Population density both in a country and in urban cities can have impact on contagious disease transmission. Both contact rates and pattern in a defined geographical spatial distribution determine the mode of infectious disease transmission (47, 48). Contact hours and contact number per person vary with age and number of household members. In a study in Hong Kong the highest contact pattern was observed among school going children, which decreased with age (48). However, older age group with economic strength also showed elevated contact rates. We found weak relation of population density per km² with percentage of test positives (Figure 1A). Generally, population density poses increased chance of disease transmission, but contact rate is not always determined by population density. At low density contact rate can increase rapidly which gets saturated at very dense area where lack of organized social contact is evident (47). At this point contact rate becomes independent of population density. Although in our analysis low density countries showed slightly higher percentage of test positives, further data analysis showed that few countries in this group e.g. Algeria, Brazil and Afghanistan had very high test positive value as outliers as they did very low number of testing. This inadequate testing increased their test positive rate as we found in our analysis too (Figure 3B).

Number of tests performed to confirm COVID-19 is still a challenging issue for many countries and it can affect decision making drastically. We found statistical linear positive relation in number of positive patients with increasing test numbers (Figure 3A). The following graph depicted that as the number of tests per 100K population increased, percentage of test positives dropped gradually (Figure 3B). In a recent press conference WHO mentioned that high percentage of test positive in a country indicate inadequate number of tests performed and they set a rough standard of 10% test positives as indicator of normal outcome (49). This strongly supports our data in Figure 3B.

Countries that performed above 500 tests per 100K population, showed around 10% test positives. Lower middle and upper middle economy countries performed symptom-based highly selective diagnosis due to socioeconomic, demographic and political reasons which increased their percentage of test positives.

Test positive rate also has close link with urban population, median age and income level. Among 91 countries in our study, around 74.73% (n=68) countries belong to high economy class, 20.88% (n=19) were from upper middle economy and rest were from lower middle economy group (Table 1). Thus, the most of the independent socioeconomic and demographic variables comply with the characteristics of developed countries. These countries possess modern health facilities, medical professionals and cutting-edge research facilities. Despite having these benefits in health sectors they could not successfully restrict infections and deaths toll. However, these amenities certainly were advantageous in some form and contributed in patient recovery (Figure 2D).

Developed countries own bigger number of city populations with a big proportion of older inhabitants who prefer to stay at home (50, 51). The test positive rates thus were lower in groups of countries with dense urban populations (Figure 1B). Median age data presented that developing countries hold more young populations compared to developed countries. Young population always have tendencies to go outside and making them vulnerable age group (32). Therefore, test positive rates were high in developing countries where median age is below 30 years (Figure 1C). Health expenditure per capita correlates with high number of tests performed, thus countries with over 501 USD investments showed decreased test positive rate (Figure 1D). As high median age belongs to countries with strong healthcare system, case recovery was also found to be high where median age is over 30 years (Figure 2C)

Case fatality and recovery from epidemics largely depends on a person’s internal and external factors along with age, presence of co-morbidities, health facilities and pattern of adherences at healthcare centers (25, 33, 50, 52). The effect of contagions on certain population is also influenced by the interplay between incubation period and the age dependent case fatality rate of the disease (53). In our study, case fatality rate was high where median age is over 40 years (Figure 1C) and in countries with more than 50% urban population (Figure 1B). Although increasing population density showed gradual rise in case fatality, countries with over 500 people per km² density (n=6) surprisingly showed reduced case fatality (Figure 1A). Most of the countries in this group have high urban population and high median age; however, they managed to control case fatality with remarkable success, except the Netherlands with 12.79% case fatality. With rapid urbanization the risk of pandemic and zoonotic diseases are increasing (54). Being infectious and contagious disease, urban population percentages is one of the important predictors in COVID-19 outcome. In countries with more people living in rural areas have low case fatality as air velocity has potential reducing effect on disease transmission (55). In contrast, urban medical facilities, availabilities of required medicines/drugs, and treatment tools accelerate disease recovery despite huge load of patients compared to rural and remote areas.

Countries with high health expenditure per capita and GNI (PPP) could not restrict the pace of death as expected due to inadequate intensive care equipment and management personnel with overwhelming number of critical patients. Although, better urban intelligence and management is partially linked with better recovery (56), as proportion of urban population around the globe is gradually increasing, policy level rethink on healthcare system is a must. We need to redesign urban intelligence, resource management and coordination to fight COVID-19 like pandemic where isolation, social distancing and quarantine are challenging (57).

Investment on health specially toward NDCs have shown reduced mortality/incidence ratio in cancer (58), stoke (59) and child mortality (60). Expenditure to contain infectious disease is still a necessary component; as controlled use of available resources can reduce disease spreading (61). Moreover, sudden pandemic induced resource constraints can critically affect treatment and patient recovery (62). Contrary to misconceptions that higher expenditure relate to better healthcare, developed countries showed no strong negative correlation of health expenditure to case fatality (63). This could be due to inappropriate way of spending money, poorly designed policy and political intrusions that inhibit policy and treaty implementation (64). So far in COVID-19 pandemic, some countries offered strict preventive measures and information technology based contact tracing to successfully contain the disease although they fall into high risk group countries. In our study, COVID-19 related case fatality rate was comparatively higher in countries with high health expenditure per capita which explains that majority of health investment is insufficient or inadequate in required segments to tackle such pandemic. In a recent study, COVID-19 incidence and case fatality was found not to be associated with health expenditure and services (34). The situation gets even worse as incidence number rise rapidly putting pressure on healthcare capacity, as observed in some European countries (25).