In a recent study published in Applied Geography, researchers explored the spatial variability in increasing public awareness of the efficacy of coronavirus disease 2019 (COVID-19) testing.
Study: Assessing the spatial variability of raising public risk awareness for the intervention performance of COVID-19 voluntary screening: A spatial simulation approach. Image Credit: ST.art/Shutterstock
Rapid viral transmission with an upsurge in pandemics like COVID-19 usually has occurred due to invisible viral transmission by asymptomatic patients. Healthcare facilities rely on voluntary screening (VS) at a large scale to detect (and isolate) such invisible transmitters and individuals with symptoms at the earliest for disease mitigation.
Increasing public risk awareness has improved epidemic prevention strategies’ efficacy since high public awareness could increase testing resource use and, therefore, elevate resource/ testing kit demands. However, testing efficacy may be affected by spatial demands (SD) for resources during different periods. Further, SD could be increased by public risk awareness in different regions based on the spatial closeness to testing facilities and human movement attractiveness.
About the study
The present study investigated spatial variability in increasing public awareness to improve medical resource usage and willingness to confront the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic.
Spatial simulation models were implemented to integrate the dynamics of an epidemic and differing public risk awareness levels in space and time. Subsequently, the impact of the proximity of testing resources and human movement attractiveness on SARS-CoV-2 testing at various public awareness levels was investigated.
Data from the Taipei region, a densely populated socioeconomic and metropolitan center of Taiwan (1363 square kilometers of area and six million population), was used for assessing the integrated model framework feasibility. Spatial analysis units comprised 543 TAZs (traffic analysis zones, with mean TAZ size and population being 2.5 square kilometers and 12,500, respectively.)
Thirty SARS-CoV-2 testing stations in hospitals that were CECC (Central Epidemic Command Center)-established were utilized for representing facilities for resource supply, all of which provided RT-PCR (real-time polymerase chain reaction) tests. The everyday average human movement volume among TAZs was estimated by TRTS-IV (Taipei rapid transit systems demand model, fourth version), and the estimations were conducted by DORTS (department of rapid transit systems) of the Taipei government in the year 2015.
The integrated model comprised three components. The first component used a modified SEPIA compartmental model for simulating the spatiotemporal epidemic progression. The second component processed dynamic demands for COVID-19 testing resources by integrating probable requesters and the willingness for testing.
The third component used the 2SFCA (two-step floating catchment area) model for evaluating SA (spatial accessibility) to test resources at each location based on dynamic demand. All the components interacted repeatedly, and the coevolution with time reflected the effects of testing behaviors against epidemics.
The ICR (increase in capture rate) and DIP (decrease of infection proportion) measures were evaluated to determine improvements in resource use and epidemic reduction, respectively. Further, the influence of geographic factors on the local benefit index was investigated by measuring the attractiveness and proximity of each place.
Results
Increasing public awareness increased testing willingness, which caused demands to be partially satisfied at peak pandemic times; however, the testing resource shortages did not significantly elevate COVID-19 severity. The increase in testing willingness or public awareness could not notably increase resource use after resource usage had attained a high level.
Consequently, an imbalance between resource demand and resource use was observed. The peak value of the fraction of individuals with severe symptoms dropped dramatically, although the epidemic peak time appeared earlier in the case of rising public awareness.
The elevated demand triggered by increasing willingness for testing, therefore, could not be satisfied completely in the case of high-level public awareness. In the case of low public awareness, focusing on unattractive areas (like urban fringe or residential regions) could increase testing benefits, and testing willingness showed similar temporal patterns.
On the contrary, in the case of high public awareness, the distance to testing stations was more critical for increasing testing benefits; allocating additional testing resources to locations distantly located from the testing stations could improve testing benefits. Testing willingness increased earlier during an epidemic and became higher at peak times.
DIP and ICR values for the majority of TAZs gradually increased with an increase in public awareness. However, the values for some TAZs were found to continue to be low, indicating that resource use and epidemic reductions were difficult to be improved in those regions. The ICR range was generally wider than that of DIP, with larger differences between the two in increasing public awareness.
The findings indicated that pattern ICR values showed greater spatial heterogeneity than DIP values and that increasing public awareness enlarged the differences between them. In general, DIP and ICR values did not correlate and were inconsistent across TAZs in the case of high public awareness. Attractiveness dominated the spatial differences occurring due to geographical variables.
Overall, the study findings highlighted the spatial variability in increasing public risk awareness for COVID-19 screening. The findings could aid healthcare authorities in allocating resources and designing testing strategies against viral outbreaks based on public awareness levels.
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