Wet Markets and Food Safety: TripAdvisor for Improved Global Digital Surveillance

Community-based symptom reporting among agro-pastoralists and their livestock in Chad in a One Health approach

One Health Syndromic Surveillance has a high potential for detecting early epidemiological events in remote and hard-to-reach populations. Chadian pastoralists living close to their animals and being socio-economically unprivileged have an increased risk for zoonosis exposure. Engaging communities in disease surveillance could also strengthen preparedness capacities for outbreaks in rural Chad. This study describes a retrospective cross-sectional survey that collected data on clinical symptoms reported in people and livestock in Chadian agro-pastoral communities. In January-February 2018, interviews were conducted in rural households living in nomadic camps or settled villages in the Yao and Danamadji health districts. The questionnaire covered demographic data and symptoms reported in humans and animals for the hot, wet, and cold seasons over the last 12 months. Incidence rates of human and animal symptoms were comparatively analyzed at the household level. Ninety-two households with a homogeneous socio-demographic distribution were included. We observed cough and diarrhea as the most frequent symptoms reported simultaneously in humans and animals. In all species, the incidence rate of cough was significantly higher during the cold season, and diarrhea tended to occur more frequently during the wet season. However, the incidence rate of cough and diarrhea in animals did not predict the incidence rate of these symptoms in humans. Overall, the variations in reported symptoms were consistent with known seasonal, regional, and sociological influences on endemic diseases. Our retrospective study demonstrated the feasibility of collecting relevant health data in humans and animals in remote regions with low access to health services by actively involving community members. This encourages establishing real-time community-based syndromic surveillance in areas such as rural Chad.

Generating Contextual Variables From Web-Based Data for Health Research: Tutorial on Web Scraping, Text Mining, and Spatial Overlay Analysis

BACKGROUND

Contextual variables that capture the characteristics of delimited geographic or jurisdictional areas are vital for health and social research. However, obtaining data sets with contextual-level data can be challenging in the absence of monitoring systems or public census data.

OBJECTIVE

We describe and implement an 8-step method that combines web scraping, text mining, and spatial overlay analysis (WeTMS) to transform extensive text data from government websites into analyzable data sets containing contextual data for jurisdictional areas.

METHODS

This tutorial describes the method and provides resources for its application by health and social researchers. We used this method to create data sets of health assets aimed at enhancing older adults' social connections (eg, activities and resources such as walking groups and senior clubs) across the 374 health jurisdictions in Catalonia from 2015 to 2022. These assets are registered on a web-based government platform by local stakeholders from various health and nonhealth organizations as part of a national public health program. Steps 1 to 3 involved defining the variables of interest, identifying data sources, and using Python to extract information from 50,000 websites linked to the platform. Steps 4 to 6 comprised preprocessing the scraped text, defining new variables to classify health assets based on social connection constructs, analyzing word frequencies in titles and descriptions of the assets, creating topic-specific dictionaries, implementing a rule-based classifier in R, and verifying the results. Steps 7 and 8 integrate the spatial overlay analysis to determine the geographic location of each asset. We conducted a descriptive analysis of the data sets to report the characteristics of the assets identified and the patterns of asset registrations across areas.

RESULTS

We identified and extracted data from 17,305 websites describing health assets. The titles and descriptions of the activities and resources contained 12,560 and 7301 unique words, respectively. After applying our classifier and spatial analysis algorithm, we generated 2 data sets containing 9546 health assets (5022 activities and 4524 resources) with the potential to enhance social connections among older adults. Stakeholders from 318 health jurisdictions registered identified assets on the platform between July 2015 and December 2022. The agreement rate between the classification algorithm and verified data sets ranged from 62.02% to 99.47% across variables. Leisure and skill development activities were the most prevalent (1844/5022, 36.72%). Leisure and cultural associations, such as social clubs for older adults, were the most common resources (878/4524, 19.41%). Health asset registration varied across areas, ranging between 0 and 263 activities and 0 and 265 resources.

CONCLUSIONS

The sequential use of WeTMS offers a robust method for generating data sets containing contextual-level variables from internet text data. This study can guide health and social researchers in efficiently generating ready-to-analyze data sets containing contextual variables.

Using fresh vegetable waste from Chinese traditional wet markets as animal feed: Material feasibility and utilization potential

To develop new animal feed sources and establish a sustainable food upcycling system, the material feasibility and feeding potential of fresh vegetable waste (FVW) were clarified in this study. First, the FVW output of wet markets in Hangzhou, China was tracked and predicted. The results showed that the retail waste ratio of FVW in wet markets reached 9.3 %, predicting that China's FVW will reach 9034 kt in 2030. Second, the study revealed that the nutritive value of FVW was comparable to that of traditional alfalfa feed, suitable for use as animal feed. However, we found a high probability of microbial contamination. Therefore, FVW should have stricter classification and collection methods. Under this premise, the feeding utilization potential of FVW in wet markets is large. In 2030, the crude protein content may replace 2737 kt of alfalfa, saving 7.7 E + 08 m3 of water and 75,018 ha of land.

A Situational Analysis of the Impact of the COVID-19 Pandemic on Digital Health Research Initiatives in South Asia

The objective of this paper was to evaluate and compare the quantity and sustainability of digital health initiatives in the South Asia region before and during the COVID-19 pandemic. The study used a two-step methodology of (a) descriptive analysis of digital health research articles published from 2016 to 2021 from South Asia in terms of stratification of research articles based on diseases and conditions they were developed, geography, and tasks wherein the initiative was applied and (b) a simple and replicable tool developed by authors to assess the sustainability of digital health initiatives using experimental or observational study designs. The results of the descriptive analysis highlight the following: (a) there was a 40% increase in the number of studies reported in 2020 when compared to 2019; (b) the three most common areas wherein substantive digital health research has been focused are health systems strengthening, ophthalmic disorders, and COVID-19; and (c) remote consultation, health information delivery, and clinical decision support systems are the top three commonly developed tools. We developed and estimated the inter-rater operability of the sustainability assessment tool ascertained with a Kappa value of 0.806 (±0.088). We conclude that the COVID-19 pandemic has had a positive impact on digital health research with an improvement in the number of digital health initiatives and an improvement in the sustainability score of studies published during the COVID-19 pandemic.

Risk assessment and preventive health behaviours toward COVID-19 amongst bushmeat handlers in Nigerian wildlife markets: Drivers and One Health challenge

Over 70% of emerging infectious diseases are zoonotic and 72% of them have wildlife reservoirs with consequent global health impacts. Both SARS-CoV-1 and SARS-CoV-2 emerged certainly through wildlife market routes. We assessed wildlife handlers' zoonotic risk perceptions and preventive health behaviour measures toward COVID-19 during pandemic waves, and its drivers at wildlife markets using Health Belief Model (HBM) constructs. A cross-sectional study was conducted at purposively selected wildlife markets in Nigeria between November 2020 and October 2021. Descriptive, univariate, and multivariable logistic regressions analyses were performed at 95% confidence interval. Of the 600 targeted handlers in 97 wildlife markets, 97.2% (n = 583) participated. Consumers were the majority (65.3%), followed by hunters (18.4) and vendors (16.3%). Only 10.3% hunters, 24.3% vendors and 21.0% consumers associated COVID-19 with high zoonotic risk. Also, only few handlers practiced social/physical distancing at markets. Avoidance of handshaking or hugging and vaccination was significantly (p = 0.001) practiced by few handlers as preventive health behaviours at the markets. All the socio-demographic variables were significantly (p<0.05) associated with their knowledge, risk perceptions, and practice of preventive health behaviours toward COVID-19 at univariate analysis. Poor markets sanitation, hygiene, and biosecurity (OR=3.35, 95% CI: 2.33, 4.82); and poor butchering practices and exchange of wildlife species between shops [(OR=1.87; 95% CI: 1.34, 2.60) and (OR=2.03; 95% CI: 1.43, 2.88), respectively] were more likely to significantly influence COVID-19 emergence and spread at the markets. To tackle the highlighted gaps, collaborations between the public health, anthropologists, and veterinary and wildlife authorities through the One Health approach are advocated to intensify awareness and health education programmes that will improve perceptions and behaviours toward the disease and other emerging diseases control and prevention.

Evaluation of Tourism Food Safety and Quality with Neural Networks

Food safety issues are inextricably linked to people's lives and, in extreme cases, endanger public safety and social stability. People are becoming increasingly concerned about food safety issues in a modern society with high-quality economic development. People's incomes are increasing day by day as the economy continues to grow, and the tourism industry has grown by leaps and bounds. However, many problems arose, such as the issue of food safety in tourism. Tourism food safety issues affect not only the development of the food industry but also the development of tourism. Food safety oversight of tourist attractions has always been a relatively concerning issue in the country, and it is also something that the general public is concerned about. It can be said that food safety supervision of tourist attractions is the most important thing in food safety supervision. In this context, it becomes an important task to evaluate the safety of tourist food. This work proposes a multiscale convolutional neural network (AMCNN) combined with neural networks and attention layers to realize the safety and quality evaluation of tourist food. The algorithm uses the lightweight Xception network as a basic model and utilizes multiscale depth-separable convolution modules of different sizes for feature extraction and fusion to extract richer food safety feature information. Furthermore, the convolutional attention module (CBAM) is embedded on the basis of the multiscale convolutional neural network, which makes the network model focus more on discriminative features.

Success in animal skin fashion in African countries or the boom of the wet market

The world and the way things are done have changed, from selling clothing in brick-and-mortar stores to online shopping through social media platforms. Population growth has significantly contributed to an increased clothing demand, which, in turn, has increased the demand for animal skin. Traditional markets, also known as wet markets, are considered as major zoonotic disease reservoirs due to human and animal contact. Some groups and individuals continue to believe in traditional medicine and clothing that is made from animal skin, and such beliefs are more accessible with the presence of wet markets. Hence, animal poaching and trafficking have increased to meet the high demands, primarily in the Western world. Poverty is a well-known motivation to commit a crime. Conservationists should not only look at the animal regulation site to propose a solution to animal poaching and trafficking but should also consider communal poverty. Thus, this review aimed to highlight the role of wet market and animal skin fashion on animal welfare and human health.

Live and Wet Markets: Food Access versus the Risk of Disease Emergence

Emerging zoonotic diseases exert a significant burden on human health and have considerable socioeconomic impact worldwide. In Asia, live animals as well as animal products are commonly sold in informal markets. The interaction of humans, live domestic animals for sale, food products, and wild and scavenging animals, creates a risk for emerging infectious diseases. Such markets have been in the spotlight as sources of zoonotic viruses, for example, avian influenza viruses and coronaviruses, Here, we bring data together on the global impact of live and wet markets on the emergence of zoonotic diseases. We discuss how benefits can be maximized and risks minimized and conclude that current regulations should be implemented or revised, to mitigate the risk of new diseases emerging in the future.

An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time

Given still-high levels of coronavirus disease 2019 (COVID-19) susceptibility and inconsistent transmission-containing strategies, outbreaks have continued to emerge across the United States. Until effective vaccines are widely deployed, curbing COVID-19 will require carefully timed nonpharmaceutical interventions (NPIs). A COVID-19 early warning system is vital for this. Here, we evaluate digital data streams as early indicators of state-level COVID-19 activity from 1 March to 30 September 2020. We observe that increases in digital data stream activity anticipate increases in confirmed cases and deaths by 2 to 3 weeks. Confirmed cases and deaths also decrease 2 to 4 weeks after NPI implementation, as measured by anonymized, phone-derived human mobility data. We propose a means of harmonizing these data streams to identify future COVID-19 outbreaks. Our results suggest that combining disparate health and behavioral data may help identify disease activity changes weeks before observation using traditional epidemiological monitoring.