ZD-Prevent: Stopping Pandemics at the Source

The 2019 Novel Coronavirus (2019-nCoV) pandemic, now referred to as COVID-19, is responsible for over 131 million cases and 2.8 million deaths worldwide entering the Spring of 2021 (COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU), 2021). Current global efforts include reacting to the pandemic through mandates on social activity, extensive testing, and emerging vaccinations. Research continues in multiple areas associated with COVID-19, including impacts of social distancing, facemasks, rapid testing, and vaccines' effectiveness. In Dobson et al. (2020), economists predict damages from COVID-19 will result in a loss ranging from $10-$20 trillion worldwide.

While the world continues the battle on the current disease, an intriguing question revolves around how pandemics originate and what steps might reduce future pandemics' probability. COVID-19 is a zoonotic disease, which occurs when a virus gravitates from wildlife to humans. In the case of COVID-19, the disease originated in bats, then moved to another host animal before jumping (also known as spillover) into the human population and rapidly spreading. One root cause of the zoonotic disease is illegal, unregulated, and high-risk wildlife trade and consumption, creating dangerous spillover opportunities. Zoonotic diseases are on an unprecedented rise and a blunt notice of the relationship between people and wildlife (De Wit et al., 2020). This paper focuses on reducing zoonotic disease growth by implementing a Big Data solution to monitor illegal and high-risk wildlife trade on the black market.

Scope

The global demand for wildlife creates an environment of wildlife extraction, dead or alive, for sale to the public across all types of cultures. For example, bushmeat is considered a delicacy in many countries and a status symbol of luxury. Also, the United States is one of the largest consumers of exotic pets, a booming industry. In China, the farming of wildlife for resell is a $20 billion industry employing millions of people (Dobson et al., 2020).

This paper proposes an early detection and control system for zoonotic diseases due to wildlife trade spillover. The distributed Big Data system, Zoonotic Disease Prevention (ZD-Prevent), is a futuristic solution that combines emerging Big Data technology to address a growing problem. ZD-Prevent has three distinct features making it suitable for zoonotic disease prevention. First, ZD-Prevent intends to collect as much data as possible on the wildlife trade industry's flow. ZD-Prevent relies on Big Data technology, scalable to handle millions of data elements of varying types and sizes to satisfy this requirement. Secondly, ZD-Prevent has rich integrations with thousands of Internet of Things (IoT) devices for real-time data collection. Finally, ZD-Prevent includes advanced Artificial Intelligence (AI) and Machine Learning (ML) algorithms that analyze the data for patterns, anomalies, and warnings that trigger high-risk wildlife trade alerts.

The monitoring and control of wildlife extraction have challenges, including the culture of indigenous tribes and those living in remote areas where wildlife is the primary protein source. While ZD-Prevent intends to reduce the illegal transfer of wildlife for monetary purposes drastically, there is no intent to restrict a primary food source in these communities. The gap in evaluating at-risk wildlife extraction where the intent is a primary food source is a current limitation to ZD-Prevent, especially since this extraction typically happens in indigenous tribes or remote areas where technology implementation is most difficult. However, future applications could include new capabilities to monitor and test these food sources.

Purpose

Although COVID-19 is the current pandemic, zoonotic diseases are not new and include diseases dating back to 1900, including Zika, HIV, SARS, H1N1, and Ebola. Today, over 200 zoonotic diseases exist, and the trend is growing (De Wit et al., 2020). While efforts to react quickly after an epidemic occurs may slow or even stop a pandemic, these measures are reactive and do nothing to address the root cause. According to the outcomes of a platform workshop on biodiversity and pandemics held in July 2020 (Workshop Report on Biodiversity and Pandemics of the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) 2020), the wildlife trade has increased 500% in value since 2005, and this knowing the data does not include the illegal or black markets.

The report also notes that the wildlife trade regulation is challenging due to the breath, species, and products. The future risk of disease emergence increases as the networks, wildlife farms, live markets, bulk transport, and international trade pipelines continue to grow. The ability to control zoonotic diseases relies on discovering and educating the biodiversity of microbes in nature, which parallels modern medicine development. Microbes compete for space and nutrition, leading to natural selection, which is critical to understanding viruses and antibodies that offset them (Workshop Report on Biodiversity and Pandemics of the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES), 2020).

ZD-Prevent is a mechanism to pivot the growth curve of zoonotic diseases by identifying high-risk trade in real-time and alerting action to contain the questionable exchange. The reduction of high-risk wildlife trade will directly impact zoonotic disease and the influx of pandemics. While stopping the threat of a pandemic at the core is the primary objective of ZD-Prevent, a secondary benefit is identifying at-risk wildlife. The identification and targeting at-risk wildlife provides a pathway for further research to inform microbe studies, yielding detail on risk, virus execution and spread, and possible antibodies.

Supporting Forces

ZD-Prevent includes the integration of three primary technological emerging areas. First, the Big Data platform manages high volumes of data and incredible data flow velocities. While the system is technically passive, the goal is to get close to real-time identification of high-risk wildlife trades. Secondly, ZD-Prevent takes IoT to new levels, where cargo scans occur constantly, are validated, and wildlife is uniquely identified and tracked from origin to destination. Lastly, ZD-Prevent relies on new models of AI/ML to leverage large amounts of data to identify patterns, anomalies, and risks in specific cargo.

ZD-Prevent tracks what testing occurs, and the results of that testing, to create a massive database of diseases, wildlife, and the relationships between them. Naturally, demographic data such as the origin and ancestry provides even greater detail towards containing a potential zoonotic disease occurrence. The testing technology is a sensor itself, with the ability to extract a sample quickly and safely from a specimen and transfer that data instantaneously for automated analysis. ZD-Prevent can process the test sample in near real-time, preventing transport of potentially affected wildlife.

Economically, the ZD-Prevent cost estimate is millions of dollars; however, it is a fraction of the expense due to damages of a pandemic such as COVID-19. Assuming the cost of ZD-Prevent is even in the billions of dollars, it is still less than 1% of the cost of COVID-19 damage. Also, sensors deployed at transit sites (airports, seaports, railways, automobile crossings) could serve additional purposes in the future, cross-checking other types of threats.

Lastly, emotional forces strengthen the desire to control pandemics. While one can measure the cost of damage due to COVID-19, the loss of life is immeasurable. Social gatherings, weddings, funerals, church services are all critical human sociological drivers that are important to society. The loss of employment, physical interaction, education, and travel are all known drivers to stress, depression, and anxiety that drive increases in suicide rates, mental illness, and crime (Dymecka et al., 2020).

Challenging Forces

Economic issues are a major challenging force for implementing ZD-Prevent due to the impact on the wildlife transaction business. In Dobson et al. (2020), the wildlife farming industry is more than $20 billion in China alone. Worldwide, the business volume is much larger and a critical source of include for millions of employees. However, it is not the intent of ZD-Prevent to curtail all wildlife exchange but instead identify zoonotic diseases. While the implementation of ZD-Prevent will impact profits by adding work to the logistic process and stopping at-risk exchanges, the intent is not to stop the business. Regardless, challenges exist with economics anytime the profit margin comes into play.

The other challenging force is political in that ZD-Prevent is most effective when all parties participate. One at-risk transaction of at-risk wildlife is enough to cause spillover that may lead to the next pandemic. Therefore, all countries and organizations must agree that pandemics are unacceptable and be willing to cooperate with the initiative. The ability to gain worldwide concurrence is challenging, and acquiring political agreement and unity is complex. Lawmakers must be willing to change policy and enforce requirements if they desire to reduce pandemic outbreaks is a sincere goal.

Methods

The Delphi method is advantageous for developing a ZD-Prevent strategic plan given the diverse and knowledgeable population's input and expertise. Given that ZD-Prevent is a global solution, a long list of considerations includes demographics, culture, sociotechnical implementation, legal statutes, and many more. The Delphi method enables an iterative approach to answer essential questions, raising new concerns, identifying challenges, and developing a pathway to success. The Delphi method enables the development of a theory through variable development and proposition generation. The expert panel strengthens the theory and likelihood of applicability through iterative rounds of questioning and developing causal relationships. The result leads to "construct validity," which is a precise definition of the model (Okoli & Pawlowski, 2004).

Models

Many theoretical models across various domains exist towards analyzing sociotechnical transition. In Dosi (1982), the author studies the relationship between technological paradigms and technology trajectories. The outcome includes a model accounting for continuous change while realizing the evolution of technological innovation. A core component of the model realizes that new technology rises through multiple forces (such as market growth) across diverse environments, including multiple and often contrasting forces. In Giménez Roche et al. (2015), the authors apply the Dosi model to entrepreneurial construction of business paradigms, which Figure 1 illustrates.

Figure 1

Entrepreneurial construction of innovative and progressive business paradigms

                While the Dosi model has a specific focus on technical trajectories and the impact of multiple forces, another popular model leverages constructs of various models in aggregation for a robust model targeting Health Information Technology (HIT) (Sittig & Singh, 2010). The sociotechnical model for HIT considers eight distinct dimensions, providing a holistic model intending to fill gaps of predecessors. Table 1 includes the eight dimensions and how they apply to ZD-Prevent.

Table 1

Eight dimensions of a sociotechnical model for Health Information Technology

Dimension	Description
Hardware and Software Computing Infrastructure	The hardware and software components are necessary to operate ZD-Prevent
Clinical Content	The data elements within ZD-Prevent including the raw, enriched and analyzed results
Human-Computer Interface	The data consumption vehicle(s) for ZD-Prevent, including sensors, tests, and scanning systems
People	The diverse set of individuals working together to produce and use ZD-Prevent
Workflow and Communication	The recognition that the success of ZD-Prevent depends on constant feedback
Internal Organizational Policies, Procedures, and Culture	The realization and pathway for organizations to work in unison for the legal deployment and operation of ZD-Prevent
External Rules, Regulations, and Pressures	The external forces (ethical, global, environmental, cultural, social, and legal) impacting ZD-Prevent success
System Measurement and Monitoring	A system for continuous monitoring and feedback for ZD-Prevent

Analytical Plan

The evaluation of ZD-Prevent resides on identifying at-risk wildlife before spillover occurs, affecting humans and leading to epidemic or pandemic levels. Therefore, the success of ZD-Prevent is critically associated with the identification of zoonotic diseases in real-time. Data on zoonotic diseases exists and serves as a baseline metric for ZD-Prevent's effectiveness (Workshop Report on Biodiversity and Pandemics of the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES), 2020). Therefore, ZD-Prevent enables a very data-driven approach to evaluation over time.

The deployment of a system such as ZD-Prevent is not trivial, given the intent to apply at a global level to maximize effectiveness. Deployment of ZD-Prevent initiates with a contained release on a management pilot group. A pilot's use enables iteration of fit-for-purpose testing, impacts, and adjustments to a scaling strategy, feedback, and improvements towards communications and training. The release process is an agile activity, where feedback from a pilot improves the next release. The process, known as a customer-driven release management framework, leverages the Information Technology Infrastructure Library's (ITIL) best practices along with the Project Management Institutes (PMI) methodology for project delivery (Cleveland & Ellis, 2014).

The critical component of deployment of ZD-Prevent and evaluation of sociotechnical acceptance and overall effectiveness requires a participatory action research (PAR) strategy, where continuous feedback addresses emerging needs. A PAR strategy addresses real-time issues by enabling researchers to observe, document, and modify the system in-flight. The term, action research, rests on the fact that the development team is highly participatory in the effort, especially during early pilots and growth. The PAR framework is cyclical in that five stages (diagnosis, action planning, actions, evaluation, and learning) continuously repeat. Figure 2 illustrates the model (Cleveland & Ellis, 2014).

Figure 2

Participatory Action Research (PAR) strategy

Anticipated Results

The initial ZD-Prevent pilot's outcome expects to identify and test the transport of wildlife from an initially bounded geographic location. The anticipated results expect to reveal a more accurate count of wildlife transports, test results of that wildlife, and any zoonotic disease's presence and associated data. As ZD-Prevent scales globally, data-driven insights will provide statistics towards the impact on pandemic threat reduction, which expects to be statistically significant at the 95% confidence interval (Field, 2013).

Since ZD-Prevent resides on a Big Data Analytic framework, the data processing and advanced Machine Learning algorithms expect to identify at-risk wildlife transports in real-time and predict future sensitivities. Examples of sensitivities include specific wildlife, ecosystems, geographical locations, and economic areas prone to spillover. These outcomes enable government organizations to more tightly manage at-risk situations and heighten awareness of possible black-market activity.

The combined use of the customer-driven release management framework and participatory action research expects ZD-Prevent to quickly adapt to changes in the environment, such that the system is adequate despite the landscape. With some forces expected, the frameworks allow for ambiguity and adapt as necessary quickly. Historically, systems deploying at the global level are often challenging to maneuver and lack nimbleness due to change, something ZD-Prevent seeks to avoid (Almeida, 2017). The realization that ZD-Prevent has opposing forces is critical for ensuring the technology handles impacts from a social, political, and legal perspective.

Conclusion

The purpose of ZD-Prevent is to stop the threat of pandemics, such as COVID-19, at the core, proactively addressing at-risk wildlife spillover.          Many solutions attempt to address the problem in a reactive state after the pandemic is already well propagated. While emergency response is a critical function, ZD-Prevent and the theory underpinning the technology are more aggressive in prevention. While the cost of technology such as ZD-Prevent is high, the research indicates it is only a tiny fraction of costs for addressing a pandemic like COVID-19.

ZD-Prevent is an innovation of monumental size that intends to positively impact society at the root level, essentially addressing possible pandemic situations before they occur. The impacts to society include a healthier environment due to prevention. ZD-Prevent stands to eliminate disease spillover, which reduces disease in humans, leading to minor illness, hospitalizations, and related deaths. The need to develop rapid vaccinations that might pose a risk due to lack of testing and runtime is unnecessary. Social requirements for masks, social distancing, and constant disinfectants are all reactive actions that ZD-Prevent eliminates at the root cause level.

The other significant societal impact is in the financial space, where cost avoidance associated with a pandemic is a primary concern. The costs associated with rises and spikes in treatment, hospitalizations, and vaccinations are expensive outcomes. Additionally, the shadow costs and risks of not having the capacity to treat "typical" patients due to a pandemic's needs are substantial. Secondly, the economy's impact due to restrictions, quarantines, and public fear leads to increasing rates in unemployment, depression, and suicide rates. Lastly, the cost of an economic drop due to a pandemic is often compensated by the federal governments, leading to more significant debt and taxes for the citizen (Dymecka et al., 2020).

The realization of ZD-Prevent as a global solution requires the innovation to mature through continuous feedback loops and scale concurrently. By applying proven methods such as Delphi and scenario planning, future barriers and obstacles are somewhat predictable, and the product is designed with flexibility for change adaptation due to unforeseen forces (Okoli & Pawlowski, 2004). The transition from planning to execution leverages frameworks such as the customer-driven release management framework and PAR, both complementary models with a high level of feedback, engagement, and agility. Given the supporting and challenging forces that accompany any global change, these frameworks' use is critical.

To obtain the ultimate objective of eliminating pandemics for current and future generations, ZD-Prevent's success depends not only on the technology but also on the collaboration and unity of countries worldwide. World leaders must commit to the ambitious goal and work together against some formidable forces. Wildlife trade and farming are substantial businesses in some countries, and that is not a trivial task. Also, the demand is so high that, as with any economy, it leads to exceptions, black markets, and illegal activity. Although ZD-Prevent does not intend to halt wildlife trading, ensuring the safety and elimination of zoonotic disease requires a level of rigor that comes with a cost. As with most global efforts of any sort, success rests on the people willing to sacrifice for society's betterment.

Areas of Future Research

While ZD-Protect targets the primary lynchpin of zoonotic diseases that cause pandemics, the wildlife trade industry is only one of two significant threats. Deforestation of tropical forests places humans on the edges of ecological environments ripe for accidental spillover. Humans are becoming more susceptible to wildlife contact as more than 25% of original forests are lost to development efforts. The impact of fragmentation in ecosystems and habitat due to construction, mining, logging, urban expansion, settlement, and war causes significant increases in virus spillovers. In contrast to the wildlife trade, a purposeful event, virus spillover due to deforestation, is even more complicated (Dobson et al., 2020).

COVID-19 and other pandemics due to zoonotic diseases expose the high risk of encroachment between humans and nature. The opportunity to address this broken relationship exists but requires a transformative and systematic change to make a substantial difference. The forces are not strictly technological but rather ethical, financial, legal, environmental, and cultural. When addressing complex environmental challenges, solutions with a single objective are less likely to be successful given the forces' cross-correlation. Solutions require collaboration between different governments, public and private sectors, and subject matter experts across scientific domains. Only when all parties participate with a common strategic objective will society see the effects of a healthier relationship with the planet earth (De Wit et al., 2020). 

References

Almeida, F. L. F. (2017). Benefits, Challenges and Tools of Big Data Management. Journal of Systems Integration, 8(4), 12-20. https://doi.org/10.20470/jsi.v8i4.311

Cleveland, S., & Ellis, T. J. (2014). Orchestrating End-User Perspectives in the Software Release Process: An Integrated Release Management Framework. Advances in Human-Computer Interaction, 2014, 805307. https://doi.org/10.1155/2014/805307

COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). (2021). Johns Hopkins University of Medicine. https://coronavirus.jhu.edu/map.html

De Wit, W., Freschi, A., & Trench, E. (2020). COVID 19: Urgent Call to Protect People and Nature. Dalberg Advisors for World Wide Fund For Nature. https://c402277.ssl.cf1.rackcdn.com/publications/1348/files/original/FINAL_REPORT_EK-Rev_2X.pdf?1592404724

Dobson, A. P., Pimm, S. L., Hannah, L., Kaufman, L., Ahumada, J. A., Ando, A. W., Bernstein, A., Busch, J., Daszak, P., Engelmann, J., Kinnaird, M. F., Li, B. V., Loch-Temzelides, T., Lovejoy, T., Nowak, K., Roehrdanz, P. R., & Vale, M. M. (2020). Ecology and economics for pandemic prevention. Science, 369(6502), 379-381. https://doi.org/10.1126/science.abc3189

Dosi, G. (1982). Technological paradigms and technological trajectories: A suggested interpretation of the determinants and directions of technical change. Research Policy, 11(3), 147-162. https://doi.org/10.1016/0048-7333(82)90016-6

Dymecka, J., Gerymski, R., & Machnik-Czerwik, A. (2020). How does stress affect our life satisfaction during COVID-19 pandemic? Moderated mediation analysis of sense of coherence and fear of coronavirus. https://doi.org/10.1080/13548506.2021.1906436

Field, A. (2013). Discovering statistics using IBM SPSS statistics. Sage.

 

Giménez Roche, G., Calcei, D., & Neri, M. (2015). Business and Technological Paradigms: The Entrepreneurial Bridge. Available at SSRN 2675544. https://doi.org/10.2139/ssrn/2675544

Okoli, C., & Pawlowski, S. D. (2004). The Delphi method as a research tool: an example, design considerations and applications. Information & Management, 42(1), 15-29. https://doi.org/10.1016/j.im.2003.11.002

Sittig, D. F., & Singh, H. (2010). A new sociotechnical model for studying health information technology in complex adaptive healthcare systems. Quality & safety in health care, 19 Suppl 3(Suppl 3), i68-i74. https://doi.org/10.1136/qshc.2010.042085

Workshop Report on Biodiversity and Pandemics of the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES). (2020). I. Secretariat.