ABSTRACT
An efficient veterinary workforce is paramount for global health security as most emerging infectious diseases are zoonotic. Being a hotspot of disease outbreaks there is a need to strengthen the veterinary field epidemiology capacity in Cambodia. The COVID-19 pandemic has highlighted the need for a strong health security workforce in the Asia-Pacific. This study was conducted with an aim to understand veterinary epidemiology training gaps in Cambodia.A mixed method study using a concurrent triangulation design was conducted targeting the veterinary workforce. Univariable and multivariable regression and an inductive, thematic analysis was used. Survey responses from 108 veterinarians indicated that most (70%) respondents did not have any training, while only 6.0% had been to a Field Epidemiology Training Program for Veterinarians (FETPV). Lack of formal training in epidemiology was associated with non-participation in outbreak response (P< 0.05). The key informants suggested system level factors, limited staff, and perceived disconnect between the central and community level as likely barriers to efficient outbreak response. The need for epidemiology training of veterinarians targeting knowledge consolidation and skill development through experiential learning was emphasized. Our assessment recommends that, a multifaceted approach targeting pedagogical and structural aspects of veterinary field epidemiology in Cambodia is required.
Subject(s)
Communicable Diseases , Communicable Diseases, Emerging , COVID-19ABSTRACT
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. 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). 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 14,106,060 (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 males (64%) than females (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 year old age group (5,481; 95% UI 5,464–5,500 years). There were 4,823,791 (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.
Subject(s)
COVID-19ABSTRACT
Background: . This paper presents, for the first time, the Epidemic Volatility Index (EVI), a conceptually simple, early warning tool for emerging epidemic waves. Methods: . EVI is based on the volatility of the newly reported cases per unit of time, ideally per day, and issues an early warning when the rate of the volatility change exceeds a threshold. Results: . Results from the COVID-19 epidemic in Italy and New York are presented here, while daily updated predictions for all world countries and each of the United States are available online. Interpretation . EVI’s application to data from the current COVID-19 pandemic revealed a consistent and stable performance in terms of detecting oncoming 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 fast and optimize containment of outbreaks.