One Health activities to reinforce intersectoral coordination at local levels in India.

India's dense human and animal populations, agricultural economy, changing environment, and social dynamics support conditions for emergence/re-emergence of zoonotic diseases that necessitate a One Health (OH) approach for control. In addition to OH national level frameworks, effective OH driven strategies that promote local intersectoral coordination and collaboration are needed to truly address zoonotic diseases in India. We conducted a literature review to assess the landscape of OH activities at local levels in India that featured intersectoral coordination and collaboration and supplemented it with our own experience conducting OH related activities with local partners. We identified key themes and examples in local OH activities. Our landscape assessment demonstrated that intersectoral collaboration primarily occurs through specific research activities and during outbreaks, however, there is limited formal coordination among veterinary, medical, and environmental professionals on the day-to-day prevention and detection of zoonotic diseases at district/sub-district levels in India. Examples of local OH driven intersectoral coordination include the essential role of veterinarians in COVID-19 diagnostics, testing of human samples in veterinary labs for Brucella and leptospirosis in Punjab and Tamil Nadu, respectively, and implementation of OH education targeted to school children and farmers in rural communities. There is an opportunity to strengthen local intersectoral coordination between animal, human and environmental health sectors by building on these activities and formalizing the existing collaborative networks. As India moves forward with broad OH initiatives, OH networks and experience at the local level from previous or ongoing activities can support implementation from the ground up.

Inherent virus characteristics and host range drive the zoonotic and emerging potential of viruses.

Understanding the zoonotic and emerging potential of viruses is critical to prevent and control spread that can cause disease epidemics or pandemics. We developed a database using the most up-to-date information from the International Committee on Taxonomy of Viruses (4958 virus species) and identified 1479 vertebrate virus species and their host ranges. Viral traits and host ranges were then used as predictors in generalized linear mixed models for three host-associated outcomes - confirmed zoonotic, potential zoonotic and disease emergence. We identified significant interactions between host range and viral characteristics, not previously reported, that influence the zoonotic and emergence potential of viruses. Bat- and livestock-adapted viruses posed high risk, and the risk increased substantially if these viruses were also present in other vertebrates or were not reported from invertebrates. Our model predicted 39 viruses of interest that have never been reported to have zoonotic potential (27) or to potentially become emerging human viruses (12). We conclude that nucleic acid type is important in identifying the zoonotic and emerging potential of viruses. We recommend enhanced surveillance and monitoring of these virus species identified with a zoonotic and emerging potential to mitigate disease outbreaks and future epidemics.

Disability-adjusted life years (DALYs) due to the direct health impact of COVID-19 in India, 2020.

COVID-19 has affected all countries. Its containment represents a unique challenge for India due to a large population (> 1.38 billion) across a wide range of population densities. Assessment of the COVID-19 disease burden is required to put the disease impact into context and support future pandemic policy development. Here, we present the national-level burden of COVID-19 in India in 2020 that accounts for differences across urban and rural regions and across age groups. Input data were collected from official records or published literature. The proportion of excess COVID-19 deaths was estimated using the Institute for Health Metrics and Evaluation, Washington data. Disability-adjusted life years (DALY) due to COVID-19 were estimated in the Indian population in 2020, comprised of years of life lost (YLL) and years lived with disability (YLD). YLL was estimated by multiplying the number of deaths due to COVID-19 by the residual standard life expectancy at the age of death due to the disease. YLD was calculated as a product of the number of incident cases of COVID-19, disease duration and disability weight. Scenario analyses were conducted to account for excess deaths not recorded in the official data and for reported COVID-19 deaths. The direct impact of COVID-19 in 2020 in India was responsible for 14,100,422 (95% uncertainty interval [UI] 14,030,129-14,213,231) DALYs, consisting of 99.2% (95% UI 98.47-99.64%) YLLs and 0.80% (95% UI 0.36-1.53) YLDs. DALYs were higher in urban (56%; 95% UI 56-57%) than rural areas (44%; 95% UI 43.4-43.6) and in men (64%) than women (36%). In absolute terms, the highest DALYs occurred in the 51-60-year-old age group (28%) but the highest DALYs per 100,000 persons were estimated for the 71-80 years old age group (5481; 95% UI 5464-5500 years). There were 4,815,908 (95% UI 4,760,908-4,924,307) DALYs after considering reported COVID-19 deaths only. The DALY estimations have direct and immediate implications not only for public policy in India, but also internationally given that India represents one sixth of the world's population.

The epidemic volatility index, a novel early warning tool for identifying new waves in an epidemic.

Early warning tools are crucial for the timely application of intervention strategies and the mitigation of the adverse health, social and economic effects associated with outbreaks of epidemic potential such as COVID-19. This paper introduces, the Epidemic Volatility Index (EVI), a new, conceptually simple, early warning tool for oncoming epidemic waves. EVI is based on the volatility of newly reported cases per unit of time, ideally per day, and issues an early warning when the volatility change rate exceeds a threshold. Data on the daily confirmed cases of COVID-19 are used to demonstrate the use of EVI. Results from the COVID-19 epidemic in Italy and New York State are presented here, based on the number of confirmed cases of COVID-19, from January 22, 2020, until April 13, 2021. Live daily updated predictions for all world countries and each of the United States of America are publicly available online. For Italy, the overall sensitivity for EVI was 0.82 (95% Confidence Intervals: 0.75; 0.89) and the specificity was 0.91 (0.88; 0.94). For New York, the corresponding values were 0.55 (0.47; 0.64) and 0.88 (0.84; 0.91). Consecutive issuance of early warnings is a strong indicator of main epidemic waves in any country or state. EVI's application to data from the current COVID-19 pandemic revealed a consistent and stable performance in terms of detecting new waves. The application of EVI to other epidemics and syndromic surveillance tasks in combination with existing early warning systems will enhance our ability to act swiftly and thereby enhance containment of outbreaks.