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.
Rabies is a devastating zoonotic disease of mammals that causes encephalitis and death. It is endemic in India, with an estimated annual 20,000 human deaths (one-third of the global rabies burden). ...The magnitude of animal rabies incidence is unknown.
In four sub-districts of Punjab, India, we monitored canine and livestock populations from August 15, 2016 to August 14, 2017. Demographic, clinical and rabies diagnostic laboratory (RDL) data were collected from suspected cases of rabies. The annual incidence rate / 10,000 animal years at risk (95% CI) in each sub-district was estimated for each species.
During 2016-2017, a total of 41 suspected rabies cases were detected in the four selected sub-districts in Punjab. Laboratory confirmed rabies (LCR) incidence was 2.03/10,000 dog years (0.69, 5.96) and 2.71/10,000 dog years (1.14, 6.43) in stray and pet dogs, respectively. The LCR incidence in farmed buffalo and cattle was 0.19/10,000 buffalo years (0.07, 0.57) and 0.23/10,000 cattle years (0.06, 0.88), respectively. The LCR incidence amongst equine was 4.28/10,000 equine years (0.48, 38.10). Stray cattle rabies incidence in the selected sub-districts was 9.49/10,000 cattle years (3.51, 25.67). If similar enhanced surveillance for rabies was conducted state-wide, we estimate that 98 (34-294) buffalo, 18 (2-156) equine, 56 (15-214) farmed cattle, 96 (35-259) stray cattle, 128 (54-303) pet dogs and 62 (21-182) stray dogs would be expected to be confirmed with rabies in Punjab annually.
These results indicate that rabies incidence in animals, particularly in dogs and stray cattle, is much higher than previously suspected. We recommend that statewide enhanced disease surveillance should be conducted to obtain more accurate estimates of rabies incidence in Punjab to facilitate better control of this important disease.
Brucellosis is endemic in the bovine population in India and causes a loss of US$ 3·4 billion to the livestock industry besides having a significant human health impact.
We developed a stochastic ...simulation model to estimate the impact of three alternative vaccination strategies on the prevalence of Brucella infection in the bovine populations in India for the next two decades: (a) annual mass vaccination only for the replacement calves and (b) vaccination of both the adult and young population at the beginning of the program followed by an annual vaccination of the replacement calves and, (c) annual mass vaccination of replacements for a decade followed by a decade of a test and slaughter strategy.
For all interventions, our results indicate that the prevalence of Brucella infection will drop below 2% in cattle and, below 3% in buffalo after 20 years of the implementation of a disease control program. For cattle, the Net Present Value (NPV) was found to be US $ 4·16 billion for intervention (a), US $ 8·31 billion for intervention (b) and, US $ 4·26 for intervention (c). For buffalo, the corresponding NPVs were US $ 8·77 billion, US $ 13·42 and, US $ 7·66, respectively. The benefit cost ratio (BCR) for the first, second and the third intervention for cattle were 7·98, 10·62 and, 3·16, respectively. Corresponding BCR estimates for buffalo were 17·81, 21·27 and, 3·79, respectively.
These results suggest that all interventions will be cost-effective with the intervention (b), i.e. the vaccination of replacements with mass vaccination at the beginning of the program, being the most cost-effective choice. Further, sensitivity analysis revealed that all interventions will be cost-effective even at the 50% of the current prevalence estimates. The results advocate for the implementation of a disease control program for brucellosis in India.
Understanding human disease, zoonoses and emergence is a global priority. A deep understanding of pathogen ecology and the complex inherent relationships at the agent–environment interface are ...essential to inform disease control and mitigation and to predict the next zoonotic pandemic. Here, we present the first analysis of social and environmental factors associated with human, zoonotic and emerging pathogen diversity at a global scale, controlling for research effort. Predictor–response associations were captured by generalized additive models. We used national level data to aid in policy development to inform control and mitigation. We show that human population density, land area, temperature and the human development index at the country level are associated with human, emerging and zoonotic pathogen diversity. Multiple models demonstrating society–agent–environment interactions demonstrate that social, environmental and geographical factors predict global pathogen diversity. The analyses demonstrate that weather variables (temperature and rainfall) have the potential to influence pathogen diversity.
The interplay between agent-host-environment characteristics is responsible for the emergence and zoonotic potential of infectious disease pathogens. Many studies have investigated key agent ...characteristics and environmental factors responsible for these phenomena. However, little is known about the role played by host characteristics in zoonoses, disease emergence and the ability of pathogens to infect multiple hosts. We compiled a dataset of 8114 vertebrate host–agent interactions from published literature. Multiple host characteristics and the pathogen's zoonotic, emergence and multi-host potential were then linked to the dataset. The associations between zoonotic, emerging human pathogen and multi-host pathogenicity and several host characteristics were explored using logistic regression models. The numbers of publications and sequences from the agent–host combinations were used to control for the research effort. Hosts in the class Aves (odds ratio OR 20.87, 95% CI 2.66–163.97) and Mammalia (OR 26.09, 95% CI 3.34–203.87) were more likely to host a zoonotic pathogen compared to the class Amphibia. Similarly, hosts having Bursa fabricii (i.e., birds) (OR 1.8, 95% CI 1.4–2.3) were more likely to host an emerging human pathogen. The odds of being a zoonotic pathogen were highest when the host female required a greater number of days for maturity, and the pathogen was able to affect a greater number of host species. In contrast, the hosts from which a higher number of pathogens were reported were less likely (OR 0.39, 95% CI 0.31–0.49) to be associated with an emerging human pathogen. The odds of an emerging human pathogen were highest when the host had a higher adult body mass, and the specific pathogen could affect more host species. The odds of a pathogen infecting multiple hosts were highest when a host had shorter female maturity days (>670–2830 days) and lower birth/hatching weight (>42.2–995 g) compared to longer female maturity days (>2830–6940 days) and greater birth/hatching weight (>3.31–1160 kg). We conclude that several host characteristics – such as mass, maturity, immune system and pathogen permissiveness- are linked with zoonoses, disease emergence or multi-host pathogenicity. These findings can contribute to preparedness for emerging infections and zoonotic diseases.
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.
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