Does a Country’s Risk Factors Impact in Spreading COVID-19 in African Countries?

The novel coronavirus or COVID-19 has extended its spread across the globe, and most of the countries have reported infections. In what started as a first case single reported in Egypt has now magnified and close to about 355000+ infections have been reported across the African region as on June 26, 2020. The transmission trajectory of COVID-19 across the globe remains a mystery, and much remains to be learned. The fear of spread among nations is a cause for concern, especially among African countries with weaker governance, high poverty levels, weak health systems etc. The density of population in urban areas could be a trigger factor and could be devastating. The management and control of COVID-19 are critical to check the spread, and most of it is reliant on the health facilities in countries to carryout repeated tests. This paper aims to analyses the various parameters and tabulates a risk matrix and places it analysis using Statistical Package for the Social Sciences based on the available data. The analysis also provides potential insight into the vulnerabilities among African countries and a relative review of factors associated with the novel coronavirus.


INTRODUCTION
or Corona Virus is an issue that the world is struggling to contain. As the numbers rise to 2 million globally, the world is facing an unprecedented crisis which is getting difficult to contain. So far, Africa has been spared by its spread when compared to the European nations, the Americas, Asia. The effects are most likely going to be progressively perceptible in Africa as time propels. Researchers and scientists are anxious and concerned over Africa as the numbers rise across the globe. Africa so far has registered 15000+ cases, and the numbers are increasing (WHO, 2020;UN, 2020). Africa's first case was registered in Egypt on 14 February. The pandemic as it stands is slowly spreading across all regions in the continent. The current outbreak with similar characteristics to Ebola on transmission has exposed the vulnerabilities in the health care system. The African culture is one of a kind, where society blends more uninhibitedly. This, in itself, is a huge introduction to the present pandemic (Gherghel and Bulai, 2020;Instabilitate guvernamentală cronică, 2017).
Numerous questions remain unanswered concerning the spread, pathogen source, transmission rate, incubation period, mortality rate, and so on. As the pathogen had its impact over the globe, so did the analogies that were drawn from studies and research on the pandemic. In spite of the fact that the Chinese specialists perceived and deciphered the strain in under ten days, a solution to the strain is far off. By the time researchers realised that the disease was spreading with human beings as the source, it was too late as many had travelled from Wuhan where the disease originated (Jiang, 2020).
As the COVID pandemic spread its tentacles across the globe, much remains unknown about the overall transmission in Africa. Many sociologists fear the worst for Africa, given the high levels of poverty, weak health care systems, crowded urban areas, lifestyle, culture, the virus could be hard and devastating. The current circumstance due to coronavirus-19 has caused largescale issues with various countries constraining social principles as social distancing, serious lockdown strategies etc. bringing about a new social change. As we witness the responses by nations driving intense methodologies to improve the infection curve and improve the overall population immunity, which is one of the methods for controlling the pandemic, it is seen affecting society (WHO, 2020).
Against this backdrop, this paper aims to study and provide a risk analysis on the various risk factors Africa is exposed to with respect to the novel Coronavirus and seeks to assess the vulnerability faced by each African country. The paper also aims to provide a statistical analysis of mapping the risk to infectious diseases. Section one introduces the article, with section two providing an overview of the pandemic. The section also highlights the various factors influencing African society. Section three discusses the methodology, and in section four, the risks are modelled.

OVERVIEW OF PANDEMICS
Six coronaviruses have been distinguished in the group of coronavirus that is known to infect, causing bronchitis, pneumonia. The death scale of disease in coronavirus is far lower than that of the common flu infection, rhinovirus. Of these six infections, SARS and MERS have been a worry that represents around 5-10% contaminations in contrast with 25-35% when contrasted with occasional flu during seasonal outbreaks. Since the spate of SARS in [2002][2003], various diseases affecting the respiratory tracts are credited to the Coronavirus family have been found. One such illness is COVID-19 which causes extreme pneumonia in individuals and individuals who are more fragile, for example, diabetic, HIV positives, particularly among the older population. (Bradburne et al., 1967;Bradburne and Somerset, 1972;Monto, 1974;Patrick et al., 2006;Lieberman et al., 2010;Nickbakhsh et al., 2016;Jiang, 2020) COVID-19 or Coronavirus 2019 first came to light in the city of Wuhan on 12 December as reported by the Wuhan Health Corporation, Hubei Province in the Peoples Republic of China (Biscayart et al., 2020). The number of reported cases in Wuhan was 27 initially, which later ballooned as more and more frequented the hospitals and health centres. The infections were traced to the wet market of Wuhan. (Lu et al., 2020;Zhou et al., 2020;Biscayart et al., 2020). The population of Wuhan city is about 11 million (Rodriguez-Morales et al., 2020a;Rodriguez-Morales et al., 2020b).
By the time the Chinese government had control measures in place, the disease had spread to many continents (Eder et al., 2020). The point of convergence of the plague moved from China to Iran later to Italy, Spain, the United Kingdom and the vast majority of European Nations and the present point of convergence being U.S.A ( Figure 1). As the number of cases soared across the globe, the spread was initially registered as a concern, later as an epidemic and as the threat level increased, was termed a pandemic by WHO in stages (Johns Hopkins School of Public Health, 2020; WHO, 2020). With rigours rules guidance by WHO on society, the governments started imposing strict measures and stressed the importance of washing hands, social distancing, and so on. Notwithstanding the way that appreciation of the gettogethers is yet to be perceived deductively, it has added to the understanding the imperative role of transmission and the spread of the disease (Rashid et al., 2008;Ebrahim and Memish, 2020). The proliferation of COVID-19 has mostly been attributed to travellers as careers in many countries as in the case of Iran, Italy, Spain and many other countries Arab-Mazar et al., 2020;Gherghel and Bulai, 2020;Biscayart et al., 2020;Pullano et al., 2020;Rodriguez-Morales, 2020b).
Africa, as a continent, had registered the first case in Egypt and was linked to a traveller who had just entered Egypt. Figure 2 portrays the timeline of virus transmission in Africa while Figure 3, depicts the outbreak across Africa.

Factors Influencing of COVID-19 on African Society
During the last decade, the world has experienced more than 20 diseases in epidemics or pandemics ranging from measles, Zika to Ebola, SARS, MERS and recent COVID-19 (WHO, 2004;WHO, 2020). These high profile cases such as Zika, Ebola illustrated the importance and exposed the most vulnerable countries which could technically require more support. The RAND report of 2016 identifies the importance of mapping of high-risk countries. The reports highlight 25 most vulnerable countries of which 22 of them are located in Africa, and which could potentially be the hub for transmission of infectious disease unless paid attention to highlighting the importance of technical support.
The primary reason attributed to the risks are conflicts, health care facilities, and so on. (Melinda et al., 2016). Africa currently accommodates about 25 million refugees displaced either due to conflict or repression, and roughly 85% of the population belong to DRC, South Sudan, Somalia, Ethiopia, Sudan, Nigeria, CAR and Cameroon (Williams, 2020).
As with every country, the coronavirus was attributed mainly to travel, tourism and business. Egypt, Morocco, Nigeria, South Africa being business hubs were first to be impacted by the virus in the initial stages. As with the spread with the other countries, the African nations also risk from local transmission due to domestic travel and international travel, and it has assumed significance in recent times. It is also important to understand that nearly 60 thousand African students are currently enrolled in various universities across china for studies (Smith, 2020).
WHO's pandemic declaration also was significantly influenced by turn of events and fears of the virus spreading across countries with exposure to weaker health care facilities in Africa like Algeria, Angola, Ivory Coast, DRC, Ethiopia, Ghana, Kenya, Nigeria, Mauritius, Tanzania, Uganda, Zambia and South Africa. They were prioritised based on travel to and from China (Smith, 2020). The second most important factor in the public health system is the skill and the ability of knowledge sharing and communication, which is key to the spread of transmission. Africa as a continent suffers due to lack of skilled medical practitioners, nurses and midwives. Qualified health practitioners are an integral part of maintaining the capacity of these health centres. The converse of this could lead to significant challenges in the form of testing and tracking, which could lead to rapid transmission. The factor that comes into the picture is the culture in the community, which could lead to underreporting of the cases. This is further complicated by the spread of misinformation in the community. Another critical factor is the community's belief in the traditional medical systems that are not tested and are generally run by community practitioners (Gilbert et al., 2020;Africa Centre for Strategic Studies, 2020). In crux, the public health systems in Africa are prone to challenges of curbing infectious diseases and further complicated depending on the density and population of the area in question.
The third factor is the urban density of the population living. Most African countries are densely populated. This effectively could act as an ideal condition for the virus to spread quickly, undetected. Except for fewer countries like Sahel, Namibia, Sudan, which are sparsely populated, the dense concentration of people crowding a smaller area is ticking timebomb. As noticed from other countries like Italy, Spain, the COVID-19 appears to spread quickly in thriving communities where there are frequent interaction and contact among the people. African cities like Addis Ababa, Lagos, Cairo, Johannesburg, Kinshasa, Abuja etc. where the per people in a square kilometre is high, the disease is likely to spared fast (Gilbert et al., 2020;Africa Centre for Strategic Studies, 2020).
The fourth important factor is the age of the population. Africa has a distributed age population spread, unlike Italy or Spain. In countries like Algeria, South Africa, Morocco etc. have a more significant population in an older age group unlike places like Mali, Niger, Chad, DRC etc. As noticed from the case fatalities, over 70% being in the range of 60+ categories, this could be a factor that is bound to have population immunity due to Africa's population being in the age group of 30-40s (Gilbert et al., 2020; Africa Centre for Strategic Studies, 2020). However, the underlying factors that need to be considered are the factors that many African countries face such as HIV, malnutrition, malaria etc. as the mortality rate varies depending on the country.
The fifth important factor is the food crisis the countries are likely to experience. As millions in countries like Zimbabwe, Sudan, Somalia dive further into hunger, the pandemic endangers millions of lives as the community is driven into desperation. Added to it, is the shortage in essentials the countries are likely to face due to hyperinflation as the crisis looms over the nations. Adding to the issue is the locust swarms, which has destroyed crops worth millions lately (BBC, 2020).
As per the world bank, the resultant effect of COVID-19 on the planet could have sweeping ramifications for many individuals who live in poverty or have just risen out of it. It is evaluated that Africa could be the most terribly affected and may lose half of GDP with food nourishment, medication, joblessness and speculation issues on drawing investments even before the nations face the full-rage of the disease. (World Bank, 2020; Sullivan and Chalkidou, 2020).

DATA MODELING AND METHODOLOGY
Data selection (global reported, death and recovered cases) for analysing the impact were collected from the John Hopkins School of public health data portal (Johns Hopkins School of Public Health, 2020). A similar exercise was done to collect the data on patient-bed ratio, patient-doctors ratio and patient-nurse ratio. The information was collected from World Health Organisation database (WHO, 2020). To understand the current scenario, the data collected was mapped into the Tables 1 and 2 as follows  Africa, 2020) The overall infection rate as on April 9, 2020 for Africa stands at 15,623 number with a recovery percentage of 19.2 and a death percentage of 5.3%. When comparing the same with the data as on June 26, 2020, the total number of reported cases had increased substantially to 352,570 cases with the total recovery percentage standing at 48.15%. While the death percentage as on April 9, 2020 was 5.3%, it had substantially come down to 2.6%. This indicates that there is a positive recovery rate across Africa (Figures 4 and 5). Table 3 details the total number of cases, recovery and the deaths (WorldoMeters, 2020; Johns Hopkins School of Public Health, 2020). However, it is important to note that. Table 4 shows the risk modelling for the African countries, while Table 5 depicts the legend to the risk. Each of the states was mapped  with their exposure to international countries, health care system, the population density in the urban areas, the age of the population, the hospital bed ratio, patient-doctor ratio, patient-nurse/midwife ratio. The risks were then tabulated as low (1) to critical (5). The country's cumulative data indicated the total risk the country is exposed to. The total risk was then mapped to the number of cases reported, recovery and death rate as per the data on April 9, 2020. The model was then analysed using Statistical Package for the Social Sciences (SPSS).

THE RISK MODELING
Examining the outlined risk factors, it can be seen that Ethiopia, Nigeria, Senegal, Chad, Ivory Coast, Togo, Tanzania, Mali, Mozambique, Madagascar, Sudan, Burkina Faso, Niger fall under the category of critical risk though most of the countries had minimum interaction with the outside countries with the exception of Nigeria (5). The cumulative risk rating on these countries ranked high due to its availability of hospital beds, patient-doctor and nurse/ midwife ratio in relation to the total population. This multilayered risk portfolio technically emphasises the vulnerabilities that these countries face due to the associated availability of the necessary health resources. Another tranche of countries, Morocco, Egypt, CAR, Cameroon, Guinea, DRC, Uganda, Marutiana, is just below the critical level and fall in the very high-risk category. These countries exhibit multiple layers of exposure. Notably, with the exception of Morocco and Egypt, none of the other countries lies in the high exposure category of international travel. Many other countries which lie in the high risk, medium risk and low-risk category benefit from other factors which include access to health care systems, low profile to international exposure with the exception of South Africa, which has high exposure to the international market.

MODEL COMPARISON, ANALYSIS AND RESULTS
The mapping of the total Coronavirus cases against data indicated some exciting results. Notably, as on April 9, 2020, countries like Egypt, Morocco rated as high risk has more number of cases registered, with morocco registering 1374 cases as against the total deaths of 97. However, the number of cases rose to 11,465 numbers in a span of 77 days this despite the country announcing lockdown since March 20 (Ahmed, 2020). Egypt registered 1699 cases and reported 118 deaths as on April 9, 2020. However,  Both these countries notably are more exposed to international travel. Egypt also has a high population density in urban areas as opposed to Morocco. Cameroon, one other country which falls in the very high-risk category, reports only 730 cases and a recovery rate of 9% as on April 9, 2020. Similarly, DRC Congo, which is conflict-prone, has reported only 60 cases and a recovery rate of 8%. Another interesting fact data is on Nigeria and Senegal. Nigeria is listed and falls in the critical risk category. However, the country has reported less number of cases with 288, with only seven deaths and a recovery of 51 cases with an impressive recovery rate of 18%. In contrast, Senegal, which has 250 cases, reports recovery rate of 49%. Except for Nigeria, which has a high exposure rate to international travel, Senegal ranks on the risk rate of 3. Both Nigeria and Senegal have lower hospital-bed, patient-physician and midwife ratio. In the critical risk category, Ivory coast reports 444 cases with only three deaths and has an impressive 12% recovery rate.
Both South Africa and Algeria, which fall in the middle-risk category as per the mapping chart, reported more cases. South Africa ranks high on international exposure as opposed to Algeria. The total number of registered cases stands at 1934 number and reports 18 death and 95 recovered with a recovery rate of only 5.5%, given the hospital-bed, patient-physician, patient-nurse ratio. In contrast, the total cases in SA stood at 118,375 cases with a steep increase. Similarly, Algeria reports 1666 cases with the highest death rate for Africa with 235 and a recovery rate of 21%. South Africa has one of the best health infrastructures when compared to the rest of the African nations. Mauritius, which ranks low in the risk template reports 314 cases with seven deaths and a recovery rate of 7.3%. Similarly, Tunisia reports 643 instances with a recovery rate of 3.8%. Figure 6 shows a detailed graph of countries exposed to the outside world and the number of cases reported. The factor is relevant as it reflects a timely address of the testing issue. The mapping of the number of reported cases indicates the pertinent reality across the African continent. There is a strong correlation between the reported cases and exposure to the outside world. Except for Nigeria which has reported lesser number of cases, the rest of the countries which rank high on exposure to the outside world like South Africa, Morocco, Egypt, Algeria all have high reporting incidents when compared to the rest of Africa. The ability to test the large influx of population in the urban areas has also seen a sharp rise in these countries. Notably, Critical, Very/high-risk countries like Zimbabwe, Uganda, Ethiopia, Kenya have reported far fewer cases though the risk to initial transmission was recorded high.
As the COVID-19 widens it spread, it is essential to watch all the countries which fall in the domain of critical, very high or high risk. It is likely that the countries such as Sudan, DRC, Cameroon, Somalia, Zimbabwe register as vulnerable to the spread of the COVID-19. Therefore, it is essential to ensure proper attention to these countries and towns and providing continued support to the public   administration, health institutions etc., though each of the states has its challenges and unique set of associated vulnerabilities or risks.
Based on the observations, the data for April 9, 2020 was fed into the SPSS package for better analysis. The risk rating was considered as the dependent variable and the reported cases, total death and total recovery as an independent variable. Two sets of studies were performed. The first analysis included data for all countries for the African continent. In the second analysis, the countries which had null values were removed. That is countries with zero reported cases or zero deaths or zero recoveries. The results are as follows

First Analysis (Data for All Countries Included)
The analysis provides a deep insight into the various aspects of the countries. The regression analysis indicates that there is a positive correlation when assessing the risk-total, total number of cases, total death and total recovery. The correlation against death indicates that there is a strong correlation relationship when risk is associated with the number of deaths and a very moderate relationship when comparing the cases registered and the total recovery though statistically not significant. Similarly, when assessing the relationship between the total number of cases registered against the total death and recovery, the relationship is weaker, and 25% and 18% of the death cases can be explained.   The regression coefficients on the total number of cases indicate that there would be a decease .01% against the risk rate. Similarly, there is a decrease of 0.46% and is not statistically significant.

Regression
The total recovery rate against the risk has a positive relationship, and for every one point, there would be an increase of by 0.34%.

Second Analysis (Cases with Null Values Removed)
The second analysis, which was carried out after removing the null values provides a different insight into the various aspects of the countries. The regression analysis indicates that there is a negative correlation when assessing the risk total and total death rate and a positive relationship for risk total and recovery rate, number of recorded cases. The correlation against death indicates that there is a decrease in death cases in the countries. The correlation relationship when risk is associated with the number of the total number of cases is significant. At the same time, there is a very moderate relationship when comparing the total recovery, though statistically not significant. The third type of analysis that was performed for further analysis to understand the significance of the model using Bivariate analysis. This was carried on the countries which had reported the cases, number death and recovery. The study included 28 countries. Though there was a positive correlation, they were not statistically significant.

Bivariate correlations for variables with the null value
As there was positive correlation on the data for the countries which had reported positive cases, death, and recovery the analysis was extended to include all the variables and included the countries which had not reported any death or recovery. The data was then analysed for bivariate correlations. There were some significant findings for both Kendall's and Spearman's correlations.

Conclusion on analysis
The study indicated that the model though is not statistically significant at this point due to data on reported cases, death and recovered are variables and are varying, it can be concluded that the model will result in a significant finding when the data for the reported cases, death and recovery stabilise. The model also indicated that the risk exposure of the country had a direct impact on the number of reported cases, death, and recovery, though there are other influential factors like the number of tests conducted, available ventilators or life-saving equipment, treatment methods, medications, immunity level of the patient, age etc. that influence the findings.

DISCUSSION AND SUGGESTIONS
Though it is too early to provide complete information, the early detection of COVID-19 is crucial to prevent the spread. However, the exposure to many markets from African nations like South Africa, Algeria, Ethiopia, Morocco, which are categorised highrisk countries, is highly heterogeneous. As the second wave of onward transmission is active, it potentially risks the weaker health systems across the globe and as indicated the infrastructure in Africa is more or less requires improvement in all phase. As much as it is essential to identify, isolate and provide treatment to those infected with the disease, it is also vital to prioritise the health workers safety. This is especially true for African countries with weaker health infrastructure. Algeria, Ethiopia, South Africa, Nigeria were part of the highest risk countries as identified by WHO based on direct business links and volume. Egypt, which has been identified as a very high risk in the risk matrix, was not part of the WHO's 13 risk identified countries. Thus, this assessment strongly reflects on the spatial pattern of the way the virus has transmitted due to importation. In as much as the data segregates and places risk based on the volume, it does not provide complete insight into the business or tourism travels across the African nations either from China or Europe and is beyond the scope of this research article.
Though some countries are ill-equipped and lack enough resources like rapid testing kits, the states have so far managed to get it done in facilities at adjoining nations or abroad. This delay could prove to be a challenge as the suspected cases need to wait for the test results confirmation, which could result in disease transmission.
To avoid this, the governments have restored to stringent selfquarantine procedures. WHO is also stepping up its efforts to support and improve the diagnostic capacities in these countries. (WHO, 2020;Steenhuysen and Nebehay, 2020). Africa has as CDC and has strengthened its capacity and capability to assist countries during the COVID-19 pandemic (Africa CDC, 2020). Most African countries have a plan for disease preparedness plan like the H1N1 pandemic which could be inadequate given the seriousness of this pandemic (Sambala et al., 2018). The consequences of the outbreaks SARS, MERS have indicated the importance of enforcing national public health programs, strengthening laboratories, improving and building on the human resources and ensure proper training programs (Marston et al., 2017;Sands et al., 2016).
It is also essential that the results in this paper should be interpreted carefully. Though the overall risk importation in African countries is much lower than the rest of the countries, it is essential for the states not to let their guard down. The preparedness by African countries towards COVID-19 has so far been adequate with many countries strengthening surveillance measures and screen at ports of entry (Monde 2020;WHO, 2020;Nkengasong, 2020;Gilbert et al., 2020).
The strengthening of the combating COVID-19 in Africa is phenomenal. Thanks to the Ebola epidemic. The critical advantage that Africa relies on is handling and fighting off infectious diseases like cholera, measles and recent plague like Ebola has so far been a boon and help in fighting off the current pandemic in a positive way. With most governments enhancing communication campaigns with guidelines from WHO, the fight towards the pandemic has intensified.

CONCLUSION
Though it is too early to provide complete information and arrive at a conclusion on the COVID-19 pandemic, the early detection of COVID-19 is crucial to prevent the spread across the globe.
Africa is no exception. The exposure of states to the outer market considered as risky could also provide a deep insight into the pandemic itself. Though it is crucial to identify and find a suitable treatment for the infection, it is also vital to strengthen the fight against the pandemic in all possible ways including providing the utmost protection for the health and safety of the frontline workers.
The research analysis on the epidemic on various factors indicates that the countries in Africa are likely to be less exposed, unlike their counterparts in America's or Asia or the European countries.
Although it is too early to conclude the statistical significance of the research, the trend indicates that once the pandemic stabilises, the results will likely show high relevance and correlations between the variables. The future course of this work will be to analyse the data as the pandemic subsides to provide a detailed insight into the pandemic, especially on the recovery and the mortality rate.