The impact of anthropogenic climate change on pediatric viral diseases.

The adverse effects of climate change on human health are unfolding in real time. Environmental fragmentation is amplifying spillover of viruses from wildlife to humans. Increasing temperatures are expanding mosquito and tick habitats, introducing vector-borne viruses into immunologically susceptible populations. More frequent flooding is spreading water-borne viral pathogens, while prolonged droughts reduce regional capacity to prevent and respond to disease outbreaks with adequate water, sanitation, and hygiene resources. Worsening air quality and altered transmission seasons due to an increasingly volatile climate may exacerbate the impacts of respiratory viruses. Furthermore, both extreme weather events and long-term climate variation are causing the destruction of health systems and large-scale migrations, reshaping health care delivery in the face of an evolving global burden of viral disease. Because of their immunological immaturity, differences in physiology (e.g., size), dependence on caregivers, and behavioral traits, children are particularly vulnerable to climate change. This investigation into the unique pediatric viral threats posed by an increasingly inhospitable world elucidates potential avenues of targeted programming and uncovers future research questions to effect equitable, actionable change. IMPACT: A review of the effects of climate change on viral threats to pediatric health, including zoonotic, vector-borne, water-borne, and respiratory viruses, as well as distal threats related to climate-induced migration and health systems. A unique focus on viruses offers a more in-depth look at the effect of climate change on vector competence, viral particle survival, co-morbidities, and host behavior. An examination of children as a particularly vulnerable population provokes programming tailored to their unique set of vulnerabilities and encourages reflection on equitable climate adaptation frameworks.

Exploring Coronavirus Disease 2019 Vaccine Hesitancy on Twitter Using Sentiment Analysis and Natural Language Processing Algorithms.

BACKGROUND: Vaccination can help control the coronavirus disease 2019 (COVID-19) pandemic but is undermined by vaccine hesitancy. Social media disseminates information and misinformation regarding vaccination. Tracking and analyzing social media vaccine sentiment could better prepare health professionals for vaccination conversations and campaigns. METHODS: A real-time big data analytics framework was developed using natural language processing sentiment analysis, a form of artificial intelligence. The framework ingests, processes, and analyzes tweets for sentiment and content themes, such as natural health or personal freedom, in real time. A later dataset evaluated the relationship between Twitter sentiment scores and vaccination rates in the United States. RESULTS: The real-time analytics framework showed a widening gap in sentiment with more negative sentiment after vaccine rollout. After rollout, using a static dataset, an increase in positive sentiment was followed by an increase in vaccination. Lag cross-correlation analysis across US regions showed evidence that once all adults were eligible for vaccination, the sentiment score consistently correlated with vaccination rate with a lag of around 1 week. The Granger causality test further demonstrated that tweet sentiment scores may help predict vaccination rates. CONCLUSIONS: Social media has influenced the COVID-19 response through valuable information and misinformation and distrust. This tool was used to collect and analyze tweets at scale in real time to study sentiment and key terms of interest. Separate tweet analysis showed that vaccination rates tracked regionally with Twitter vaccine sentiment and might forecast changes in vaccine uptake and/or guide targeted social media and vaccination strategies. Further work is needed to analyze the interplay between specific populations, vaccine sentiment, and vaccination rates.

Prognostic Indicators for Ebola Patient Survival.

To determine whether 2 readily available indicators predicted survival among patients with Ebola virus disease in Sierra Leone, we evaluated information for 216 of the 227 patients in Bo District during a 4-month period. The indicators were time from symptom onset to healthcare facility admission and quantitative real-time reverse transcription PCR cycle threshold (Ct), a surrogate for viral load, in first Ebola virus-positive blood sample tested. Of these patients, 151 were alive when detected and had reported healthcare facility admission dates and Ct values available. Time from symptom onset to healthcare facility admission was not associated with survival, but viral load in the first Ebola virus-positive blood sample was inversely associated with survival: 52 (87%) of 60 patients with a Ct of >24 survived and 20 (22%) of 91 with a Ct of <24 survived. Ct values may be useful for clinicians making treatment decisions or managing patient or family expectations.

Assessing ventilation through ambient carbon dioxide concentrations across multiple healthcare levels in Ghana.

Infection prevention and control (IPC) measures safeguard primary healthcare systems, especially as the infectious disease landscape evolves due to climate and environmental change, increased global mobility, and vaccine hesitancy and inequity, which can introduce unexpected pathogens. This study explores the importance of an "always-on," low-cost IPC approach, focusing on the role of natural ventilation in health facilities, particularly in low-resource settings. Ambient carbon dioxide (CO2) levels are increasingly used as a measure of ventilation effectiveness allowing for spot checks and targeted ventilation improvements. Data were collected through purposive sampling in Northern Ghana over a three-month period. Levels of CO2 ppm (parts per million) were measured by a handheld device in various healthcare settings, including Community-Based Health Planning and Services (CHPS) facilities, municipal and teaching hospitals, and community settings to assess ventilation effectiveness. Analyses compared CO2 readings in community and hospital settings as well as in those settings with and without natural ventilation. A total of 40 facilities were evaluated in this study; 90% were healthcare facilities and 75% had natural ventilation (with an open window, door or wall). Facilities that relied on natural ventilation were mostly community health centers (60% vs 0%) and more commonly had patients present (83% vs 40%) compared with facilities without natural ventilation. Facilities with natural ventilation had significantly lower CO2 concentrations (CO2 ppm: 663 vs 1378, p = 0.0043) and were more likely to meet international thresholds of CO2 < 800 ppm (87% vs 10%, p = <0.0001) and CO2 < 1000 ppm (97% vs 20%, p = <0.0001). The adjusted odds ratio of low CO2 in the natural facilities compared with non-natural were: odds ratios, OR (95% CI): 21.7 (1.89, 247) for CO2 < 800 ppm, and 16.8 (1.55, 183) for CO2 < 1000 ppm. Natural ventilation in these facilities was consistently significantly associated with higher likelihood of low CO2 concentrations. Improved ventilation represents one cost-effective layer of IPC. This study highlights the continuing role natural ventilation can play in health facility design in community health care clinics. Most health facilities met standard CO2 thresholds, particularly in community health facilities. Further research is needed to optimize the use of natural ventilation. The use of a handheld devices to track a simple metric, CO2 levels, could improve appreciation of ventilation among healthcare workers and public health professionals and allow for them to target improvements. This study highlights potential lessons in the built environment of community primary health facilities as a blueprint for low-cost, integrated multi-layer IPC measures to mitigate respiratory illness and anticipate future outbreaks.

HIV and Schistosoma haematobium prevalences correlate in sub-Saharan Africa.

OBJECTIVE: Epidemiological studies have observed that genital schistosomiasis increases the risk of HIV infection in Africa. We analysed the correlation between Schistosoma haematobium prevalence and HIV prevalence across sub-Saharan African countries. DESIGN: Regression analysis of prevalence of HIV and S. haematobium across sub-Saharan African countries. METHODS: Using compiled country-level S. haematobium prevalence, HIV prevalence and other demographic and economic data from published sources, we applied univariate and multivariate regression models to assess the correlations between S. haematobium prevalence and HIV prevalence while controlling for risk factors associated with each infection. RESULTS: In 43 sub-Saharan African countries, the mean prevalence of S. haematobium was 22.4% [standard deviation (SD): 9.8%] and for HIV was 6.21% (SD: 5.71%). In multivariate analysis, adjusted for prevalence of male circumcision, years since a country's first HIV/AIDS diagnosis, geographical region and immunization coverage, each S. haematobium infection per 100 individuals was associated with a 2.9% (95% CI: 0.2-5.8%) relative increase in HIV prevalence. S. haematobium was not associated with Schistosoma mansoni, HSV-2, hepatitis C, malaria or syphilis. CONCLUSIONS: Schistosoma haematobium prevalence was associated with HIV prevalence in sub-Saharan Africa. Controlling S. haematobium may be an effective means of reducing HIV transmission in sub-Saharan Africa.

To explain or not to explain?-Artificial intelligence explainability in clinical decision support systems.

Explainability for artificial intelligence (AI) in medicine is a hotly debated topic. Our paper presents a review of the key arguments in favor and against explainability for AI-powered Clinical Decision Support System (CDSS) applied to a concrete use case, namely an AI-powered CDSS currently used in the emergency call setting to identify patients with life-threatening cardiac arrest. More specifically, we performed a normative analysis using socio-technical scenarios to provide a nuanced account of the role of explainability for CDSSs for the concrete use case, allowing for abstractions to a more general level. Our analysis focused on three layers: technical considerations, human factors, and the designated system role in decision-making. Our findings suggest that whether explainability can provide added value to CDSS depends on several key questions: technical feasibility, the level of validation in case of explainable algorithms, the characteristics of the context in which the system is implemented, the designated role in the decision-making process, and the key user group(s). Thus, each CDSS will require an individualized assessment of explainability needs and we provide an example of how such an assessment could look like in practice.

Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients.

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does "trustworthy AI" mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic.

COVID-19 early-alert signals using human behavior alternative data.

Google searches create a window into population-wide thoughts and plans not just of individuals, but populations at large. Since the outbreak of COVID-19 and the non-pharmaceutical interventions introduced to contain it, searches for socially distanced activities have trended. We hypothesize that trends in the volume of search queries related to activities associated with COVID-19 transmission correlate with subsequent COVID-19 caseloads. We present a preliminary analytics framework that examines the relationship between Google search queries and the number of newly confirmed COVID-19 cases in the United States. We designed an experimental tool with search volume indices to track interest in queries related to two themes: isolation and mobility. Our goal was to capture the underlying social dynamics of an unprecedented pandemic using alternative data sources that are new to epidemiology. Our results indicate that the net movement index we defined correlates with COVID-19 weekly new case growth rate with a lag of between 10 and 14 days for the United States at-large, as well as at the state level for 42 out of 50 states with the exception of 8 states (DE, IA, KS, NE, ND, SD, WV, WY) from March to June 2020. In addition, an increasing caseload was seen over the summer in some southern US states. A sharp rise in mobility indices was followed by a sharp increase, respectively, in the case growth data, as seen in our case study of Arizona, California, Florida, and Texas. A sharp decline in mobility indices is often followed by a sharp decline, respectively, in the case growth data, as seen in our case study of Arizona, California, Florida, Texas, and New York. The digital epidemiology framework presented here aims to discover predictors of the pandemic's curve, which could supplement traditional predictive models and inform early warning systems and public health policies.

Comorbidity and clinical factors associated with COVID-19 critical illness and mortality at a large public hospital in New York City in the early phase of the pandemic (March-April 2020).

BACKGROUND: Despite evidence of socio-demographic disparities in outcomes of COVID-19, little is known about characteristics and clinical outcomes of patients admitted to public hospitals during the COVID-19 outbreak. OBJECTIVE: To assess demographics, comorbid conditions, and clinical factors associated with critical illness and mortality among patients diagnosed with COVID-19 at a public hospital in New York City (NYC) during the first month of the COVID-19 outbreak. DESIGN: Retrospective chart review of patients diagnosed with COVID-19 admitted to NYC Health + Hospitals / Bellevue Hospital from March 9th to April 8th, 2020. RESULTS: A total of 337 patients were diagnosed with COVID-19 during the study period. Primary analyses were conducted among those requiring supplemental oxygen (n = 270); half of these patients (135) were admitted to the intensive care unit (ICU). A majority were male (67.4%) and the median age was 58 years. Approximately one-third (32.6%) of hypoxic patients managed outside the ICU required non-rebreather or non-invasive ventilation. Requirement of renal replacement therapy occurred in 42.3% of ICU patients without baseline end-stage renal disease. Overall, 30-day mortality among hypoxic patients was 28.9% (53.3% in the ICU, 4.4% outside the ICU). In adjusted analyses, risk factors associated with mortality included dementia (adjusted risk ratio (aRR) 2.11 95%CI 1.50-2.96), age 65 or older (aRR 1.97, 95%CI 1.31-2.95), obesity (aRR 1.37, 95%CI 1.07-1.74), and male sex (aRR 1.32, 95%CI 1.04-1.70). CONCLUSION: COVID-19 demonstrated severe morbidity and mortality in critically ill patients. Modifications in care delivery outside the ICU allowed the hospital to effectively care for a surge of critically ill and severely hypoxic patients.

Déjà vu: Stimulating open drug discovery for SARS-CoV-2.

In the past decade we have seen two major Ebola virus outbreaks in Africa, the Zika virus in Brazil and the Americas and the current pandemic of coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). There is a strong sense of déjà vu because there are still no effective treatments. In the COVID-19 pandemic, despite being a new virus, there are already drugs suggested as active in in vitro assays that are being repurposed in clinical trials. Promising SARS-CoV-2 viral targets and computational approaches are described and discussed. Here, we propose, based on open antiviral drug discovery approaches for previous outbreaks, that there could still be gaps in our approach to drug discovery.

Modelling the impact of migration on the HIV epidemic in South Africa.

OBJECTIVE: To use observed data to develop a mathematical model that estimates the impact of migration on the spread of HIV in South Africa. METHODS: A deterministic mathematical model was designed to evaluate the dynamic interactions between mobility, sexual behaviour, HIV, and sexually transmitted infections. The model was based on a population study of 488 adults, which included male migrants, male non-migrants and their rural partners in KwaZulu/Natal, South Africa. RESULTS: The model predicted that the impact of migration depends upon the epidemic's stage and the pattern of migration. Early in the epidemic, frequent migration between populations with different HIV prevalence rates accelerated HIV spread; however, local sexual risk behaviour determined the eventual scale of the epidemic. If migration is coupled with increased sexual risk behaviour by migrant men, as has been reported in the South African communities studied, HIV prevalence would increase 10 times among migrants' female partners (1.8 to 19%). In contrast, if migration were to occur infrequently, with migration-associated risk behaviour assumed to be at current levels, the predicted epidemic would be one fifth that currently observed (2.8 versus 15.1%). CONCLUSIONS: Migration primarily influences HIV spread by increasing high-risk sexual behaviour, rather than by connecting areas of low and high risk. Frequent return of migrants is an important risk factor when coupled with increased sexual risk behaviour. Accordingly, intervention programmes in South Africa need to target the sexual behaviour of short-term migrants specifically, even though these individuals may be more difficult to identify.

Case Report: A Cluster of Three Leptospirosis Cases in a New York City Abattoir and an Unusual Complication in the Index Case.

We report the case of a 46-year-old male abattoir worker who developed myalgias, shortness of breath, and irritability 2 weeks after sustaining a laceration to the hand with a knife at work. During his hospital evaluation for septic shock he was noted to be febrile, hypotensive, profoundly jaundiced with aseptic meningitis, and renal failure, and was diagnosed with Leptospirosis interrogans infection confirmed by serum and urine polymerase chain reaction. After standard antibiotic therapy and recovery from severe clinical illness, he developed unilateral orchitis with pyuria secondary to leptospirosis, a well-established complication in the veterinary literature, but of which we offer the first report in humans in the English literature. The case presented was also the index case that uncovered a cluster outbreak of leptospirosis in New York City during the winter of 2016-2017, involving a total of three patients who lived or worked within a block of the abattoir. Two patients survived whereas the third died of pulmonary hemorrhage shortly after seeking medical care.

Use of a community-led prevention strategy to enhance behavioral changes towards Ebola virus disease prevention: a qualitative case study in Western Côte d'Ivoire.

BACKGROUND: Starting in December 2013, the Ebola virus disease (EVD) epidemic spread in West Africa through five countries (Sierra Leone, Liberia, Guinea, Nigeria, and Mali), killing over 11,300 people. In partnership with Côte d'Ivoire's Ministry of Health, the International Rescue Committee instigated a community-led strategy aimed at promoting behavior change in order to prevent potential Ebola outbreaks in the country. The strategy was implemented in Western districts bordering Liberia, Guinea, and Mali. This study aims to analyze the community-led strategy, to document lessons learned from the experience, and to capitalize on the achievements. METHODS: A case study in four districts of Western Côte d'Ivoire, i.e. Biankouma, Danané, Odienné and Touba districts was carried out. Qualitative data in 12 villages (i.e., three villages per district) was collected from 62 healthcare workers, community leaders, and ordinary community members. Data was de-identified, coded and analyzed using a thematic approach. RESULTS: The community-led strategy was socially accepted in the villages. Even though some community leaders reported that sensitization had been, at times, constrained by a lack of equipment, the people interviewed demonstrated accurate understanding of information about prevention practices. Some practices were easily adopted, while others remained difficult to implement (e.g., ensuring safe and dignified dead body management). CONCLUSION: This research demonstrates that sensitization efforts led by well-integrated and respected community leaders can be conducive of behavior change. Lessons learned from the community-led strategy could be applied to future disease outbreaks.

Illustrating and homology modeling the proteins of the Zika virus.

The Zika virus (ZIKV) is a flavivirus of the family Flaviviridae, which is similar to dengue virus, yellow fever and West Nile virus. Recent outbreaks in South America, Latin America, the Caribbean and in particular Brazil have led to concern for the spread of the disease and potential to cause Guillain-Barré syndrome and microcephaly. Although ZIKV has been known of for over 60 years there is very little in the way of knowledge of the virus with few publications and no crystal structures. No antivirals have been tested against it either in vitro or in vivo. ZIKV therefore epitomizes a neglected disease. Several suggested steps have been proposed which could be taken to initiate ZIKV antiviral drug discovery using both high throughput screens as well as structure-based design based on homology models for the key proteins. We now describe preliminary homology models created for NS5, FtsJ, NS4B, NS4A, HELICc, DEXDc, peptidase S7, NS2B, NS2A, NS1, E stem, glycoprotein M, propeptide, capsid and glycoprotein E using SWISS-MODEL. Eleven out of 15 models pass our criteria for selection. While a ZIKV glycoprotein E homology model was initially described in the immature conformation as a trimer, we now describe the mature dimer conformer which allowed the construction of an illustration of the complete virion. By comparing illustrations of ZIKV based on this new homology model and the dengue virus crystal structure we propose potential differences that could be exploited for antiviral and vaccine design. The prediction of sites for glycosylation on this protein may also be useful in this regard. While we await a cryo-EM structure of ZIKV and eventual crystal structures of the individual proteins, these homology models provide the community with a starting point for structure-based design of drugs and vaccines as well as a for computational virtual screening.

Open drug discovery for the Zika virus.

The Zika virus (ZIKV) outbreak in the Americas has caused global concern that we may be on the brink of a healthcare crisis. The lack of research on ZIKV in the over 60 years that we have known about it has left us with little in the way of starting points for drug discovery. Our response can build on previous efforts with virus outbreaks and lean heavily on work done on other flaviviruses such as dengue virus. We provide some suggestions of what might be possible and propose an open drug discovery effort that mobilizes global science efforts and provides leadership, which thus far has been lacking. We also provide a listing of potential resources and molecules that could be prioritized for testing as in vitro assays for ZIKV are developed. We propose also that in order to incentivize drug discovery, a neglected disease priority review voucher should be available to those who successfully develop an FDA approved treatment. Learning from the response to the ZIKV, the approaches to drug discovery used and the success and failures will be critical for future infectious disease outbreaks.

FDA approved drugs as potential Ebola treatments.

In the search for treatments for the Ebola Virus, multiple screens of FDA drugs have led to the identification of several with promising in vitro activity. These compounds were not originally developed as antivirals and some have been further tested in mouse in vivo models. We put forward the opinion that some of these drugs could be evaluated further and move into the clinic as they are already FDA approved and in many cases readily available. This may be important if there is a further outbreak in future and no other therapeutic is available.

Finding small molecules for the 'next Ebola'.

The current Ebola virus epidemic may provide some suggestions of how we can better prepare for the next pathogen outbreak. We propose several cost effective steps that could be taken that would impact the discovery and use of small molecule therapeutics including: 1. text mine the literature, 2. patent assignees and/or inventors should openly declare their relevant filings, 3. reagents and assays could be commoditized, 4. using manual curation to enhance database links, 5. engage database and curation teams, 6. consider open science approaches, 7. adapt the "box" model for shareable reference compounds, and 8. involve the physician's perspective.

A common feature pharmacophore for FDA-approved drugs inhibiting the Ebola virus.

We are currently faced with a global infectious disease crisis which has been anticipated for decades. While many promising biotherapeutics are being tested, the search for a small molecule has yet to deliver an approved drug or therapeutic for the Ebola or similar filoviruses that cause haemorrhagic fever. Two recent high throughput screens published in 2013 did however identify several hits that progressed to animal studies that are FDA approved drugs used for other indications. The current computational analysis uses these molecules from two different structural classes to construct a common features pharmacophore. This ligand-based pharmacophore implicates a possible common target or mechanism that could be further explored. A recent structure based design project yielded nine co-crystal structures of pyrrolidinone inhibitors bound to the viral protein 35 (VP35). When receptor-ligand pharmacophores based on the analogs of these molecules and the protein structures were constructed, the molecular features partially overlapped with the common features of solely ligand-based pharmacophore models based on FDA approved drugs. These previously identified FDA approved drugs with activity against Ebola were therefore docked into this protein. The antimalarials chloroquine and amodiaquine docked favorably in VP35. We propose that these drugs identified to date as inhibitors of the Ebola virus may be targeting VP35. These computational models may provide preliminary insights into the molecular features that are responsible for their activity against Ebola virus in vitro and in vivo and we propose that this hypothesis could be readily tested.

Effect of the one-child policy on influenza transmission in China: a stochastic transmission model.

BACKGROUND: China's one-child-per-couple policy, introduced in 1979, led to profound demographic changes for nearly a quarter of the world's population. Several decades later, the consequences include decreased fertility rates, population aging, decreased household sizes, changes in family structure, and imbalanced sex ratios. The epidemiology of communicable diseases may have been affected by these changes since the transmission dynamics of infectious diseases depend on demographic characteristics of the population. Of particular interest is influenza because China and Southeast Asia lie at the center of a global transmission network of influenza. Moreover, changes in household structure may affect influenza transmission. Is it possible that the pronounced demographic changes that have occurred in China have affected influenza transmission? METHODS AND FINDINGS: To address this question, we developed a continuous-time, stochastic, individual-based simulation model for influenza transmission. With this model, we simulated 30 years of influenza transmission and compared influenza transmission rates in populations with and without the one-child policy control. We found that the average annual attack rate is reduced by 6.08% (SD 2.21%) in the presence of the one-child policy compared to a population in which no demographic changes occurred. There was no discernible difference in the secondary attack rate, -0.15% (SD 1.85%), between the populations with and without a one-child policy. We also forecasted influenza transmission over a ten-year time period in a population with a two-child policy under a hypothesis that a two-child-per-couple policy will be carried out in 2015, and found a negligible difference in the average annual attack rate compared to the population with the one-child policy. CONCLUSIONS: This study found that the average annual attack rate is slightly lowered in a population with a one-child policy, which may have resulted from a decrease in household size and the proportion of children in the population.

Dynamically modeling SARS and other newly emerging respiratory illnesses: past, present, and future.

The emergence and rapid global spread of the severe acute respiratory syndrome (SARS) coronavirus in 2002-2003 prompted efforts by modelers to characterize SARS epidemiology and inform control policies. We overview and discuss models for emerging infectious diseases (EIDs), provide a critical survey of SARS modeling literature, and discuss promising future directions for research. We reconcile discrepancies between published estimates of the basic reproductive number R0 for SARS (a crucial epidemiologic parameter), discuss insights regarding SARS control measures that have emerged uniquely from a modeling approach, and argue that high priorities for future modeling of SARS and similar respiratory EIDs should include informing quarantine policy and better understanding the impact of population heterogeneity on transmission patterns.