Motivation and objective

Measures of physical distancing in African countries came with immense economic costs. All countries in Africa are categorized as low-income (US$ 1025 or less per year and capita) or middle-income (US$ 1026 to US$ 12,375 per year and capita) as defined by the World Bank16. Therefore, these countries have very limited financial resources to mitigate any negative economic effects both at the macro- and the micro-level. The International Monetary Fund estimates that sub-Saharan Africas gross domestic product (GDP) shrunk by 1.9% in 202017, which will result in a sharp increase in poverty18,19 for the first time in 30 years. Estimates from the United Nations Development Programme indicate a sharp reduction in the Human Development Index in 2020 for the first time since its introduction in 199020. Large shares of the populations are employed in the informal sector, with estimates varying between 35% for South Africa and 92% for Mali, with no social security net2. If people cannot go to work, the result is an instant income loss for most of these people, leading to an immediate rise in food insecurity21,22. As a result, many African countries quickly started to lift measures of physical distancing in summer 2020. However, schools remained closed in most African countries throughout 2020 and 202123,24.

Our study aims to contribute to a deeper understanding of the geographic distribution of critical infrastructure patterns to respond to the current and future epidemics and pandemics, placing particular emphasis on Africa. Our study alsocontributes to measuring a countrys preparedness to prevent, detect, and cope with infectious disease outbreaks such as COVID-1925,26,27,28,29,30. We argue that the effectiveness of governmental regulations in many African countries to increase physical distancing andto reduce transmission rates of infectious diseases does not only lead to poverty but is also limited given the lack of essential private infrastructure, which makes it impossible for populations to follow WHO regulations to keep sufficient distance. Although vaccinations and treatments against COVID-19 became available in 2021, international and national barriers toward high vaccination coverage in many African countries will remain and these have also been discussed as a driver of future mutations of SARS-CoV-231. Hence, to both contain the spread of SARS-CoV-2 and future viruses governmental measures to encourage physical distancing remain important policy responses.

Using principal component analysis, we propose a physical distancing index (PDI) composed of five indicators: households with (1) a lack of private toilet facilities; (2) lack of a private drinking water source; (3) lack of ICT infrastructure; (4) lack of private transportation means; and (5) lack of space. The indicator is weighted with population density to account for the fact that the capacity to keep physical distance is both influenced by the lack of private infrastructure and population density. We compute the PDI for 34 African countries as well as for 519 first-level subnational regions. Moreover, based onBayesian distributional regression,the PDI is computed at the pixel level (grid size of 55km) for specific countries.

The proposed index complements existing indices that have attempted to measure a countrys capacity to respond to an infectious disease outbreak. Most existing indices focus on measuring the overall capacity of the countrys health and governance system to detect and respond rather than on households capabilities to prevent the spread of an infectious disease through physical distancing. For example, one attempt to measure the preparedness of a countrys health system to deal with an infectious diseaseoutbreak is the monitoring of the International Health Regulations (IHR) by the WHO25. The aggregated index to monitor progress in a countrys health system was introduced in 2010 and is based on 13 different capacity dimensions: (1) legislation and financing; (2) IHR coordination and national IHR focal point functions; (3) zoonotic event and the human-animal interface; (4) food safety; (5) laboratory; (6) surveillance; (7) human resources; (8) national health emergency framework; (9) health service provisions; (10) risk communication; (11) points of entry; (12) chemical events; and (13) radiation emergencies. The most recent data from the year 2018 show a global improvement across all 13 IHR capacity dimensions. However, countries in Africa lag behind most other countries in the world25. A second index to analyze the vulnerability of countries with respect to infectious disease outbreaks is the Infectious Disease Vulnerability Index, developed by the RAND Corporation. The aggregated index is based on seven dimensions of factors influencing a countrys vulnerability to infectious diseases: (1) demographic; (2) health care; (3) public health; (4) disease dynamics; (5) political-domestic; (6) political-international; and (7) economic26. The estimates of the index in 2016 show that of the 25 most vulnerable countries, 22 are in Africa (the other three are Afghanistan, Haiti, and Yemen). Particular disease hotspots are identified in West Africa, and the authors of the study point to a dangerous mix of political instability and limited capacity of health systems in countries such as Somalia, Central African Republic, and South Sudan26.

The results of these two indices are limited to country-level aggregates and provide no within-country variation. Although estimates at the country level are useful for international and inter-temporal comparisons, they do not provide any information on within-country heterogeneity in preparedness to contain a disease. At the subnational level, where differences in policies and behavior within a country are less severe than across countries, a subnational PDI can be used for a more precise monitoring and targeting of outbreaks of infectious diseases. Moreover, the two indices provide no estimate on how the spread of infectious viruses, for example of the SARS-CoV-2 Delta and Omicron variants, can be contained through physical distancing practiced by the general public. Here, Brown et al.29 provide a first attempt, but our approach differs in four fundamental dimensions. First, Brown et al. study different indicators: (1) household has access to internet, phone, TV, or radio; (2) no more than two people per sleeping room; (3) household has access to a private toilet; (4) household has a dwelling that can be closed; (5) household has access to piped water; and (6) household has a place for handwashing. We focus on indicators that are more directly linked to social interaction: for example, whether a household has a TV or a place for handwashing says little about social interaction. Second, we exploit the availability of geo-referenced information in the Demographic and Health Surveys (DHS) to provide new insights about the capability to physical distance at the subnational level, whereas Brown et al. only aggregate at the national level. Geo-referenced data can help to identify potential diseases hotspots within a country for better policy targeting. Third, Brown et al. use a simple country average of their indicators to calculate their index. Although this is a straightforward approach, it also implies an arbitrary weighting scheme where one has to assume that, for example, access to a TV has the same informative power as sharing a room in explaining the capacities of households to protect themselves from getting infected. We employ the PCA method to avoid the equal weighting assumption, which is a commonly used approach in the empirical literature. The PCA is a more data-driven approach and combines the variation of all included variables in the index. Fourth, we take into account population density, which we argue is critical in studying the capabilities of households to physically distance, as higher population density is associated with higher infection risk when private infrastructure is lacking (see also Fig.3). As a result of all these differences, the correlation between the home environment for protection index (HEP) and our PDI is very low (=0.2, see also Fig.5), also resulting in a different ranking of countries with respect to their capability to distance physically.

While our results show some similarities to the results of existing indices that measure the functioning of a countrys health system and the vulnerability of countries with respect to infectious disease outbreaks, our results also show some interesting differences. For example, Ghana and Senegal are, relative to other African countries, ranked high in the existing indices; however, due to their high population density and limited private infrastructure, the risk of disease transmission is still high. Furthermore, some countries even show a double burden of a high PDI (very limited capability to keep physical distance) and a low capacity of the health system to deal with an outbreak of an infectious disease, such as Benin, The Gambia, Sierra Leone, and Togo.

Figure1 depicts the results of the geospatial estimates of the population weighted PDI at the country and regional level for all 34 countries in Africa for which we have data. A higher index value and darker color represent a lower capability to physical distance, and hence, a higher risk of disease transmission. The corresponding country average values for each indicator of the PDI as well as the normalized index value are presented in Supplementary Table2.1. Moreover, Supplementary Fig.1.2 shows the population density at the country and regional level and disaggregated at the pixel level. As expected, countries with a high population density show an increase in the index (or a decrease in the capability for physical distancing) when adjusting by population density.

Country (a) and regional (b) level PDI. The panel depicts the capabilities of households to follow social distancing measures based on a simple multidimensional measure calculated based on (1) number of households sharing toilet facilities, (2) usage of public water source, (3) persons per room, (4) no access to ICT, (5) bicycle or other vehicle is not present, based on a PCA. The estimates are normalized between zero and one. Source: DHS and Center for International Earth Science Information NetworkCIESINColumbia University52; calculations by the authors.

We find considerable heterogeneity in the PDI across Africa. High-risk areas of disease transmission are particularly concentrated in the western part of Africa, such as Ghana, The Gambia, Togo, Sierra Leone, Benin, Liberia, Senegal, and Cte dIvoire. A relatively high population density (for example Ghana, The Gambia, and Togo had population densities between 121 and 200 people per km2 in 2015), coupled with limited infrastructure for physical distancing, could make these countries highly susceptible to infectious diseases that are transmitted through droplets. Countries with lower population densities and relatively better essential private infrastructure, such as Namibia, Gabon, Mozambique, and South Africa, show (relative to other countries in Africa) a lower PDI. Figure1 also shows that countries such as Niger and Chad, despite facing a severe lack of infrastructure, might still face slower transmission rates compared to countries with an equally severe infrastructural challenge, such as Liberia or Ghana, due to lower population densities. The interpretation of the geospatial estimates need to be made in relation to other African countries. For example, although South Africa shows a much brighter color in Fig.1, this does not mean that South Africa has all the infrastructure in place for people to keep distance, in particular in socio-economically deprived and marginalized settings. Moreover, even if such infrastructure is in place, it does not mean that people necessarily follow physical distancing recommendations32.

More interestingly, Fig.1 (right panel) also reveals considerable spatial heterogeneity of high-risk areas within countries. Whereas some countries show a relatively consistent risk pattern, such as Sierra Leone, Liberia, and Ethiopia, other countries reveal hotspots within countries that are hidden in the estimates of the national average. For example, western Kenya is a very high-risk region (Kisumu, Mombasa, and Nairobi), as is southern/central Cte dIvoire (Abidjan, Bas-Sassandra, and Yamoussoukro), north-western Tanzania (Geita, Shinyanga, Simiyu, and Tabora), or north-east South Africa (KwaZulu-Natal and Gauteng). Hence, although country-level estimates are useful for international or inter-temporal comparison, they mask important differences in the risk of disease transmission due to lack of infrastructure at lower administrative levels. This is pivotal to prioritizing national interventions, such as increased testing efforts or vaccination campaigns in the most vulnerable regions of countries.

Figure2 shows the results of the Bayesian regression at the pixel level for Ghana, Ethiopia, Kenya, and South Africa, four countries with some of the highest numbers of SARS-CoV-2 cases in Africa registered as of August, 2021. These countries are ranked amongst the least (South Africa), the middle (Ethiopia and Kenya) and the most (Ghana) challenging in the infrastructure-based PDI. For all countries, subnational heterogeneity is high and high-risk areas exist in all countries where people cannot protect themselves by keeping distance and are, hence, highly susceptible to the spread of infectious diseases by droplets. Moreover, in these areas lockdowns of public life will be difficult to enforce as people will have to leave the house not only to buy food and access health services, but also to access other public infrastructure.

Estimates of the PDI at pixel level (55km) for Ghana (a); Ethiopia (b); Kenya (c) and South Africa (d). Source: DHS and Center for International Earth Science Information NetworkCIESINColumbia University52; calculations by the authors.

To assess whether the PDI indeed hints to potential hotspots of disease transmission, we checked all countries in our sample to see if data on reported COVID-19 cases is available at the subnational level, and identified nine countries with subnational regional information. The countries are: Democratic Republic of the Congo, Ethiopia, Mozambique, Namibia, Nigeria, Niger, Senegal, South Africa, and Togo. Figure3 illustrates the close association between the PDI and number of COVID-19 cases for South Africa. The comparisons of PDI and COVID-19 confirmed cases for the other eight countries are shown in Supplementary Figs.1.3 and 1.4. The correlation coefficient between the PDI and COVID-19 cases for all nine countries ranges from 0.4 to 0.9, pointing to an overall close association between our PDI index and the observed regional caseload. This simple ex-post comparison provides evidence about the predictive power of the PDI to identify potential disease hotspotswithin countries.

Population weighted PDI (a), and observed cumulative caseload (b) at the regional level for South Africa. Note that we used the latest available information on the aggregated regional caseload. Source: Information on the regional caseload for South Africa is publicly available data which we are happy to share upon request and DHS; calculations by the authors.

A closer analysis of the different indicators entering the PDI (see Fig.4) helps to explain the occurrence of hotspots and provides guidance to countries where infrastructure investments are most needed. Different countries face different challenges. For example, Ghana and Liberia have severe private sanitation constraints, Rwanda and Burundi face severe private water infrastructure constraints, The Gambia and Senegal show more crowded housing, and the populations of Madagascar and the Democratic Republic of the Congo, do not have access to private communication or transportation. Across the African continent, 45% of households share toilets. On average, households share toilets with two other households, but the average number ranges from 1.32 households in Mozambique to 6.17 households in Ghana. To expect these households not to meet other people on a regular basis is simply unrealistic. The average number of people per room is 3.2, showing the difficulties of households and families to effectively isolate if a household member becomes sick. In Senegal and The Gambia this number goes up to five people sharing the same room for sleeping. Shared sanitation and sharing a room with many other household members are the two indicators with the highest weight in the PCA (see Supplementary Fig.1.7), meaning that regions and countries that show severe infrastructure constraints in access to a private sanitation facility and private room show the highest PDI values. For 40% of the households in our sample, the only access to water is from a public water source; these households need to leave their house to gather water, which increases the risk of infection. The share of households that do not own a mobile phone ranges from 56% in Madagascar to 3% in Senegal. Similarly, the share of households that own a bicycle, motorbike or car ranges from 5% in Ethiopia to 94% in Burkina Faso, againemphasizing the high heterogeneity between countries.

The figure shows the shares at the regional level from top left to bottom right: (a) number of households sharing sanitation facilities, (b) number of people per room, (c) share of households using open and public water sources, (d) share of households without a mobile phone, e share of households with no bike, car or motorbike, and (f) population density (people per km2) estimates for 2020. Source: DHS, and Center for International Earth Science Information NetworkCIESINColumbia University52; calculations by the authors.

Comparing the results of the PDI to other indices that measure a countrys vulnerability to a pandemic outbreak or the general ability of a health system to deal with an outbreak shows a weak correlation between the indices in general (see Fig.5). Countries such as Benin, The Gambia, Sierra Leone, and Togo have both weak health systems to deal with a sudden outbreak of an infectious disease, as well as a lack of essential private infrastructuresuch as access to private water, toilets, transportation, ICT, and spacethat undermine measures aimed at slowing the spread of a pandemic. Countries like Ghana face severe infrastructural constraints to slow down the spread of a pandemic such as COVID-19, but have the capacity of the national health system to respond to it. Countries like Rwanda, South Africa, and Namibia have both a functioning health system to respond, and access to essential private infrastructure to facilitate COVID-19 prevention measures. Case numbers in South Africa have still been the highest in Africa, which shows that infrastructure is not sufficient and countries heavily depend on their people to adhere to public health measures, such as physical distancing32. The high general caseload in South Africa can also be seen as a reflection of the high number of tests relative to other countries in Africa11,12, the higher importation risk of COVID-1928, and the older population33. Hence, the PDI does not, on its own, provide any predictions about outbreaks on the national level but can help to identify regions within a country where infectious diseases might spread faster once they enter these regions (see Fig.3 and Supplementary Figs.1.3 and1.4). Last, we observe only a weak negative association between the PDI and per capita GDP (Fig.5), indicating that economically more advanced countries have a lower risk of disease transmission. However, the relationship seems to be non-linear and, particularly for poorer countries, heterogeneity seems to be high. This means that despite being poor, some countries have managed to provide basic essential infrastructure, which helps to protect their populations and improves their livelihoods. This argument is further emphasized by Supplementary Fig.1.5, which plots the PDI against the regional poverty headcount rate at the first administrative level for countries with available data, indicating that simply targeting poor regionswith intensified COVID-19 prevention measures is not sufficient.

Scatter plot of the PDI (using the latest available Demographic Health Survey of a country) against the infectious disease vulnerability index (a)26; the WHO international health regulations index (b)25; the home environment for protection index (c)29; and the country's GDP (d)2. Note that a high PDI implies a lack of essential private infrastructure, and hence, high risk of disease transmission; accordingly, countries in the top of boxes have a lack of private infrastructure, and hence, lack the capacity to limit disease transmission. Moreover, note that for the infectious disease vulnerability index, the WHO international health regulations index, and the home environment for protection index, a higher value indicates a better preparedness, or protection. In addition, ISO-3 country codes are used to abbreviate the countries in this figure. See Supplementary Table2.1 for details on the ISO-3 country code. Source: DHS, Moore et al.26, Gilbert et al.28, and World Development Indicators2; calculations by the authors.

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