PODD: Participatory One Health Disease Detection

Turning local farmers into disease detectives: A participatory surveillance platform that empowers communities to prevent zoonotic spillovers

Patipat Susumpow

Cofounder, Opendream

We are combating the spread of zoonotic emerging infectious diseases before they spread to humans and become pandemics. An estimated 60% of known infectious diseases—such as SARS, MERS, Avian Flu, Swine Flu, and HIV—are zoonotic in origin, meaning they “spillover” from animals to humans. Zoonosis represents over 75% of all emerging diseases, with COVID-19 serving as the latest example of how spillover events impact our world. 

Zoonosis is a major barrier to poverty alleviation around the world, particularly in less developed countries that lack disease surveillance infrastructure and depend on animals for daily life. As our cities grow, expanding farmland and deforestation encroach on wild animal habitats, increasing the likelihood of spillover. This is especially worrying since the majority of livestock owners practice backyard farming in close proximity to other animals and people. According to most infectious disease experts, it’s not a matter of if—but when the next pandemic will happen.

The problem isn’t only that we eat meat or have overcrowded farms; it’s the missing link between farmers and local government authorities who are capable of responding to an outbreak. Effective and ubiquitous early warning systems, like PODD, are the difference between a few sick chickens… and COVID-19.

Livestock owners have a vested interest in reporting suspected illness on PODD, knowing it comes with expert veterinarian care for their animals—often provided even with false alarms. For livestock owners, this means improved animal health and a lessened risk of disease transmission. And if the case turns out to be a highly probable outbreak, then local health officials will quarantine the sick animals, thus saving the rest of the livestock and possibly their own families’ lives. 

The participation of “disease detectives” powers digital disease surveillance systems for local governments by better equipping them with relevant and timely data. Local health authorities can monitor the status of sick animals in real-time from their offices as farmers take pictures and upload them to the PODD system.

Without government buy-in, data is just unactionable information. Indeed, national governments benefit as PODD adds surveillance capacities previously unavailable to them. Ministries of agriculture and public health have access to granular local data, which helps to improve understanding of disease trends, burden of disease, and inform allocation of (sometimes scarce) national resources for disease control. 

Lastly, the general public benefits because fewer animal outbreaks lowers the frequency of spillover events, and therefore—fewer human pandemics.

The entire suite of software running PODD is open sourced — 100% free to copy & use and globally accessible to anyone with an internet connection. 

Additionally, the animal health reports are submitted to our general public website Sicksense.org, which aggregates human, animal, and environmental reports into a single One Health database accessible to anyone with an internet connection.

When you empower local communities with technology to solve problems themselves, they care more about the solution. Our theory of change is that giving communities tools to monitor the health of their environment, animals, and themselves is in fact the best way to stop spillovers before it’s too late. 

In Thailand, 40% of farming households are living below the poverty line, depending on $2.78 dollars per day for the entire family. Their lives truly depend on the health of their animals. PODD enables these families to detect small-scale animal outbreaks, and contain the spread before the national government shuts down the entire province (limiting trade) or orders their entire livestock to be culled, plunging them deeper into poverty.

While stopping “The Next Big One” has clear and tangible impacts for the world, preventing the frequent minor outbreaks matters as well. These smaller animal-only outbreaks matter not only because saving a local government $4 million and keeping farmers in business are positive, but focusing on challenges that directly impact farmers’ lives leads to better preparedness for a pandemic when it arrives. Engaging farmers beyond rare pandemic events results in more participatory data—and stronger early detection systems in the future.

PODD has had measurable and sustained impact in Thailand. In order to scale beyond Thailand, we have created a “PODD Toolkit,” which any partner can use to implement a local version of the system for their country. 

The Toolkit consists of two components:

1. Open-source PODD software

2. A “Disease Detective Programme” with instructions for recruitment, training, and management of volunteers

The goal for the Toolkit is to democratize the ability for lower to middle income countries—like Thailand—to identify and respond to future pandemics. 

We’ve targeted academic veterinary science institutions and public health authorities and offered the open source PODD software. The institution then acts as our local PODD partner, who either implements the software themselves (accessible by open source) or asks us to help them (“One Health as a Service / OHaaS”). Enlisting the support of regional partners, who are experts within their own community, and packaging PODD to easily implement in any country allows our solution to scale rapidly. 

We already have the Toolkit deployed across Thailand, and plan to have Cambodia, Laos, Indonesia, Vietnam, India, and Uganda onboarded with their own PODD implementations within the next 3 years.

Improving early warning systems and response times saves lives and prevents catastrophic economic damage. We look at 4 key impact areas when evaluating a local implementation of PODD:

Environmental Impact

• Time from animal illness to time of PODD report. 

• Time from PODD report to response.

• Animal outbreaks detected and controlled within the community of origin.

Human Impact

• Participation and retention of local community members. Every three months, we train volunteers on animal and humans diseases and the PODD application. Using qualitative and quantitative questions, we survey volunteers to measure programme commitment and user experience with PODD.

Economic Impact

• Monitor economic benefits of PODD disease surveillance for local governments by comparing economic loss from prior outbreaks.

• Track local resources utilized to maintain training and on-going implementation. This shows how communities and local governments are sustaining PODD.

Socio-Economic Impact

• The Social Return on Investment (SROI) will be measured for monitoring impact on social, economic, and environmental levels. SROI focuses on the positive or negative shifts of those three components, which will indicate the project’s direction (such as the need to expand in terms of time period or area).

A community project with local ownership of data, while faster to implement and easier to adjust according to rapidly changing environmental conditions (such as outbreaks), can at first glance be difficult to combine with siloed data initiatives from national governments. 

It turns out that national governments benefit tremendously from localized surveillance systems—extending and complimenting their existing systems. In Thailand, we engage directly with the central government, informing them of our relevant findings in a format that enhances their existing systems without integrating with them, and collaborating in order to identify future target areas for PODD surveillance. We provide actionable insights, and in return, we receive access to other provincial public health and agriculture agencies for further PODD expansion.

Over the next 3 years, we would benefit from a partnership with the Brunswick Group, whose Public Affairs team might help us engage with government stakeholders as we expand PODD internationally.

1. Opendream: technology lead

2. Chiang Mai University: operations lead

3. Ending Pandemics: funding, mentorship, advisor, epidemiological expertise

4. MIT Solve: funding, mentorship

5. People’s Prize: funding, mentorship, incubation

6. Afyadata: regional partner in Tanzania

7. Makerere University School of Public Health: regional partner in Uganda

8. College of Veterinary Sciences in Kerala: regional partner in India
   

9. SEAHUN
   

When the Covid-19 pandemic hit, we were already six years into implementing our PODD platform within communities across Thailand. We’ve designed this technology specifically to assist in identifying, responding, and recovering from public health crises. So when we learned about The Trinity Challenge, it felt like a natural fit—an opportunity that could help us scale PODD into new territory, while still maintaining the affordability, equity, and democratization that are essential elements of our mission.

Locally in Thailand, we’ve witnessed the shortcomings of traditional bureaucratic approaches to problems, and that’s become our impetus to create better solutions and give access to everyday citizens. This means teaming up with experts across multilateral organizations and iterating our products until we know they can achieve localized impact. 

PODD has created lasting impact in Thailand, but internationally we need help. The Trinity Challenge Members (and Judges) can make a positive and lasting impact for the public good by partnering with us. We’ve selected some ideal partners (listed below) but given the opportunity we would hope to grow this list.

Our goal is to connect with all academic institution Members and pitch their public health/veterinary/epidemiology departments to become part of the PODD regional network.

We have also identified key technology and global health leaders who would be ideal partners:

• Professor Tan Chorh Chuan’s experience leading Singapore’s response to the 2003 SARS outbreak would be helpful for expanding PODD’s coverage in Asia. 

• Sheetal Silal’s research using mathematical models to predict infectious diseases could help strengthen PODD’s early warning system.

• Jia Li’s deep knowledge of machine vision could help us improve our AI to recognize true outbreaks from image data.

• BlueDot - we want to contribute our participatory data to their event based surveillance system.

• Zenysis’ machine learning platform could improve our ML, and we could be a social impact case study for them. We also seek advice in building tech for government. 

• Dr Dylan George’s career in forecasting infectious disease risks could elevate our pre-spillover technology.

• Global Virome Project’s global consortium is without a doubt the perfect launchpad for PODD, specifically Dr Jonna Mazet because of her One Health leadership and her directorship of PREDICT; potential advisor for PODD.