Urban Vulnerability Assessment for Pandemic surveillance

A Pandemic devastates the life of global citizens and causes significant economic, social, and political disruption. Evidence suggests that Pandemic's likelihood has increased over the past century because of increased global travel and integration, urbanization, and changes in land use. Further, evidence concerning the urban character of the Pandemic has underlined the role of cities in disease transmission. An early assessment of the severity of infection and transmissibility can help quantify the Pandemic potential and prioritize surveillance to control of urban areas in Pandemics. In this paper, an Urban Vulnerability Assessment (UVA) methodology is proposed. UVA investigates the possible vulnerable factors related to Pandemics to assess the vulnerability in urban areas. A vulnerability index is constructed by the aggregation of multiple vulnerability factors computed on each urban area (i.e., urban density, poverty index, informal labor, transmission routes). UVA provides insights into early vulnerability assessment using publicly available data. The applicability of UVA is shown by the identification of high-vulnerable areas where surveillance should be prioritized in the COVID-19 Pandemic in Bogota, Colombia.


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On the other hand, the recently published response plan 57 for the current COVID-19 Pandemic, UN-Habitat has un-58 derlined the urban-centric character of the infectious disease 59 (UN Habitat, 2020). It says, more than 1430 cities are af-60 fected by the Pandemic in 210 countries and well above 95% 61 of the total cases are located in urban areas. Further, the 62 World Health Organization (WHO) emphasized that the first 63 transmission in the COVID-19 Pandemic did happen in the 64 internationally connected megacities(WHO, 2020). Even though 65 there is urban universality of the disease, the cities of the 66 global south are more susceptible given their population den-67 sities, low income, risky occupations, and lack of affordable 68 health services (Mitlin, 2020).

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In this paper, a conceptual framework for Urban Vulner-70 ability Assessment (UVA) in Pandemics is proposed. UVA 71 conducted a comprehensive review of relevant literature to 72 identify vulnerable factors influencing Pandemics. These 73 factors are used to generate an index that allows us to iden-74 tify and rank potentially vulnerable urban areas. UVA helps 75 decision-makers to review how the strengthening of surveil-76 lance and control measures would mitigate the vulnerabil-77 ity of Pandemic. UVA is tested in the current COVID-19 78 Pandemic in Bogotá, the crowdest city of Colombia. Us-79 ing widely available data of Bogotá (i.e., from the National   search retrieved studies for which the study's title, abstract, 112 or keywords indicated the study examined a type of vulnera-113 bility in Pandemics. Then, a manual assessment is made for 114 every study against eligibility criteria:

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• The study provided a quantitative or conceptual anal-116 ysis of a type(s) of vulnerability factors related to in-117 fectious diseases (or Pandemics).

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• The actual analysis or argument of the study earnestly 119 included vulnerability.

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• The study focuses on urban areas.

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• To be eligible the study focuses more on the vulnera-122 bility analysis at geographic area than on the individ-123 ual vulnerability of infectious diseases.

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Then, the vulnerability factor the study focused on, the 125 geographic focus of the study and the methods used to assess 126 the vulnerability is recorded. This involved examining the ti-127 tle, abstract, or keywords, or full-text version if required. We 128 listed the country or region(s) where the study focused. For 129 theoretical studies, studies that presented examples without 130 a geographic focus, or studies with an unclear geographic 131 focus, the geographic location is listed as Not Applicable 132 (NA). is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted November 16, 2020. ; https://doi.org/10.1101/2020.11.13.20231282 doi: medRxiv preprint  (best) to 1 (worst), see Fig. 1(b), by estimating the proba-143 bility density at specific spatial unit using the Kernel 144 Density Estimation (KDE) method.

Statistical data analysis
according to their probability distribution for the vulner-152 able factors, see Fig. 1(c).

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Since the datasets' information are in different spatial 213 units (i.e., Urban sectors, UPZ), the spatial unit using in this 214 study is the Urban sector (more atomic), and the informa-215 tion at UPZ level is then transformed into Urban sectors by 216 spatial transformation (i.e., an Urban sector belongs to one 217 and only one UPZ, then the UPZ values are assigned to the 218 Urban sector). is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted November 16, 2020. ; https://doi.org/10.1101/2020.11.13.20231282 doi: medRxiv preprint Urban Vulnerability Assessment for Pandemic surveillance is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted November 16, 2020. ; https://doi.org/10.1101/2020.11.13.20231282 doi: medRxiv preprint Urban Vulnerability Assessment for Pandemic surveillance   is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted November 16, 2020. ; https://doi.org/10.1101/2020.11.13.20231282 doi: medRxiv preprint Urban Vulnerability Assessment for Pandemic surveillance  to lower values) getting 14 ranks (one for each vulnerable 298 factor). Then, Borda's count method is used to get a unique 299 ranking for each cluster.

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Finally, a vulnerability label is assigned for each cluster 301 based on the ranking (i.e., higher rank indicates higher vul-302 nerability). Fig 5 shows the final vulnerability index for the 303 three different vulnerability index constructed with UVA. 304 For Vulnerability index I, the results show high vulnerable 305 urban sectors in the south and west part of the city. On the 306 other hand, the Vulnerability index II shows how some Ur-307 ban sectors change from medium-vulnerability (in compari-308 son with the Vulnerability index I) to low or high-vulnerability. 309 The Vulnerability index III presents an interesting scenario, 310 where the spatial correlation between urban sectors (showed 311 in Vulnerability index I and II) is not remarkable getting an 312 unbias vulnerability index for COVID-19 in Bogotá, Colom-313 bia.

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An Urban Vulnerability Assessment (UVA) for Pandemic 316 surveillance is proposed. UVA highlights the vulnerability 317 based on a set of 14 vulnerable factors found in the litera-318 ture. The vulnerable factors are ranked for each spatial unit 319 under study using some statistical methods like Kernel Den-320 sity Estimation (KDE) and Clustering Analysis. To generate 321 a unique vulnerability index, Borda's count method is used. 322 The proposed UVA is tested in the current COVID-19 323 Pandemic in Bogotá city, the largest and crowded city in 324 Colombia. UVA creates not only one, but a set of vulner-325 . CC-BY 4.0 International license It is made available under a perpetuity.
is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted November 16, 2020. ; https://doi.org/10.1101/2020.11.13.20231282 doi: medRxiv preprint Urban Vulnerability Assessment for Pandemic surveillance (c) Vulnerability index III Fig. 5: Vulnerability indices generated using UVA for the current COVID-19 Pandemic in Bogotá, Colombia. Vulnerability index I has 3 levels from low to high (left); Vulnerability index II has 5 levels from lowest to highest (middle); and Vulnerability index III has 10 levels from 1 to 10 (right).
ability indices (i.e., low-high, lowest-highest, and 1-10) to . CC-BY 4.0 International license It is made available under a perpetuity.
is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted November 16, 2020. ; https://doi.org/10.1101/2020.11.13.20231282 doi: medRxiv preprint Urban Vulnerability Assessment for Pandemic surveillance