Drivers of Emerging Infectious Disease Events as a Framework for Digital Detection

A protocol and a data-based prediction to investigate virus spillover at the wildlife interface in human-dominated and protected habitats in Thailand: The Spillover Interface project

The Spillover Interface Project aims at assessing the encounter of wildlife, domestic animals, and humans along a landscape gradient from a protected area to a residential community, through areas of reforestation and agricultural land. Here, we present the protocols of the project that combine virus screening in humans, bats, rodents and dogs with camera trapping, land-use characterization, and network analyses. The project is taking place in the sub-district of Saen Thong (Nan Province, Thailand) in collaboration with local communities, the District Public Health Office, and Nanthaburi National Park. To formulate a predictive hypothesis for the Spillover Interface Project, we assess the wildlife diversity and their viral diversity that could be observed in Saen Thong through a data science analysis approach. Potential mammalian species are estimated using data from the International Union for Conservation of Nature (IUCN) and their associated viral diversity from a published open database. A network analysis approach is used to represent and quantify the transmission of the potential viruses hosted by the mammals present in Saen Thong, according to the IUCN. A total of 57 viruses are expected to be found and shared between 43 host species, including the domestic dog and the human species. By following the protocols presented here, the Spillover Interface Project will collect the data and samples needed to test this data-driven prediction.

A global examination of ecological niche modeling to predict emerging infectious diseases: a systematic review

Introduction

As emerging infectious diseases (EIDs) increase, examining the underlying social and environmental conditions that drive EIDs is urgently needed. Ecological niche modeling (ENM) is increasingly employed to predict disease emergence based on the spatial distribution of biotic conditions and interactions, abiotic conditions, and the mobility or dispersal of vector-host species, as well as social factors that modify the host species' spatial distribution. Still, ENM applied to EIDs is relatively new with varying algorithms and data types. We conducted a systematic review (PROSPERO: CRD42021251968) with the research question: What is the state of the science and practice of estimating ecological niches via ENM to predict the emergence and spread of vector-borne and/or zoonotic diseases?

Methods

We searched five research databases and eight widely recognized One Health journals between 1995 and 2020. We screened 383 articles at the abstract level (included if study involved vector-borne or zoonotic disease and applied ENM) and 237 articles at the full-text level (included if study described ENM features and modeling processes). Our objectives were to: (1) describe the growth and distribution of studies across the types of infectious diseases, scientific fields, and geographic regions; (2) evaluate the likely effectiveness of the studies to represent ecological niches based on the biotic, abiotic, and mobility framework; (3) explain some potential pitfalls of ENM algorithms and techniques; and (4) provide specific recommendation for future studies on the analysis of ecological niches to predict EIDs.

Results

We show that 99% of studies included mobility factors, 90% modeled abiotic factors with more than half in tropical climate zones, 54% modeled biotic conditions and interactions. Of the 121 studies, 7% include only biotic and mobility factors, 45% include only abiotic and mobility factors, and 45% fully integrated the biotic, abiotic, and mobility data. Only 13% of studies included modifying social factors such as land use. A majority of studies (77%) used well-recognized ENM algorithms (MaxEnt and GARP) and model selection procedures. Most studies (90%) reported model validation procedures, but only 7% reported uncertainty analysis.

Discussion

Our findings bolster ENM to predict EIDs that can help inform the prevention of outbreaks and future epidemics.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier (CRD42021251968).

Modeling the enigma of complex disease etiology

BACKGROUND

Complex diseases often present as a diagnosis riddle, further complicated by the combination of multiple phenotypes and diseases as features of other diseases. With the aim of enhancing the determination of key etiological factors, we developed and tested a complex disease model that encompasses diverse factors that in combination result in complex diseases. This model was developed to address the challenges of classifying complex diseases given the evolving nature of understanding of disease and interaction and contributions of genetic, environmental, and social factors.

METHODS

Here we present a new approach for modeling complex diseases that integrates the multiple contributing genetic, epigenetic, environmental, host and social pathogenic effects causing disease. The model was developed to provide a guide for capturing diverse mechanisms of complex diseases. Assessment of disease drivers for asthma, diabetes and fetal alcohol syndrome tested the model.

RESULTS

We provide a detailed rationale for a model representing the classification of complex disease using three test conditions of asthma, diabetes and fetal alcohol syndrome. Model assessment resulted in the reassessment of the three complex disease classifications and identified driving factors, thus improving the model. The model is robust and flexible to capture new information as the understanding of complex disease improves.

CONCLUSIONS

The Human Disease Ontology's Complex Disease model offers a mechanism for defining more accurate disease classification as a tool for more precise clinical diagnosis. This broader representation of complex disease, therefore, has implications for clinicians and researchers who are tasked with creating evidence-based and consensus-based recommendations and for public health tracking of complex disease. The new model facilitates the comparison of etiological factors between complex, common and rare diseases and is available at the Human Disease Ontology website.

High Temperature Limits of Survival and Oviposition of Phormia regina (Meigen) and Lucilia sericata (Meigen)

The temperature dependent development rates of blow flies allow blow flies to be used as biological clocks in forensic death investigations. However, the upper thermal limits of adult survival and oviposition, both required for producing larvae, remains largely unknown. Therefore, in this study we examined the impact of a range of temperatures between 37 °C and 44 °C on the likelihood of survival and egg-laying behavior of two species of medicolegal forensic importance, Lucilia sericata (Meigen) and Phormia regina (Meigen) (Diptera: Calliphoridae). To quantify the upper temperature limits of survival, adult fly colonies were exposed to 37 °C, 41 °C, 42 °C, 43 °C, and 44 °C for 24 h. Similarly for oviposition trials, adults of both species were exposed to 40 °C, 42 °C, and 43 °C with P. regina oviposition also observed at 41 °C. Trials lasted for 24 h with oviposition substrate replenished at the 12 h mark. A yes/no determination on egg deposition was made, eggs were counted, and a yes/no determination was made on egg hatch. Survival did not differ by species (p = 0.096). Overall, survival decreased with increasing temperatures, with ~100% at 37 °C, ~50% at 41 °C, ~37% at 42 °C, ~15% at 43 °C and 0% at 44 °C. Lucilia sericata laid eggs capable of hatch up to 43 °C, while Phormia regina egg-hatch was observed up to 41 °C. These results indicate a greater thermal tolerance of adult survival than for egg deposition and successful egg hatch, which supports previous experiments indicating blow flies stop laying eggs at sub-lethal temperatures. Furthermore, these data indicate that adult blow flies may find remains at or near time of death but may delay egg deposition until temperatures drop below an acceptable threshold.

The Korean 3T Practice: New Biosurveillance Model Utilizing New Information Technology and Digital Tools

In South Korea, COVID-19 pandemic responses, namely the 3T (testing, tracing, and treating) strategy, emerged as a new biosurveillance regime actively using new information technology (IT) and digital tools. The foundation of the Korean 3T system is epidemiological investigation efforts and clinical practices exploiting the use of new digital and IT tools. Due to these unique features, the Korean 3T system can be referred to as a “contact-based biosurveillance system,” which is an advanced version of the traditional biosurveillance models (indicator-based or event-based models). This article illustrates how the contact-based biosurveillance system originated from the experience with the 2015 Middle East Respiratory Syndrome (MERS) outbreak. The post-MERS Korean biosurveillance regime actively adopted the utility of new digital and IT tools to strengthen not only the ex-ante epidemic intelligence capabilities (by traditional models) but also the ex-post response and recovery capabilities (digital contact tracing and digital health intervention). However, critics claim that the Korean 3T system may violate individuals’ privacy and human rights by addressing the fact that the Korean biosurveillance system would strengthen social surveillance and population control by the government as a “digital big brother” in the cyber age. Nevertheless, 3T biosurveillance promises a positive future direction for digital health practice in the current biosurveillance regimes.

Strengthening preparedness against global health threats: A paradigm shift based on One Health approaches

The implementation of preparedness strategies to prevent and mitigate the impact of global health threats poses several challenges. It should promptly identify cross-cutting drivers of pandemic threats, assess context-specific risks, engage multiple stakeholders, and translate complex data from multiple sources into accessible information for action. This requires a coordinated, multidisciplinary and multisectoral effort engaging systems that, most of the time, work in isolation.

The One Health (OH) approach promotes the collaboration and communication among different disciplines and sectors, and could be applied across the preparedness phases at national and international level.

We discuss here gaps and needs in preparedness strategies, which can benefit from the OH approach, and a set of actionable recommendations, as shared with the G20–2021 with a dedicated Policy Brief.

The discussion adds to the current debate about OH operationalization and promotes a paradigm shift towards coordinated prevention and preparedness strategies for early assessment and management of global health threats.

HumOSCM for pandemic response

Purpose

Humanitarian organizations have faced the unprecedented consequences of the coronavirus disease 2019 (COVID-19) pandemic. In this article, the authors therefore discuss how epidemics and pandemics, specifically Ebola and COVID-19, have affected humanitarian operations and supply chain management (HumOSCM), and how HumOSCM has contributed to preparedness for and response to epidemics and pandemics. The authors present lessons learned from responses to Ebola and COVID-19.

Design/methodology/approach

For this study, the authors review the scholarly HumOSCM literature, use documentary evidence from practitioner literature and apply a theory synthesis approach to derive recommendations on how HumOSCM could strengthen future responses to epidemics and pandemics.

Findings

The conceptualizations highlight the importance of strengthening collaboration, capability and capacity for the response to epidemics and pandemics. Furthermore, the components that can enhance the degree of collaboration, and hence, response formation, are discussed.

Research limitations/implications

As a non-empirical article, it suffers from the limitations of conceptual research. Hence, empirical testing of the proposed framework is recommended. The framework and propositions can serve as a basis for future studies.

Practical implications

The conceptual framework can help humanitarian organizations and other actors in the humanitarian sector to better understand how to prepare for future responses to epidemics and pandemics, in particular by considering the components that enhance the degree of collaboration, as well as through capability development and capacity building.

Originality/value

This article begins a discussion of how HumOSCM should evolve to better respond to future epidemics and pandemics.

Identifying scenarios and risk factors for Q fever outbreaks using qualitative analysis of expert opinion

Q fever is an important zoonotic disease perceived to be an occupational hazard for those working with livestock. Outbreaks involving large numbers of people are uncommon, but the increasing case incidence coupled with changing environmental and industry conditions that promote transmission of Q fever has raised concerns that large and serious outbreaks could become more frequent. The aim of this study was to use expert opinion to better understand how large Q fever outbreaks might occur in an Australian context and to document factors believed to be drivers of disease transmission. Focus groups were conducted with human and animal health professionals across several Australian states. All discussions were recorded, transcribed verbatim and imported into NVIVO for thematic analysis. Four anthropogenic risk factors (disease awareness, industry practices, land use, human behaviour) and three ecological risk factors (physical environment, agent dissemination, animal hosts) emerged from the data. Analysis of expert opinions pointed to the existence of numerous scenarios in which Q fever outbreaks could occur, many of which depict acquisition in the wider community outside of traditional at-risk occupations. This perception of the expansion of Q fever from occupational-acquisition to community-acquisition is driven by greater overarching economic, political and socio-cultural influences that govern the way in which people live and work. Findings from this study highlight that outbreaks are complex phenomena that involve the convergence of diverse elements, not just that of the pathogen and host, but also the physical, political and socioeconomic environments in which they interact. A review of the approaches to prevent and manage Q fever outbreaks will require a multisectorial approach and strengthening of community education, communication and engagement so that all stakeholders become an integrated part of outbreak mitigation and response.

Challenges in the development of digital public health interventions and mapped solutions: Findings from a scoping review

Background

“Digital public health” has emerged from an interest in integrating digital technologies into public health. However, significant challenges which limit the scale and extent of this digital integration in various public health domains have been described. We summarized the literature about these challenges and identified strategies to overcome them.

Methods

We adopted Arksey and O’Malley's framework (2005) integrating adaptations by Levac et al. (2010). OVID Medline, Embase, Google Scholar, and 14 government and intergovernmental agency websites were searched using terms related to “digital” and “public health.” We included conceptual and explicit descriptions of digital technologies in public health published in English between 2000 and June 2020. We excluded primary research articles about digital health interventions. Data were extracted using a codebook created using the European Public Health Association's conceptual framework for digital public health.

Results and analysis

Overall, 163 publications were included from 6953 retrieved articles with the majority (64%, n = 105) published between 2015 and June 2020. Nontechnical challenges to digital integration in public health concerned ethics, policy and governance, health equity, resource gaps, and quality of evidence. Technical challenges included fragmented and unsustainable systems, lack of clear standards, unreliability of available data, infrastructure gaps, and workforce capacity gaps. Identified strategies included securing political commitment, intersectoral collaboration, economic investments, standardized ethical, legal, and regulatory frameworks, adaptive research and evaluation, health workforce capacity building, and transparent communication and public engagement.

Conclusion

Developing and implementing digital public health interventions requires efforts that leverage identified strategies to overcome diverse challenges encountered in integrating digital technologies in public health.

The Pearl River Declaration: a timely call for enhancing health security through fostering a regional one health collaboration in the Asia-Pacific

The Second International Symposium on One Health Research (ISOHR) was held in Guangzhou city, China on 23-24 November 2019. A transdisciplinary collaborative approach, One Health (OH), was the central theme of the symposium which brought together more than 260 experts, scholars and emerging researchers from human health, veterinary health, food safety, environmental health and related disciplines and sectors. More than 50 organizations including World Health Organization, Centers for Disease Control (USA), and Queensland Government (Australia) participated in the symposium. Scholars, experts and emerging researchers, policy-makers and practitioners in their respective fields delivered over 50 presentations at the symposium, highlighting the collective vulnerability to some of the emerging health challenges the region was combating. These included emerging infectious diseases, antimicrobial resistance, climate change, food safety and the growing burden of non-communicable diseases. The Pearl River Declaration, emanated from the symposium, called for establishing a One Health Cooperation Network in the Southeast Asia-Pacific region with a vision to strengthen regional health security through sharing each other's knowledge and experience, and making investments in workforce development, scientific innovations such as vaccine research and development, sharing epidemic intelligence, risk identification, risk communication and appropriate response measures against emerging health threats.

Synthesis and antimicrobial evaluation of new halogenated 1,3-Thiazolidin-4-ones

The ongoing prevalence of multidrug-resistant bacterial pathogens requires the development of new effective antibacterial agents. In this study, two series of halogenated 1,3-thiazolidin-4-ones were synthesized and characterized. All the synthesized thiazolidinone derivatives were evaluated for their antimicrobial activity. Biological screening of the tested compounds revealed the antibacterial activity of the chlorinated thiazolidinones 4a, 4b and 4c against Escherichia coli TolC-mutant, with MIC values of 16 µg/mL. A combination of a sub-inhibitory concentration of colistin (0.25 × MIC) with compounds 4a, 4b or 4c showed antibacterial activity against different Gram-negative bacteria (MICs = 4-16 µg/mL). Interestingly, compounds 4a, 4b and 4c were not cytotoxic to murine fibroblasts and Caco-2 cells. The chlorinated thiazolidinone derivative 16d demonstrated a bacteriostatic activity against a panel of pathogenic Gram-positive bacteria, including clinical isolates of methicillin and vancomycin-resistant Staphylococcus aureus, Listeria monocytogenes and multidrug-resistant Staphylococcus epidermidis (MICs = 8 - 64 µg/mL), with no cytotoxicity against both Caco-2 and L929 cells. Compound 16d was superior to vancomycin in disruption of the pre-formed MRSA biofilm. Furthermore, the three fluorinated thiazolidinone derivatives 26c, 30c and 33c showed a hindrance to hemolysin activity, without cytotoxicity against L929 cells.

The Geographic Variation of Surveillance and Zoonotic Spillover Potential of Influenza Viruses in Domestic Poultry and Swine

Background

Avian and swine influenza viruses circulate worldwide and pose threats to both animal and human health. The design of global surveillance strategies is hindered by information gaps on the geospatial variation in virus emergence potential and existing surveillance efforts.

Methods

We developed a spatial framework to quantify the geographic variation in outbreak emergence potential based on indices of potential for animal-to-human and secondary human-to-human transmission. We then compared our resultant raster model of variation in emergence potential with the global distribution of recent surveillance efforts from 359105 reports of surveillance activities.

Results

Our framework identified regions of Southeast Asia, Eastern Europe, Central America, and sub-Saharan Africa with high potential for influenza virus spillover. In the last 15 years, however, we found that 78.43% and 49.01% of high-risk areas lacked evidence of influenza virus surveillance in swine and domestic poultry, respectively.

Conclusions

Our work highlights priority areas where improved surveillance and outbreak mitigation could enhance pandemic preparedness strategies.

Zoonotic disease research in East Africa

BACKGROUND

The East African region is endemic with multiple zoonotic diseases and is one of the hotspots for emerging infectious zoonotic diseases with reported multiple outbreaks of epidemic diseases such as Ebola, Marburg and Rift Valley Fever. Here we present a systematic assessment of published research on zoonotic diseases in the region and thesis research in Kenya to understand the regional research focus and trends in publications, and estimate proportion of theses research transitioning to peer-reviewed journal publications.

METHODS

We searched PubMed, Google Scholar and African Journals Online databases for publications on 36 zoonotic diseases identified to have occurred in the East Africa countries of Burundi, Ethiopia, Kenya, Tanzania, Rwanda and Uganda, for the period between 1920 and 2017. We searched libraries and queried online repositories for masters and PhD theses on these diseases produced between 1970 and 2016 in five universities and two research institutions in Kenya.

RESULTS

We identified 771 journal articles on 22, and 168 theses on 21 of the 36 zoonotic diseases investigated. Research on zoonotic diseases increased exponentially with the last 10 years of our study period contributing more than half of all publications 460 (60%) and theses 102 (61%) retrieved. Endemic diseases were the most studied accounting for 656 (85%) and 150 (89%) of the publication and theses studies respectively, with publications on epidemic diseases associated with outbreaks reported in the region or elsewhere. Epidemiological studies were the most common study types but limited to cross-sectional studies while socio-economics were the least studied. Only 11% of the theses research transitioned to peer-review publications, taking an average of 2.5 years from theses production to manuscript publication.

CONCLUSION

Our findings demonstrate increased attention to zoonotic diseases in East Africa but reveal the need to expand the scope, focus and quality of studies to adequately address the public health, social and economic threats posed by zoonoses.

Towards a genomics-informed, real-time, global pathogen surveillance system

The recent Ebola and Zika epidemics demonstrate the need for the continuous surveillance, rapid diagnosis and real-time tracking of emerging infectious diseases. Fast, affordable sequencing of pathogen genomes - now a staple of the public health microbiology laboratory in well-resourced settings - can affect each of these areas. Coupling genomic diagnostics and epidemiology to innovative digital disease detection platforms raises the possibility of an open, global, digital pathogen surveillance system. When informed by a One Health approach, in which human, animal and environmental health are considered together, such a genomics-based system has profound potential to improve public health in settings lacking robust laboratory capacity.

Framework for Infectious Disease Analysis: A comprehensive and integrative multi-modeling approach to disease prediction and management

The impact of infectious disease on human populations is a function of many factors including environmental conditions, vector dynamics, transmission mechanics, social and cultural behaviors, and public policy. A comprehensive framework for disease management must fully connect the complete disease lifecycle, including emergence from reservoir populations, zoonotic vector transmission, and impact on human societies. The Framework for Infectious Disease Analysis is a software environment and conceptual architecture for data integration, situational awareness, visualization, prediction, and intervention assessment. Framework for Infectious Disease Analysis automatically collects biosurveillance data using natural language processing, integrates structured and unstructured data from multiple sources, applies advanced machine learning, and uses multi-modeling for analyzing disease dynamics and testing interventions in complex, heterogeneous populations. In the illustrative case studies, natural language processing from social media, news feeds, and websites was used for information extraction, biosurveillance, and situation awareness. Classification machine learning algorithms (support vector machines, random forests, and boosting) were used for disease predictions.

Global hotspots and correlates of emerging zoonotic diseases

Zoonoses originating from wildlife represent a significant threat to global health, security and economic growth, and combatting their emergence is a public health priority. However, our understanding of the mechanisms underlying their emergence remains rudimentary. Here we update a global database of emerging infectious disease (EID) events, create a novel measure of reporting effort, and fit boosted regression tree models to analyze the demographic, environmental and biological correlates of their occurrence. After accounting for reporting effort, we show that zoonotic EID risk is elevated in forested tropical regions experiencing land-use changes and where wildlife biodiversity (mammal species richness) is high. We present a new global hotspot map of spatial variation in our zoonotic EID risk index, and partial dependence plots illustrating relationships between events and predictors. Our results may help to improve surveillance and long-term EID monitoring programs, and design field experiments to test underlying mechanisms of zoonotic disease emergence.

VetCompass Australia: A National Big Data Collection System for Veterinary Science

VetCompass Australia is veterinary medical records-based research coordinated with the global VetCompass endeavor to maximize its quality and effectiveness for Australian companion animals (cats, dogs, and horses). Bringing together all seven Australian veterinary schools, it is the first nationwide surveillance system collating clinical records on companion-animal diseases and treatments. VetCompass data service collects and aggregates real-time, clinical records for researchers to interrogate, delivering sustainable and cost-effective access to data from hundreds of veterinary practitioners nationwide. Analysis of these clinical records will reveal geographical and temporal trends in the prevalence of inherited and acquired diseases, identify frequently prescribed treatments, revolutionize clinical auditing, help the veterinary profession to rank research priorities, and assure evidence-based companion-animal curricula in veterinary schools. VetCompass Australia will progress in three phases: (1) roll-out of the VetCompass platform to harvest Australian veterinary clinical record data; (2) development and enrichment of the coding (data-presentation) platform; and (3) creation of a world-first, real-time surveillance interface with natural language processing (NLP) technology. The first of these three phases is described in the current article. Advances in the collection and sharing of records from numerous practices will enable veterinary professionals to deliver a vastly improved level of care for companion animals that will improve their quality of life.

Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level

BACKGROUND

Infectious diseases are a leading threat to public health. Accurate and timely monitoring of disease risk and progress can reduce their impact. Mentioning a disease in social networks is correlated with physician visits by patients, and can be used to estimate disease activity. Dengue is the fastest growing mosquito-borne viral disease, with an estimated annual incidence of 390 million infections, of which 96 million manifest clinically. Dengue burden is likely to increase in the future owing to trends toward increased urbanization, scarce water supplies and, possibly, environmental change. The epidemiological dynamic of Dengue is complex and difficult to predict, partly due to costly and slow surveillance systems.

METHODOLOGY / PRINCIPAL FINDINGS

In this study, we aimed to quantitatively assess the usefulness of data acquired by Twitter for the early detection and monitoring of Dengue epidemics, both at country and city level at a weekly basis. Here, we evaluated and demonstrated the potential of tweets modeling for Dengue estimation and forecast, in comparison with other available web-based data, Google Trends and Wikipedia access logs. Also, we studied the factors that might influence the goodness-of-fit of the model. We built a simple model based on tweets that was able to 'nowcast', i.e. estimate disease numbers in the same week, but also 'forecast' disease in future weeks. At the country level, tweets are strongly associated with Dengue cases, and can estimate present and future Dengue cases until 8 weeks in advance. At city level, tweets are also useful for estimating Dengue activity. Our model can be applied successfully to small and less developed cities, suggesting a robust construction, even though it may be influenced by the incidence of the disease, the activity of Twitter locally, and social factors, including human development index and internet access.

CONCLUSIONS

Tweets association with Dengue cases is valuable to assist traditional Dengue surveillance at real-time and low-cost. Tweets are able to successfully nowcast, i.e. estimate Dengue in the present week, but also forecast, i.e. predict Dengue at until 8 weeks in the future, both at country and city level with high estimation capacity.

The Potential of Social Media and Internet-Based Data in Preventing and Fighting Infectious Diseases: From Internet to Twitter

Health threats due to infectious diseases used to be a major public health concerns around the globe till mid of twentieth century when effective public health interventions helped in eradicating a number of infectious diseases around the world. Over the past 15 years, there has been a rise in the number of emerging and reemerging infectious diseases being reported such as the Acute Respiratory Syndrome (SARS) in 2002, HINI in 2009, Middle East Respiratory Syndrome (MERS) in 2012, Ebola in 2014, and Zika in 2016. These emerging viral infectious diseases have led to serious public health concerns leading to death and causing fear and anxiety among the public. More importantly, at the moment, the prevention and control of viral infectious diseases is difficult due to a lack of effective vaccines. Thus having real-time reporting tools are paramount to alert relevant public health surveillance systems and authorities about taking the right and necessary actions to control and minimize the potential harmful effects of viral infectious diseases. Social media and Internet-based data can play a major role in real-time reporting to empower active public health surveillance systems for controlling and fighting infectious diseases.

One Health proof of concept: Bringing a transdisciplinary approach to surveillance for zoonotic viruses at the human-wild animal interface

As the world continues to react and respond inefficiently to emerging infectious diseases, such as Middle Eastern Respiratory Syndrome and the Ebola and Zika viruses, a growing transdisciplinary community has called for a more proactive and holistic approach to prevention and preparedness - One Health. Such an approach presents important opportunities to reduce the impact of disease emergence events and also to mitigate future emergence through improved cross-sectoral coordination. In an attempt to provide proof of concept of the utility of the One Health approach, the US Agency for International Development's PREDICT project consortium designed and implemented a targeted, risk-based surveillance strategy based not on humans as sentinels of disease but on detecting viruses early, at their source, where intervention strategies can be implemented before there is opportunity for spillover and spread in people or food animals. Here, we share One Health approaches used by consortium members to illustrate the potential for successful One Health outcomes that can be achieved through collaborative, transdisciplinary partnerships. PREDICT's collaboration with partners around the world on strengthening local capacity to detect hundreds of viruses in wild animals, coupled with a series of cutting-edge virological and analytical activities, have significantly improved our baseline knowledge on the zoonotic pool of viruses and the risk of exposure to people. Further testament to the success of the project's One Health approach and the work of its team of dedicated One Health professionals are the resulting 90 peer-reviewed, scientific publications in under 5 years that improve our understanding of zoonoses and the factors influencing their emergence. The findings are assisting in global health improvements, including surveillance science, diagnostic technologies, understanding of viral evolution, and ecological driver identification. Through its One Health leadership and multi-disciplinary partnerships, PREDICT has forged new networks of professionals from the human, animal, and environmental health sectors to promote global health, improving our understanding of viral disease spillover from wildlife and implementing strategies for preventing and controlling emerging disease threats.

Predicting Subnational Ebola Virus Disease Epidemic Dynamics from Sociodemographic Indicators

Background

The recent Ebola virus disease (EVD) outbreak in West Africa has spread wider than any previous human EVD epidemic. While individual-level risk factors that contribute to the spread of EVD have been studied, the population-level attributes of subnational regions associated with outbreak severity have not yet been considered.

Methods

To investigate the area-level predictors of EVD dynamics, we integrated time series data on cumulative reported cases of EVD from the World Health Organization and covariate data from the Demographic and Health Surveys. We first estimated the early growth rates of epidemics in each second-level administrative district (ADM2) in Guinea, Sierra Leone and Liberia using exponential, logistic and polynomial growth models. We then evaluated how these growth rates, as well as epidemic size within ADM2s, were ecologically associated with several demographic and socio-economic characteristics of the ADM2, using bivariate correlations and multivariable regression models.

Results

The polynomial growth model appeared to best fit the ADM2 epidemic curves, displaying the lowest residual standard error. Each outcome was associated with various regional characteristics in bivariate models, however in stepwise multivariable models only mean education levels were consistently associated with a worse local epidemic.

Discussion

By combining two common methods—estimation of epidemic parameters using mathematical models, and estimation of associations using ecological regression models—we identified some factors predicting rapid and severe EVD epidemics in West African subnational regions. While care should be taken interpreting such results as anything more than correlational, we suggest that our approach of using data sources that were publicly available in advance of the epidemic or in real-time provides an analytic framework that may assist countries in understanding the dynamics of future outbreaks as they occur.

The disease of corruption: views on how to fight corruption to advance 21st century global health goals

Corruption has been described as a disease. When corruption infiltrates global health, it can be particularly devastating, threatening hard gained improvements in human and economic development, international security, and population health. Yet, the multifaceted and complex nature of global health corruption makes it extremely difficult to tackle, despite its enormous costs, which have been estimated in the billions of dollars. In this forum article, we asked anti-corruption experts to identify key priority areas that urgently need global attention in order to advance the fight against global health corruption. The views shared by this multidisciplinary group of contributors reveal several fundamental challenges and allow us to explore potential solutions to address the unique risks posed by health-related corruption. Collectively, these perspectives also provide a roadmap that can be used in support of global health anti-corruption efforts in the post-2015 development agenda.

Correlation Between UpToDate Searches and Reported Cases of Middle East Respiratory Syndrome During Outbreaks in Saudi Arabia

UpToDate search activity is useful for detecting and monitoring outbreaks of Middle East respiratory syndrome in Saudi Arabia.

Background. UpToDate is an online clinical decision support resource that is used extensively by clinicians around the world. Digital surveillance techniques have shown promise to aid with the detection and monitoring of infectious disease outbreaks. We sought to determine whether UpToDate searches for Middle East respiratory syndrome (MERS) could be used to detect and monitor MERS outbreaks in Saudi Arabia.

Methods. We analyzed daily searches related to MERS in Jeddah and Riyadh, Saudi Arabia during 3 outbreaks in these cities in 2014 and 2015 and compared them with reported cases during the same periods. We also compared UpToDate MERS searches in the affected cities to those in a composite of 4 negative control cities for the 2 outbreaks in 2014.

Results. UpToDate MERS searches during all 3 MERS outbreaks in Saudi Arabia showed a correlation to reported cases. In addition, UpToDate MERS search volume in Jeddah and Riyadh during the outbreak periods in 2014 was significantly higher than the concurrent search volume in the 4 negative control cities. In contrast, during the baseline periods, there was no difference between UpToDate searches for MERS in the affected cities compared with the negative control cities.

Conclusions. UpToDate search activity seems to be useful for detecting and monitoring outbreaks of MERS in Saudi Arabia.