Cystic echinococcosis (CE), caused by Echinococcus granulosus s.l. is a neglected zoonosis posing a significant public health challenge. Little is known about human CE in Bhutan. This study was conducted to gain an understanding of the burden, distribution, and potential risk factors of CE in Bhutan. From January 2015 to December 2019 data from Jigme Dorji Wangchuck National Referral Hospital (JDWNRH) and 6 other district-level hospitals were reviewed. Descriptive statistics were used to summarize the data. DALYs and Poisson regression models were used to estimate the burden and explore the relationship between cases and possible risk factors. A total of 159 cases were recorded. Most cases (145) were admitted to the surgical ward and 14 cases were referred to India. The average annual incidence was 4.4 cases per 100 000 population. The burden of disease was estimated to be approximately 39 DALYs per year for treatment-seeking cases, or possibly 80 DALYs per year including non-treatment seeking cases. This translates to approximately to 5.2 DALYs and 10.2 per 100 000 per year respectively. The commonest sites of infection were the liver (78%) and lungs (13%). Most cases were treated with surgery (>82%), and more than 47% were admitted to the hospital for >4 days. Policy interventions targeting community engagement, awareness, education, high risk occupational groups, females, and those living in the endemic districts of the central and western regions may yield larger gains. More studies and the institution of a surveillance system can help better guide policy interventions.

Using the case of chronic wasting disease (CWD) in white-tailed deer, we estimated hunters’ willingness-to-pay (WTP) for testing their game and determined how their perception of disease risk, trust, and confidence in wildlife agency affected hunter participation in ongoing disease surveillance. The average WTP for testing was $23.75 per deer, and it was positively related to trust and confidence in the wildlife agency and the perception of risk about deer populations in the declining area and the pathogen spreading to other areas. These findings imply that implementing active outreach programs can improve hunter participation in user-paid systems for CWD surveillance.

Avian influenza (AI) is an important disease that has significant implications for animal and human health. High pathogenicity AI (HPAI) has emerged in consecutive seasons within the UK to cause the largest outbreaks recorded. Statutory measures to control outbreaks of AI virus (AIV) at poultry farms involve disposal of all birds on infected premises. Understanding of the timing of incursions into the UK could facilitate decisions on improved responses. During the autumnal migration and wintering period (autumn 2019– spring 2020), three active sampling approaches were trialled for wild bird species considered likely to be involved in captive AI outbreaks with retrospective laboratory testing undertaken to define the presence of AIV.

Faecal sampling of birds (n = 594) caught during routine and responsive mist net sampling failed to detect AIV. Cloacal sampling of hunter-harvested waterfowl (n = 146) detected seven positive samples from three species with the earliest detection on the 17 October 2020. Statutory sampling first detected AIV in wild and captive birds on 3 November 2020. We conclude that hunter sourced sampling of waterfowl presents an opportunity to detect AI within the UK in advance of outbreaks on poultry farms and allow for early intervention measures to protect the national poultry flock.

The coronavirus disease 2019 (COVID-19) pandemic continues to present unique public health challenges both within the United States and across the globe. Institutions of higher learning are tasked with preventing and responding to COVID-19 on campus while also considering implications for the surrounding communities. The process of re-opening campus, whether at full or partial capacity, has tasked these institutions with overcoming complex challenges associated with balancing the resumption of campus operations while simultaneously protecting university affiliates and surrounding community members from COVID-19 through robust surveillance, contact tracing, and testing efforts. Here, we provide a concise outline related to the development and implementation of the comprehensive and sustainable COVID-19 surveillance program at the University of Florida. We also critically discuss the successes and pitfalls of this program while also providing recommendations for the development of similar programs in the future.

This study aimed to analyse the trend and spatial–temporal clusters of risk of transmission of COVID-19 in northeastern Brazil. We conducted an ecological study using spatial and temporal trend analysis. All confirmed cases of COVID-19 in the Northeast region of Brazil were included, from 7 March to 22 May 2020. We used the segmented log-linear regression model to assess time trends, and the local empirical Bayesian estimator, the global and local Moran indexes for spatial analysis. The prospective space–time scan statistic was performed using the Poisson probability distribution model. There were 113 951 confirmed cases of COVID-19. The average incidence rate was 199.73 cases/100 000 inhabitants. We observed an increasing trend in the incidence rate in all states. Spatial autocorrelation was reported in metropolitan areas, and 178 municipalities were considered a priority, especially in the states of Ceará and Maranhão. We identified 11 spatiotemporal clusters of COVID-19 cases; the primary cluster included 70 municipalities from Ceará state. COVID-19 epidemic is increasing rapidly throughout the Northeast region of Brazil, with dispersion towards countryside. It was identified high risk clusters for COVID-19, especially in the coastal side.

With the push towards control and elimination of soil-transmitted helminthiasis and schistosomiasis in low- and middle-income countries, there is a need to develop alternative diagnostic assays that complement the current in-country resources, preferably at a lower cost. Here, we describe a novel high-resolution melt (HRM) curve assay with six PCR primer pairs, designed to sub-regions of the nuclear ribosomal locus. Used within a single reaction and dye detection channel, they are able to discriminate Ancylostoma duodenale, Necator americanus, Strongyloides stercoralis, Ascaris lumbricoides, Trichuris trichiuria and Schistosoma spp. by HRM curve analysis. Here we describe the primers and the results of a pilot assessment whereby the HRM assay was tested against a selection of archived fecal samples from Ghanaian children as characterized by Kato–Katz and real-time PCR analysis with species-specific TaqMan hydrolysis probes. The resulting sensitivity and specificity of the HRM was 80 and 98.6% respectively. We judge the assay to be appropriate in modestly equipped and resourced laboratories. This method provides a potentially cheaper alternative to the TaqMan method for laboratories in lower resource settings. However, the assay requires a more extensive assessment as the samples used were not representative of all target organisms.

The West African 2014 Ebola outbreak has highlighted the need for a better information network. Hybrid information networks, an integration of both hierarchical and formalized command control-driven and community-based, or ad hoc emerging networks, could assist in improving public health responses. By filling the missing gaps with social media use, the public health response could be more proactive rather than reactive in responding to such an outbreak of global concern. This article provides a review of the current social media use specifically in this outbreak by systematically collecting data from ProQuest Newsstand, Dow Jones Factiva, Program for Monitoring Emerging Diseases (ProMED) as well as Google Trends. The period studied is from 19 March 2014 (first request for information on ProMED) to 15 October 2014, a total of 31 weeks. The term ‘Ebola’ was used in the search for media reports. The outcome of the review shows positive results for social media use in effective surveillance response mechanisms – for improving the detection, preparedness and response of the outbreak – as a complement to traditional, filed, work-based surveillance approach.

In the European Union, Meat Inspection (MI) aims to protect public health by ensuring that minimal hazardous material enters in the food chain. It also contributes to the detection and monitoring of animal diseases and welfare problems but its utility for animal surveillance has been assessed partially for some diseases only. Using the example of poultry production, we propose a complete assessment of MI as a health surveillance system. MI allows a long-term syndromic surveillance of poultry health but its contribution is lowered by a lack of data standardization, analysis and reporting. In addition, the probability of case detection for 20 diseases and welfare conditions was quantified using a scenario tree modelling approach, with input data based on literature and expert opinion. The sensitivity of MI appeared to be very high to detect most of the conditions studied because MI is performed at batch level and applied to a high number of birds per batch.

Animal disease surveillance is watching an animal population closely to determine if a specific disease or a group of diseases makes an incursion so that a prior plan of action can be implemented. The purpose of this paper is to review existing tools and techniques for an animal disease-surveillance system that can incorporate the monitoring of climate factors and related data to enhance understanding of disease epidemiology. In recent decades, there has been interest in building information systems by combining various data sources for different purposes. Within the field of animal health, there have only been limited attempts at the integration of surveillance data with relevant climate conditions. Statistical techniques for data integration, however, have been explored and used by several disciplines. Clearly the application of available techniques for linking climate data with surveillance systems should be explored with the aim of facilitating prevention, mitigation, and adaptation responses in the surveillance setting around climate change and animal disease risks. Drawing on this wider body of work, three of the available techniques that can be utilized in the analysis of surveillance data with the available climate data sets are reviewed.

Sentinel species are increasingly used by disease managers to detect and monitor the prevalence of zoonotic diseases in wildlife populations. Characterizing home-range movements of sentinel hosts is thus important for developing improved disease surveillance methods, especially in systems where multiple host species co-exist. We studied ranging activity of major hosts of bovine tuberculosis (TB) in an upland habitat of New Zealand: we compared home-range coverage by ferrets (Mustela furo), wild deer (Cervus elaphus), feral pigs (Sus scrofa), brushtail possums (Trichosurus vulpecula) and free-ranging farmed cattle (Bos taurus). We also report in detail the proportional utilization of a seasonal (4-monthly) range area for the latter four species. Possums covered the smallest home range (<30 ha), ferrets covered ∼100 ha, pigs ∼4 km2, deer and cattle both >30 km2. For any given weekly period, cattle, deer and pigs were shown to utilize 37–45% of their estimated 4-month range, while possums utilized 62% during any weekly period and 85% during any monthly period of their estimated 4-month range. We suggest that present means for estimating TB detection kernels, based on long-term range size estimates for possums and sentinel species, probably overstate the true local surveillance coverage per individual.

This paper provides an overview of disaster public health preparedness, response, and recovery activities with particular reference to examples that have occurred in California. It discusses the public health considerations from two aspects: 1) general public health effects; and 2) public and environmental health control measures. The latter discussion is divided into: 1) drinking water; 2) human wastes; 3) food; 4) personal hygiene; 5) mass care and shelter; 6) solid waste and debris; 7) hazardous materials; 8) injury prevention programs and public health information; 9) vector control; and 10) disease control and surveillance. Two tables summarize the disaster medical and health functions as they relate to public health.