Fogarty International Center collaborative networks in infectious disease modeling: Lessons learnt in research and capacity building

Transboundary determinants of avian zoonotic infectious diseases: challenges for strengthening research capacity and connecting surveillance networks

As the climate changes, global systems have become increasingly unstable and unpredictable. This is particularly true for many disease systems, including subtypes of highly pathogenic avian influenzas (HPAIs) that are circulating the world. Ecological patterns once thought stable are changing, bringing new populations and organisms into contact with one another. Wild birds continue to be hosts and reservoirs for numerous zoonotic pathogens, and strains of HPAI and other pathogens have been introduced into new regions via migrating birds and transboundary trade of wild birds. With these expanding environmental changes, it is even more crucial that regions or counties that previously did not have surveillance programs develop the appropriate skills to sample wild birds and add to the understanding of pathogens in migratory and breeding birds through research. For example, little is known about wild bird infectious diseases and migration along the Mediterranean and Black Sea Flyway (MBSF), which connects Europe, Asia, and Africa. Focusing on avian influenza and the microbiome in migratory wild birds along the MBSF, this project seeks to understand the determinants of transboundary disease propagation and coinfection in regions that are connected by this flyway. Through the creation of a threat reduction network for avian diseases (Avian Zoonotic Disease Network, AZDN) in three countries along the MBSF (Georgia, Ukraine, and Jordan), this project is strengthening capacities for disease diagnostics; microbiomes; ecoimmunology; field biosafety; proper wildlife capture and handling; experimental design; statistical analysis; and vector sampling and biology. Here, we cover what is required to build a wild bird infectious disease research and surveillance program, which includes learning skills in proper bird capture and handling; biosafety and biosecurity; permits; next generation sequencing; leading-edge bioinformatics and statistical analyses; and vector and environmental sampling. Creating connected networks for avian influenzas and other pathogen surveillance will increase coordination and strengthen biosurveillance globally in wild birds.

Global Health Perspectives on Race in Research: Neocolonial Extraction and Local Marginalization

Best practices in global health training prioritize leadership and engagement from investigators from low- and middle-income countries (LMICs), along with conscientious community consultation and research that benefits local participants and autochthonous communities. However, well into the 20th century, international research and clinical care remain rife with paternalism, extractive practices, and racist ideation, with race presumed to explain vulnerability or protection from various diseases, despite scientific evidence for far more precise mechanisms for infectious disease. We highlight experiences in global research on health and illness among indigenous populations in LMICs, seeking to clarify what is both scientifically essential and ethically desirable in research with human subjects; we apply a critical view towards race and racism as historically distorting elements that must be acknowledged and overcome.

A multivalent nucleoside-modified mRNA vaccine against all known influenza virus subtypes

Seasonal influenza vaccines offer little protection against pandemic influenza virus strains. It is difficult to create effective prepandemic vaccines because it is uncertain which influenza virus subtype will cause the next pandemic. In this work, we developed a nucleoside-modified messenger RNA (mRNA)-lipid nanoparticle vaccine encoding hemagglutinin antigens from all 20 known influenza A virus subtypes and influenza B virus lineages. This multivalent vaccine elicited high levels of cross-reactive and subtype-specific antibodies in mice and ferrets that reacted to all 20 encoded antigens. Vaccination protected mice and ferrets challenged with matched and mismatched viral strains, and this protection was at least partially dependent on antibodies. Our studies indicate that mRNA vaccines can provide protection against antigenically variable viruses by simultaneously inducing antibodies against multiple antigens.

Monitoring COVID-19 in Colombia during the first year of the pandemic

Background:

COVID-19 cases in Medellín, the second largest city in Colombia, were monitored during the first year of the pandemic using both mathematical models based on transmission theory and surveillance from each observed epidemic phase.

Design and Methods:

Expected cases were estimated using mandatory reporting data from Colombia’s national epidemiological surveillance system from March 7, 2020 to March 7, 2021. Initially, the range of daily expected cases was estimated using a Borel-Tanner stochastic model and a deterministic Susceptible-Infected-Removed (SIR) model. A subsequent expanded version of the SIR model was used to include asymptomatic cases, severe cases and deaths. The moving average, standard deviation, and goodness of fit of estimated cases relative to confirmed reported cases were assessed, and local transmission in Medellin was contrasted with national transmission in Colombia.

Results:

The initial phase was characterized by imported case detection and the later phase by community transmission and increases in case magnitude and severity. In the initial phase, a maximum range of expected cases was obtained based on the stochastic model, which even accounted for the reduction of new imported cases following the closure of international airports. The deterministic estimate achieved an adequate fit with respect to accumulated cases until the conclusion of the mandatory national quarantine and gradual reopening, when reported cases increased. The estimated new cases were reasonably fit with the maximum reported incidence.

Conclusion:

Adequate model fit was obtained with the reported data. This experience of monitoring epidemic trajectory can be extended using models adapted to local conditions.

Biodefence research two decades on: worth the investment?

For the past 20 years, the notion of bioterror has been a source of considerable fear and panic worldwide. In response to the terror attacks of 2001 in the USA, extensive research funding was awarded to investigate bioterror-related pathogens. The global scientific legacy of this funding has extended into the present day, highlighted by the ongoing COVID-19 pandemic. Unsurprisingly, the surge in biodefence-related research and preparedness has been met with considerable apprehension and opposition. Here, we briefly outline the history of modern bioterror threats and biodefence research, describe the scientific legacy of biodefence research by highlighting advances pertaining to specific bacterial and viral pathogens, and summarise the future of biodefence research and its relevance today. We sought to address the sizeable question: have the past 20 years of investment into biodefence research and preparedness been worth it? The legacy of modern biodefence funding includes advancements in biosecurity, biosurveillence, diagnostics, medical countermeasures, and vaccines. In summary, we feel that these advances justify the substantial biodefence funding trend of the past two decades and set a precedent for future funding.

Anthrax in the Amhara regional state of Ethiopia; spatiotemporal analysis and environmental suitability modeling with an ensemble approach

Anthrax is one of the most neglected tropical disease affecting humans, livestock, and wildlife worldwide. The disease is caused by soil-borne spore-forming bacteria called Bacillus anthracis. A machine learning algorithm with the biomod2 package of R software was used to develop a predictive map for the Amhara regional state of Ethiopia. One hundred twenty-eight georeferenced confirmed outbreak reports of anthrax in livestock and 11 bioclimatic, eight soil characteristics, and three livestock density variables were used to train the model. The algorithm was set to run 3-fold with a total of 27 outputs for the nine selected models. An ensemble model was developed with ROC evaluation metrics set at 0.8. The ensemble model showed an improved performance than the individual models (KAPPA, TSS, and ROC values of 0.86, 0.93, and 0.99, respectively). Variables like annual precipitation (22.51 %), precipitation of warmest quarter (14.17 %), precipitation of wettest month (11.61 %), cattle density (9.67 %), sheep density (6.6 %), annual maximum temperature (6.17 %), altitude/elevation (5.24 %), and sand content (4.83 %) contributed the highest share in the ensemble model. The predicted suitable areas were primarily in the Central and Southern parts of the region. West Gojam and South Gondar zones were found highly suitable; while parts of Waghemira, North Wollo, and South Wollo were not significantly suitable. Besides, East Gojam, North Gondar, and Awi administrative zones were also reasonably suitable to Bacillus anthracis. The study can be used as a basis in the planning of prevention and control approaches of anthrax outbreaks in the region. Administrative zones like West Gojam, South Gondar, Awi, and East Gojam have to be prioritized as a risky-areas in the planning of preventive measures of anthrax in the region.

Strong spatial embedding of social networks generates nonstandard epidemic dynamics independent of degree distribution and clustering

Some directly transmitted human pathogens, such as influenza and measles, generate sustained exponential growth in incidence and have a high peak incidence consistent with the rapid depletion of susceptible individuals. Many do not. While a prolonged exponential phase typically arises in traditional disease-dynamic models, current quantitative descriptions of nonstandard epidemic profiles are either abstract, phenomenological, or rely on highly skewed offspring distributions in network models. Here, we create large socio-spatial networks to represent contact behavior using human population-density data, a previously developed fitting algorithm, and gravity-like mobility kernels. We define a basic reproductive number [Formula: see text] for this system, analogous to that used for compartmental models. Controlling for [Formula: see text], we then explore networks with a household-workplace structure in which between-household contacts can be formed with varying degrees of spatial correlation, determined by a single parameter from the gravity-like kernel. By varying this single parameter and simulating epidemic spread, we are able to identify how more frequent local movement can lead to strong spatial correlation and, thus, induce subexponential outbreak dynamics with lower, later epidemic peaks. Also, the ratio of peak height to final size was much smaller when movement was highly spatially correlated. We investigate the topological properties of our networks via a generalized clustering coefficient that extends beyond immediate neighborhoods, identifying very strong correlations between fourth-order clustering and nonstandard epidemic dynamics. Our results motivate the observation of both incidence and socio-spatial human behavior during epidemics that exhibit nonstandard incidence patterns.

Ontology-Based Graphs of Research Communities: A Tool for Understanding Threat Reduction Networks

Scientific research communities can be represented as heterogeneous or multidimensional networks encompassing multiple types of entities and relationships. These networks might include researchers, institutions, meetings, and publications, connected by relationships like authorship, employment, and attendance. We describe a method for efficiently and flexibly capturing, storing, and extracting information from multidimensional scientific networks using a graph database. The database structure is based on an ontology that captures allowable types of entities and relationships. This allows us to construct a variety of projections of the underlying multidimensional graph through database queries to answer specific research questions. We demonstrate this process through a study of the U.S. Biological Threat Reduction Program (BTRP), which seeks to develop Threat Reduction Networks to build and strengthen a sustainable international community of biosecurity, biosafety, and biosurveillance experts to address shared biological threat reduction challenges. Networks like these create connectional intelligence among researchers and institutions around the world, and are central to the concept of cooperative threat reduction. Our analysis focuses on a series of seven BTRP genome sequencing training workshops, showing how they created a growing network of participants and countries over time, which is also reflected in coauthorship relationships among attendees. By capturing concept and relationship hierarchies, our ontology-based approach allows us to pose general or specific questions about networks within the same framework. This approach can be applied to other research communities or multidimensional social networks to capture, analyze, and visualize different types of interactions and how they change over time.

Coordinating the real-time use of global influenza activity data for better public health planning

Health planners from global to local levels must anticipate year-to-year and week-to-week variation in seasonal influenza activity when planning for and responding to epidemics to mitigate their impact. To help with this, countries routinely collect incidence of mild and severe respiratory illness and virologic data on circulating subtypes and use these data for situational awareness, burden of disease estimates and severity assessments. Advanced analytics and modelling are increasingly used to aid planning and response activities by describing key features of influenza activity for a given location and generating forecasts that can be translated to useful actions such as enhanced risk communications, and informing clinical supply chains. Here, we describe the formation of the Influenza Incidence Analytics Group (IIAG), a coordinated global effort to apply advanced analytics and modelling to public influenza data, both epidemiological and virologic, in real-time and thus provide additional insights to countries who provide routine surveillance data to WHO. Our objectives are to systematically increase the value of data to health planners by applying advanced analytics and forecasting and for results to be immediately reproducible and deployable using an open repository of data and code. We expect the resources we develop and the associated community to provide an attractive option for the open analysis of key epidemiological data during seasonal epidemics and the early stages of an influenza pandemic.

A Case History in Cooperative Biological Research: Compendium of Studies and Program Analyses in Kazakhstan

Kazakhstan and the United States have partnered since 2003 to counter the proliferation of weapons of mass destruction. The US Department of Defense (US DoD) has funded threat reduction programs to eliminate biological weapons, secure material in repositories that could be targeted for theft, and enhance surveillance systems to monitor infectious disease outbreaks that would affect national security. The cooperative biological research (CBR) program of the US DoD’s Biological Threat Reduction Program has provided financing, mentorship, infrastructure, and biologic research support to Kazakhstani scientists and research institutes since 2005. The objective of this paper is to provide a historical perspective for the CBR involvement in Kazakhstan, including project chronology, successes and challenges to allow lessons learned to be applied to future CBR endeavors. A project compendium from open source data and interviews with partner country Kazakhstani participants, project collaborators, and stakeholders was developed utilizing studies from 2004 to the present. An earlier project map was used as a basis to determine project linkages and continuations during the evolution of the CBR program. It was determined that consistent and effective networking increases the chances to collaborate especially for competitive funding opportunities. Overall, the CBR program has increased scientific capabilities in Kazakhstan while reducing their risk of biological threats. However, there is still need for increased scientific transparency and an overall strategy to develop a capability-based model to better enhance and sustain future research. Finally, we offer a living perspective that can be applied to further link related studies especially those related to One Health and zoonoses and the assessment of similar capability-building programs.