomatic transmission, and basic reproduction number (R0) for COVID-19.Results: The estimated mean incubation period was 4.8 days (95% confidence interval [CI], 4.1-5.6), and mean generation time was 5.7 days (95% CI, 4.8-6.5). The estimated R0 based on the estimated generation time was 2.2 (95% CI, 1.9-2.4). A simulation study suggested that our approach could provide unbiased estimates, insensitive to the width of exposure windows.Conclusions: Properly accounting for the timing and process of data collection is critical to have correct estimates of generation time and incubation period. R0 can be biased when it is derived based on serial interval as the proxy of generation time. [ABSTRACT FROM AUTHOR]

This article provides a short history of the methods and tools developed by the Pelagios initiative: a series of seven projects dedicated to linking digital historical resources based on the geographic places to which they relate and refer. The first section of the article situates the work within the wider field of semantic and geospatial technologies. It then describes the initial development of the Pelagios methodology through an open consortium of ancient Mediterranean data resource providers, in association with the Pleiades Gazetteer of the Ancient World. The second section details how this approach was expanded for application in other historical and archaeological periods and domains, with a special focus on tool development. In particular, we reflect on the development and application of Recogito, an online semantic geoannotation platform that allows anyone to identify place references within historical texts, imagery or datasets. [ABSTRACT FROM AUTHOR]

Estimation of the effective reproductive number Rt is important for detecting changes in disease transmission over time. During the Coronavirus Disease 2019 (COVID-19) pandemic, policy makers and public health officials are using Rt to assess the effectiveness of interventions and to inform policy. However, estimation of Rt from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make recommendations. For near real-time estimation of Rt, we recommend the approach of Cori and colleagues, which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis, are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for retrospective analyses of how individuals infected at different time points contributed to the spread. We advise caution when using methods derived from the approach of Bettencourt and Ribeiro, as the resulting Rt estimates may be biased if the underlying structural assumptions are not met. Two key challenges common to all approaches are accurate specification of the generation interval and reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in Rt estimation. Author summary: The effective reproductive number Rt is a key epidemic parameter used to assess whether an epidemic is growing, shrinking, or holding steady. Rt estimates can be used as a near real-time indicator of epidemic growth or to assess the effectiveness of interventions. But due to delays between infection and case observation, estimating Rt in near real time, and correctly inferring the timing of changes in Rt, is challenging. Here, we provide an overview of challenges and best practices for accurate and timely Rt estimation. [ABSTRACT FROM AUTHOR]

To rapidly evaluate the safety and efficacy of COVID-19 vaccine candidates, prioritizing vaccine trial sites in areas with high expected disease incidence can speed endpoint accrual and shorten trial duration. Mathematical and statistical forecast models can inform the process of site selection, integrating available data sources and facilitating comparisons across locations. We recommend the use of ensemble forecast modeling – combining projections from independent modeling groups – to guide investigators identifying suitable sites for COVID-19 vaccine efficacy trials. We describe an appropriate structure for this process, including minimum requirements, suggested output, and a user-friendly tool for displaying results. Importantly, we advise that this process be repeated regularly throughout the trial, to inform decisions about enrolling new participants at existing sites with waning incidence versus adding entirely new sites. These types of data-driven models can support the implementation of flexible efficacy trials tailored to the outbreak setting. [ABSTRACT FROM AUTHOR]

• Adapting the WHO protocol for the treatment of severe malaria to a local context resulted in a marked reduction in mortality from severe malaria in pediatric cases • Using a point-of-care decision support tool is a sustainable way to build capacity in nursing and pharmacy cadres in Sierra Leone • Standardizing admissions, treatments, and communication among pediatric ward staff also created efficiency in the workday, which is a valuable benefit in settings with human resource constraints To reduce childhood mortality from severe malaria by implementing the World Health Organization's standardized malarial treatment protocol. Observational study comparing the mortality rate from malaria before and after the intervention. Inpatient pediatric ward in a district referral hospital of Sierra Leone. A total of 1298 pediatric patients (ages 0-13 years, male and female) received the intervention, representing 100% of the pediatric patients admitted with severe malaria during the dates of implementation (there were no exclusion criteria). We implemented the World Health Organization's standardized malarial protocol on September 30, 2015. Based on monthly run reports of mortality and root cause analysis, we adapted the malaria protocol by adding sublingual glucose as a treatment to target hypoglycemia complications in March 2016. The primary outcome was a change in monthly percent mortality from severe malaria, and the secondary outcome was the percent of mortality attributed to hypoglycemia. The monthly average percent mortality from severe malaria dropped from 9% to 3.6% after the intervention, which was borderline statistically significant (p 0.06, CI 95% 1.5 to 5.6). The secondary outcome, percent of malarial deaths attributable to hypoglycemia via chart reviews, dropped from 83% to 44% across the study period. There was an increase in the average number of admissions for severe malaria from 71 to 153 children per month in the second half of the year (range from 49-212 per month). Implementing the WHO malaria treatment protocol with bedside tracking of protocol steps reduced malaria mortality and improved our ward's efficiency without adding any human or medical resources. [ABSTRACT FROM AUTHOR]

(B-C) The case reproductive number is defined as the average number of new infections that an individual who becomes infected on day ti (green arrows in B) or symptomatic on day ts (yellow arrows in C) will eventually go on to cause (blue downward arrows in B and C). There is an error in the caption for Fig 1, "Instantaneous reproductive number as estimated by the method of Cori et al. vs. cohort reproductive number estimated by Wallinga and Teunis" sentence four. [Extracted from the article]