## 4 Discussion
### EO Data for COVID-19 Responses in Slums  
Geo-referenced socio-economic and demographic data are essential for any planning action, especially in emergency situations when that assistance must be fast and accurate. The availability of national data cubes or city-level data ecosystems would allow better management of such emergencies. With the declaration of the Sars-CoV-19 pandemic in March 2020 [74], there was an immediate and growing concern about its spread and impact in urban slums. Initially, the virus's arrival in LMICs through international travellers and first spread in wealthy neighbourhoods [75]. However, large inequalities that go from limited internet access, lack of tests and of medical care, to poor health conditions resulted in the fast spread beyond deprived communities (in particular slums) [76]. Mortality rates are showing to be high in deprived communities [77; 78]. For example, in Salvador, Brazil, poor neighbourhoods had a mortality rate of 98.9 per 100,000 inhabitants, while in wealthier neighbourhoods, this rate was 86, considering the sum of cases from March to August 2020 [79].  
Many observed challenges related to COVID-19 responses could be solved by the proposed EO-based data ecosystem. One first challenge relates to the location of risks, that is, the geo-referencing of positive cases in the intra urban space. The laboratory or medical records usually contain information on the patient's address, even though they may be incomplete, divergent or in an analogue format. For privacy reasons, the location of the residence should not be disclosed. However, few cities [42] publish their data in aggregation units such as streets, zip codes, grids up to 100 m or census tracts. The dissemination of information by neighbourhood, for example, requires the exclusion of non-residential areas from the analysis areas to avoid bias. For this, it is necessary to have updated data on urbanised areas and land use (ground information). Another problem is caused by the heterogeneity of large aggregation units. They generate distortions in the process of recognising the socio-economic profile, making epidemiological analyses difficult. Investigations end up being restricted to those who have access to the individual'sdata, and results are not always disclosed to society. A second challenge is related to virus exposure, which is directly associated with social distance [80]. To support local strategies, estimates of household size (based on buildings footprints extracted from EO) and local land use classification are important to support dimension and distribution of awareness campaigns, testing, distribution of hygiene and protection items, disinfection, etc. The development of community-based reactions to the disease is the third challenge in slums. Once a person is suspected of having the disease, they need to be monitored, tested, isolated and treated and have every contact traced [81]. However, the problem may start at the moment of suspicion, if this person does not have and cannot pay for individual transport to the next health unit (most slums are far from such units--example in Figure 11. Therefore, primary health care, with a call centre or home visit, for example, becomes essential for monitoring the symptoms of COVID-19, but also for the control of pre-existing health conditions such as chronic diseases, e.g., diabetes, hypertension [82], other diseases that may be related to unhealthy and humid areas and homes, such as respiratory diseases [83] and infectious diseases (e.g., malaria or leptospirosis [84]).  
Economic activities must go back to a \"new normal\" [42]; when the vaccines arrive, how will they be distributed? How many are there, and where are those people who need it most? Those are some examples where up-to-date socio-economic and demographic data are key (e.g., [85]). However, the last census in Brazil was in 2010, showing the urgency of an EO-based data ecosystem.