3 P’s of a Pandemic: Predict, Prevent and Preserve

The COVID-19 epidemic caught everyone off guard, but there has never been a more significant time to reflect on the past and prepare for the future. While the magnitude might not be at the scale of the COVID-19 pandemic, those emergencies in no way can be ignored. In 2014, the gravity of this unpreparedness was manifested when Ebola struck West Africa, claiming 11,000 lives. This is not the time to stop but to persevere in learning how to deal with the Zika virus that incessantly unravels new developments in epidemics. Each of those epidemics/pandemics has advanced the science and public health sector even further, but now is the time to move from responding to disease outbreaks to preparing for them in advance. As Peter Daszak, director of the EcoHealth Alliance, rightly expressed in his interview with The Guardian (Vidal, 2020), in this ‘age of pandemics’ the requirement is to start working on prevention along with rapid responses and to “identify all possible sources and dismantle those before the next pandemic strikes.”

The current pandemic has cost millions of lives and trillions of dollars as direct and indirect costs. A 3-P approach (Predict, Prevent, Preserve) approach is needed to address the sustainability of public healthcare delivery mechanisms. Infectious diseases can occur due to close contact between humans, animals, and the environment, increasing their likelihood. As a result, preparing for future epidemics and pandemics necessitates a particular focus on comprehending the intricacies of interactions between humans, animals, and the environment, as well as the various farming systems and their levels of biosecurity, disease prevention (for example, through vaccines) and rapid outbreak control across containment measures. Beyond “normal” infrastructures, networks, and disciplines, the epidemic has demonstrated the crucial need for collaboration.

Even though each pathogen presents specific challenges, certain common themes may apply to other outbreak situations in general. Echoing Kelly-Cirino et al. (2019), challenges such as “fragmented and unreliable funding pathways, limited access to specimens and reagents, inadequate diagnostic testing capacity at both national and community levels of healthcare and lack of incentives for companies to develop and manufacture diagnostics for priority pathogens during non-outbreak periods”, have impacted diagnostic preparedness which is fundamental for successful outbreak containment. In addition to this, with an ever-increasing population in the developing world, there will always be resource constraints in diagnostics preparedness and the overall public health surveillance systems to estimate the status of population health and ascertain emerging and evolving pathogens within the community.

Hence, there is a need for a system or a process that can routinely diagnose samples and signal an early warning whenever some pathogen of pandemic potential is detected. One such tool and concept that emerged out of its dormancy in the early COVID times is environmental Surveillance or Wastewater-based Epidemiology. This involves testing wastewater samples to detect traces of a pathogen and thus determine whether a population is at risk of infection. The presence of the pathogen in wastewater is a surrogate indicator based on the fact that the faecal discharge from the infected persons, sometimes in a very early stage of infection (even asymptomatic carriers), contains the same pathogen. This surrogate indicator, if tracked, can serve as an early warning of an outbreak in a new area, particularly in densely populated urban slums and congested urban pockets.

Environmental Surveillance has a long history of use in public health, particularly for poliovirus and antimicrobial resistance (AMR), but it has been mainly of limited help. In February 2020, a Netherlands-based agency KWR reported the first detection of SARS-CoV-2 in sewage and published their findings in March 2020. This publication triggered and pushed many researchers and public health agencies to test and implement environmental Surveillance for COVID outbreaks in several cities.

According to COVIDPoops19, a global COVID-19 WBE Collaborative, as many as 126 environmental surveillance initiatives across 64 countries show viral load trends in their respective dashboards, supporting local governments in tracking early signals from environmental Surveillance, as of 2nd March 2022. Environmental Surveillance can play a crucial role in addressing this issue. The system tests wastewater from different sources through a pooled sample representing human and animal genes. This could be a viable and highly cost-effective method of surveilling the biome in resource-constrained settings. These facts increase the scope of environmental Surveillance and push the idea of routine Surveillance of community wastewater in all cities, with a more multidisciplinary focus.

Collaborative approaches such as One-Health have gained traction, with numerous individuals and organisations claiming to be conducting OneHealth research. ‘One Health,’ according to the World Health Organization, is a method of planning and executing programmes, policies, laws, and research in which multiple sectors communicate and collaborate to improve public health outcomes. Despite the discussions, policy declarations, and research programmes, some significant recommendations were not adopted. A formal expert panel was recently formed to advise the tripartite (WHO, FAO, OIE) and the UN Environmental Programme (UNEP) on the future demand for One Health surveillance. This group takes a broader definition of One Health. It is currently evaluating regional examples of integrated One Health surveillance to examine the scalability of such systems to other regions, beginning with a systematic review of models for West Nile, avian influenza, rabies, Rift valley fever, and many others. Even in this scenario, environmental Surveillance could be the best possible method, provided that sample sites and collection methods are opted considering the representation of all living organisms in the biome.

Genomic Surveillance has taken centre stage as a result of the pandemic. SARS-CoV-2 genome sequencing has surpassed all other monitoring methods, with more genetic variants available after 18 months for SARS-CoV-2 than for influenza, HIV, and all foodborne bacterial species combined. Genomic sequencing has been accepted as an essential tool in public health, resulting in significant investments in expanding sequencing capacity. Given the widespread use of developed vaccines, considered effective against new variants, this capacity is unlikely to be required on the same scale shortly. As a result, it’s essential to consider what it would take to adapt the existing infrastructure and technologies to prepare for future outbreaks and pandemic risks.

In an attempt to acknowledge the gravity of the situation, developing an early warning and detection system and being prepared for the next pandemic might provide an effective solution, accompanied by a more robust international response. With regular Surveillance, data samples and forecast models, we can advance toward predicting pandemics using artificial intelligence and natural language processing to create early warning systems and predict possible disease outbreaks in the future.

However, very few people and organisations in the pandemic prediction and forecasting sphere reasons are multifold. First, historically speaking, there is inadequate data from the past pandemics (Vidal, J., 2020), possibly because of constraints caused by rapidly evolving pathogens and the absence of information systems. As a result, the data collected is of limited value and used after a particular time. Secondly, although most new infections are zoonotic, not all are transmissible to humans (The Lancet, 2016), resulting in a lackadaisical governance response mechanism and the implications thereof. These novel infectious diseases are likely to circulate in animals and humans before being detected in clinical cases. At this time, it becomes increasingly hard to contain the spread, especially in densely populated areas. As a result, standardised tests for samples representing the human and animal microbiome (bacteriome and virome) comparably over time and across countries. Given the dynamic nature of animal-human interaction, it is difficult to predict which animal group or virus group will cause the next pandemic (Wille et al., 2021). Furthermore, the world is at the initial stages of pandemic prediction, having barely scratched the surface.

Citizens, care providers, veterinarians, farmers, and others involved in sample collection are critical to the success of early detection programmes, whether risk-targeted monitoring, clinical diagnostic evaluation or syndromic Surveillance. Studies have demonstrated that capacity building of new clinicians and scientists occurs especially when the individuals who need to build a novel capacity also believe they are contributing and have a role to play. One possible way to encourage this is to give frontline settings the ability to analyse their data and contribute to conclusions and publications before sharing them publicly. Thus, we can direct resources toward open science, non-commercial web-based solutions to enable relatively inexperienced scientists on the clinical and veterinary frontlines. The aim is to complete simple analyses of their data and thus participate in global issues research rather than simply seeing their data exploited by others. Furthermore, international clinical trial networks established to respond quickly to new infections can improve collaboration (Aarestrup F. et al., 2021).

The R&D Blueprint (WHO) promises to build advocacy around rapid activation of research and development activities during epidemics “to accelerate diagnostics, vaccines and therapeutics for this novel coronavirus.” Moreover, strategies such as effective Surveillance, healthcare system diagnostics, capacity strengthening, and the establishment of sustainable financing and market strategies are potential ways to tackle this global emergency (Kelly-Cirino et al., 2019). Also, a coordinated implementation plan and multi-stakeholder governance can bolster the movement towards developing a global/countrywide centralised institution mechanism to predict pandemics and work towards effective management of epidemics.

The recent efforts in global genome monitoring have a lot of promise if we don’t make the same mistake as before. Environmental Surveillance promises to tackle most of the issues mentioned above. If taken on a large scale, it can change the existing public health surveillance systems and help city administration adequately control and manage pandemics caused by pathogens. Rather than recreating and maintaining the pre-existing silo, bringing these innovative critical alliances together would be a significant step forward. There is an urgent need to learn from the unexpectedly catastrophic times collectively we have witnessed in the hope of acting proactively and not reactively and strengthening our efforts to make evidence-informed policy action for building resilience globally.

Keywords: pandemic, epidemics, prediction, outbreaks, one Health, Surveillance

Contributing authors: Sabhimanvi Dua, Shirish Harshe, Spriha Atray, Sammy Shreedhar