Spatial Distribution of COVID-19 in Honduras at the Early Phase of the Pandemic Using Geographic Information Systems (GIS)<strong> </strong>

Abstract

The epidemic of Coronavirus Disease 2019 (COVID-19) have affected all the regions of the world, nevertheless, in some countries there is a lack of studies on its main clinical and epidemiological features. We analyzed the incidence, incidence rates, and evolution of COVID-19 cases in Honduras from February 18-April 24, 2020.Methods: Using daily epidemiological data from surveillance about COVID-19 in Honduras, we calculated the rates of incidence (cases/100,000 population), and developed at national, departmental, and municipal levels GIS-based maps.Results: February 18 - April 24, 2020, a sum of 3,169 suspected COVID-19 cases have been assessed by RT-PCR, 533 (16.8%) of them were positive, for an incidence rate of 5.73 cases/100,000 pop. The highest peak was reached on March 31 (48 cases). The department with the highest number of cases and incidence rate was Cortes (383 cases, 71.9% of the total, 21.45 cases/100,000 pop). Discussion: The pattern and evolution of COVID-19 epidemic in Honduras has been particularly focused in the major urban areas, San Pedro Sula and Tegucigalpa, the capital city. Studies using geographical information systems linked with clinical disease characteristics are necessary to attain accurate epidemiological data for public health systems. Such information is also useful for assessment of the evolution of the pandemic and monitoring interventions.

Geospatial dynamics of COVID‐19 clusters and hotspots in Bangladesh

“…Previous studies also identified similar districts of Bangladesh, for example Dhaka, Narayanganj, Gazipur, Narshingdi, Munshigonj and Manikgonj as hotspots of COVID-19 in terms of infection and related deaths(Islam et al, 2020;Rahman et al, 2020). Other countries also reported the epidemic to affect mostly urban populated centres and municipalities and the capital district(Zambrano et al, 2020) Masrur et al (2020). found that Dhaka is the highest active risk cluster since early April which supports our study.…”

Epidemiological geography at work: An exploratory review about the overall findings of spatial analysis applied to the study of CoViD-19 propagation along the first pandemic year

“…In the Americas, Melin et al ( 2020 ) drafted the phases through which the pandemic took root in Mexican states; Zambrano et al ( 2020 , Honduras, NPR) noticed that emerging positive cases seemed to come mainly from the biggest urban settlements; Cuartas et al, ( 2020 ) studied the infection pattern in the Colombian city of Cali, while in Brazil Santos et al ( 2020 ) did the same for Rio de Janeiro. Still at the urban scale, but halfway around the world, Kalabikhina and Panin ( 2020 , Russia) estimated the virus propagation speed across the metropolitan area of Moscow.…”

“…Health service accessibility was a recurrent issue: by applying big-data raster analysis, some continent-wide studies were carried out to compare the healthcare venue availability (Geldsetzer et al, 2020 ). Finally, a group of papers analyzed the role of chronic conditions and their geographic prevalence in affecting the pandemic trends, treating them as a predictive risk factor (Melin et al, 2020 , Mexico), or seeking statistical correlations at the local level (Ramirez and Lee, 2020 , USA), or in retrospective cohort studies (Zambrano et al, 2020 , Honduras, NPR).…”

“…Some studies coupled the GIS methods with traditional retrospective analyses: Dagnino et al. ( 2020 , Brasil, NPR) found that the incidence peak in Rio Grande do Sul occurred in the 30–69 years cohorts; Zambrano et al ( 2020 , Honduras, NPR), noticed that almost 60% of confirmed cases were 60–79 years old, while the least affected cohorts were younger than 10 years (< 5%).…”

On the use of clinical based infection data for pandemic case studies

“…Concerning recent pandemic Covid-19, many studies have been presented to furnish relevant information useful to study and characterize this viral infection [16], [17]. For example, authors [18] in used daily epidemiological data from surveillance about COVID-19 in Honduras to calculate the rates of incidence and its developed at national, departmental, and municipal levels by implementing GIS-based maps.…”