Communicating infectious disease prevalence through graphics: Results from an international survey

From Snow's map of cholera transmission to dynamic catchment boundary delineation: current front lines in spatial analysis

The history of mapping infectious diseases dates back to the 19th century when Dr John Snow utilised spatial analysis to pinpoint the source of the 1854 cholera outbreak in London, a ground-breaking work that laid the foundation for modern epidemiology and disease mapping (Newsom, 2006). As technology advanced, so did mapping techniques. In the late 20th century, geographic information systems (GIS) revolutionized disease mapping by enabling researchers to overlay diverse datasets to visualise and analyse complex spatial patterns (Bergquist & Manda 2019; Hashtarkhani et al., 2021). The COVID-19 pandemic showed that disease mapping is particularly valuable for optimising prevention and control strategies of infectious diseases by prioritising geographical targeting interventions and containment strategies (Mohammadi et al., 2021). Today, with the aid of highresolution satellite imagery, geo-referenced electronic data collection systems, real-time data feeds, and sophisticated modelling algorithms, disease mapping has become a feasible and accessible tool for public health officials in tracking, managing, and mitigating the spread of infectious diseases at global, regional and local scales (Hay et al., 2013). [...].

Risk heatmaps as visual displays: Opening movie studios after the COVID-19 shutdown

Upon shutting down operations in early 2020 due to the COVID-19 pandemic, the movie industry assembled teams of experts to help develop guidelines for returning to operation. It resulted in a joint report, The Safe Way Forward, which was created in consultation with union members and provided the basis for negotiations with the studios. A centerpiece of the report was a set of heatmaps displaying SARS-CoV-2 risks for a shoot, as a function of testing rate, community infection prevalence, community transmission rate (R0), and risk measure (either expected number of cases or probability of at least one case). We develop and demonstrate a methodology for evaluating such complex displays, in terms of how well they inform potential users, in this case, workers deciding whether the risks of a shoot are acceptable. We ask whether individuals making hypothetical return-to-work decisions can (a) read display entries, (b) compare display entries, and (c) make inferences based on display entries. Generally speaking, respondents recruited through the Amazon MTurk platform could interpret the display information accurately and make coherent decisions, suggesting that heatmaps can communicate complex risks to lay audiences. Although these heatmaps were created for practical, rather than theoretical, purposes, these results provide partial support for theoretical accounts of visual information processing and identify challenges in applying them to complex settings.

How people use information about changes in infections and disease prevalence

OBJECTIVE

To determine the influence of two representative metrics of epidemiological risk, changes in new infections, and disease prevalence, on people's risk judgments and disease-prevention behaviors.

METHOD

Four experiments were conducted from August 2020 to May 2021. In Experiments 1 and 2, participants were exposed to information about different directions of change (upward and downward) and varying levels of prevalence of an infectious disease. In Experiments 3 and 4, participants were exposed to information about only one direction of change (upward or downward) and varying levels of prevalence. Participants reported risk judgments and intentions to engage in disease-prevention behaviors for each disease situation presented to them.

RESULTS

When both the direction of change and levels of prevalence varied, risk judgments and intentions were more influenced by change (vs. prevalence) information. Participants' reliance on prevalence information to guide risk judgments increased when they were presented with only an upward or downward change, particularly for situations with worsening infections. In all cases, the effects of epidemiological information on behavioral intentions were mediated by its effects on risk judgments.

CONCLUSIONS

Information about changes in infections consistently influences people's risk judgments and drive subsequent behavioral response. The impact of prevalence information, however, is limited to situations in which changes in infections are stable, such that it affects risk judgments and behavior decisions only when changes in infections demonstrate a constant upward or downward direction. The results point to the need for public health interventions to increase the impact of prevalence information. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Communicating about COVID-19 vaccine development and safety

PURPOSE

Beliefs that the risks from a COVID-19 vaccine outweigh the risks from getting COVID-19 and concerns that the vaccine development process was rushed and lacking rigor have been identified as important drivers of hesitancy and refusal to get a COVID-19 vaccine. We tested whether messages designed to address these beliefs and concerns might promote intentions to get a COVID-19 vaccine.

METHOD

We conducted an online survey fielded between March 8-23, 2021 with US Veteran (n = 688) and non-Veteran (n = 387) respondents. In a between-subjects experiment, respondents were randomly assigned to a control group (with no message) or to read one of two intervention messages: 1. a fact-box styled message comparing the risks of getting COVID-19 compared to the vaccine, and 2. a timeline styled message describing the development process of the COVID-19 mRNA vaccines.

RESULTS

Most respondents (60%) wanted a COVID-19 vaccine. However, 17% expressed hesitancy and 23% did not want to get a COVID-19 vaccine. The fact-box styled message and the timeline message did not significantly improve vaccination intentions, F(2,358) = 0.86, p = .425, [Formula: see text] = .005, or reduce the time respondents wanted to wait before getting vaccinated, F(2,306) = 0.79, p = .453, [Formula: see text] = .005, compared to no messages.

DISCUSSION

In this experimental study, we did not find that providing messages about vaccine risks and the development process had an impact on respondents' vaccine intentions. Further research is needed to identify how to effectively address concerns about the risks associated with COVID-19 vaccines and the development process and to understand additional factors that influence vaccine intentions.

Functionalized non-woven surfaces for combating the spread of the COVID-19 pandemic

The worldwide outbreak of SARS-CoV-2 infection has necessitated mandatory use of face masks, personal protective equipment and intake of a healthy diet for immunity boosting. As per WHO's recommendation, continuous use of masks has been proven effective in decreasing the SARS-CoV-2 infection rate. The present study reports on the bacterial filtration efficacy (BFE) of a novel 4-ply functionalized non-woven face mask. We synthesized a polypropylene-based fabric with inner layers of melt-blown fine fibres coated with polylactic acid and immune-boosting herbal phytochemicals. Experimental studies on the synthesized face mask demonstrated a BFE of greater than 99% against Staphylococcus aureus (a bacterium species frequently found in mammalian respiratory tract). A thorough computational analysis using LibDock algorithm demonstrated an effective docking performance of herbal phytochemicals against harmful virus structures. More importantly, the face mask also showed sufficient and stable breathability as per regulatory standards. A breathing resistance of 30 Pa at an aerosol flow rate of 30 l h⁻¹ was reported under standard temperature and pressure conditions, indicating a high potential for real-world applications.

The attributes of the images representing the SARS-CoV-2 coronavirus affect people's perception of the virus

BACKGROUND

The recent COVID-19 pandemic has seen an explosion of coronavirus-related information. In many cases, this information was supported by images representing the SARS-CoV-2.

AIM

To evaluate how attributes of images representing the SARS-CoV-2 coronavirus that were used in the initial phase of the coronavirus crisis in 2020 influenced the public's perceptions.

METHODS

We have carried out an in-depth survey using 46 coronavirus images, asking individuals how beautiful, scientific, realistic, infectious, scary and didactic they appeared to be.

RESULTS

We collected 91,908 responses, obtaining 15,315 associations for each category. While the reference image of SARS-CoV-2 used in the media is a three-dimensional, colour, illustration, we found that illustrations of the coronavirus were perceived as beautiful but not very realistic, scientific or didactic. By contrast, black and white coronavirus images are thought to be the opposite. The beauty of coronavirus images was negatively correlated with the perception of scientific realism and didactic value.

CONCLUSION

Given these effects and the consequences on the individual's perception, it is important to evaluate the influence that different images of SARS-CoV-2 may have on the population.

Using a simplified ATP algorithm to improve data reliability and improve cleanliness standards for surface and medical device hygiene

BACKGROUND

An algorithm has been improved to mitigate variability in cleanliness measurements of various surfaces using rapid Adenosine Triphosphate (ATP) testing. A cleaning intervention step (CIS) verifies the cleanability of those surfaces.

METHODS

ATP testing was performed on surfaces which were pre-approved as "clean" and ready for re-use. Adjacent (duplicate) ATP sampling was undertaken on 421 environmental surfaces, medical devices and other implements. The CIS was conducted on 270 surfaces using an aseptic technique and disposable cleaning wipes.

RESULTS

The two initial ATP results were plotted against each other with a 100 RLU threshold grading the results as clean (2x < 100RLU), dirty (2x > 100RLU) or equivocal (1x < 100RLU and 1x > 100RLU). Of the surfaces sampled, 68.5 % were clean (288/421), 13.5 % were dirty (57/421) and 18 % were equivocal (76/421). The duplicate testing demonstrated a false negative rate of 10 % (44/421) where the first swab was <100 RLU and the second swab >100 RLU. For the equivocal group, the gap between the two swabs was >100 RLU for 7.5 % of surfaces (33/421). The CIS was conducted on 270 of the surfaces tested and showed that cleaning could be improved (P=<0.001) on 88.5 % of surfaces (239/270).

CONCLUSION

The simplified ATP testing algorithm provides real-time discrimination between surface cleanliness levels and improved certainty over surface hygiene. The duplicate swab sampling approach mitigates uncontrolled variability in the results and the CIS provides a nuanced understanding of the measurable cleanliness of any surface.

Needleless electrospun phytochemicals encapsulated nanofibre based 3-ply biodegradable mask for combating COVID-19 pandemic

The emergence of COVID-19 pandemic has severely affected human health and world economies. According to WHO guidelines, continuous use of face mask is mandatory for personal protection for restricting the spread of bacteria and virus. Here, we report a 3-ply cotton-PLA-cotton layered biodegradable face-mask containing encapsulated phytochemicals in the inner-filtration layer. The nano-fibrous PLA filtration layer was fabricated using needleless electrospinning of PLA & phytochemical-based herbal-extracts. This 3-layred face mask exhibits enhanced air permeability with a differential pressure of 35.78 Pa/cm² and superior bacterial filtration efficiency of 97.9% compared to conventional face masks. Close-packed mesh structure of the nano-fibrous mat results in effective adsorption of particulate matter, aerosol particles, and bacterial targets deep inside the filtration layer. The outer hydrophobic layer of mask exhibited effective blood splash resistance up to a distance of 30 cm, ensuring its utilization for medical practices. Computational analysis of constituent phytochemicals using the LibDock algorithm predicted inhibitory activity of chemicals against the protein structured bacterial sites. The computational analysis projected superior performance of phytochemicals considering the presence of stearic acid, oleic acid, linoleic acid, and Arachidic acid exhibiting structural complementarity to inhibit targeted bacterial interface. Natural cotton fibers and PLA bio-polymer demonstrated promising biodegradable characteristics in the presence of in-house cow-dung based biodegradation slurry. Addition of jaggery to the slurry elevated the biodegradation performance, resulting in increment of change of weight from 07% to 12%. The improved performance was attributed to the increased sucrose content in biodegradation slurry, elevating the bacterial growth in the slurry. An innovative face mask has shown promising results for utilization in day-to-day life and medical frontline workers, considering the post-pandemic environmental impacts.

Spatial prediction of malaria prevalence in Papua New Guinea: a comparison of Bayesian decision network and multivariate regression modelling approaches for improved accuracy in prevalence prediction

Background

Considerable progress towards controlling malaria has been made in Papua New Guinea through the national malaria control programme’s free distribution of long-lasting insecticidal nets, improved diagnosis with rapid diagnostic tests and improved access to artemisinin combination therapy. Predictive prevalence maps can help to inform targeted interventions and monitor changes in malaria epidemiology over time as control efforts continue. This study aims to compare the predictive performance of prevalence maps generated using Bayesian decision network (BDN) models and multilevel logistic regression models (a type of generalized linear model, GLM) in terms of malaria spatial risk prediction accuracy.

Methods

Multilevel logistic regression models and BDN models were developed using 2010/2011 malaria prevalence survey data collected from 77 randomly selected villages to determine associations of Plasmodium falciparum and Plasmodium vivax prevalence with precipitation, temperature, elevation, slope (terrain aspect), enhanced vegetation index and distance to the coast. Predictive performance of multilevel logistic regression and BDN models were compared by cross-validation methods.

Results

Prevalence of P. falciparum, based on results obtained from GLMs was significantly associated with precipitation during the 3 driest months of the year, June to August (β = 0.015; 95% CI = 0.01–0.03), whereas P. vivax infection was associated with elevation (β = − 0.26; 95% CI = − 0.38 to − 3.04), precipitation during the 3 driest months of the year (β = 0.01; 95% CI = − 0.01–0.02) and slope (β = 0.12; 95% CI = 0.05–0.19). Compared with GLM model performance, BDNs showed improved accuracy in prediction of the prevalence of P. falciparum (AUC = 0.49 versus 0.75, respectively) and P. vivax (AUC = 0.56 versus 0.74, respectively) on cross-validation.

Conclusions

BDNs provide a more flexible modelling framework than GLMs and may have a better predictive performance when developing malaria prevalence maps due to the multiple interacting factors that drive malaria prevalence in different geographical areas. When developing malaria prevalence maps, BDNs may be particularly useful in predicting prevalence where spatial variation in climate and environmental drivers of malaria transmission exists, as is the case in Papua New Guinea.

Communicating Scientific Uncertainty About the COVID-19 Pandemic: Online Experimental Study of an Uncertainty-Normalizing Strategy

Background

Communicating scientific uncertainty about public health threats such as COVID-19 is an ethically desirable task endorsed by expert guidelines on crisis communication. However, the communication of scientific uncertainty is challenging because of its potential to promote ambiguity aversion—a well-described syndrome of negative psychological responses consisting of heightened risk perceptions, emotional distress, and decision avoidance. Communication strategies that can inform the public about scientific uncertainty while mitigating ambiguity aversion are a critical unmet need.

Objective

This study aimed to evaluate whether an “uncertainty-normalizing” communication strategy—aimed at reinforcing the expected nature of scientific uncertainty about the COVID-19 pandemic—can reduce ambiguity aversion, and to compare its effectiveness to conventional public communication strategies aimed at promoting hope and prosocial values.

Methods

In an online factorial experiment conducted from May to June 2020, a national sample of 1497 US adults read one of five versions of an informational message describing the nature, transmission, prevention, and treatment of COVID-19; the versions varied in level of expressed scientific uncertainty and supplemental focus (ie, uncertainty-normalizing, hope-promoting, and prosocial). Participants then completed measures of cognitive, emotional, and behavioral manifestations of ambiguity aversion (ie, perceived likelihood of getting COVID-19, COVID-19 worry, and intentions for COVID-19 risk-reducing behaviors and vaccination). Analyses assessed (1) the extent to which communicating uncertainty produced ambiguity-averse psychological responses; (2) the comparative effectiveness of uncertainty-normalizing, hope-promoting, and prosocial communication strategies in reducing ambiguity-averse responses; and (3) potential moderators of the effects of alternative uncertainty communication strategies.

Results

The communication of scientific uncertainty about the COVID-19 pandemic increased perceived likelihood of getting COVID-19 and worry about COVID-19, consistent with ambiguity aversion. However, it did not affect intentions for risk-reducing behaviors or vaccination. The uncertainty-normalizing strategy reduced these aversive effects of communicating scientific uncertainty, resulting in levels of both perceived likelihood of getting COVID-19 and worry about COVID-19 that did not differ from the control message that did not communicate uncertainty. In contrast, the hope-promoting and prosocial strategies did not decrease ambiguity-averse responses to scientific uncertainty. Age and political affiliation, respectively, moderated the effects of uncertainty communication strategies on intentions for COVID-19 risk-reducing behaviors and worry about COVID-19.

Conclusions

Communicating scientific uncertainty about the COVID-19 pandemic produces ambiguity-averse cognitive and emotional, but not behavioral, responses among the general public, and an uncertainty-normalizing communication strategy reduces these responses. Normalizing uncertainty may be an effective strategy for mitigating ambiguity aversion in crisis communication efforts. More research is needed to test uncertainty-normalizing communication strategies and to elucidate the factors that moderate their effectiveness.

Exposure to Common Geographic COVID-19 Prevalence Maps and Public Knowledge, Risk Perceptions, and Behavioral Intentions

This survey study examines knowledge, risk perceptions, and behavioral intentions among survey respondents exposed to different types of COVID-19 prevalence maps.

Prior Vaccination and Effectiveness of Communication Strategies Used to Describe Infectious Diseases

We tested the effect of prior vaccination on response to communication strategies in a hypothetical news article about an influenza pandemic. Vaccinated were more likely than nonvaccinated participants to plan future vaccination, and future vaccination intent was greater with certain communication strategies. Using these findings to target communication may increase vaccination rates.