KNOWLEDGE AND PRACTICE OF HEALTH PROTECTIVE MEASURES OF EARLY CHILDHOOD LEARNERS IN BENIN CITY, NIGERIA

A healthy lifestyle is desired by everybody, and this involves health-promoting and health-protecting behaviours that are complementary. Health protective behaviours must be learned early in life and particularly in preparation to face pandemics. Children must learn them as part of the socialization process in school and at home to prevent  the transmission of diseases. This study explored the knowledge and practices of health-protective behaviours by early childhood learners. The population for this study was learners in early childhood schools in Benin City, Nigeria. A sample of 200 early childhood learners whose parents consented was selected purposively from early childhood schools. An interview schedule that was validated with a reliability of .83 was used in collecting data by the researchers. The data collected was analysed using frequencies, percentages and Pearson product-moment correlation. More than 60% of the learners had knowledge of washing hands regularly, using of hand sanitizers, covering their mouth with elbow when coughing among others as health protective measures which are part of non-pharmaceutical initiatives. Furthermore, more than 50% of the learners practice protective measures. Overall, the level of knowledge and practice of the measures was encouraging. Schools should continue to teach learners non-pharmaceutical initiatives in preparation for any outbreak of pandemics; the practice of such initiatives should also be enforced.  Article visualizations

Knowledge Aware and Culturally Sensitive SIR Models for Infectious Disease Spread

Mathematical models, such as the Susceptible-Infected-Removed (SIR) epidemiological model, have been proven successful in predicting the spread of disease. Studies show that knowledge held by people, coupled with cultural influences, play important roles in identifying preventive behavior of people during an epidemic spread. In this research, two complementary extensions to the basic SIR framework are proposed. The first extension includes building a knowledge aware SIR (KSIR) model, adding a knowledge factor, where knowledge represents preventive behavior during a disease spread. The second extension provides for a population learning model and thus introduces a culturally sensitive KSIR model. A basic agent based model incorporating SIR model has been built as an initial framework wherein cultural algorithms are employed to create a culturally evolving population during an epidemic spread. A case study based on a cross cultural survey was used to initialize the data and validate the framework. Experimental results show that during a disease spread cultural knowledge influences people\u27s behavior and thus is a deciding factor in risk assessment

The Effects of Avian Influenza News on Consumer Purchasing Behavior: A Case Study of Italian Consumers' Retail Purchases

To better understand how information about potential health hazards influences food demand, this case study examines consumers’ responses to newspaper articles on avian influenza, informally referred to as bird flu. The focus here is on the response to bird flu information in Italy as news about highly pathogenic H5N1 avian influenza (HPAI H5N1) unfolded in the period October 2004 through October 2006, beginning after reports of the first outbreaks in Southeast Asia and extending beyond the point at which outbreaks were reported in Western Europe. Estimated poultry demand, as influenced by the volume of newspaper reports on bird flu, reveals the magnitude and duration of newspaper articles’ impacts on consumers’ food choices. Larger numbers of bird flu news reports led to larger reductions in poultry purchases. Most impacts were of limited duration, and all began to diminish within 5 weeks.Avian influenza, bird flu, consumer behavior, food safety, poultry sales and consumption, risk perception and response, Agricultural and Food Policy, Health Economics and Policy, Institutional and Behavioral Economics, International Relations/Trade,

Particle filtering in compartmental projection models

Simulation models are important tools for real-time forecasting of pandemics. Models help health decision makers examine interventions and secure strong guidance when anticipating outbreak evolution. However, models usually diverge from the real observations. Stochastics involved in pandemic systems, such as changes in human contact patterns play a substantial role in disease transmissions and are not usually captured in traditional dynamic models. In addition, models of emerging diseases face the challenge of limited epidemiological knowledge about the natural history of disease. Even when the information about natural history is available -- for example for endemic seasonal diseases -- transmission models are often simplified and are involved with omissions. Availability of data streams can provide a view of early days of a pandemic, but fail to predict how the pandemic will evolve. Recent developments of computational statistics algorithms such as Sequential Monte Carlo and Markov Chain Monte Carlo, provide the possibility of creating models based on historical data as well as re-grounding models based on ongoing data observations. The objective of this thesis is to combine particle filtering -- a Sequential Monte Carlo algorithm -- with system dynamics models of pandemics. We developed particle filtering models that can recurrently be re-grounded as new observations become available. To this end, we also examined the effectiveness of this arrangement which is subject to specifics of the configuration (e.g., frequency of data sampling). While clinically-diagnosed cases are valuable incoming data stream during an outbreak, new generation of geo-spatially specific data sources, such as search volumes can work as a complementary data resource to clinical data. As another contribution, we used particle filtering in a model which can be re-grounded based on both clinical and search volume data. Our results indicate that the particle filtering in combination with compartmental models provides accurate projection systems for the estimation of model states and also model parameters (particularly compared to traditional calibration methodologies and in the context of emerging communicable diseases). The results also suggest that more frequent sampling from clinical data improves predictive accuracy outstandingly. The results also present that assumptions to make regarding the parameters associated with the particle filtering itself and changes in contact rate were robust across adequacy of empirical data since the beginning of the outbreak and inter-observation interval. The results also support the use of data from Google search API along with clinical data

Persistence of the Omicron variant of SARS-CoV-2 in Australia: The impact of fluctuating social distancing

We modelled emergence and spread of the Omicron variant of SARS-CoV-2 in Australia between December 2021 and June 2022. This pandemic stage exhibited a diverse epidemiological profile with emergence of co-circulating sub-lineages of Omicron, further complicated by differences in social distancing behaviour which varied over time. Our study delineated distinct phases of the Omicron-associated pandemic stage, and retrospectively quantified the adoption of social distancing measures, fluctuating over different time periods in response to the observable incidence dynamics. We also modelled the corresponding disease burden, in terms of hospitalisations, intensive care unit occupancy, and mortality. Supported by good agreement between simulated and actual health data, our study revealed that the nonlinear dynamics observed in the daily incidence and disease burden were determined not only by introduction of sub-lineages of Omicron, but also by the fluctuating adoption of social distancing measures. Our high-resolution model can be used in design and evaluation of public health interventions during future crises.Comment: 30 pages, 12 figures, source code: https://doi.org/10.5281/zenodo.732567

Testing and being tested in pandemic times

The coronavirus pandemic is witness to a great proliferation of two types of tests. The first type is testing – new medical diagnostic tests as well as epidemiological models that simulate and project the course of the virus. In the second type, actors, organizations, and institutions are being tested in this moment of social and political crisis. This essay analyzes the similarities and differences between these two major types of tests in order to understand their entanglements in the crisis. In the process, we find a great diversity of tests operating in multiple registers, themselves not clearly demarcated, often combining and sometimes conflating, for example, scientific and public discourse. The study opens by identifying three aspects of testing, drawn from the sociology of testing. First, tests are frequently proxies (or projections) that stand for something. Second, a test is a critical moment that stands out – whether because it is a moment deliberately separated out or because it is a puzzling or troublesome “situation” that disrupts the flow of social life. Third, when someone or something is put to the test, of interest is whether it stands up to the challenge. These insights serve as the building blocks for addressing three major issues – representation, selection, and accountability – regarding testing in the time of the coronavirus crisis