Modeling Transmission Dynamics and Risk Assessment for COVID-19 in Namibia Using Geospatial Technologies

Abstract

The SARS-CoV-2 infections continue to increase in Namibia and globally. Assessing and mapping the COVID-19 risk zones and modeling the response of COVID-19 using different scenarios are very vital to help decision-makers to estimate the immediate number of resources needed and plan for future interventions of COVID-19 in the area of interest. This study is aimed to identify and map COVID-19 risk zones and to model future COVID-19 response of Namibia using geospatial technologies. Population density, current COVID-19 infections, and spatial interaction index were used as proxy data to identify the different COVID-19 risk zones of Namibia. COVID-19 Hospital Impact Model for Epidemics (CHIME) V1.1.5 tool was used to model future COVID-19 responses with mobility restrictions. Weights were assigned for each thematic layer and thematic layer classes using the Analytical Hierarchy Process (AHP) tool. Suitably ArcGIS overlay analysis was conducted to produce risk zones. Current COVID-19 infection and spatial mobility index were found to be the dominant and sensitive factors for risk zoning in Namibia. Six different COVID-19 risk zones were identified in the study area, namely highest, higher, high, low, lower, and lowest. Modeling result revealed that mobility reduction by 30% within the country had a notable effect on controlling COVID-19 spread: a flattening of the peak number of cases and delay to the peak number. The research output could help policy-makers to estimate the immediate number of resources needed and plan for future interventions of COVID-19 in Namibia, especially to assess the potential positive effects of mobility restriction.

Keywords: CHIME modeling; COVID-19; Geospatial technology; Namibia; Spatial interaction index.

Fig. 1

Contextual map of Namibia

Fig. 2

Steps followed to create the spatial interaction index layer of Namibia

Fig. 3

Population density, confirmed cases, and spatial interaction index for COVID-19 overlay analysis to produce risk mapping

Fig. 4

Regional-level mobility changes during the four stages of lock down in Namibia. Source: Authors Produced based on data derived from Google mobility index 2020

Fig. 5

Percentage influence of factors for overlay analysis and COVID-19 risk mapping

Fig. 6

Regional-based COVID-19 risk zones of Namibia, and IDW interpolation based on the 08 May 2020 confirmed cases. The risk zones in this study can be interpreted only as areas with high transmission of COVID-19 and high number of affected population is in comparisons to the other regions of the country

Fig. 7

a Hospitalized census, b new daily admission projection, c change in hospitalized census over date per region, and d daily hospital census projection in Namibia for next 60 days with no social distancing starting from 08 May 2020

Fig. 8

a Hospitalized census, b change in Hospitalized census over date per region, c new daily admission projection, and d daily hospital census projection in Namibia for next 60 days with no 30% social distancing

Fig. 9

a Hospitalized census, b new daily admission projection, c change in hospitalized census over date per region, and d daily hospital census projection in Namibia for next 90 days with no social distancing

Fig. 10

a Hospitalized census, b new daily admission projection, c change in Hospitalized census over date per region, and d daily hospital census projection in Namibia for next 90 days with 30% social distancing

Fig. 11

a Hospitalized census, b new daily admission projection, c change in hospitalized census over date per region, and d daily hospital census projection in Namibia for next 90 days from 18 September 2020